WO2022048361A1 - Blockchain-based data processing method and apparatus, and storage medium - Google Patents

Abstract

The present application relates to the technical field of blockchains, and in particular to a blockchain-based data processing method and apparatus, and a storage medium. The method comprises: sending a pre-commit operation instruction to each second node device among a plurality of second node devices, instructing each second node device to execute a pre-commit operation that corresponds to the pre-commit operation instruction, so as to lock a resource corresponding to the pre-commit operation, and returning, to a first node device, a success response message which indicates that the pre-commit operation is successfully executed; when the first node device is in a crash state, adding a preset compensation mechanism to an interface adapter of the first node device, so as to acquire, by means of the preset compensation mechanism, a pre-commit operation execution situation of each second node device; and when the pre-commit operation execution situation shows that each of the plurality of second node devices has successfully executed the pre-commit operation, executing a next-stage operation. By using the embodiments of the present application, the efficiency of blockchain-based data processing can be improved.

Description

Blockchain-based data processing method, device and storage medium

This application claims the priority of the Chinese patent application with the application number 202010913948.1 and the invention titled "Blockchain-based data processing method, device and storage medium" submitted to the China Patent Office on September 3, 2020, the entire content of which is approved by Reference is incorporated in this application.

technical field

The present application relates to the technical field of blockchain, and in particular to a method, device and storage medium for data processing based on blockchain.

Background technique

The blockchain is essentially a continuously growing distributed database maintained by multiple parties, also known as a distributed shared ledger. The inventor realized that its core lies in a distributed cryptographic ledger whose timing cannot be tampered with. and distributed consensus mechanism to establish a trust relationship between each other, programming and operating data through smart contracts composed of automated scripts, and finally realizing the evolution from information interconnection to value interconnection. At present, the problem of how to ensure the data consistency of cross-chain operations needs to be solved urgently.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a blockchain-based data processing method, device, and storage medium, which can ensure data consistency in cross-chain operations.

In a first aspect, an embodiment of the present application provides a blockchain-based data processing method, which is applied to a first node device in a blockchain system, where the blockchain system includes the first node device and a plurality of first node devices. For a two-node device, the method includes:

Send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to perform a pre-debug operation on the Locking the resource corresponding to the pre-commissioning operation, and returning a success response message indicating that the pre-commissioning operation is successful to the first node device;

When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism condition;

When the execution status of the pre-commissioning operation shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, the next stage of operation is performed.

In a second aspect, an embodiment of the present application provides a blockchain-based data processing device, which is applied to a first node device in a blockchain system, where the blockchain system includes the first node device and a plurality of first node devices. Two-node device, the device includes:

a sending unit, configured to send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction , to lock the resource corresponding to the pre-debugging operation, and return a success response message that the pre-debugging operation is successfully executed to the first node device;

a compensation unit, configured to add a preset compensation mechanism to the interface converter of the first node device when the first node device is in a down state, so as to obtain each second node device through the preset compensation mechanism Execution of pre-commissioning operations;

The executing unit is configured to execute the next stage operation when the execution status of the pre-debugging operation shows that each second node device in the plurality of second node devices successfully executes the pre-debugging operation.

In a third aspect, embodiments of the present application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be processed by the above-mentioned processing implements the following methods:

Sending a pre-debug operation instruction to each second node device in the plurality of second node devices, and instructing each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to perform a pre-debug operation on the pre-debug Locking the resource corresponding to the operation, and returning a success response message indicating that the pre-debugging operation is successful to the first node device;

When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism condition;

When the execution status of the pre-commissioning operation shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, the next stage of operation is performed.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the following method:

Sending a pre-debug operation instruction to each second node device in the plurality of second node devices, and instructing each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to perform a pre-debug operation on the pre-debug Locking the resource corresponding to the operation, and returning a success response message indicating that the pre-debugging operation is successful to the first node device;

When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism condition;

When the execution status of the pre-commissioning operation shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, the next stage of operation is performed.

