HK1259033B - Data processing method and apparatus - Google Patents

Description

Data processing method and device

Technical Field

The present application relates to the field of computer technology, and in particular to a data processing method and device.

Background Art

Carbon emissions is a general term or abbreviation for greenhouse gas emissions. Any human activity can cause carbon emissions, such as automobile exhaust and coal-fired power plants.

Today, most scientists and governments acknowledge that greenhouse gases are already and will continue to pose a catastrophic threat to the Earth and humanity. To prevent this situation from worsening, the international community established the United Nations Framework Convention on Climate Change (UNFCCC) in 1992, and in December 1997, the Kyoto Protocol (Kyoto Protocol) was agreed upon at the third Conference of the Parties to the Convention, held in Kyoto, Japan. The Kyoto Protocol requires more than 30 Annex I countries (including developed countries and countries with economies in transition) to reduce their greenhouse gas emissions by an average of at least 5.2% compared to 1990 levels between 2008 and 2012. After being ratified by developed countries representing more than 55% of their 1990 CO2 emissions, the Kyoto Protocol officially entered into force on February 16, 2005. This marked the beginning of a period of substantial greenhouse gas emission reductions for the international community. For the first time in human history, an international legal framework existed to limit the impact of human activities on the Earth's carbon cycle and climate change. Reducing carbon emissions has become a key objective of socioeconomic development and production and business activities in the signatory countries.

In other words, carbon emissions have become a major social issue, and how to encourage businesses and individuals to proactively control carbon emissions is a direction that all of humanity must strive for. A key step in this is ensuring that businesses and individuals clearly understand the carbon emissions of their daily activities and the potential savings from adopting more environmentally friendly practices (hereinafter referred to as carbon savings).

Existing technologies facilitate the calculation and control of carbon emissions and carbon savings from businesses, as their activities are relatively centralized. However, individual behavior is more fragmented and haphazard, leading to numerous sources of carbon emissions and a vast amount of information. Furthermore, ordinary people rarely consider the carbon emissions associated with their own actions. Therefore, how to leverage this fragmented information to calculate the carbon savings of each individual's daily behavior has become an urgent challenge.

Summary of the Invention

The embodiment of the present application provides a data processing method to solve the problem of how to use individual fragmented behavior information to enable each person to know the carbon saving amount of daily behavior.

An embodiment of the present application provides a data processing device to solve the problem of how to use fragmented personal behavior information to enable each person to know the carbon savings of their daily behavior.

An embodiment of the present application provides a data processing method, including:

Obtaining user behavior data, the behavior data being generated when the user uses an Internet service, the behavior data including: a user identifier indicating the user's identity, and identification information indicating the Internet service corresponding to the behavior data; the Internet service including: an Internet service that can save paper products and/or an Internet service that can reduce travel by means of transportation;

determining at least one preset carbon savings quantification algorithm based on the identification information of the Internet service;

Calculating the user's carbon savings based on the behavior data and the determined preset carbon savings quantification algorithm;

According to the calculated carbon saving amount of the user, the carbon saving amount of the user is processed into specific data for reflecting the carbon saving amount of the user, and the processing at least includes: converting the calculated carbon saving amount into a form of points.

An embodiment of the present application provides a data processing device, including:

an acquisition module for acquiring user behavior data, the behavior data being generated when the user uses an Internet service, the behavior data including: a user identifier indicating the user's identity, and identification information indicating the Internet service corresponding to the behavior data; the Internet service including: an Internet service that can save paper products and/or an Internet service that can reduce the use of transportation;

a determination module, which determines at least one preset carbon saving quantification algorithm based on the identification information of the Internet service;

a calculation module, calculating the carbon savings of the user based on the behavior data and a predetermined carbon savings quantification algorithm;

The processing module processes the user's carbon saving amount into specific data reflecting the user's carbon saving amount according to the calculated carbon saving amount of the user, and the processing at least includes: converting the calculated carbon saving amount into a form of points.

At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

Through the technical solution in this application, the fragmented behavioral data of users can be aggregated, and based on the aggregated various behavioral data, combined with the corresponding carbon saving quantification algorithm, the carbon emissions reduced by the user, that is, the user's carbon saving, can be calculated. The corresponding service provider can further process the data corresponding to the user based on the calculated carbon saving of the user. This method will enable users to know their own carbon saving more intuitively without having to query and calculate it by themselves, which is more convenient for users. In addition, the service provider can process the user's data based on the user's carbon saving, such as accumulating points and upgrading account levels, and associate the corresponding service with the user's carbon saving.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

FIG1 is a schematic diagram of a data processing process according to an embodiment of the present application;

Figures 2a to 2e are schematic diagrams of data processing processes for different user behavior data in different scenarios provided by an embodiment of the present application;

FIG3 is a schematic diagram of an architecture for implementing a data processing process according to an embodiment of the present application;

FIG4a is a schematic diagram of a control for accumulating points for a user provided in an embodiment of the present application;

FIG4 b is a schematic diagram showing the total amount of points provided in an embodiment of the present application;

FIG4c is another schematic diagram showing the total amount of points provided in an embodiment of the present application;

Figures 5a and 5b are schematic diagrams of points acquisition between users according to an embodiment of the present application;

FIG6 is a schematic diagram of the structure of a data processing device provided in an embodiment of the present application;

DETAILED DESCRIPTION

To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

This application aims to calculate the user's carbon emissions and carbon savings by collecting and aggregating the user's fragmented behavioral data over a period of time.

Based on this, an embodiment of the present application provides a data processing process, as shown in FIG1 , which specifically includes the following steps:

S101: Obtaining user behavior data, wherein the behavior data is generated when the user uses an Internet service, and the behavior data includes a user identifier indicating the user's identity and identification information indicating the Internet service corresponding to the behavior data.

In the embodiment of the present application, the obtained user behavior data is usually a plurality of fragmented behavior data.

For behavioral data, each behavioral data includes a user identifier (such as user ID, user account, etc.) and identification information indicating the Internet service corresponding to the behavioral data. The Internet services mentioned here may include at least one of electronic payment, online reservations, online ticketing, online payment, and health services. The health service can be a service in the mobile phone system or APP for monitoring user exercise behavior, and the health service includes at least one of a step counting service or a distance calculation service.

Behavioral data from different Internet services can be distinguished based on the identification information contained in the behavioral data.

In the process of aggregating the fragmented behavioral data of users, it is necessary to ensure that the aggregated behavioral data all belong to the same user. For online behavioral data, in actual applications, users may use Internet services through different applications (or servers), such as: users use online payment services through payment applications, users use online meal ordering services through meal ordering applications, etc. When using these Internet services, users may use different accounts. So, in order to ensure that the behavioral data obtained are all behavioral data of the user, in one way, different accounts of the user (i.e., user identification) can be obtained. During actual operation, the user can enter their different account names, and then the different account names of the user can be saved. Subsequently, the behavioral data associated with the account can be obtained from the corresponding application (or server) through the user's account name. Of course, the above method is not limited to the acquisition of user accounts, but also applies to other user identifications, which will not be repeated here.

At the same time, different Internet services have different identification information, where the identification information may include: service type identification, type identification bit in the order number, etc. Then, the type of Internet service corresponding to the behavior data can be further determined through the identification information in the behavior data.

In actual operation, after obtaining the user's behavioral data, the field representing the type in the behavioral data can be clarified according to the agreement, so based on the content in this field, the type of Internet service corresponding to the behavioral data is determined. Of course, the types of Internet services provided by certain applications (or servers) are relatively fixed, such as: a ticketing website server only provides ticketing services. Then, if the behavioral data is obtained from these applications (or servers), the type of Internet service corresponding to the behavioral data can be directly identified based on the name, domain name, website address and other information of the application (or server). This does not constitute a limitation of the present application.

It should be noted that the execution entity of this method can be either an application or a server. When the execution entity is an application, as a possible method, the application can provide various Internet services to users. In this case, the application itself can generate user behavior data, and accordingly, carbon emissions can be calculated directly based on the generated behavior data. In other words, under this method, users only need to register an account in the application as the execution entity to use the various Internet services in the application. The generated behavior data is then associated with the account, so the application only needs to obtain the behavior data related to the account.

