CN109685573A - Business circle data processing method and device, electronic equipment and storage medium - Google Patents

Abstract

Embodiments of the present invention relate to the field of data analysis, and disclose a business circle data processing method, device, electronic device and storage medium. In the present invention, the method for processing business circle data includes: receiving an analysis request, where the analysis request carries the user category and the business circle to which the user belongs; obtaining a corresponding analysis result according to the user category and the business circle to which the user belongs; Describe the analysis results, adapt to the needs of different types of user groups, and provide personalized analysis results.

Description

A method, device, electronic device and storage medium for processing business circle data

technical field

The invention relates to the field of data analysis, in particular to a method, device, electronic device and storage medium for processing business circle data.

Background technique

In today's era of big data, the number of rows in a single report for a specific business scenario has even reached billions. In the online service industry, different clients have different data requirements. The customer side is more sensitive to price, taste, etc., and the logistics side is more sensitive to the pressure of distribution, the difficulty of distribution, etc., which requires the takeaway platform to transparently transmit different data to different users.

The inventor found that the most commonly used technology is data kanban, which is to display the data they care about to different users through the integration of data. Then most of the dimensions of Kanban are time, city, crowd, business category and other dimensions, which are too macro to guide the application of data in actual business circles.

SUMMARY OF THE INVENTION

The purpose of the embodiments of the present invention is to provide a business circle data processing method, device, electronic device and storage medium, which can adapt to the needs of different types of user groups and provide personalized analysis results.

To solve the above technical problems, embodiments of the present invention provide a method for processing business circle data, including: receiving an analysis request, where the analysis request carries a user category and a business circle to which the user belongs; The affiliated business district obtains the corresponding analysis results; and feeds back the analysis results.

An embodiment of the present invention also provides a business circle data processing device, comprising: a receiving module for receiving an analysis request, where the analysis request carries a user category and a business circle to which the user belongs; an acquisition module for receiving an analysis request according to the user The category and the business circle to which the user belongs to obtain the corresponding analysis result; the sending module is used for feeding back the analysis result.

Embodiments of the present invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores data executable by the at least one processor The instruction is executed by the at least one processor, so that the at least one processor can execute: receive an analysis request, the analysis request carries the user category and the business circle to which the user belongs; Obtain corresponding analysis results from the business circle to which the user belongs; and feed back the analysis results.

Embodiments of the present invention also provide a non-volatile storage medium for storing a computer-readable program, and the computer-readable program is used for a computer to execute the above-mentioned business circle data processing method.

Compared with the prior art, the main difference and effect of the embodiments of the present invention are: after receiving the analysis request, the analysis result is obtained according to the user list carried in the analysis request and the business district to which the user belongs, and the obtained analysis is then fed back. result. Different types of user groups can get different analysis results, in line with the different actual situations of sensitive data of different user groups, and adapt to the actual needs of different user categories, thereby providing personalized analysis results.

As a further improvement, the analysis result is obtained according to the following methods: acquiring the scene labels of the merchants belonging to the first business district and order data in the first business district; classifying the merchants according to the scene labels; summarizing the same category order data of the merchants placed; analyze the aggregated order data according to a preset analysis strategy, and obtain the analysis result of the first business district. Use scene tags to classify merchants in a business circle, and further summarize order data under the same category, and analyze the results according to the summary results, so as to obtain the analysis results of the business circle.

As a further improvement, there are multiple analysis strategies, and different analysis strategies correspond to different user categories; the analyzing the aggregated order data according to the preset analysis strategy includes: according to the analysis request The user category determines the analysis strategy; the aggregated order data is analyzed according to the determined analysis strategy. Set different analysis strategies for different user categories to ensure accurate analysis results corresponding to various users.

As a further improvement, before classifying the merchants according to the scene tags, the method further includes: acquiring order data of merchants with the same scene tags belonging to other business districts; classifying the merchants according to the scene tags , specifically: classifying each of the merchants in the first business district and each of the merchants in other business districts according to the scene tag. When performing data analysis, the data of merchants belonging to a category with the same scene label is added to the data of merchants in other business districts, so that more data samples are involved in the analysis and the accuracy of the analysis results is improved.

