HK1248349B - 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. Blockchain technology can be used to build blockchain networks. Blocks, as a logical data structure within a blockchain network, can be used to store business data. However, blocks in a blockchain network are generated dynamically.

In actual applications, blocks are usually generated at set time intervals (for example, one block is generated every ten minutes, or one block is generated at a longer time interval), and the blocks store business data generated during the set time interval.

However, with the development of science and technology, the amount of business data is changing, and the amount of data can be large or small. Therefore, based on the existing block generation mechanism, it will be impossible to achieve a balance between business needs and system resource consumption.

Summary of the Invention

In view of this, the embodiments of the present application provide a blockchain-based data processing method and device for achieving a balance between business needs and system resource consumption.

The embodiments of this application adopt the following technical solutions:

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

Monitor the amount of consensus processing business data processed within a set time period;

Dynamically adjust the block generation time according to the processing quantity;

A new block is generated according to the adjusted time.

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

A monitoring unit monitors the amount of consensus processing business data processed within a set time period;

An adjustment unit, dynamically adjusting the time of block generation according to the processing quantity;

The processing unit generates a new block according to the adjusted time.

The present application also provides a blockchain-based data processing device, comprising: at least one memory and at least one processor, wherein the memory stores a program and is configured to execute the following steps by the at least one processor:

Monitor the amount of consensus processing business data processed within a set time period;

Dynamically adjust the block generation time according to the processing quantity;

A new block is generated according to the adjusted time.

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

The embodiment of the present application monitors the amount of consensus-processed business data within a set time period; dynamically adjusts the time for block generation based on the amount of processing; and then generates new blocks according to the adjusted time. In this way, it can effectively ensure that the speed of block generation matches the amount of change in business data, on the one hand meeting the requirements of business needs; on the other hand, when consensus processing is performed on business data, it can also ensure the utilization rate of system resources, and will not cause fluctuations in the utilization rate of system resources, effectively achieving a balance between business needs and system resource consumption.

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

FIG3 is a flow chart of a data processing method based on blockchain 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;

FIG5 is a schematic diagram of the structure of a blockchain-based data processing device provided in an embodiment of the present application.

DETAILED DESCRIPTION

Based on the existing block generation mechanism, if the speed of business data generation is much faster than the speed of block generation, a large amount of business data will accumulate, reducing the efficiency of business data processing; if the speed of business data generation is slower than the speed of block generation, it means that the amount of business data processed by consensus will be relatively small, which will cause a waste of system resources (because the system resources consumed by initiating a consensus remain unchanged).

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 entity of the embodiment of the present application may be a blockchain node in a blockchain network, or other device independent of the blockchain node, used to control the block generation rate.

Step 101: Monitor the amount of consensus processing business data processed within a set time period.

In an embodiment of the present application, a blockchain node can be used as the monitoring object to monitor the amount of business data processed by consensus on the blockchain node within a set time period; or multiple blockchain nodes can be used as monitoring objects to monitor the amount of business data processed by consensus on different blockchain nodes within a set time period. No specific limitation is made here.

The following example illustrates monitoring the amount of business data processed by a blockchain node within a set time period.

In the embodiment of the present application, before the generation of blocks is adjusted, blocks can be generated according to the set reference time, that is, when the reference time arrives, a new block is generated. Then, business data will be generated from the previous reference time to the current reference time.

The reference time here may refer to a length of time or a point in time, and is not specifically limited here.

After a new block is generated, consensus processing will be performed on the generated business data, and then the business data that passes the consensus will be stored in the new block.

The set time period here can be determined based on the base time of block generation, or can be determined based on actual needs, and is not specifically limited here.

Then, in the embodiment of the present application, if the amount of business data generated is relatively small, then the business data processed by consensus within the set time period can be understood as the amount of business data generated; if the amount of business data generated is relatively large (the storage capacity of a block cannot meet the amount of business data generated), then the business data processed by consensus within the set time period can be understood as the amount of business data actually retrieved by a consensus.

When monitoring the amount of consensus processing business data processed within a set time period, in order to ensure the accuracy of subsequent adjustments to the time of block generation, the amount of consensus processing business data processed within n consecutive set time periods can also be monitored, where n is a natural number, and the set time period is determined according to the time of block generation.

Preferably, in the embodiment of the present application, a benchmark processing capacity of business data can also be determined. The benchmark processing capacity can be determined based on the capacity of the block or based on actual needs, and is not specifically limited here.

Step 103: Dynamically adjust the block generation time according to the processing quantity.

In the embodiment of the present application, if the processing quantity is less than a set first threshold, the time for generating the block is extended by a set first time period;

If the processing quantity is greater than a set second threshold, the time for generating the block is shortened by a set second time period.