As another possible approach, if the application is not able to provide Internet services, then the application can initiate a behavior data acquisition request to a third-party application (or third-party server) that can provide Internet services and receive the user's behavior data fed back by the third-party application, or synchronize data with the third-party application to receive the user's behavior data generated by the third-party application. In this approach, based on the above content, the user can enter the user's third-party accounts and the third-party applications (or third-party servers) corresponding to different third-party accounts in the application as the execution subject. The application as the execution subject will then associate each third-party account with the account registered by the user in the application as the execution subject. Taking the third-party application as an example, in the process of acquiring behavior data, since the user enters the third-party applications (or third-party servers) corresponding to each third-party account, the application as the execution subject can determine the third-party application corresponding to the third-party account based on the third-party account and can send a acquisition request with the third-party account to the third-party application. Then, the third-party application will search for the behavior data related to the third-party account based on the third-party account and feedback, thereby completing the process of acquiring behavior data. Of course, the process here does not constitute a limitation of this application.

Of course, in actual operation, if a third-party application is involved, the application as the executing entity can pre-register the corresponding permissions with the third-party application in order to receive the user behavior data fed back by the third-party application. If a third-party server is involved, the application as the executing entity can obtain the user behavior data fed back by the third-party server through the data transmission protocol agreed upon with the third-party application.

Furthermore, in actual application scenarios, for behavioral data generated by a third-party server or third-party application in a commonly used data format, such as a two-dimensional table format, a HyperText Markup Language (HTML) format, an Extensible Markup Language (XML) format, etc., the application or server as the execution subject can read and parse the behavioral data based on the corresponding format after obtaining the behavioral data.

For certain specific data formats, the application that executes the data can agree on a data transmission format with a third-party application, such as JSON format. In addition, parsing methods for data in different formats can be pre-added to the application's own application programming interface (API) to implement parsing of behavioral data in different formats. This does not constitute a limitation of the present application.

The acquired behavior data can be stored locally on the terminal or on a server.

S102: Determine at least one preset carbon saving quantification algorithm according to the identification information of the Internet service.

In the embodiment of the present application, a correspondence is pre-established between the internet service and the carbon savings quantification algorithm. Therefore, at least one preset carbon savings quantification algorithm can be determined based on the identification information of the internet service and the correspondence between the internet service and the carbon savings quantification algorithm. The carbon savings quantification algorithm includes, but is not limited to, a quantification formula, a quantification model, and the like.

In other words, different Internet services (i.e., behavioral data containing different identification information) may correspond to different carbon saving quantification algorithms. For example, electronic payment services can save paper products, while walking saves carbon emissions from vehicles. At the same time, Internet services and carbon saving quantification algorithms may also have a one-to-many correspondence, that is, multiple carbon saving quantification algorithms may be used in one Internet service. For example, in the scenario where users use online ticket purchase services, online ticketing services can save users from going to the corresponding ticketing locations, thereby reducing carbon emissions caused by users taking public transportation to the ticketing locations. Therefore, when calculating the carbon savings of users using online ticket purchase services, the carbon saving quantification algorithm for reducing travel by public transportation can be used. In addition, users purchasing tickets online can also avoid printing paper receipts (i.e., paper products) when paying, which can also reduce carbon emissions. Therefore, when calculating the carbon savings of users using online ticket purchase services, the carbon saving quantification algorithm for saving paper products can also be used.

It should be noted here that in the embodiment of the present application, the preset carbon saving quantification algorithm is also divided into different types, including but not limited to: a first preset algorithm, which is a carbon saving quantification algorithm for saving paper products (for example: a carbon saving quantification algorithm for saving printed paper receipts), and a second preset algorithm, which is a carbon saving quantification algorithm for reducing travel by public transportation (for example: a carbon saving quantification algorithm for walking).

The first preset algorithm is a quantification algorithm for carbon saving by saving paper products.

In traditional offline methods, users may generate corresponding paper products (such as paper payment vouchers, consumption receipts, appointment forms, etc.) when performing the above-mentioned types of business. Taking paper bills as an example, the generation of paper bills will further cause carbon emissions. Therefore, in this method, the corresponding carbon savings can be calculated by calculating the carbon emissions corresponding to the bill paper.

Specifically, the following formula can be used:

Where _ERy is the carbon savings (in tons of CO2) from paper receipts saved for each online payment. It should be noted that _ERy actually represents the carbon emissions corresponding to the printed paper receipts for each offline payment. Since users can avoid printing paper receipts by paying online, _ERy is used here as the carbon savings from paper receipts saved for each online payment.

i, the merchant type for offline payment;

F _i , is the proportion (percentage) of merchants of type i that use POS payment;

AD _i,y is the number of times a user makes offline payments at merchant type i in year y (unit: times);

EF _y is the baseline emission factor for offline payments in year y (unit: g CO2/time).

It should be noted that EF _y can be determined based on the emission intensity of bill paper manufacturers in different regions. For example, Table 1 shows the emission intensity of bill paper manufacturers in several provinces:

Yunnan Zhejiang Shaanxi Other provinces Emission intensity (tons of CO2/ton of paper) 1.9296 2.0072 1.8834 1.4622

Table 1

The above formula can be used to determine the carbon savings associated with reducing bill generation online. Because the carbon savings associated with reducing bill generation for each online payment transaction are often too small, the above formula uses an annualized calculation. In practice, a threshold for the number of transactions can also be set, and when the number of transactions reaches that threshold, the carbon savings are also calculated. This does not constitute a limitation of the present application.

The second preset algorithm is an algorithm for quantifying the amount of carbon savings from reducing travel by public transportation.

In traditional offline methods, when users perform the above-mentioned types of business, they may go to corresponding business locations (such as banks, shops, restaurants, etc.) to perform the corresponding business. In this process, the transportation used by users may generate carbon emissions. Therefore, in this method, the carbon emissions caused by the transportation used by users for travel can be reduced online, and the corresponding carbon savings can be calculated.

Specifically, the following formula can be used:

P = L * W;

Where L is the distance between the user's location when performing online business and the nearest business location;

W is the average carbon emissions generated by vehicles (the vehicles here can include all types of vehicles using old energy)

P is the carbon emissions generated by using transportation within this distance.

For the above formula, if the user performs the corresponding business online, then the user does not need to go to the corresponding business location. Therefore, the value of P can be used as the carbon saving amount of the user performing the corresponding business online.

S103: Calculating the user's carbon savings based on the behavior data and the determined preset carbon savings quantification algorithm.

Regarding the calculation process, it should be noted that when using the aforementioned first preset algorithm to calculate the carbon savings, each time the user uses the Internet service, the production of paper products can be reduced, so the user's carbon savings is related to the number of times the user uses the Internet service. At the same time, since the carbon emission standards in different regions are different from each other, the user's carbon savings is also related to the region where the user is located. Therefore, in an embodiment of the present application, when using the first preset algorithm to calculate the carbon savings, the user's carbon savings is calculated based on the behavioral data and the determined preset carbon savings quantification algorithm, specifically including: according to the behavioral data, at least determining the number of times the user executes the Internet service, and the user's geographical location when executing the Internet service, and calculating the user's carbon savings based on the determined number of times the user executes the Internet service, the geographical location and the first preset algorithm.

When using the aforementioned second preset algorithm to calculate the carbon savings, the user's carbon savings are related to the distance or number of steps the user walks. Therefore, when using the second preset algorithm to calculate the carbon savings, the user's carbon savings are calculated based on the behavioral data and the determined preset carbon savings quantification algorithm, specifically including: determining at least the number of steps or distance walked by the user based on the behavioral data, and calculating the user's carbon savings based on the determined number of steps or distance walked by the user and the second preset algorithm.

In actual calculations, if there is a one-to-one correspondence between the internet service and the carbon savings quantification algorithm, the carbon savings quantification algorithm can be used to calculate the user's carbon savings. If there is a one-to-many correspondence between the internet service and the carbon savings quantification algorithm, the carbon savings quantification algorithm can be combined to calculate the user's carbon savings. The specific method can be determined based on actual application conditions and does not constitute a limitation of this application.

In addition, in some practical application scenarios, the obtained behavioral data may contain redundant data. For example, the obtained behavioral data of users using online ticketing services may contain amount data, but this amount data is not needed in the process of calculating carbon savings.