As a further improvement, it includes: the scene label of the merchant is obtained according to the following methods: extracting the characteristic parameters of the order data in the business circle; analyzing the characteristics of the characteristic parameters of the order data in the business circle; according to the analysis result A scene tag of the merchant is determined. Data analysis is performed according to the predetermined characteristic parameters, so as to obtain the scene tags of the merchants according to the characteristics expressed by the characteristic parameters, and the classification of merchants by the scene tags is closer to the user's needs.

As a further improvement, the characteristic parameters are: space coordinates, time or category. Specify specific characteristic parameters.

As a further improvement, the analyzing the characteristics of the characteristic parameters of the order data in the business circle includes: obtaining an entropy value of the characteristic parameters of the order data; determining the order data in the business circle according to the entropy value the characteristics of the characteristic parameters. It is more scientific and accurate to use the method of entropy to obtain the characteristics of the characteristic parameters.

As a further improvement, before acquiring the scene labels of the merchants belonging to the first business district and the order data in the first business district, the method further includes: collecting order data and the business districts to which each order belongs; Coordinates and subordinate orders are merged with business circles, and the first business circle is one of the merged business circles. Specifically, according to the spatial coordinates of the business district and subordinate orders, the business district is merged to achieve deduplication and desensitization of the business district, reduce the number of business districts after the merger, and reduce the number of overall analysis.

As a further improvement, the business circle to which the user belongs is determined according to the positional relationship between the user's location and each business circle. Clarify the method of determining the business district to which you belong.

As a further improvement, the user categories include: merchants, customers, logistics and platforms. Identify the category of the user.

As a further improvement, the user category carried in the analysis request is a merchant, and the analysis result at least includes: commodity planning suggestions.

As a further improvement, obtaining the corresponding analysis result according to the user category and the business circle to which the user belongs includes: searching the system for the corresponding analysis result according to the user category and the business circle to which the user belongs. The analysis results can be pre-generated and stored in the system for direct search and acquisition after receiving the analysis request to speed up the acquisition.

Description of drawings

1 is a flowchart of a method for processing business district data according to the first embodiment of the present invention;

FIG. 2 is a flow chart of an acquisition method of an analysis result in a method for processing business district data according to a second embodiment of the present invention;

3 is a flowchart of a method for obtaining a scene label in the method for processing business district data according to the second embodiment of the present invention;

FIG. 4 is a flow chart of a method for obtaining an analysis result in a method for processing business district data according to a third embodiment of the present invention;

5 is a schematic diagram of a processing device for business district data according to a fourth embodiment of the present invention;

FIG. 6 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can appreciate that, in the various embodiments of the present invention, many technical details are set forth in order for the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can be realized.

The first embodiment of the present invention relates to a method for processing business circle data. This embodiment can be applied to the terminal side, such as in electronic devices such as mobile phones and tablet computers, and can also be applied in the server on the network side. The specific process As shown in Figure 1, it includes:

Step 101, receiving an analysis request.

Specifically, the analysis request can be issued by a user input instruction, or can be set to be issued automatically by the application. For example, there is a button for receiving an analysis report in a certain interface of the application. When the user clicks the button, an analysis request is sent, and correspondingly, the electronic device receives the analysis request. Analysis request, then when the user opens the application for the first time every day, the sending of the analysis request is triggered, and correspondingly, the electronic device receives the analysis request.

Continuing to explain, the analysis request carries user categories, and the user categories may include: merchants, customers, logistics, and platforms. Among them, merchants such as restaurants, massage shops, etc., logistics such as riders, delivery staff, etc.

More specifically, the analysis request carries the business district to which the user belongs. Specifically, the business district of the user can be determined according to the positional relationship between the user's location and each business district. For example, the business district closest to the user's location is regarded as the business district to which the user belongs.

