HK1247301B - Data processing method and equipment based on blockchain - Google Patents

Description

A data processing method and device based on blockchain

Technical Field

The present application relates to the fields of Internet information processing technology and computer technology, and in particular to a data processing method and device based on blockchain.

Background Art

Blockchain technology, also known as distributed ledger technology, is a distributed internet database technology characterized by decentralization, transparency, immutability, and trustworthiness. A network built on blockchain technology is called a blockchain network. This network consists of network nodes (also called blockchain nodes). Each network node corresponds to at least one blockchain, and each blockchain contains at least one block.

Since a network node sends the data to be stored to other network nodes in the blockchain network by broadcasting when receiving the data to be stored, each network node in the blockchain network stores the full amount of data of the blockchain network, and the data stored on each network node is consistent.

Specifically, data storage in a blockchain network generally involves two phases: First, the blockchain network receives the data to be stored and broadcasts it across the entire network. This allows all nodes in the blockchain network to receive the data and, at this point, write it into their cache. Second, the nodes in the blockchain network that have acquired storage rights for the data, upon obtaining recording rights, write the data into a block and then add the block to the existing blockchain. This demonstrates that blockchain networks employ an asynchronous write strategy for data storage.

However, in actual business scenarios, an asynchronous write strategy is adopted to store business data in the blockchain network. Usually, a data processing queue is maintained in the first stage, and the data to be stored is written into the data processing queue in sequence according to the timestamp of the data to be stored. In this way, in the second stage, the data to be stored in the data processing queue is written into the block according to the first-in-first-out principle.

Research has found that storing data in the blockchain network in the above manner has the following problems: for businesses with higher business priorities, the business data processing efficiency is relatively low, which in turn leads to relatively low processing efficiency of these businesses.

Summary of the Invention

In view of this, the embodiments of the present application provide a blockchain-based data processing method and device for solving the problem of low efficiency in processing business data for businesses with relatively high business priorities in the prior art.

The present invention provides a data processing method based on blockchain, including:

A node in the blockchain obtains at least one piece of business data generated in a set time period, wherein the business data includes a processing level for characterizing the business generating the business data;

The node stores the service data in a data processing queue matching the processing level according to the processing level;

When a new block is generated, the nodes read the business data that meets the set conditions from different data processing queues respectively, and store the read business data in the block.

The present application also provides a blockchain-based data processing device, including:

an acquiring unit, configured to acquire at least one piece of service data generated in a set time period, wherein the service data includes a processing level for characterizing a service generating the service data;

a storage unit, storing the service data in a data processing queue matching the processing level according to the processing level;

The processing unit reads the business data that meets the set conditions from different data processing queues when a new block is generated, and stores the read business data in the block.

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

By storing different business data in different data processing queues according to processing levels, and reading a certain amount of business data from different data processing queues according to set conditions when storing data in the block, business data of different processing levels can be processed, breaking the regulations of the existing technology that only process business data according to time factors, and avoiding the problem of low processing efficiency of businesses with higher business priorities in the existing technology. The solution provided in the embodiment of the present application not only ensures the processing efficiency of business data of businesses with high business priorities, but also ensures the processing efficiency of business data of businesses with low business priorities. When meeting the business processing priority, it increases the flexibility of business data processing in the blockchain and enhances the use value of the blockchain in the field of business applications.

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 flow chart of a data processing method based on blockchain provided in an embodiment of the present application;

FIG2( a ) is a schematic diagram of data processing queues corresponding to service data of different processing levels provided in an embodiment of the present application;

FIG2( b ) is a schematic diagram of data processing queues corresponding to service data of different processing levels provided in an embodiment of the present application;

FIG3 is a schematic diagram of a scenario of a blockchain-based data processing method provided in an embodiment of the present application;

FIG4 is a schematic diagram of the structure of a blockchain-based 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.