In other practical application scenarios, the obtained behavioral data may not be used directly. For example, when calculating carbon savings based on the behavioral data of users using online ticketing services, the calculation is mainly based on the number of times the user uses the online ticketing service. Therefore, it is necessary to perform statistical processing on the multiple behavioral data obtained to determine the corresponding number of times.

Therefore, in the embodiment of the present application, before performing the calculation, data sorting operations such as statistics, screening, and elimination can also be performed on the acquired behavior data. As a feasible way in actual operation, the application (or server) as the execution subject can perform data sorting operations on the behavior data. As another feasible way in actual operation, the application (or server) as the execution subject can make an agreement with the behavior data provider to clearly calculate the behavior data required. In this way, the behavior data provider can perform the above-mentioned data sorting operations on the user's behavior data, and then provide it to the application (or server) as the execution subject. This does not constitute a limitation of the present application.

Based on the above-mentioned behavioral data and carbon saving quantification algorithm, the calculated quantitative value represents the carbon emissions reduced by the user, that is, the carbon saving amount of the user.

Of course, in actual applications, the carbon savings of a user can be calculated according to a set period, or according to the number of times the user uses the Internet service. This does not constitute a limitation of the present application.

S104: Processing specific data corresponding to the user based on the calculated carbon savings of the user and the user ID, wherein the specific data is related to the carbon savings.

As one approach in an embodiment of the present application, after calculating a user's carbon savings, the user's carbon savings over a period of time can be processed based on this carbon savings, such as statistics and analysis. As another approach in an embodiment of the present application, the calculated carbon savings can be converted into points, with the higher the point value, the greater the carbon savings achieved by the user. Accordingly, the service provider can provide different services to the user based on the number of points. This does not constitute a limitation of the present application.

In the embodiment of the present application, the user identifier may include the user's account. Therefore, the specific data may include data within the user's account, including but not limited to: carbon saving points, carbon saving levels, carbon saving medals, and/or virtual items related to carbon saving within the user's account. Of course, the specific data described in the present application may also be other data that can reflect the user's carbon saving amount.

The following describes the method described in Figure 1 in detail in combination with different scenarios:

Nowadays, the relationship between individuals and the Internet is becoming increasingly close. At the same time, with the popularization of mobile Internet, many personal behaviors can be reflected on the Internet, such as using ride-hailing apps for travel and ordering takeout for meals. In other words, users are using Internet services more and more frequently, and Internet services can reduce users' carbon emissions compared to traditional offline methods. Specifically:

Scenario 1: A user uses online ticketing services.

Online ticketing services include online booking, purchase, and refund services for train, air, boat, movie, and other ticket types. Compared to traditional ticketing services, where users visit ticketing locations, online ticketing services can reduce travel, particularly carbon emissions from public transportation. They can also reduce the amount of paper products (e.g., printed receipts) generated during ticket purchase and refund processes.

When a user uses the online ticketing service, the service provider (such as a ticketing website) will generate online ticketing data based on the user's online ticketing behavior. This online ticketing data is the user's behavior data when using the online ticketing service. Then, the user's carbon savings can be calculated based on this behavior data.

In this scenario, the execution entity can be either an application client with carbon savings calculation functionality (hereinafter referred to as the "calculation application") or a server with carbon savings calculation functionality. The execution entity is described as the calculation application. Furthermore, since online ticketing services are typically provided by ticketing websites, users can access these services through the corresponding applications of these ticketing websites (hereinafter referred to as the "ticketing application"). The behavioral data generated by users using these services can be generated by the server of the ticketing website (hereinafter referred to as the "ticketing server").

Based on this, as shown in Figure 2a, assuming that the user purchases tickets online, the process of obtaining and calculating the carbon savings in this scenario is as follows:

S201: The computing application sends an acquisition request carrying user information to a ticketing server to obtain the ticketing data of the user.

In actual applications, when a user needs to purchase a ticket online, he or she can send a ticket purchase request to the ticket server through the corresponding ticket application. The ticket purchase request may carry user information (such as the user's ID number, name, ticket account registered in the ticket application, etc.) and ticket purchase information (such as the type of ticket to be purchased, time, location, etc.). Then, after receiving the ticket purchase request issued by the ticket application, the ticket server will issue a ticket according to the online ticket purchase request and generate the user's ticket data for record.

Based on the above content, various types of user information of the user are pre-stored in the computing application, so the user information carried in the acquisition request may include: the user's ID number, name, ticketing account registered in the ticketing application, etc.

During the actual acquisition process, the computing application can send a request containing the ticket account information pre-entered by the user and the ticket server corresponding to the ticket account to the ticket server, thereby obtaining ticket data related to the ticket account information. Of course, the computing application can also obtain the user's ticket data for a specified period of time according to a set cycle. For example, the computing application can send an acquisition request to the ticket server on a daily basis and specify in the acquisition request that only the user's ticket data within 24 hours be obtained. In other words, the acquisition request also carries time information. Of course, this does not constitute a limitation of the present application.

Regarding the acquisition process in this step, in addition to the above-mentioned method of active acquisition by the computing application, the computing application can also send the user's account registered in the computing application (hereinafter referred to as the computing account) and the user's ticketing account to the ticketing server in advance, so that the ticketing server can dynamically obtain the ticketing data related to the ticketing account based on the ticketing account, and the ticketing server can actively push the ticketing data related to the ticketing account to the computing application based on the computing account. Of course, if the computing application itself has an online ticketing service and the user uses the online ticketing service provided by the computing application, then in this case, the computing application directly obtains the ticketing data generated by it. This does not constitute a limitation of the present application.

S202: The ticketing server receives the acquisition request, and determines the ticketing data corresponding to the user information according to the user information carried in the acquisition request, and feeds back the determined ticketing data to the computing application.

The ticketing data at least includes the user ID and identification information reflecting the type of ticketing service. The ticketing data here refers to the user's behavior data when using the online ticketing service.

Of course, if the acquisition request includes time information, the ticketing server will acquire the ticketing data of the user that matches the time information based on the time information.

Furthermore, as described in the aforementioned method, the computing application can request the ticketing server to provide the ticketing data required by the computing application. This allows the ticketing server to perform data sorting on the user's ticketing data and then send the sorted ticketing data to the computing application. For example, the ticketing server may store ticketing data such as the purchase amount, origin, and destination. However, this data is not useful for calculating carbon savings. Therefore, the ticketing server may sort the user's ticketing data, removing such data, and then send the sorted ticketing data to the computing application.

S203: After the calculation application obtains the ticketing data, it determines that the ticketing data is associated with the user's account based on the user ID contained in the ticketing data, and determines the carbon saving quantification algorithm required for calculation based on the identification information contained in the ticketing data.

Among them, similar to the above content, in one way, the correspondence between the identification information and the carbon saving quantification algorithm is established in advance. Then, the calculation application can determine the carbon saving quantification algorithm used to calculate the carbon saving for the ticketing data based on the correspondence between the identification information and the carbon saving quantification algorithm: the carbon saving quantification algorithm that avoids users from traveling by public transportation, and the carbon saving quantification algorithm that reduces the printing of paper tickets.

In another way, the computing application can determine through identification information that the application scenario is an online ticketing service. The corresponding carbon saving quantification algorithm is pre-defined for this scenario. Therefore, the computing application can determine through identification information that the carbon saving quantification algorithm in the online ticketing service scenario is further: a carbon saving quantification algorithm that avoids users from traveling by public transportation, and a carbon saving quantification algorithm that reduces the printing of paper tickets.

S204: Calculate the carbon savings of the user using the online ticketing service based on the determined carbon savings quantification algorithm and the acquired ticketing data, and process the specific data corresponding to the user based on the calculated carbon savings.

In this scenario, the ticketing application has a positioning function, which can determine the user's location information (ie, user location information) when the user issues an online ticketing instruction.