It should be further explained that the business districts in this embodiment are merged according to certain rules, and try to avoid the situation where the business districts to which users belong are unclear due to overlapping business districts. Among them, the rules for merging business districts can be merged according to the order data reported by the business districts respectively. For example, if the repetition rate of the order data reported by the two business districts exceeds a certain percentage, it can be determined that the actual degree of overlap between the two business districts is very high. Therefore, the business district with more abundant order data is used as the combined data of the two business districts; if the spatial coordinate difference between the two business districts is less than a certain threshold, it can also be determined that the distance between the two business districts is very close, so that the two business districts are determined as two business districts. The order data reported by the circle is merged and then deduplicated to determine the order data of the merged business circle.

Step 102: Acquire corresponding analysis results according to the user category and the business district to which the user belongs.

In this embodiment, data analysis may be performed in units of business districts to obtain analysis results. Among them, different user categories may correspond to different analysis strategies, and according to the analysis strategy determined by the user category, the analysis results of the business district to which they belong are obtained.

Specifically, the user category is a merchant, and the analysis results include at least: product planning suggestions, and may also include the time period with high customer demand in this business district and the corresponding order volume, the category with high customer demand in this business district, and the merchants of each category in this business district. The actual analysis results can be set according to actual needs, which is not limited here.

More specifically, the analysis results can be pre-generated and stored in the system. For example, a business district can be analyzed with different analysis strategies in advance, and multiple analysis results corresponding to different user categories can be obtained and stored separately, and an analysis result library can also be formed. . Then, after receiving the analysis request, the corresponding analysis result is searched from the system according to the user category and the business district to which the user belongs.

Step 103, feedback the analysis result.

Specifically, the analysis result obtained in step 102 is fed back to the user, for example, fed back to the user's terminal, and displayed by the terminal.

More specifically, after receiving the analysis result, the terminal can also process various types of application data according to the analysis result. Taking the user as the customer as an example, after receiving the analysis result, the terminal can also recommend the single product for the user according to the hot selling single product in the business area in the analysis result.

Continuing to explain, after the terminal obtains the analysis result, it can also be temporarily stored, and then displayed when the user needs to refer to it.

Compared with the prior art, the main difference and effect of this embodiment are: after the analysis request is received, the analysis result is obtained according to the user list carried in the analysis request and the business district to which the user belongs, and the obtained analysis result is then fed back. . Different types of user groups can get different analysis results, in line with the different actual situations of sensitive data of different user groups, and adapt to the actual needs of different user categories, thereby providing personalized analysis results.

The second embodiment of the present invention relates to a method for processing business circle data, and this embodiment further specifies the way of obtaining the analysis result.

The specific flow of the acquisition method of the analysis result in the business circle data processing method in this embodiment is shown in Figure 2, including:

Step 201: Obtain scene tags of merchants belonging to the first business circle and order data in the first business circle.

Specifically, the order data may include all orders in the subordinate first business district, and data such as order time, consumption products, and consumption amount in each order are obtained.

Continuing to explain, in this embodiment, merchants can be classified by scene tags, and the scene tags can be obtained in the manner shown in FIG. 3 , as follows:

Step 301: Extract characteristic parameters of order data in the business circle.

Specifically, the feature parameters to be extracted can be preset, such as spatial coordinates, time or category. The feature parameters to be extracted can be one or multiple. If there are more than one feature parameters to be extracted, they can be extracted separately. extraction and subsequent analysis.

Step 302, analyze the characteristics of the characteristic parameters of the order data in the business circle.

Specifically, when analyzing the characteristic of the characteristic parameter, the entropy value of the characteristic parameter of the order data can be obtained first, and then the characteristic of the characteristic parameter of the order data in the business circle is determined according to the entropy value.

Taking the characteristic parameter as the category as an example, the calculation formula of category entropy is:

period_entropy=sigma((-1)*rate _i *log(rate _i ));

Among them, for the summation of multiple categories, rate _i = order quantity of the ith category/total order quantity.