The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

Figure 1 is a flow chart of a blockchain-based data processing method provided in an embodiment of the present application. The method may be as follows. The execution subject in the embodiment of the present application may be any business node in the blockchain (also referred to as a blockchain node, hereinafter referred to as a node), without specific limitation here. In the embodiment of the present application, the execution subject is taken as an example to be described in detail.

Step 101: A node in the blockchain obtains at least one piece of business data generated in a set time period.

The service data includes a processing level for characterizing the service generating the service data.

In the embodiment of the present application, first, a service priority is determined for different services. The service priority here corresponds to the processing level of the service data, that is, the higher the service priority, the higher the processing level of the service data generated by the service. Secondly, a field representing the service priority (or processing level) of the service that generates the service data is added to the service data, so that when a node receives the service data, it can determine the processing level of the service data through this field.

Typically, the fields included in business data include but are not limited to: hash value, version number, public key, signature, hash value of the block to which it belongs, timestamp (i.e., the time when the node accepts the business data), etc., and the fields included in the business data recorded in the embodiment of the present application include but are not limited to: as shown in Table 1. Table 1 describes the specific attributes of each field:

Table 1

Preferably, in the embodiment of the present application, data processing queues can also be configured for different business priorities based on the determined business priorities, so that when business data is received, it can be stored separately according to the business priority.

In the embodiment of the present application, the following description is based on the example of service priorities being divided into "high" and "low". If the service priorities are divided into "high" and "low", then the corresponding processing levels are also divided into "high" and "low", i.e., high processing level and low processing level.

It should be noted that since there is a time interval between two adjacent blocks generated in the blockchain, this time interval can be used as a reference condition for setting the time period in the embodiments of the present application, and is not specifically limited here.

Step 102: The node stores the service data in a data processing queue that matches the processing level according to the processing level.

In the embodiment of the present application, since different data processing queues can be created in advance according to the service priority (or processing level of the service), a mapping relationship between the processing level and the identifier of the data processing queue is established.

When a node receives business data, it extracts the processing level of the business data (the extraction method here can be to read the processing level field in the business data and determine the processing level of the business data based on the processing level field); based on the pre-established mapping relationship between the processing level and the identifier of the data processing queue, it determines the data processing queue corresponding to the processing level contained in the business data; and stores the business data in the determined data processing queue.

Figure 2(a) is a schematic diagram of data processing queues corresponding to business data of different processing levels provided in an embodiment of the present application. As can be seen from Figure 2(a), it contains two data processing queues, one of which corresponds to a high processing level, that is, the business data stored in this data processing queue is generated by businesses with high business priority (or high business processing level); and the other data processing queue corresponds to a low processing level, that is, the business data stored in this data processing queue is generated by businesses with low business priority (or low business processing level).

It should be noted that the business data stored in the data processing queue can be arranged according to the acceptance time of the business data, or according to the business attributes contained in the business data, or according to the queue principle (first in, first out), which is not specifically limited here.

Step 103: When a new block is generated, the node reads the business data that meets the set conditions from different data processing queues, and stores the read business data in the block.

In the embodiment of the present application, each node in the blockchain network processes business data in two stages. The first stage can correspond to steps 101 and 102 of the embodiment of the present application, and the second stage can correspond to step 103 of the embodiment of the present application, that is, how to store the business data in the block. It should be noted that the focus of the solution described in the embodiment of the present application is to describe which business data is read from different data processing queues to be stored in the block. Therefore, the verification, consensus, etc. are not described in detail in the embodiment of the present application. These processing methods can be carried out according to the existing technology.

Preferably, the setting conditions recorded in the embodiments of the present application can be determined based on the storage capacity of the block and the preset processing ratio of business data of different processing levels, or other methods can be adopted as long as it is ensured that business data of different business processing levels can be written into the block evenly.

The following describes in detail how the node reads the service data that meets the set conditions from different data processing queues.