During the calculation process, the computing application can count the number of times a user uses the online ticketing service based on the ticket number in the acquired ticketing data (understandably, the ticket number uniquely identifies a single online ticketing service). Furthermore, the computing application can obtain the user's location information from the ticketing application when using the online ticketing service and, based on the region corresponding to the user's location information, determine _EFy in the aforementioned formula. Thus, based on this information, the carbon savings from reducing paper ticket printing for each use of the online ticketing service can be calculated. If a user uses the online ticketing service multiple times in the same region, since _EFy corresponds to the same region, the carbon savings from reducing paper ticket printing for the user = n* _ERy . If a user uses the online ticketing service multiple times in different regions, since _EFy corresponds to different regions, the carbon savings from reducing paper ticket printing for the user is the cumulative carbon savings for each region.

In addition, the calculation application can also determine the location of the ticketing location closest to the user's location (such as a train station) based on the user's location information when the user uses the online ticketing service, and calculate the distance L between the user's location and the ticketing location. Then, based on the average carbon emissions W generated by the transportation in the above formula, calculate the amount of carbon saved by avoiding the user's travel by transportation within this distance.

Therefore, in this scenario, the user's carbon savings may include the carbon savings from avoiding traveling by public transportation, and may also include the carbon savings from reducing the printing of paper receipts.

The amount of carbon saved by using the online ticketing service can then be converted into points. Once the user logs in to the app that executes the service, they can view their accumulated points. In other words, this approach allows users to intuitively understand the amount of carbon emissions reduced, or carbon saved, by using the online ticketing service.

Scenario 2: Users use online payment services.

Online payment services may include face-to-face payment, transfer and other services conducted online. Compared with traditional payment services, they can reduce the amount of paper products (such as printed paper receipts) generated during the payment process, thereby reducing carbon emissions.

When a user uses the online payment service, the service provider of the online payment will generate online payment data based on the user's online payment behavior. The online payment data is the user's behavior data of using the online payment service. Then, the user's carbon savings can be calculated based on the behavior data.

Similar to the previous scenario, the execution entity in this scenario can also be a computing application (or server) with carbon savings calculation capabilities. The description will still be based on the execution entity being the computing application. Furthermore, in actual scenarios, service providers that can provide online payment services may include: product websites, payment platforms, and/or banks. For example, using the payment platform as an example, users can access online payment services through the payment platform's corresponding application (hereinafter referred to as the "payment application"). The behavioral data generated by users using the online payment service can be generated by the payment platform's server (hereinafter referred to as the "payment server").

Based on this, as shown in Figure 2b, assuming that a user makes an online payment to a target user through a payment platform, and both the user and the target user have corresponding accounts registered on the payment platform, the process of obtaining and calculating the carbon savings in this scenario is as follows:

S211: The computing application sends an acquisition request carrying user information to the payment server to obtain the payment log data of the user.

It should be noted that in actual applications, when a user needs to make an online payment, they can send a payment request to the payment server through the corresponding payment application. The payment request carries user information (such as the user's payment account registered on the payment platform), target user information (such as the target user's target account registered on the payment platform), and payment information (such as the payment amount). After receiving the payment request sent by the payment application, the payment server will obtain the funds matching the payment amount from the user's payment account based on the received payment request, allocate them to the target user's target account, and generate payment log data.

Based on the above content, the user information carried in the acquisition request may include: the payment account registered by the user on the payment platform. Similar to the above scenario, in this scenario, the computing application may also send an acquisition request carrying the payment account to the payment server based on the payment account pre-entered by the user and the payment server corresponding to the ticketing account, so as to obtain the payment log data related to the payment account. In addition, the computing application may also send an acquisition request to the payment server according to a set period to obtain the user's payment log data, and the acquisition request may also carry time information, which allows the computing application to obtain the user's payment log data within a specified time period. I will not go into details here.

Regarding the acquisition process in this step, in addition to the above-mentioned method of active acquisition by the computing application, the computing application can also send the computing account registered by the user in the computing application and the user's payment account to the payment server in advance, so that the payment server can dynamically obtain the payment log data related to the payment account based on the payment account, and the payment server can actively push the payment log data related to the payment account to the computing application based on the computing account. Of course, if the computing application itself has an online payment service and the user uses the online payment service provided by the computing application, then in this case, the computing application directly obtains the payment log data generated by it. This does not constitute a limitation of the present application.

S212: The payment server receives the acquisition request, and determines the payment log data corresponding to the user information based on the user information carried in the acquisition request, and feeds back the determined payment log data to the computing application.

The payment log data includes at least the user ID and identification information reflecting the payment service type. The payment log data here refers to the user's behavior data regarding their use of online payment services. Of course, if the acquisition request includes time information, the payment server will retrieve the payment log data for the user matching the time information based on the time information.

Similarly, in the process of calculating carbon savings, data such as the payment amount, target user, and payment time in the payment log data are not required. Therefore, the calculation application can perform data sorting operations on the received payment log data to eliminate unnecessary data in the calculation process, or, based on the agreement with the payment server, enable the payment server to perform corresponding data sorting operations before sending the payment log data.

S213: After obtaining the payment log data, the computing application will determine that the payment log data is associated with the user's account based on the user ID contained in the payment log data, and determine the carbon savings quantification algorithm required for calculation based on the identification information contained in the payment log data.

Among them, similar to the above content, in one way, the correspondence between the identification information and the carbon saving quantification algorithm is established in advance. Then, the calculation application can determine the carbon saving quantification algorithm used to calculate the carbon saving amount for the payment log data based on the correspondence between the identification information and the carbon saving quantification algorithm: the carbon saving quantification algorithm that reduces the printing of paper receipts.

In another way, the computing application can determine through identification information that the application scenario is an online payment service. The scenario predefines the corresponding carbon saving quantification algorithm. Therefore, the computing application can determine through identification information that the carbon saving quantification algorithm of the online payment service is further: a carbon saving quantification algorithm for reducing the printing of paper receipts.

S214: Calculate the carbon savings of the user using the online payment service based on the determined carbon savings quantification algorithm and the acquired payment log data, and process the specific data corresponding to the user based on the calculated carbon savings.

In this scenario, the payment application has a positioning function and can determine the user's location information (ie, user location information) when the user issues an online payment instruction.

Therefore, during the calculation process, the calculation application can count the number of times a user uses the online payment service based on the payment order number (understandably, the payment order number uniquely identifies an online payment service) obtained from the payment log data. Furthermore, the calculation application can obtain the user's location information when using the online payment service through the payment application and, based on the region corresponding to this user's location information, determine _EFy in the aforementioned formula. Based on this information, the carbon savings from reducing paper receipt printing can be calculated for each use of the online payment service. If a user uses the online payment service multiple times in the same region, since _EFy corresponds to the same region, the carbon savings from reducing paper receipt printing = n* _ERy . If a user uses the online payment service multiple times in different regions, since _EFy corresponds to different regions, the carbon savings from reducing paper receipt printing are the cumulative carbon savings from each region.

Similarly, in this scenario, the calculated carbon savings can be converted into points, so that users can intuitively know the carbon savings they have made by using online payment services.

Scenario 3: Users use the online reservation service.

Online reservation services may include: online restaurant reservations, hotel reservations, venue reservations, hospital registration and other services. Compared with the traditional method of users going to the business location to make reservations, online reservation services can save users from going to the business location, thereby reducing the carbon emissions generated by users' use of transportation.

When a user uses the online appointment service, the service provider that provides the online appointment (such as a hospital website) will generate online appointment data based on the user's online appointment behavior. The online appointment data is the user's behavior data of using the online appointment service. Then, the user's carbon savings can be calculated based on the behavior data.

Similar to the previous scenario, the execution entity in this scenario can also be an application or server with carbon savings calculation capabilities. The description will still be based on the execution entity being a calculation application. Furthermore, in actual scenarios, service providers that can provide online reservation services may include: reservation platforms, hospitals, hotels, and/or restaurants. Taking the reservation platform as an example, users can access the online reservation service through the reservation platform application (hereinafter referred to as the "reservation application"), and the behavioral data generated by users when using the online reservation service can be generated by the reservation platform server (hereinafter referred to as the "reservation server").

Based on this, as shown in Figure 2c, assuming that the user registers online through the appointment platform, the process of obtaining and calculating the carbon savings in this scenario is as follows:

S221: The computing application sends an acquisition request carrying user information to the reservation server to obtain the user's registration data.