Specifically, the total order quantity or the order quantity of a certain category in the above formula can be calculated and used when obtaining the order data in the business circle.

Continuing to explain, the larger the entropy, that is, the larger the period_entropy, the more scattered the category distribution of orders, and vice versa, the more concentrated. Then determine the characteristics of the category according to the entropy value. If the barbecue category data used to calculate the entropy value is the smallest, that is, the (-1)*rate*log(rate) corresponding to the barbecue category is the smallest, indicating that the order quantity of the barbecue category is the largest in the order. Therefore, it can be determined that the proportion of barbecue orders placed in this business district is relatively high, and the characteristic can be that they like barbecue categories. If the data of each category is not much different, it means that the order volume of each category in the order is relatively average, and the characteristics can be that each category is more liked or has no prominent favorite category.

Taking the characteristic parameter as time as an example, the calculation formula of time entropy is:

period_entropy=sigma((-1)*rate _i *log(rate _i ));

Wherein, summing is performed for five time periods (eg, breakfast, lunch, afternoon tea, dinner, and supper), rate _i = order quantity in the ith time period/total order quantity.

Similarly, the larger the entropy, the more scattered the time period distribution of orders, and vice versa, the more concentrated. Then determine the characteristics of the period according to the entropy value. If the noon period (lunch) data used to calculate the entropy value is the smallest, that is, the (-1)*rate*log(rate) corresponding to the afternoon period is the smallest, indicating the order quantity of lunch in the order. Therefore, it can be determined that the proportion of orders placed during lunch is relatively high in this business district, and the characteristic can be lunch. If the values of each time period are not much different, it means that the order volume in each time period in the order is relatively average, and the characteristic can be that each time period is relatively average.

Step 303: Determine the scene label of the merchant according to the analysis result.

Specifically, the scene label of the merchant can be determined according to the characteristics of the characteristic parameter. Taking the feature parameters including spatial coordinates and time as an example, the scene label of the merchant can be determined according to the corresponding relationship in the following table. The scene label listed in the table below is used as an example, and can also be set to other in practical applications.

business district time space coordinates breakfast Lunch dinner supper residential area Resident Breakfast Weekend Resident Lunch Weekday Resident Dinner Resident supper building white collar breakfast weekday white collar lunch white collar dinner white-collar supper School —— student lunch student dinner student supper shopping mall shopping mall staff breakfast weekday mall employee lunch mall staff dinner shopping mall employees Hospital hospital breakfast hospital lunch hospital dinner —— hotel Vacation/Business trip breakfast weekday lunch Vacation/Business Trip Dinner Vacation / business trip late night Industrial Park Industrial Park Breakfast Tech Staff Lunch —— —— company Company employee breakfast Company employee lunch —— ——

According to the corresponding relationship in the above table, the scene label corresponding to each merchant can be determined.

Continue to explain, in practical applications, after listing the possible scene labels in the business district, the scene attributes of the business district can be summarized according to the proportion of orders in the business district in different scenarios, such as: according to the business district in different scenarios The proportion of orders in the scene shows that the proportion of white-collar lunches, technology employees' lunches, company employees' lunches, and white-collar supper is relatively high on weekdays, so that these scenes are strong scenes in the business district.

Through the above steps 301 to 303, it can be realized to determine the scene label for each merchant in the business circle.

It should also be noted that the first business district in step 201 of this embodiment is also a merged business district, and the specific merging method can be as follows: collect order data and the business district to which each order belongs; according to the spatial coordinates of each business district and the subordinate orders Merge business districts, of which the first business district is one of the merged business districts. Specifically, the business circles in this embodiment are merged according to certain rules, and try to avoid the situation where the business circles to which users belong are unclear due to overlapping business circles. The rules for merging business circles may be combined according to the order data respectively reported by the business circles, which are similar to the merging rules in the first embodiment, and will not be repeated here.

Step 202, classify each merchant according to the scene tag.

Step 203: Aggregate order data of merchants under the same category.