Specifically, the node performs the following operations for different data processing queues:

Determining a processing level corresponding to the first data processing queue;

If the processing level is the first processing level, determining a processing ratio of the business data of a preset second processing level, the first processing level being higher than the second processing level;

determining a first processing quantity of the business data of the first processing level according to the determined processing ratio and the storage capacity of the block;

According to the first processing quantity, business data that meets the processing quantity is read sequentially from the first data processing queue.

It should be noted that the “first” and “second” in the “first data processing queue” and “second data processing queue” recorded in the embodiments of the present application have no special meanings and only represent two different data processing queues.

The "first" and "second" in the "first processing level" and "second processing level" recorded in the embodiments of the present application have no special meanings and only represent two different processing levels. In the embodiments of the present application, it is assumed that the first processing level is higher than the second processing level.

It should be noted that the “first” and “second” in the “first processing quantity” and “second processing quantity” recorded in the embodiments of the present application have no special meaning and only represent two different numerical values.

The first data processing cohort was taken as the research object.

First, determine the processing level corresponding to the first data processing queue.

Second, determine whether the processing level is the first processing level or the second processing level. If it is the first processing level, execute the third step; if it is the second processing level, execute the fourth step.

Third, determine the processing ratio of the business data of the preset second processing level, and determine the first processing quantity of the business data of the first processing level based on the determined processing ratio and the storage capacity of the block; and based on the first processing quantity, read the business data that meets the first processing quantity from the first data processing queue in sequence.

Fourth, determine the processing ratio of the business data of the preset second processing level, and determine the second processing quantity of the business data of the second processing level based on the determined processing ratio and the storage capacity of the block; and based on the second processing quantity, read the business data that meets the second processing quantity from the first data processing queue in sequence.

It should be noted that the "preset processing ratio of business data of the second processing level" in the embodiment of the present application can be understood as the minimum ratio of business data with a lower processing level in the embodiment of the present application, that is, when storing data in the block this time, the minimum ratio of business data with a lower processing level that needs to be stored to the storage capacity of the block. The processing ratio here can be adjusted according to actual needs and is not limited here.

Assuming that the processing ratio of the business data of the preset second processing level is p, and the storage capacity of the block (which can also be understood as the upper limit of the business data that the block can reprint) is x, then the first processing quantity of the business data of the first processing level can be expressed as (1-p)x, and the second processing quantity of the business data of the second processing level can be expressed as px.

In an embodiment of the present application, there is another situation where the processing quantity of the business data stored in the first data processing queue is less than the first processing quantity, which means that after determining the first processing quantity, the business data stored in the first data processing queue cannot meet the requirements of the first processing quantity. In order to achieve reasonable use of resources, in this case, the business data is read from the first data processing queue in sequence according to the processing quantity of the business data stored in the first data processing queue. That is, all the business data stored in the first data processing queue is read out.

At this time, the processing quantity of business data stored in the first data processing queue and the storage capacity of the block are determined, and the business data are read from the second data processing queue in sequence according to the determined processing quantity of business data read from the second data processing queue.

Still expressing this in the above manner, assuming the number of service data stored in the first data processing queue is m, since the value corresponding to (1-p)x is greater than m, the first processing number should be m, and the second processing number determined in this way should be (x-m). In other words, when the amount of service data with higher service priority is small, while ensuring that service data with higher service priority is processed first, as much service data with lower service priority as possible can be processed.

In the embodiment of the present application, sequentially reading service data that meets the first processing quantity from the first data processing queue includes:

The business data that meets the first processing quantity is read in sequence from the first data processing queue according to the order of the acceptance times corresponding to the business data.

Similarly, business data that meets the second processing quantity may be read from the second data processing queue in sequence according to the order of the acceptance time corresponding to the business data.

In the embodiment of the present application, storing the read business data in the block includes:

The read business data are stored in the blocks in sequence according to the order of the acceptance times corresponding to the business data.