It should be noted that in actual applications, when a user needs to register online, he or she can send a registration request to the reservation server through the corresponding reservation application. The registration request carries user information (such as: user's medical insurance information, user name, ID number, reservation account registered by the user in the reservation application, etc.), registration type information (such as: expert number, ordinary number, etc.), and the hospital information selected by the user (such as: hospital grade, hospital name, etc.). Then, after receiving the registration request sent by the reservation application, the reservation server will register with the corresponding hospital according to the registration request, and after the registration is successful, it will feedback the electronic registration form to the reservation application, and generate the user's registration data based on the electronic registration form and record it.

Based on the above, the computing application pre-stores various user information about the user. Therefore, the user information included in the retrieval request may include the user's medical insurance information, name, ID number, and the user's registered appointment account in the appointment application. Similar to the above scenario, the computing application can periodically send retrieval requests to the appointment server, requesting the user's registration data for the specified time period. This will not be described in detail here.

Regarding the acquisition process in this step, in addition to the above-mentioned method of active acquisition by the computing application, the computing application can also make an agreement with the reservation server. Specifically, the computing application can send the computing account registered by the user in the computing application and the user's reservation account to the reservation server, so that the reservation server can dynamically obtain the registration data related to the reservation account based on the reservation account, and the reservation server can actively push the registration data related to the payment account to the computing application. Of course, if the computing application itself has an online reservation service and the user uses the online reservation service provided by the computing application, then in this case, the computing application directly obtains the registration data generated by it. This does not constitute a limitation of the present application.

S222: The reservation server receives the acquisition request, and determines the registration data corresponding to the user information according to the user information carried in the acquisition request, and feeds back the determined registration data to the computing application.

The registration data at least includes the user ID and identification information reflecting the type of reservation service. The registration data here refers to the user's behavior data of using the online reservation service.

Similarly, data such as the registration type and date of consultation in the registration data are not useful for calculating carbon savings, so similar data sorting operations can be used to sort the registration data. For details, please refer to the above content and will not be explained in detail here.

S223: After the calculation application obtains the registration data, it determines that the registration data is associated with the user's account based on the user ID contained in the registration data, and determines the carbon saving quantification algorithm required for calculation based on the identification information contained in the registration data.

In one approach, a correspondence between identification information and a carbon-saving quantification algorithm is pre-established. The calculation application, based on the correspondence between the identification information and the carbon-saving quantification algorithm, can determine that the carbon-saving quantification algorithm used to calculate the carbon savings for registration data is a carbon-saving quantification algorithm that avoids users from traveling by public transportation.

In another way, the computing application can determine through identification information that the application scenario is an online reservation service. The scenario predefines the corresponding carbon saving quantification algorithm. Therefore, the computing application can determine through identification information that the carbon saving quantification algorithm of the online reservation service is further: a carbon saving quantification algorithm that avoids users from traveling by public transportation.

S224: Calculate the carbon savings of the user using the online reservation service based on the determined carbon savings quantification algorithm and the acquired registration data, and process the specific data corresponding to the user based on the calculated carbon savings.

During the calculation process, if the appointment booking app has a positioning function and can determine the user's location (i.e., user location) when the user issues an online registration instruction, the registration data obtained by the calculation app will also include the user's location when using the online registration service. At the same time, based on the hospital address included in the registration data, the calculation app can determine the location of the hospital and calculate the distance L between the user's location and the hospital. Then, based on the average carbon emissions W generated by transportation in the above formula, the calculation app can calculate the carbon savings from avoiding transportation within this distance.

Similarly, in this scenario, the calculated carbon savings can be converted into points, so that users can intuitively know the carbon savings they have made by using the online reservation service.

Scenario 4: The user uses the online payment service.

Online bill payment services include those for water, electricity, natural gas, traffic fines, and other bills. These services allow users to pay bills without having to visit a payment location, reducing carbon emissions from commuting to the payment location. They also reduce the amount of paper receipts needed to be printed during the payment process.

When a user uses the online payment service, the service provider will generate online payment data based on the user's online payment behavior. The online payment data is the user's behavior data of using the online payment service. Then, the user's carbon savings can be calculated based on the behavior data.

Similar to the previous scenario, this scenario is described using a computing application as the executing entity. Furthermore, in actual scenarios, service providers capable of providing online payment services may include online payment platforms, payment websites, and/or banks. Taking the payment platform as an example, users can access the online payment service through the payment platform's corresponding application (hereinafter referred to as the payment application). The behavioral data generated by users using the online payment service may be generated by the payment platform's server (hereinafter referred to as the payment server).

Based on this, as shown in Figure 2d, assuming that a user pays a traffic fine online through a payment platform, and that the user has a corresponding account registered on the payment platform with sufficient funds, the process of obtaining and calculating the carbon savings in this scenario is as follows:

S231: The computing application sends an acquisition request carrying user information to the payment server to obtain the payment data of the user.

It should be noted that in actual applications, when a user needs to pay online, they can send a payment request to the payment server through the corresponding payment application. The payment request carries user information (such as the user's driver's license number, ID number, fine ticket number, the payment account registered by the user on the payment platform, etc.). Then, after receiving the payment request sent by the payment application, the payment server will deduct the corresponding amount from the user's account according to the payment request, and then pay the fee on the corresponding transportation fee payment website. After the payment is successful, the electronic payment voucher will be fed back to the payment application, and the user's payment data will be generated based on the electronic payment voucher and recorded.

Based on the above, the computing application pre-stores various user information about the user. Therefore, the user information carried in the acquisition request may include: the user's driver's license number, ID number, fine ticket number, and the payment account registered by the user on the payment platform. Similar to the above scenario, the computing application can send acquisition requests to the payment server on a set periodic basis, requesting the user's payment data for the specified time period. This will not be further explained here.

Regarding the acquisition process in this step, in addition to the above-mentioned method of active acquisition by the computing application, the computing application can also make an agreement with the payment server (the agreement process can be referred to the aforementioned scenario and will not be repeated here), and the payment server actively pushes the payment data carrying the above-mentioned user information to the computing application. Of course, if the computing application itself has an online payment service and the user uses the online payment service provided by the computing application, then, in this case, the computing application directly obtains the payment data generated by it. This does not constitute a limitation of the present application.

S232: The payment server receives the acquisition request, and determines the payment data corresponding to the user information based on the user information carried in the acquisition request, and feeds back the determined payment data to the calculation application.

The payment data includes at least the user ID and identification information reflecting the payment service type. The payment data here refers to the user's behavior data regarding their use of the online payment service. In practical applications, data collation can be performed on the payment data. For details, please refer to the aforementioned content and will not be further explained here.

S233: After the calculation application obtains the payment data, it determines that the payment data is associated with the user's account based on the user ID contained in the payment data, and determines the carbon saving quantification algorithm required for calculation based on the identification information contained in the payment data.

Among them, similar to the above content, in one way, the correspondence between the identification information and the carbon saving quantification algorithm is established in advance. Then, the calculation application can determine the carbon saving quantification algorithm used to calculate the carbon saving amount for the payment data based on the correspondence between the identification information and the carbon saving quantification algorithm: the carbon saving quantification algorithm that avoids users from traveling by public transportation, and the carbon saving quantification algorithm that reduces the printing of paper receipts.

In another way, the computing application can determine through identification information that the application scenario is an online payment service. The scenario predefines the corresponding carbon saving quantification algorithm. Therefore, the computing application can determine through identification information that the carbon saving quantification algorithm in the online payment service scenario is further: a carbon saving quantification algorithm that avoids users from traveling by public transportation, and a carbon saving quantification algorithm that reduces the printing of paper receipts.

S234: Calculate the carbon savings of the user using the online payment service based on the determined carbon savings quantification algorithm and the acquired payment data, and process the specific data corresponding to the user based on the calculated carbon savings.

In this scenario, the payment application has a positioning function, which can determine the user's location information (ie, user location information) when the user issues an online payment instruction.

Therefore, during the calculation process, the calculation application can count the number of times a user uses the online payment service based on the payment slip number in the obtained payment data (understandably, the payment slip number uniquely identifies an online payment service). Furthermore, the calculation application can also obtain the user's location information when using the online payment service through the payment application, and determine the _EFy in the aforementioned formula based on the region corresponding to the user's location information. Based on this, the carbon savings from reducing paper receipt printing can be calculated for each use of the online payment service. If a user uses the online payment service multiple times in the same region, since the _EFy corresponding to that region is the same, the carbon savings from reducing paper receipt printing = n* _ERy . If a user uses the online payment service multiple times in different regions, since the _EFy corresponding to each region is different, the carbon savings from reducing paper receipt printing is the cumulative carbon savings from each region.