Specifically, in steps 202 to 203, the merchants are classified and aggregated by scene tags. The customers corresponding to the merchants in each category are similar, and the merchants in each category can be regarded as competitors, and the data of each merchant about the competitors is also updated. Therefore, the analysis results after classification according to the scene tags are more valuable for merchants.

Step 204 , analyze the aggregated order data according to a preset analysis strategy, and obtain an analysis result of the first business district.

Specifically, multiple analysis strategies can be set, and different analysis strategies correspond to different user categories.

In practical applications, each business district can preset an analysis strategy, and can also preset an analysis strategy for similar merchants, which is not limited here.

It can be seen that this embodiment uses scene tags to classify merchants in a business district, further aggregates the order data under the same category, and makes specific analysis according to the aggregated results, thereby obtaining an analysis result of the business district.

The third embodiment of the present invention relates to a method for processing business circle data.

The specific flow of the acquisition method of the analysis result in the processing method of the business circle data in this embodiment is shown in FIG. 4 , including:

Step 401 is similar to step 201 in the second embodiment, and will not be repeated here.

Step 402: Obtain order data of merchants with the same scene tag belonging to other business districts.

Specifically, one or more business districts can be selected around the business district to be analyzed, and the order data in the selected business district can be added to the order data of the business district to be analyzed to enrich the order data of the business district to be analyzed. The amount of data in the business district makes the analysis results more accurate.

Step 403, classify each merchant according to the scene tag.

Specifically, in this embodiment, the merchants belonging to the first business district and the merchants belonging to other business districts are classified according to the scene tags.

Steps 404 to 405 are similar to steps 203 to 204 in the second embodiment, and are not repeated here.

It can be seen that during data analysis, the data of merchants with the same scene label in other business districts are added to the data of merchants belonging to a category, so that more data samples are involved in the analysis and the accuracy of the analysis results is improved.

The fourth embodiment of the present invention relates to a processing device for business circle data, as shown in FIG. 5 , including:

The receiving module is used to receive the analysis request, and the analysis request carries the user category and the business circle to which the user belongs.

The obtaining module is used to obtain the corresponding analysis results according to the user category and the business district to which the user belongs.

The sending module is used to feed back the analysis results.

Compared with the prior art, the main difference and effect of this embodiment are: after the analysis request is received, the analysis result is obtained according to the user list carried in the analysis request and the business district to which the user belongs, and the obtained analysis result is then fed back. . Different types of user groups can get different analysis results, in line with the different actual situations of sensitive data of different user groups, and adapt to the actual needs of different user categories, thereby providing personalized analysis results.

It is not difficult to find that this embodiment is a device example corresponding to the first embodiment, and this embodiment can be implemented in cooperation with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and are not repeated here in order to reduce repetition. Correspondingly, the related technical details mentioned in this embodiment can also be applied to the first embodiment.

In one example, analysis results are obtained through the following modules:

The second acquisition module is used to acquire the scene tags of the merchants belonging to the first business circle and the order data in the first business circle.

The classification module is used to classify each merchant according to the scene label.

The aggregation module is used to aggregate the order data of merchants under the same category.

The first analysis module is configured to analyze the aggregated order data according to the preset analysis strategy, and obtain the analysis result of the first business district.

In an example, there are multiple analysis strategies, and different analysis strategies correspond to different user categories; the first analysis module includes:

The first determination submodule is used to determine the analysis strategy according to the user category in the analysis request.

The analysis sub-module is used to analyze the aggregated order data according to the determined analysis strategy.

In one example, the apparatus further includes:

The third acquisition module is used to acquire order data of merchants with the same scene tag belonging to other business districts.

The classification module specifically classifies the merchants in the first business district and the merchants in other business districts according to the scene tags.

In one example, the apparatus further includes: the scene tag of the merchant is obtained according to the following modules:

The extraction module is used to extract the characteristic parameters of the order data in the business circle.

The second analysis module is used to analyze the characteristics of the characteristic parameters of the order data in the business circle.

The determining module is used to determine the scene label of the merchant according to the analysis result.