The first threshold here can be determined based on the benchmark processing volume, or it can be determined based on the minimum capacity of the blockchain node business data processing, and no specific limitation is made here; the first threshold here can also be determined based on the benchmark processing volume, or it can be determined based on the maximum capacity of the blockchain node business data processing, and no specific limitation is made here.

Preferably, in an embodiment of the present application, if the processing quantity monitored in step 101 is the processing quantity of consensus-processed business data within n consecutive set time periods, then in an embodiment of the present application, the time for block generation is dynamically adjusted according to the processing quantity corresponding to each of the set time periods.

Specifically, if the processing quantity corresponding to each of the set time periods shows an increasing trend, and the maximum processing quantity is greater than a set second threshold, the time for generating a block is shortened by a set second duration.

If the processing quantities corresponding to the set time periods show a decreasing trend and the minimum processing quantity is less than a set first threshold, the time for generating a block is extended by a set first time period.

The first duration here can be an integer multiple of the base time of block generation. For example, assuming the base time is a duration, such as T, then the first duration can refer to a*T. Then, after the first duration is extended, the time of block generation will become T+a*T, where a is a positive number. The second duration here can be a decimal multiple of the base time of block generation. Assuming the base time is a duration, such as T, then the second duration can refer to b*T. Then, after the second duration is shortened, the time of block generation will become T-b*T, where b is a positive number and less than 1.

Step 105: Generate a new block according to the adjusted time.

In the embodiment of the present application, new blocks are dynamically generated according to the adjusted time.

If the adjusted time is extended relative to the base time, the computational difficulty can be increased, slowing down the speed of block generation; if the adjusted time is shortened relative to the base time, the computational difficulty can be reduced, speeding up the speed of block generation.

Through the technical solution provided by this application, the amount of consensus-processed business data processed within a set time period is monitored; and based on the processing amount, the time of block generation is dynamically adjusted; and then new blocks are generated according to the adjusted time. In this way, it is possible to effectively ensure that the speed of block generation matches the amount of change in business data, on the one hand, meeting the requirements of business needs, and on the other hand, when consensus processing is performed on business data, it is also possible to ensure the utilization rate of system resources, and there will be no fluctuations in the utilization rate of system resources, effectively achieving a balance between business needs and system resource consumption.

Figure 2 is a flow chart of a blockchain-based data processing method provided in an embodiment of the present application. The method can be as follows. If the block generation time remains unchanged, in this embodiment of the present application, the amount of consensus processing business data can be dynamically adjusted to improve system resource utilization and achieve a balance between business needs and system resource consumption.

Step 202: Monitor the amount of business data generated within m consecutive set time periods.

Here m is less than n and is a natural number.

Step 204: If the time at which the determined block is generated is equal to the reference time, determine whether the number is greater than the reference processing amount, and determine whether the number is less than the adjusted processing amount. If it is greater than the reference processing amount, execute step 206; if it is less than the adjusted processing amount, execute step 208.

In the embodiment of the present application, it is assumed that the time when the block is generated is the benchmark time, but the business data will change as the business is executed. If the amount of business data is low, that is, lower than the benchmark processing capacity, then when a new block is generated, all the generated business data can be processed by consensus.

If the number of businesses is relatively large, once it exceeds the benchmark processing capacity, it is necessary to trigger the execution of step 206, that is, to increase the processing quantity of each business data consensus processing to ensure the utilization of system resources.

Step 206: If the amount of business data generated within m consecutive set time periods is greater than the benchmark processing capacity, then adjust the processing amount of consensus-processed business data within the set time period and jump to step 210.

In the embodiment of the present application, adjusting the processing quantity of consensus processing business data within a set time period can be understood as increasing the processing quantity, and the increase range here can be determined based on the baseline processing volume of the business data.

Step 208: If the amount of business data generated within m consecutive set time periods is less than the adjusted processing quantity, then the processing quantity of consensus-processed business data within the set time period is adjusted, where m is less than n and is a natural number.

In an embodiment of the present application, the amount of business data generated within m consecutive set time periods is less than the adjusted processing quantity, indicating that the speed of the business data is decreasing. At this time, it is necessary to lower the processing quantity of consensus-processed business data within the set time period.

Step 210: Perform consensus processing on the business data according to the adjusted processing quantity within m+1 set time periods.

FIG3 is a flow chart of a data processing method based on blockchain provided in an embodiment of the present application. The method can be as follows.

Step 301: Acquire business data generated within the benchmark time period of block generation.