For example, Figure 2(b) is a schematic diagram of data processing queues corresponding to business data of different processing levels provided in an embodiment of the present application. As can be seen from Figure 2(b), different amounts of business data are read from the two data processing queues in sequence according to the method described in step 103.

Assume that the preset processing ratio of low-level business data stored in the block each time is 30%, and the storage capacity of the block is 10, that is, it can store 10 business data. Among them, the data processing queue with a high processing level stores 4 business data, and the data processing queue with a low processing level stores 8 business data. Through judgment, according to the system configuration strategy, the processing volume of high-level business data that needs to be selected is (1-30%)*10=7. Obviously, 4 is less than 7. Therefore, all 4 business data stored in the high-level data processing queue are taken out, and then 6 business data are taken out from the low-level data processing queue. The resulting 10 business data are stored in the block in the order of the business data acceptance time.

Assume that the preset processing ratio of storing low-level business data in the block each time is 30%, and the storage capacity of the block is 10, that is, it can store 10 business data. Among them, 8 business data are stored in the data processing queue with a high processing level, and 8 business data are stored in the data processing queue with a low processing level. Through judgment, according to the system configuration strategy, the processing volume of high-level business data that needs to be selected is (1-30%)*10=7. Obviously, 8 is greater than 7. Therefore, 7 business data are read from the data processing queue with a high processing level, and then 3 business data are taken from the data processing queue with a low processing level. The resulting 10 business data are stored in the block in the order of the business data acceptance time.

In the embodiment of the present application, the nodes read the service data that meets the set conditions from different data processing queues respectively, and can also be executed in the following manner:

The node performs the following operations respectively for different data processing queues:

Determining a processing level corresponding to the first data processing queue, and obtaining a preset processing ratio corresponding to the processing level;

The processing quantity is determined according to the determined processing ratio and the storage capacity of the block, and the business data that meets the processing quantity is read sequentially from the first data processing queue.

Specifically, for each data processing queue, a processing level corresponding to the data processing queue is determined, and then a processing ratio corresponding to the preset processing level is determined for the data processing queue. Based on the determined processing ratio and the storage capacity of the block, a processing quantity is determined, and business data that meets the processing quantity is sequentially read from the data processing queue.

For example, if there are n data processing queues, then these n data processing queues correspond to n processing levels. The processing ratios corresponding to different processing levels can be represented by _ai. That is, the processing ratio corresponding to the first processing level is _a1 , the processing ratio corresponding to the i-th processing level is _ai , and the processing ratio corresponding to the n-th processing level is _an . Then, ( _a1 + _a2 +... + _ai +... + _an ) = 1. For each data processing queue, if the storage capacity of the block is X, then the processing quantity corresponding to the data processing queue corresponding to the first processing level is _a1 *X; the processing quantity corresponding to the data processing queue corresponding to the i-th processing level is _ai *X; and the processing quantity corresponding to the data processing queue corresponding to the n-th processing level is _an *X.

Through the technical solution provided in the embodiment of the present application, different business data are stored in different data processing queues according to the processing level, and when storing data in the block, a certain amount of business data is read from different data processing queues according to the set conditions, so that business data of different processing levels can be processed. This breaks the provision of the existing technology that only processes business data according to time factors, and avoids the problem of low processing efficiency of businesses with higher business priorities in the existing technology. The solution provided in the embodiment of the present application not only ensures the processing efficiency of business data of businesses with high business priority, but also ensures the processing efficiency of business data of businesses with low business priority. When meeting the business processing priority, it increases the flexibility of business data processing in the blockchain and enhances the use value of the blockchain in the field of business applications.

FIG3 is a schematic diagram of a scenario of a blockchain-based data processing method provided in an embodiment of the present application. As can be seen from FIG3, when a node in the blockchain receives business data, it can select a data processing queue for it based on the processing level of the business data and write the business data into the data processing queue. When a new block is generated, business data is selected from different data processing queues according to the business data selection strategy (i.e., the method described in step 103 in the above embodiment), and the selected business data is stored in the block.