In one example, the feature parameters are: spatial coordinates, time or category.

In one example, the second analysis module includes:

The entropy submodule is used to obtain the entropy value of the characteristic parameters of the order data.

The second determination submodule is used for determining the characteristic of the characteristic parameter of the order data in the business circle according to the entropy value.

In one example, also include:

The collection module is used to collect order data and the business district to which each order belongs.

Business districts are merged according to the spatial coordinates and subordinate orders of each business district, and the first business district is one of the merged business districts.

In one example, the business circle to which the user belongs is determined according to the positional relationship between the user's location and each business circle.

In one example, the user categories include: merchant, customer, logistics, and platform.

In an example, the user category carried in the analysis request is a merchant, and the analysis result at least includes: product planning suggestions.

In one example, the first obtaining module searches the system for the corresponding analysis result according to the user category and the business district to which the user belongs.

It is worth mentioning that each module involved in this embodiment is a logical module. In practical applications, a logical unit may be a physical unit, a part of a physical unit, or multiple physical units. A composite implementation of the unit. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

A fifth embodiment of the present invention relates to an electronic device.

As shown in FIG. 6 , the electronic device includes: at least one processor 601 ; and a memory 602 communicatively connected to the at least one processor 601 ; Receive and send data under the control of the memory 602; wherein, the memory 602 stores instructions that can be executed by the at least one processor 601, and the instructions are executed by the at least one processor 601 to achieve:

Extract the query tasks that exist in the application.

Set a timer to periodically execute query tasks and cache query results.

A query instruction is received, and the query instruction includes a query task.

In response to the query task, the query result corresponding to the query instruction is determined according to the query task and the cached result.

Specifically, the electronic device includes: one or more processors 601 and a memory 602, and one processor 601 is taken as an example in FIG. 6 . The processor 601 and the memory 602 may be connected by a bus or in other ways, and the connection by a bus is taken as an example in FIG. 6 . As a non-volatile computer-readable storage medium, the memory 602 can be used to store non-volatile software programs, non-volatile computer-executable programs and modules. The processor 601 executes various functional applications and data processing of the device by running the non-volatile software programs, instructions and modules stored in the memory 602, ie, realizes the above-mentioned processing method of business district data.

The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function; the storage data area may store an option list and the like. Additionally, memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 602 may optionally include memory located remotely from the processor 601, and these remote memories may be connected to external devices via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

One or more modules are stored in the memory 602, and when executed by the one or more processors 601, execute the method for processing business district data in any of the above method embodiments.

The above product can execute the method provided by the embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present application.

The sixth embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, and the computer-readable program is used for a computer to execute some or all of the above method embodiments.

That is, those skilled in the art can understand that all or part of the steps in the method for implementing the above embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium and includes several instructions to make a device ( It may be a single chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: U disk, removable hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes.

Those skilled in the art can understand that the above-mentioned embodiments are specific examples for realizing the present invention, and in practical applications, various changes in form and details can be made without departing from the spirit and the spirit of the present invention. scope.

Embodiments of the present application provide A1. A method for processing business district data, comprising:

receiving an analysis request, where the analysis request carries the user category and the business district to which the user belongs;

Obtain corresponding analysis results according to the user category and the business district to which the user belongs;

Feedback the analysis results.

A2. According to the processing method for business district data described in A1, the analysis result is obtained according to the following methods:

Obtain the scene tags of the merchants belonging to the first business circle and the order data in the first business circle;

classify each of the merchants according to the scene tag;

Aggregate order data of merchants under the same category;

The aggregated order data is analyzed according to the preset analysis strategy, and the analysis result of the first business district is obtained.

A3. According to the processing method of the business district data described in A2, the analysis strategy has many kinds, and different analysis strategies correspond to different user categories;

The analysis of the aggregated order data according to the preset analysis strategy includes:

determining an analysis policy according to the user category in the analysis request;

The aggregated order data is analyzed according to the determined analysis strategy.