Step 303: Determine whether the amount of business data is less than the benchmark processing capacity of business data. If so, perform consensus processing on the acquired business data when a new block is generated, or if the processing capacity of business data after m consecutive consensus processes is less than the benchmark processing capacity of business data, extend the time for block generation; if so, execute step 305.

Step 305: When a new block is generated, business data corresponding to the benchmark processing volume is obtained from the acquired business data for consensus processing.

Step 307: If the processing volume of the business data obtained from the monitoring of m consecutive consensus processes is the benchmark processing volume, then the benchmark processing volume of the business data is increased, and the increased benchmark processing volume is used as the processing volume for the next consensus process.

Step 309: According to the increased benchmark processing volume, corresponding business data is obtained during the m+1 consensus processing, and consensus processing is performed on the obtained business data.

Step 302: When the increased baseline processing capacity reaches a set maximum value, shorten the block generation time.

Step 304: Monitor the processing quantity of consensus processing business data. When the processing quantity is less than the set maximum value, extend the block generation time.

Step 306: When the block generation time reaches the reference time, reduce the number of business processes until the processing volume reaches the reference volume.

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: a monitoring unit 401, an adjustment unit 402, and a processing unit 403, wherein:

Monitoring unit 401 monitors the amount of consensus processing business data processed within a set time period;

An adjusting unit 402 dynamically adjusts the time of block generation according to the processing quantity;

The processing unit 403 generates a new block according to the adjusted time.

In another embodiment of the present application, the adjusting unit 402 dynamically adjusts the block generation time according to the processing quantity, including:

If the processing quantity is less than the set first threshold, the block generation time is extended by the set first time period;

If the processing quantity is greater than a set second threshold, the time for generating the block is shortened by a set second time period.

In another embodiment of the present application, the monitoring unit 401 monitors the amount of consensus processing business data processed within a set time period, including:

Monitor the amount of consensus-processed business data processed within n consecutive set time periods, where n is a natural number and the set time period is determined by the time when the block is generated;

The adjusting unit 402 dynamically adjusts the block generation time according to the processing quantity, including:

The time for generating blocks is dynamically adjusted according to the processing quantity corresponding to each set time period.

In another embodiment of the present application, the adjusting unit 402 dynamically adjusts the block generation time according to the processing quantity corresponding to each set time period, including:

If the processing quantities corresponding to the set time periods show an increasing trend and the maximum processing quantity is greater than a set second threshold, the time for generating a block is shortened by a set second time period.

In another embodiment of the present application, the adjusting unit 402 dynamically adjusts the block generation time according to the processing quantity corresponding to each set time period, including:

If the processing quantities corresponding to the set time periods show a decreasing trend and the minimum processing quantity is less than a set first threshold, the time for generating a block is extended by a set first time period.

In another embodiment of the present application, the adjustment unit 402 adjusts the processing quantity of the consensus-processed business data within the set time period if the time when the determined block is generated is equal to the reference time and the cumulative amount of business data generated within m consecutive set time periods is greater than the reference processing capacity, where m is less than n and is a natural number.

In another embodiment of the present application, the adjustment unit 402 adjusts the processing quantity of consensus-processed business data within the set time period if the time when the determined block is generated is equal to the reference time and the cumulative amount of business data generated within m consecutive set time periods is less than the adjusted processing volume, where m is less than n and is a natural number.

In another embodiment of the present application, the processing unit 403 performs consensus processing on the business data according to the adjusted processing quantity within m+1 set time periods.

It should be noted that the data processing device provided in the embodiment of the present application can be implemented by hardware or software, and no specific limitation is made here. The data processing device monitors the number of consensus-processed business data processed within a set time period; and dynamically adjusts the time for block generation based on the processing number; and then generates new blocks according to the adjusted time. In this way, it can effectively ensure that the speed of block generation matches the amount of change in business data, on the one hand, meeting the requirements of business needs, and on the other hand, when consensus processing is performed on business data, it can also ensure the utilization of system resources, and there will be no fluctuations in the utilization of system resources, effectively achieving a balance between business needs and system resource consumption.

Figure 5 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: at least one memory 501 and at least one processor 502. The memory 501 stores a program and is configured to execute the following steps by the at least one processor 502:

Monitor the amount of consensus processing business data processed within a set time period;

Dynamically adjust the block generation time according to the processing quantity;

A new block is generated according to the adjusted time.

Among them, the functions of the processor can be found in the contents recorded in the above embodiments, and will not be described in detail here.