Example 2

Figure 4 is a schematic diagram of the structure of a blockchain-based data processing device provided in an embodiment of the present application. The data processing device includes: an acquisition unit 401, a storage unit 402, and a processing unit 403, wherein:

An acquiring unit 401 acquires at least one piece of service data generated in a set time period, wherein the service data includes a processing level for characterizing a service generating the service data;

The storage unit 402 stores the service data in a data processing queue that matches the processing level according to the processing level;

The processing unit 403 reads the business data that meets the set conditions from different data processing queues when a new block is generated, and stores the read business data in the block.

In another embodiment of the present application, the setting condition is determined according to the storage capacity of the block and the processing ratio of business data of different preset processing levels.

In another embodiment of the present application, the processing unit 403 reads the service data that meets the set conditions from different data processing queues, including:

For different data processing queues, the following operations are performed respectively:

Determining a processing level corresponding to the first data processing queue;

If the processing level is the first processing level, determining a processing ratio of the business data of a preset second processing level, the first processing level being higher than the second processing level;

determining a first processing quantity of the business data of the first processing level according to the determined processing ratio and the storage capacity of the block;

According to the first processing quantity, business data that meets the first processing quantity is read sequentially from the first data processing queue.

In another embodiment of the present application, the processing unit 403 sequentially reads the business data that meets the first processing quantity from the first data processing queue according to the first processing quantity, including:

When the processing quantity of the business data stored in the first data processing queue is less than the first processing quantity, the business data is read from the first data processing queue in sequence according to the processing quantity of the business data stored in the first data processing queue.

In another embodiment of the present application, the data processing device further includes: a determining unit 404, wherein:

The determining unit 404 determines the processing quantity of the business data to be read from the second data processing queue according to the processing quantity of the business data stored in the first data processing queue and the storage capacity of the block;

The business data is read from the second data processing queue in sequence according to the determined processing quantity of the business data to be read from the second data processing queue.

In another embodiment of the present application, the processing unit 403 further determines a processing ratio of the business data of a preset second processing level if the processing level is the second processing level;

determining a second processing quantity of the business data of a second processing level according to the determined processing ratio and the storage capacity of the block;

According to the second processing quantity, business data that meets the second processing quantity is read sequentially from the first data processing queue.

In another embodiment of the present application, the processing unit 403 sequentially reads the business data that meets the first processing quantity from the first data processing queue, including:

The business data that meets the first processing quantity is read in sequence from the first data processing queue according to the order of the acceptance times corresponding to the business data.

In another embodiment of the present application, the processing unit 403 stores the read business data in the block, including:

The read business data are stored in the blocks in sequence according to the order of the acceptance times corresponding to the business data.

In another embodiment of the present application, the data processing device further includes: a creation unit 405, wherein:

The creation unit 405 creates different data processing queues in advance according to the processing level of the business;

The storage unit 402 stores the service data in a data processing queue matching the processing level according to the processing level, including:

determining, according to a correspondence between processing levels and data processing queues, a data processing queue corresponding to the processing level contained in the business data;

The business data is stored in the determined data processing queue.

In another embodiment of the present application, the processing unit 403 reads the service data that meets the set conditions from different data processing queues, including:

For different data processing queues, the following operations are performed respectively:

Determining a processing level corresponding to the first data processing queue, and obtaining a preset processing ratio corresponding to the processing level;

The processing quantity is determined according to the determined processing ratio and the storage capacity of the block, and the business data that meets the processing quantity is read sequentially from the first data processing queue.