A4. The method for processing business district data according to A2, before classifying each of the merchants according to the scene label, further comprising:

Obtain the order data of merchants with the same scene label in other business districts;

In the classification of each of the merchants according to the scene tags, the specific steps are:

The merchants belonging to the first business district and the merchants belonging to other business districts are classified according to the scene tags.

A5. The processing method of business district data according to A2, comprising: the scene label of the merchant is obtained according to the following method:

extracting characteristic parameters of the order data in the business circle;

analyzing the characteristics of the characteristic parameters of the order data in the business district;

The scene tag of the merchant is determined according to the analysis result.

A6. The processing method for business district data according to A5, wherein the characteristic parameters are: spatial coordinates, time or category.

A7. The method for processing business district data according to A5, wherein the analysis of the characteristics of the characteristic parameters of the order data in the business district includes:

Obtain the entropy value of the characteristic parameter of the order data;

The characteristic of the characteristic parameter of the order data in the business circle is determined according to the entropy value.

A8. The method for processing business district data according to A2, before acquiring the scene labels of the merchants belonging to the first business district and the order data in the first business district, further comprising:

Collect order data and the business district to which each order belongs;

Business circles are merged according to the spatial coordinates and subordinate orders of each of the business circles, and the first business circle is one of the combined business circles.

A9. The business circle data processing method according to A1, the business circle to which the user belongs is determined according to the positional relationship between the user's location and each business circle.

A10. The business district data processing method according to A1, wherein the user categories include: merchants, customers, logistics and platforms.

A11. The business district data processing method according to A1, wherein the user category carried in the analysis request is a merchant, and the analysis result at least includes: commodity planning suggestions.

A12. The method for processing business circle data according to A1, wherein obtaining the corresponding analysis results according to the user category and the business circle to which the user belongs, including:

The corresponding analysis result is searched from the system according to the user category and the business circle to which the user belongs.

Embodiments of the present application also provide B13. A device for processing business circle data, comprising:

a receiving module, configured to receive an analysis request, where the analysis request carries the user category and the business circle to which the user belongs;

a first obtaining module, configured to obtain corresponding analysis results according to the user category and the business circle to which the user belongs;

A sending module is used to feed back the analysis result.

B14. The processing device for business circle data according to B13, wherein the analysis result is obtained through the following modules:

The second obtaining module is used to obtain the scene tags of the merchants belonging to the first business circle and the order data in the first business circle;

a classification module, configured to classify each of the merchants according to the scene label;

The aggregation module is used to aggregate the order data of merchants under the same category;

The first analysis module is configured to analyze the aggregated order data according to a preset analysis strategy to obtain the analysis result of the first business district.

B15. The device for processing business district data according to B14, the analysis strategies include multiple types, and different analysis strategies correspond to different user categories;

The first analysis module includes:

a first determination submodule, configured to determine an analysis strategy according to the user category in the analysis request;

The analysis sub-module is used to analyze the aggregated order data according to the determined analysis strategy.

B16. The device for processing business circle data according to B14, further comprising:

The third acquisition module is used to acquire the order data of merchants with the same scene label in other business districts;

The classification module specifically classifies each of the merchants in the first business district and each of the merchants in other business districts according to the scene tags.

B17. The apparatus for processing business circle data according to B14, comprising: the scene label of the merchant is obtained according to the following modules:

an extraction module for extracting characteristic parameters of the order data in the business circle;

a second analysis module, configured to analyze the characteristics of the characteristic parameters of the order data in the business district;

A determination module, configured to determine the scene label of the merchant according to the analysis result.

B18. The apparatus for processing business circle data according to B17, wherein the characteristic parameter is: spatial coordinates, time or category.

B19. The processing device for business circle data according to B17, the second analysis module includes:

An entropy sub-module, used to obtain the entropy value of the characteristic parameter of the order data;

The second determination submodule is configured to determine the characteristic of the characteristic parameter of the order data in the business circle according to the entropy value.