In addition, the calculation application can also determine the payment location closest to the user's location (such as a bank) based on the user's location information when the user uses the online payment service, and calculate the distance L between the user's location and the payment location. Then, based on the average carbon emissions W generated by the transportation in the above formula, calculate the carbon savings by avoiding the user's travel by transportation within this distance.

Therefore, in this scenario, the user's carbon savings may include the carbon savings from avoiding traveling by public transportation, and may also include the carbon savings from reducing the printing of paper receipts.

In this scenario, the calculation application will convert the calculated carbon savings into points, allowing users to intuitively know the carbon savings they have made by using the online payment service.

In addition to the above scenarios, walking can also help reduce carbon emissions, as shown in the following scenarios:

Scenario 5: The user travels on foot and uses health services to monitor walking data.

Walking can reduce the carbon emissions associated with using public transportation. In the aforementioned scenarios, users can walk to the aforementioned service locations, such as to a hospital for registration, to a ticketing location for ticket purchase, or to a payment location for payment, all of which can help reduce carbon emissions.

The walking data can be generated by a health service application with a walking data collection function (hereinafter referred to as a "walking application"), and then the user's carbon savings can be calculated based on the walking data. The walking data includes at least one of the number of steps and the walking distance.

Similar to the previous scenario, this scenario is described with the computing application as the execution subject. Based on this, as shown in Figure 2e, the process of obtaining and calculating the carbon savings in this scenario is as follows:

S241: The computing application sends an acquisition request carrying user information to the walking application to obtain the walking data of the user.

It should be noted that, in actual application, the walking data can be obtained by the walking application through corresponding acquisition algorithms, models and/or sensing devices (such as smart bracelets, smart watches, etc.). This does not constitute a limitation of the present application. It is assumed that the walking data includes: the number of steps of the user, the location information of the user during the walking process, the walking distance, etc. At the same time, the walking data also carries user information (such as the account registered by the user in the walking application).

Accordingly, the acquisition request sent by the computing application carries the user's account, so as to obtain the walking data associated with the account.

Regarding the acquisition process in this step, in addition to the aforementioned method of active acquisition by the computing application, the computing application can also enter into an agreement with the walking application, whereby the walking application actively pushes walking data containing the aforementioned user information to the computing application. Of course, if the computing application itself has the ability to collect walking data, then in this case, the computing application directly acquires the walking data generated by it. This does not constitute a limitation of the present application.

S242: The walking application receives the acquisition request, and determines walking data corresponding to the user information according to the user information carried in the acquisition request, and feeds back the determined walking data to the computing application.

The walking data at least includes a user ID and identification information reflecting the walking behavior type. The walking data here refers to the behavior data corresponding to the user's walking.

S243: After the computing application obtains the walking data, it determines that the walking data is associated with the account based on the user ID included in the walking data, and determines the carbon saving quantification algorithm required for calculation based on the identification information included in the walking data.

Among them, similar to the above content, in one way, the correspondence between the identification information and the carbon saving quantification algorithm is established in advance. Then, the calculation application can determine the carbon saving quantification algorithm used to calculate the carbon saving for walking data based on the identification information: a carbon saving quantification algorithm that avoids users from traveling by public transportation.

In another way, the computing application can determine through identification information that the application scenario is user walking. The scenario predefines the corresponding carbon saving quantification algorithm. Therefore, the computing application can determine through identification information that the carbon saving quantification algorithm for user walking is: a carbon saving quantification algorithm that avoids users from traveling by public transportation.

S244: Calculate the carbon savings of the user's walking according to the determined carbon savings quantification algorithm and the acquired walking data, and process the specific data corresponding to the user according to the calculated carbon savings.

When calculating the carbon savings corresponding to walking data, since the walking data contains the user's location information during the walking process, the calculation application can determine the walking distance of the user on the traffic route based on the location information in the walking data. Therefore, based on the walking distance and the carbon savings quantification algorithm of walking, the carbon savings of the user's walking can be determined.

Of course, if the computing application (or server) serving as the execution entity itself has the function of collecting user walking data, then in this case, the walking data generated by it can be directly obtained by the computing application (or server).

Based on the above content, the data processing method in this application can aggregate the fragmented behavioral data of users, and based on the aggregated various behavioral data, combined with the corresponding carbon saving quantification algorithm, calculate the carbon emissions reduced by the user, that is, the user's carbon saving. The corresponding service provider can further process the data corresponding to the user based on the calculated carbon saving of the user. This method will enable users to more intuitively know their own carbon saving without having to query and calculate it by themselves, which is more convenient for users. In addition, the service provider can perform data processing methods such as point accumulation and account level improvement on users based on the user's carbon saving, and associate the corresponding service with the user's carbon saving.

As a practical application method, the content described in the embodiments of the present application (including the method shown in Figure 1 above, the scenarios shown in Figures 2a to 2e, and subsequent content) can all be based on the architecture shown in Figure 3. Specifically, in Figure 3, the application client obtains fragmented user behavior data generated by users, third-party applications, and third-party services. These behavior data include behavior data generated by users using different Internet services. The application client sends the obtained behavior data to the server to calculate the user's carbon savings and perform corresponding data processing based on the carbon savings. The processing results are then displayed to the user through the application client. This does not constitute a limitation on the present application.

In an embodiment of the present application, in the process of calculating the user's carbon savings, since the obtained user's behavior data contains different types of behavior data, and there are certain differences between the carbon savings quantification algorithms corresponding to each type of behavior data, the user's carbon savings can be calculated by separately calculating the carbon savings corresponding to different types of behavior data. That is, in an embodiment of the present application, the user's carbon savings is calculated based on the obtained behavior data and the preset carbon savings quantification algorithm. Specifically, for each type of behavior data, the carbon savings corresponding to the type of behavior data is determined based on the behavior data of that type and the preset carbon savings quantification algorithm.

As mentioned above, the acquired behavior data usually contains corresponding identification information, and the type of the behavior data can be determined based on the identification information. The identification information may include: the type identification bit in the business order number, type information, etc.

For example, the business data of an online payment service includes the order number of each online payment service, and the identification bit in the order number can be used to determine that the service is an online payment service. For another example, the business data of an online reservation service includes business type information, and the business type information can be used to determine that the service is an online reservation service. This does not constitute a limitation of the present application.

For each type of business data identified, the corresponding carbon savings quantification algorithm can be used to calculate the carbon savings corresponding to that type of business data. For details, please refer to the above content and will not be elaborated here.

After the above content, after the user's carbon saving amount is determined, the specific data corresponding to the user can be processed according to the user's carbon saving amount. As a method in an embodiment of the present application, the specific data corresponding to the user is processed according to the calculated carbon saving amount of the user, which may include: obtaining the user's carbon saving amount in multiple Internet services within a preset period of time, accumulating the obtained carbon saving amounts, and processing the specific data according to the accumulated carbon saving amount.

Specifically, in actual operation, users may use different Internet services at any time. The above content provides a method for calculating the user's carbon savings according to the preset cycle time, so the user's carbon savings within the preset cycle time can be obtained and accumulated.

More specifically, the process of processing the specific data according to the accumulated carbon savings can be: adding the calculated accumulated carbon savings of the user within a preset period to the total carbon savings of the user to obtain an updated total carbon savings, and processing the specific data according to the updated total carbon savings.

In the above-mentioned process of accumulating carbon savings, the carbon savings corresponding to each type of business can be counted separately and added to the historical total carbon savings corresponding to each business. Alternatively, after calculating the carbon savings of each type of business, the carbon savings of each type of business can be accumulated to obtain the user's total carbon savings. This total carbon savings reflects the statistical value of the carbon savings corresponding to the user's fragmented behavioral data. Of course, the method of accumulating carbon savings does not constitute a limitation of this application.

In the embodiment of the present application, the user continuously performs new behaviors, and accordingly, behavior data is continuously generated. Then, the user's carbon savings can be calculated based on the newly generated behavior data and accumulated with the user's total carbon savings.