It should be noted that the data processing device provided in the embodiment of the present application can be implemented by software or hardware, which is not specifically limited here. The data processing device stores different business data in different data processing queues according to the processing level, and when storing data in the block, reads a certain amount of business data from different data processing queues according to the set conditions, so that business data of different processing levels can be processed, breaking the provisions of the prior art that only process business data according to time factors, and avoiding the problem of low processing efficiency of businesses with higher business priorities in the prior art. The solution provided in the embodiment of the present application not only ensures the processing efficiency of business data of businesses with high business priorities, but also ensures the processing efficiency of business data of businesses with low business priorities. When meeting the business processing priority, it increases the flexibility of business data processing in the blockchain and enhances the use value of the blockchain in the field of business applications.

In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD through their own programming, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and/or hardware.

It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention 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 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.

The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

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 (18)

1. A data processing method based on blockchain, characterized in that it includes: Nodes in the blockchain acquire at least one piece of business data generated within a set time period. The business data includes a processing level that characterizes the business that generated the business data. Specifically, different business priorities are pre-determined, and the business priority corresponds to the processing level of the business data. That is, the higher the business priority, the higher the processing level of the business data generated by that business. The node stores the business data into a data processing queue that matches the processing level, so that the processing level of the business data corresponds to the processing level of the data processing queue; wherein, the data processing queue has a corresponding processing level. When a new block is generated, the nodes read business data that meets the set conditions from different data processing queues and store the read business data into the block to ensure that business data of different processing levels can be written into the block in a balanced manner; wherein, the set conditions are determined based on the storage capacity of the block and the preset processing ratio of business data of different processing levels. 2. The data processing method according to claim 1, characterized in that the nodes read business data that meets set conditions from different data processing queues, including: The node performs the following operations for each of the different data processing queues: Determine the processing level corresponding to the first data processing queue; If the processing level is the first processing level, then the processing ratio of the business data of the preset second processing level is determined, wherein the first processing level is higher than the second processing level; Based on the determined processing ratio and the storage capacity of the block, determine the first processing quantity of business data for the first processing level; Based on the first processing quantity, business data that meets the first processing quantity is read sequentially from the first data processing queue. 3. The data processing method according to claim 2, characterized in that, according to the first processing quantity, business data satisfying the first processing quantity is sequentially read from the first data processing queue, including: When the number of business data to be processed stored in the first data processing queue is less than the first processing quantity, business data is read sequentially from the first data processing queue according to the number of business data to be processed stored in the first data processing queue. 4. The data processing method according to claim 3, characterized in that the method further comprises: The number of business data to be read from the second data processing queue is determined based on the number of business data to be processed stored in the first data processing queue and the storage capacity of the block. Based on the determined number of business data to be processed from the second data processing queue, business data is read sequentially from the second data processing queue. 5. The data processing method according to claim 2, characterized in that the method further comprises: If the processing level is the second processing level, then the processing ratio of the business data at the preset second processing level is determined; Based on the determined processing ratio and the storage capacity of the block, determine the second processing quantity of the business data of the second processing level; Based on the second processing quantity, business data that meets the second processing quantity is read sequentially from the first data processing queue. 6. The data processing method according to any one of claims 2 to 5, characterized in that, sequentially reading business data satisfying the first processing quantity from the first data processing queue includes: Based on the order of the acceptance time corresponding to the business data, business data that meets the first processing quantity is read sequentially from the first data processing queue. 7. The data processing method according to claim 6, characterized in that storing the read business data into the block includes: Based on the order of the acceptance time corresponding to the business data, the read business data is stored in the block in sequence. 8. The data processing method according to claim 1, characterized in that the method further comprises: Create different data processing queues in advance based on the processing level of the business; According to the processing level, storing the business data in a data processing queue that matches the processing level includes: Based on the correspondence between processing levels and data processing queues, determine the data processing queue corresponding to the processing level contained in the business data; The business data is stored in the designated data processing queue. 