B20. The device for processing business circle data according to B14, further comprising:

The collection module is used to collect order data and the business district to which each order belongs;

Business circles are merged according to the spatial coordinates and subordinate orders of each of the business circles, and the first business circle is one of the combined business circles.

B21. The apparatus for processing business circle data according to B13, wherein the business circle to which the user belongs is determined according to the positional relationship between the user's location and each business circle.

B22. The apparatus for processing business circle data according to B13, wherein the user categories include: merchants, customers, logistics and platforms.

B23. The apparatus for processing business circle data according to B13, wherein the user category carried in the analysis request is a merchant, and the analysis result at least includes: commodity planning suggestions.

B24. The apparatus for processing business circle data according to B13, wherein the first acquisition module searches the system for a corresponding analysis result according to the user category and the business circle to which the user belongs.

Embodiments of the present application also provide C25. An electronic device, comprising:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute:

receiving an analysis request, where the analysis request carries the user category and the business district to which the user belongs;

Obtain corresponding analysis results according to the user category and the business district to which the user belongs;

Feedback the analysis results.

C26. The electronic device according to C25, wherein the instruction is executed by the at least one processor, so that the at least one processor can execute the business area data processing method according to any one of the above A2 to A12 .

Embodiments of the present application also provide D27. A non-volatile storage medium for storing a computer-readable program, and the computer-readable program is used for a computer to execute the business circle data as described in any one of A1 to A12. Approach.

Claims (10)

1. a processing method of business district data, is characterized in that, comprises: receiving an analysis request, where the analysis request carries the user category and the business district to which the user belongs; Obtain corresponding analysis results according to the user category and the business district to which the user belongs; Feedback the analysis results. 2. The processing method of business district data according to claim 1, wherein the analysis result is obtained according to the following methods: Obtain the scene tags of the merchants belonging to the first business circle and the order data in the first business circle; classify each of the merchants according to the scene tag; Aggregate order data of merchants under the same category; The aggregated order data is analyzed according to the preset analysis strategy, and the analysis result of the first business district is obtained. 3. The method for processing business district data according to claim 2, wherein the analysis strategy has multiple types, and different analysis strategies correspond to different user categories; The analysis of the aggregated order data according to the preset analysis strategy includes: determining an analysis policy according to the user category in the analysis request; The aggregated order data is analyzed according to the determined analysis strategy. 4. The method for processing business district data according to claim 2, wherein before classifying each of the merchants according to the scene label, the method further comprises: Obtain the order data of merchants with the same scene label in other business districts; In the classification of each of the merchants according to the scene tags, the specific steps are: The merchants belonging to the first business district and the merchants belonging to other business districts are classified according to the scene tags. 5. The method for processing business district data according to claim 2, wherein the method comprises: the scene label of the merchant is obtained according to the following method: extracting characteristic parameters of the order data in the business circle; analyzing the characteristics of the characteristic parameters of the order data in the business district; The scene tag of the merchant is determined according to the analysis result. 6 . The method for processing business district data according to claim 5 , wherein the characteristic parameters are: spatial coordinates, time or category. 7 . 7. The method for processing business circle data according to claim 5, wherein the analyzing the characteristics of characteristic parameters of the order data in the business circle comprises: Obtain the entropy value of the characteristic parameter of the order data; The characteristic of the characteristic parameter of the order data in the business circle is determined according to the entropy value. 8. A processing device for business district data, comprising: a receiving module, configured to receive an analysis request, where the analysis request carries the user category and the business circle to which the user belongs; a first obtaining module, configured to obtain corresponding analysis results according to the user category and the business circle to which the user belongs; The sending module is used for feeding back the analysis result. 9. An electronic device, characterized in that, comprising: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute: receiving an analysis request, where the analysis request carries the user category and the business district to which the user belongs; Obtain corresponding analysis results according to the user category and the business district to which the user belongs; Feedback the analysis results. 10. A non-volatile storage medium for storing a computer-readable program, wherein the computer-readable program is used for a computer to execute the processing of the business circle data according to any one of claims 1 to 7 method.