The updated carbon saving amount can be displayed to the user so that the user can intuitively know his or her own carbon saving amount. Of course, as a method in an embodiment of the present application, for the service provider, it can also be based on the updated carbon saving amount converted into points. In other words, the calculated carbon saving amount accumulated by the user within a preset period is accumulated with the total carbon saving amount of the user. It may include: according to a preset conversion rule, the calculated carbon saving amount of the user is converted into points, and the converted points are accumulated with the total points of the user to obtain the updated total points.

The preset conversion rules may include a corresponding conversion coefficient, which can then be used to convert the user's carbon savings into corresponding points. In practice, service providers can provide different services to users based on the number of points they have, such as providing users with products that can be redeemed using points or offering discounts based on the number of points they have.

The above conversion method is not limited to points, but can also be the user's account level, medals, etc. This does not constitute a limitation of this application.

The above-mentioned accumulation process of points can be automatically accumulated according to a set period, such as: accumulating the points converted on that day with the user's total points every day. Accumulation can also be performed according to the user's confirmation instruction. For this method, a control for accumulating points can be provided to the user. The control can take various forms such as floating controls, embedded controls, pop-up controls, etc. For example, as shown in Figure 4a, the application interface is embedded with a control for accumulating points.

Based on this, adding the converted points to the total points of the user may include: receiving a confirmation instruction issued by the user through the control, and adding the converted points to the total points of the user.

For example, for the control in FIG4a , the user can accumulate points by clicking the control.

In practical applications, one optional method is to calculate and display the total points for different types of behavior (including different types of business behavior and walking). That is, as shown in Figure 4b, the interface includes different types of behavior items, and the total points corresponding to each type of behavior is displayed in each type of item. In addition, in practical applications, another optional method is to display the global total points. That is, as shown in Figure 4c, the total points in Figure 4c is the global total points corresponding to all user behavior data. This does not constitute a limitation of this application.

In addition, as an extension method in practical applications, different users can obtain each other's unaccumulated points, that is, the method also includes: receiving an acquisition instruction issued by the user for the unaccumulated points of other users, and according to the acquisition instruction, obtaining all or part of the unaccumulated points of other users, and adding all or part of the obtained points to the total points of the user.

The other users are users who have an association relationship with the user, such as the other users are the contact users in the user's contact list.

In actual applications, the server will calculate points for each user, and the server will store the association relationships between different users. Then, for any user, the server will determine the users who have an association relationship with the user based on the stored association relationships between different users, and send the points data of each user to the user, so that the points data of each user can be displayed through the application used by the user.

The points data of each user includes at least one of the current total points of each user, the total points corresponding to each type of behavior data, and unaccumulated points.

In this way, the user can intuitively view data such as the unaccumulated points of each contact user in the contact list, the total points of each contact user, the total points corresponding to each type of behavioral data, etc. through the application he uses.

If the user clicks to obtain the unaccumulated points of a contact user, then the application used by the user will send a request to the server. The server will transfer the unaccumulated points of the contact user to the user who issued the request based on the user ID in the request.

Of course, in an optional method, when a user obtains points that have not been accumulated by other users, he or she does not obtain all the points that have not been accumulated by other users, but there is a certain quantity limit, such as: the actual amount obtained is 10% of the points that have not been accumulated by other users.

As shown in Figure 5a, a practical application scenario is provided. As can be seen in Figure 5a, each contact item in the user's contact list displays unaccumulated points. At this time, the user can click on any contact to obtain its unaccumulated points. Of course, as shown in Figure 5b, when the user clicks on any contact, the detailed interface of the contact will be entered, which displays the unaccumulated points corresponding to different types of behaviors of the contact. The user can further click on different behavior items to obtain the unaccumulated points corresponding to each type of behavior. It can be seen that the above method can increase interaction and fun between users. Of course, the scenarios shown in Figures 5a and 5b do not constitute a limitation of this application.

In an embodiment of the present application, a method of allocating virtual items to users may also be adopted, that is, the method further includes: determining the updated total points of the user, and allocating virtual items that match the updated total points to the user based on the updated total points.

The virtual items may include: virtual trees, virtual medals, virtual medals, etc.

Of course, in the embodiment of the present application, virtual items have different display states depending on the total points. Specifically, based on the pre-defined points intervals, the points interval into which the user's total points fall is determined, and the display state of the user's virtual item is determined based on the pre-set correspondence between the points intervals and the virtual item display state. The display state of the virtual item includes, but is not limited to, the size, shape, color, etc. of the virtual item.

For example, assuming the system is pre-divided into three integral intervals, ordered from smallest to largest, and the sizes of the virtual trees corresponding to these three integral intervals also increase from smallest to largest, then the user's total points can be determined based on their carbon savings. The interval into which their total points fall can then be determined to determine the size of the virtual tree assigned to them. As the user's carbon savings increase, their total points also increase accordingly. When their total points reach the next integral interval, the size of their virtual tree also increases accordingly.

For another example, the user's virtual item can also be a virtual medal. As the user's carbon savings increase, the user's total points also increase, and the virtual medal can change from bronze to silver and then to gold.

Of course, the above examples do not constitute a limitation to this application.

In actual applications, there are different types of virtual trees, and the total amount of points corresponding to each type of virtual tree is different. The total amount of points of the user reflects the user's carbon savings. Therefore, the points corresponding to the virtual tree actually reflect the amount of carbon emissions that this type of tree can absorb in the natural environment.

As an extension of this method, the service provider can plant trees in the name of the user with the help of a third-party fund sponsorship. Specifically, the service provider will determine the tree information (including tree species, tree age, etc.) that matches the total number of points of the user based on the total number of points, and send the tree information and the user information of the user to the third-party fund, so that the third-party fund can determine the corresponding trees based on the tree information and plant trees in the name of the user.

This method converts the user's carbon savings into actual trees, which is beneficial to environmental protection and can also promote users' environmental awareness.

The above is a data processing method provided in the embodiment of the present application. Based on the same idea, the embodiment of the present application also provides a data processing device, as shown in FIG6 , including:

Acquisition module 601, acquiring user behavior data, wherein the behavior data is generated when the user uses an Internet service, and the behavior data includes a user identifier indicating the user's identity and identification information indicating the Internet service corresponding to the behavior data;

A determination module 602 determines at least one preset carbon savings quantification algorithm based on the identification information of the Internet service;

A calculation module 603 calculates the carbon savings of the user based on the behavior data and the determined preset carbon savings quantification algorithm;

The processing module 604 processes specific data corresponding to the user according to the calculated carbon saving amount of the user and the user identifier, wherein the specific data is related to the carbon saving amount.

The at least one preset carbon saving quantification algorithm specifically includes: a first preset algorithm, which is a carbon saving quantification algorithm for saving paper products; and a second preset algorithm, which is a carbon saving quantification algorithm for reducing travel by public transportation.

The determining module 602 determines at least one preset carbon saving quantification algorithm based on the identification information of the Internet service and the pre-stored correspondence between the Internet service and the carbon saving quantification algorithm.

When the first preset algorithm is used to calculate the carbon savings, the calculation module 603 determines at least the number of times the user performs the Internet service and the geographical location of the user when performing the Internet service based on the behavioral data, and calculates the carbon savings of the user based on the determined number of times the user performs the Internet service, the geographical location and the first preset algorithm.

When the second preset algorithm is used to calculate the carbon savings, the calculation module 603 determines at least the number of steps walked or the distance walked by the user based on the behavioral data, and calculates the carbon savings of the user based on the determined number of steps walked or the distance walked by the user and the second preset algorithm.

The Internet services specifically include: at least one of electronic payment, online reservation, online ticketing, online payment service, and health service.

The processing module 604 obtains the carbon savings of the user in multiple Internet services within a preset period, accumulates the obtained carbon savings, and processes the specific data according to the accumulated carbon savings.

The processing module 604 adds the calculated carbon savings accumulated by the user within a preset period to the user's total carbon savings to obtain an updated total carbon savings, and processes the specific data according to the updated total carbon savings.

Furthermore, the processing module 604 converts the calculated carbon saving amount of the user into points according to a preset conversion rule, and accumulates the converted points with the total points of the user to obtain an updated total points.