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 modifications 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 (17)

1. A blockchain-based data processing method, comprising: Monitor the amount of consensus processing business data processed by blockchain nodes within a set time period; The time for block generation is dynamically adjusted based on the number of blocks to be processed. A new block is generated according to the adjusted time. 2. The data processing method according to claim 1, wherein the time for generating blocks is dynamically adjusted according to the number of blocks to be processed, comprising: If the number of processes is less than a set first threshold, the time for generating the block will be extended by a set first duration. If the number of processes exceeds a set second threshold, the time for generating blocks will be shortened by a set second duration. 3. The data processing method according to claim 1, monitoring the amount of consensus processing business data processed within a set time period, includes: Monitor the number of consensus processing business data processed within n consecutive set time periods, where n is a natural number, and the set time periods are determined based on the time when the block is generated. Based on the processing quantity, the time for block generation is dynamically adjusted, including: The time for generating blocks is dynamically adjusted based on the number of processes corresponding to each set time period. 4. The data processing method according to claim 3, wherein the time for generating blocks is dynamically adjusted according to the number of processes corresponding to each set time period, comprising: If the number of processes corresponding to each of the set time periods shows an increasing trend, and the largest number of processes is greater than the set second threshold, the time for generating the block will be shortened by the set second duration. 5. The data processing method according to claim 3, wherein the time for generating blocks is dynamically adjusted according to the number of processes corresponding to each set time period, comprising: If the number of processes corresponding to each set time period shows a decreasing trend, and the minimum number of processes is less than a set first threshold, the time for generating the block will be extended by a first duration. 6. The data processing method according to claim 1, further comprising: If the time when a block is generated is equal to the base time, and the amount of business data generated within m consecutive set time periods is greater than the base processing volume, then the amount of consensus processing business data processed within the set time periods is adjusted, where m is less than n and is a natural number. 7. The data processing method according to claim 1, further comprising: If the time when a block is generated is equal to the base time, and the amount of business data generated within m consecutive set time periods is less than the adjusted processing quantity, then the processing quantity of consensus processing business data within the set time periods is adjusted, where m is less than n and is a natural number. 8. The data processing method according to claim 6 or 7, further comprising: Within m+1 set time periods, consensus processing is performed on business data according to the adjusted processing quantity. 9. A blockchain-based data processing device, comprising: The monitoring unit monitors the amount of consensus processing business data processed by blockchain nodes within a set time period. The adjustment unit dynamically adjusts the time of block generation based on the number of blocks to be processed. The processing unit generates new blocks according to the adjusted time. 10. The data processing device according to claim 9, wherein the adjustment unit dynamically adjusts the block generation time according to the processing quantity, comprising: If the number of processes is less than a set first threshold, the time for generating the block will be extended by a set first duration. If the number of processes exceeds a set second threshold, the time for generating blocks will be shortened by a set second duration. 11. The data processing device according to claim 9, wherein the monitoring unit monitors the amount of consensus processing business data processed within a set time period, including: Monitor the number of consensus processing business data processed within n consecutive set time periods, where n is a natural number, and the set time periods are determined based on the time when the block is generated. The adjustment unit dynamically adjusts the block generation time based on the processing quantity, including: The time for generating blocks is dynamically adjusted based on the number of processes corresponding to each set time period. 12. The data processing device according to claim 11, wherein the adjustment unit dynamically adjusts the time of block generation according to the processing quantity corresponding to each set time period, comprising: If the number of processes corresponding to each of the set time periods shows an increasing trend, and the largest number of processes is greater than the set second threshold, the time for generating the block will be shortened by the set second duration. 13. The data processing device according to claim 11, wherein the adjustment unit dynamically adjusts the time of block generation according to the processing quantity corresponding to each of the set time periods, comprising: If the number of processes corresponding to each set time period shows a decreasing trend, and the minimum number of processes is less than a set first threshold, the time for generating the block will be extended by a first duration. 14. The data processing device according to claim 9, If the time at which the determined block is generated is equal to the base time, and the cumulative amount of business data generated within m consecutive set time periods is greater than the base processing amount, then the adjustment unit adjusts the amount of consensus processing business data processed within the set time period, where m is less than n and is a natural number. 15. The data processing device according to claim 9, If the time at which the determined block is generated is equal to the base time, and the cumulative amount of business data generated within m consecutive set time periods is less than the adjusted processing volume, then the adjustment unit adjusts the processing volume of consensus processing business data within the set time periods, where m is less than n and is a natural number. 16. The data processing apparatus according to claim 14 or 15, The processing unit performs consensus processing on the business data according to the adjusted processing quantity within m+1 set time periods. 17. A blockchain-based data processing device, comprising: at least one memory and at least one processor, wherein the memory stores a program and is configured to execute the following steps by the at least one processor: Monitor the amount of consensus processing business data processed within a set time period; The time for block generation is dynamically adjusted based on the number of blocks to be processed. A new block is generated according to the adjusted time.