9. The data processing method according to claim 1, characterized in that the nodes read business data that meets set conditions from different data processing queues, including: The node performs the following operations for each of the different data processing queues: Determine the processing level corresponding to the first data processing queue, and obtain the preset processing ratio corresponding to the processing level; Based on the determined processing ratio and the storage capacity of the block, the processing quantity is determined, and business data sufficient for the processing quantity is read sequentially from the first data processing queue. 10. A blockchain-based data processing device, characterized in that it comprises: The acquisition unit acquires at least one piece of business data generated within a set time period. The business data includes a processing level that characterizes the business that generated the business data. Specifically, different business priorities are pre-determined, and the business priorities correspond to the processing level of the business data. That is, the higher the business priority, the higher the processing level of the business data generated by that business. The storage unit stores the business data into a data processing queue that matches the processing level, according to the processing level, so that the processing level of the business data corresponds to the processing level of the data processing queue; wherein, the data processing queue has a corresponding processing level; When a new block is generated, the processing unit reads business data that meets the set conditions from different data processing queues and stores the read business data into the block to ensure that business data of different processing levels can be written into the block in a balanced manner; wherein, the set conditions are determined based on the storage capacity of the block and the preset processing ratio of business data of different processing levels. 11. The data processing apparatus according to claim 10, characterized in that the processing unit reads business data that meets set conditions from different data processing queues, including: For each of the aforementioned data processing queues, the following operations are performed: Determine the processing level corresponding to the first data processing queue; If the processing level is the first processing level, then the processing ratio of the business data of the preset second processing level is determined, wherein the first processing level is higher than the second processing level; Based on the determined processing ratio and the storage capacity of the block, determine the first processing quantity of business data for the first processing level; Based on the first processing quantity, business data that meets the first processing quantity is read sequentially from the first data processing queue. 12. The data processing device according to claim 11, characterized in that the processing unit sequentially reads business data satisfying the first processing quantity from the first data processing queue according to the first processing quantity, including: When the number of business data to be processed stored in the first data processing queue is less than the first processing quantity, business data is read sequentially from the first data processing queue according to the number of business data to be processed stored in the first data processing queue. 13. The data processing apparatus according to claim 12, characterized in that the data processing apparatus further comprises: a determining unit, wherein: The determining unit determines the number of business data to be read from the second data processing queue based on the number of business data to be processed stored in the first data processing queue and the storage capacity of the block. Based on the determined number of business data to be processed from the second data processing queue, business data is read sequentially from the second data processing queue. 14. The data processing device according to claim 11, characterized in that, If the processing level is the second processing level, the processing unit determines the processing ratio of the business data at the preset second processing level. Based on the determined processing ratio and the storage capacity of the block, determine the second processing quantity of the business data of the second processing level; Based on the second processing quantity, business data that meets the second processing quantity is read sequentially from the first data processing queue. 15. The data processing apparatus according to any one of claims 11 to 14, characterized in that the processing unit sequentially reads business data satisfying the first processing quantity from the first data processing queue, comprising: Based on the order of the acceptance time corresponding to the business data, business data that meets the first processing quantity is read sequentially from the first data processing queue. 16. The data processing apparatus according to claim 15, wherein the processing apparatus stores the read business data into the block, comprising: Based on the order of the acceptance time corresponding to the business data, the read business data is stored in the block in sequence. 17. The data processing apparatus according to claim 10, wherein the data processing apparatus further comprises: a creation unit, wherein: The creation unit pre-creates different data processing queues according to the processing level of the business; The storage unit stores the business data into a data processing queue that matches the processing level, according to the processing level, including: Based on the correspondence between processing levels and data processing queues, determine the data processing queue corresponding to the processing level contained in the business data; The business data is stored in the designated data processing queue. 18. The data processing apparatus according to claim 10, characterized in that the processing unit reads business data that meets set conditions from different data processing queues, including: For each of the aforementioned data processing queues, the following operations are performed: Determine the processing level corresponding to the first data processing queue, and obtain the preset processing ratio corresponding to the processing level; Based on the determined processing ratio and the storage capacity of the block, the processing quantity is determined, and business data sufficient for the processing quantity is read sequentially from the first data processing queue.