A control for accumulating points is provided to the user, and the processing module 604 receives a confirmation instruction issued by the user through the control and accumulates the converted points with the total points of the user.

The device also includes: a points acquisition module 605, which receives an acquisition instruction issued by the user for points not accumulated by other users, and acquires all or part of the points not accumulated by other users according to the acquisition instruction, and accumulates all or part of the acquired points with the total points of the user.

The apparatus further includes an allocation module 606 for determining the updated total points of the user and allocating virtual items that match the updated total points to the user based on the updated total points. The virtual items have different display states depending on the total points.

The present invention is described with reference to the flowcharts and/or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of processes and/or boxes in the flowchart and/or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and/or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims (26)

1. A data processing method, characterized in that it includes: Acquire user behavior data, which is generated when the user uses Internet services. The behavior data includes: a user identifier indicating the user's identity, and identifier information indicating the Internet service corresponding to the behavior data; the Internet service includes at least: Internet services that can save paper products, and/or Internet services that can reduce the need for transportation. Based on the identification information of the Internet service, at least one preset carbon saving quantification algorithm is determined; The user's carbon savings are calculated based on the behavioral data and the predetermined carbon-saving quantification algorithm. Based on the calculated carbon savings of the user, the carbon savings of the user are processed into specific data to reflect the carbon savings of the user. The processing includes at least: converting the calculated carbon savings into an integral form. 2. The method as described in claim 1, wherein the at least one preset carbon-saving quantification algorithm specifically includes: The first preset algorithm is a carbon saving quantification algorithm for saving paper products. The carbon saving amount of the first preset algorithm is calculated based on the reduction of paper tickets corresponding to Internet services that can save paper products. and/or The second preset algorithm is a carbon saving quantification algorithm for reducing the amount of travel by public transportation. The second preset algorithm calculates the amount of carbon saving based on the reduced travel distance corresponding to Internet services that can reduce the amount of travel by public transportation. 3. The method as described in claim 2, characterized in that, when using the first preset algorithm to calculate carbon savings, the carbon savings of the user are calculated based on the behavioral data and the determined preset carbon savings quantification algorithm, specifically including: Based on the behavioral data, at least the number of times the user performed the Internet service and the geographical location of the user when performing the Internet service can be determined. The carbon savings of the user are calculated based on the number of times the user has used the Internet service, the user's geographical location, and a first preset algorithm. 4. The method as described in claim 2, characterized in that, when using the second preset algorithm to calculate carbon savings, the carbon savings of the user are calculated based on the behavioral data and the determined preset carbon savings quantification algorithm, specifically including: Based on the behavioral data, at least the number of steps the user took or the distance they walked can be determined; The carbon savings of the user are calculated based on the number of steps or the distance walked by the user, and a second preset algorithm. 5. The method as described in claim 1 or 2, characterized in that, based on the identification information of the Internet service, at least one preset carbon saving quantification algorithm is determined, specifically including: Based on the identification information of Internet services and the pre-saved correspondence between Internet services and carbon saving quantification algorithms, at least one preset carbon saving quantification algorithm is determined. 6. The method as described in claim 1, wherein the Internet service specifically includes at least one of: electronic payment, online reservation, online ticketing, online payment service, and health service. 7. The method as described in claim 1, characterized in that, based on the calculated carbon savings of the user, the user's carbon savings are processed into specific data reflecting the user's carbon savings, specifically including: The system obtains the amount of carbon savings a user makes across multiple internet services within a preset time period, accumulates the obtained carbon savings, and processes the accumulated carbon savings. 8. The method as described in claim 7, characterized in that the processing based on the accumulated carbon savings specifically includes: The calculated carbon savings amount accumulated by the user within a preset period is added to the user's total carbon savings amount to obtain the updated total carbon savings amount. 9. The method as described in claim 8, characterized in that, the calculated carbon savings accumulated by the user within a preset period are added to the user's total carbon savings, specifically including: According to the preset conversion rules, the calculated carbon savings of the user are converted into points; The converted points are added to the user's total points to obtain the updated total points. 10. The method as described in claim 9, characterized in that a control for accumulating points is provided to the user; The converted points will be added to the user's total points, specifically including: The system receives a confirmation command from the user via the control and adds the converted points to the user's total points. 11. The method of claim 9, wherein the method further comprises: Receive the user's command to acquire points that other users have not accumulated; According to the acquisition instruction, acquire all or part of the points that other users have not accumulated; All or part of the points obtained will be added to the user's total points. 12. The method of claim 9, wherein the method further comprises: Determine the updated total points for the user; Based on the updated total points, virtual items matching the updated total points are allocated to the user. 13. The method as described in claim 12, wherein the virtual items have different display states depending on the total points. 14. A data processing apparatus, characterized in that it comprises: The acquisition module acquires user behavior data, which is generated when the user uses Internet services. The behavior data includes: a user identifier indicating the user's identity, and identification information indicating the Internet service corresponding to the behavior data; the Internet service includes at least: Internet services that can save paper products, and/or Internet services that can reduce the need for transportation. The determination module determines at least one preset carbon saving quantification algorithm based on the identification information of the Internet service; The calculation module calculates the user's carbon savings based on the behavioral data and the determined preset carbon saving quantification algorithm. The processing module processes the calculated carbon savings of the user into specific data that reflects the user's carbon savings. The processing includes at least converting the calculated carbon savings into an integral form. 15. The apparatus as described in claim 14, wherein the at least one preset carbon-saving quantification algorithm specifically includes: The first preset algorithm is a carbon saving quantification algorithm for saving paper products. The carbon saving amount of the first preset algorithm is calculated based on the reduction of paper tickets corresponding to Internet services that can save paper products. and/or The second preset algorithm is a carbon saving quantification algorithm for reducing the amount of travel by public transportation. The second preset algorithm calculates the amount of carbon saving based on the reduced travel distance corresponding to Internet services that can reduce the amount of travel by public transportation. 16. The apparatus of claim 15, wherein when the first preset algorithm is used to calculate carbon savings, the calculation module determines, based on the behavioral data, at least the number of times the user performs the Internet service and the geographical location of the user when performing the Internet service, and calculates the user's carbon savings based on the determined number of times the user performs the Internet service, the geographical location, and the first preset algorithm. 17. The apparatus as described in claim 15, wherein when the second preset algorithm is used to calculate carbon savings, the calculation module determines at least the number of steps or the distance walked by the user based on the behavioral data, and calculates the user's carbon savings based on the determined number of steps or the distance walked by the user and the second preset algorithm. 18. The apparatus as described in claim 14 or 15, wherein the determining module determines at least one preset carbon saving quantification algorithm based on the identification information of the Internet service and the pre-saved correspondence between the Internet service and the carbon saving quantification algorithm. 19. The apparatus of claim 14, wherein the Internet service specifically includes at least one of: electronic payment, online reservation, online ticketing, online bill payment service, and health service. 20. The apparatus as described in claim 14, wherein the processing module acquires the amount of carbon saved by a user in multiple Internet services within a preset period, accumulates the acquired carbon savings, and processes the specific data based on the accumulated carbon savings. 21. The apparatus as described in claim 20, wherein the processing module adds the calculated carbon savings amount accumulated by the user within a preset period to the user's total carbon savings amount to obtain an updated total carbon savings amount. 22. The apparatus as described in claim 21, wherein the processing module converts the calculated carbon savings of the user into points according to a preset conversion rule, and adds the converted points to the user's total points to obtain an updated total points. 23. The apparatus of claim 22, characterized in that a control for accumulating points is provided to the user; the processing module receives a confirmation instruction issued by the user through the control and accumulates the converted points with the user's total points. 24. The apparatus of claim 22, wherein the apparatus further comprises: a points acquisition module, which receives an acquisition instruction issued by the user for points not accumulated by other users, acquires all or part of the points not accumulated by other users according to the acquisition instruction, and accumulates the acquired points with the total points of the user. 25. The apparatus of claim 22, further comprising: an allocation module, for determining the updated total points of the user, and for allocating virtual items to the user that match the updated total points based on the updated total points. 26. The apparatus of claim 25, wherein the virtual items have different display states depending on the total points.