By implementing the embodiments of the present application, transaction compensation is implemented through the interface converter, which solves the problem of locked resources, and does not need to add other services, so that the network architecture is concise, resources are saved, the complexity of the architecture is reduced, and the cross-chain operation can be ensured. Data consistency.

Description of drawings

1 is a schematic flowchart of a blockchain-based data processing method provided by an embodiment of the present application;

2 is a schematic flowchart of another blockchain-based data processing method provided by an embodiment of the present application;

FIG. 3 is a schematic structural diagram of a first node device provided by an embodiment of the present application;

4A is a block diagram of functional units of a blockchain-based data processing device provided by an embodiment of the present application;

FIG. 4B is a block diagram of functional units of another blockchain-based data processing apparatus provided by an embodiment of the present application.

detailed description

This application may relate to the field of artificial intelligence technology, for example, relevant data may be acquired and processed based on artificial intelligence technology. This application can be applied to data processing scenarios based on blockchain, for example, it can be specifically applied to the data processing scenarios of medical data in digital medicine, such as medical images, or can be specifically applied to the data processing scenarios of transaction data in financial technology. ,etc. The data involved in this application, such as medical data and transaction data, can be stored in the blockchain.

In the embodiments of the present application, regardless of whether the first node or the second node devices are electronic devices, the electronic devices involved in the embodiments of the present application may include various handheld devices with wireless communication functions, desktop computers, vehicle-mounted devices, wearable devices Devices (smart watches, smart bracelets, wireless headsets, AR/VR devices, smart glasses), computing devices or other processing devices connected to wireless modems, and various forms of user equipment (UE), mobile Station (mobile station, MS), terminal device (terminal device) and so on. For the convenience of description, the devices mentioned above are collectively referred to as electronic devices, and each node in the blockchain can be referred to as a node device.

The embodiments of the present application will be described in detail below.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a blockchain-based data processing method provided by an embodiment of the present application. As shown in the figure, applied to a first node device in a blockchain system, the block The chain system includes the first node device and a plurality of second node devices, and the blockchain-based data processing method includes:

101. Send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to The resource corresponding to the pre-commissioning operation is locked, and a success response message indicating that the pre-commissioning operation is performed successfully is returned to the first node device.

In specific implementation, the first node device may be a coordinator, the second node device may be a participant, and both the first node device and the second node device are a user in the blockchain. The embodiments of this application are applied to the blockchain The first node device in the chain system, the blockchain system may include the first node device and a plurality of second node devices. The first node device may send a precommit (precommit) operation instruction to each of the plurality of second node devices, and the plurality of second node devices may be other than the first node device in the blockchain system part or all of the node devices, instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction based on the pre-debug operation instruction, so as to lock the resources corresponding to the pre-debug operation and execute the pre-debug operation. A success response message of successful operation is returned to the first node device, so that each second node device can be debugged. The first node device and the second node device may be in the same chain or different chains, and when they are in different chains, cross-chain data consistency can be guaranteed.

Wherein, in this embodiment of the present application, the relevant resource may be a resource corresponding to the pre-commissioning operation instruction, for example, may be a bill for a preset time period or a preset location. The preset time period can be set by the user or the system defaults, for example, from 14:25 on September 16, 2018 to 18:16 on July 27, 2020, the preset location can be set by the user or the system defaults, for example , Shenzhen Kexing Science Park.

In the specific implementation, when the second node device performs the precommit operation, it can use git to submit the code, and use the git hook called precommit to automatically execute the corresponding script detection code when the git commit command is called. If there is an error in the detection, the commit code is blocked. , it will not be able to push, ensuring that the error code is only local, and the problem will not be submitted to the remote warehouse.

Optionally, in the specific implementation, the first node device can also call the interface converter adapter transaction query interface through the web page, and the adapter then calls the peer's unfinished transaction query interface to check the execution of the first stage of the unfinished transaction.

102. When the first node device is in a down state, add a preset compensation mechanism to the interface converter of the first node device, so as to obtain the pre-compensation of each second node device through the preset compensation mechanism Operation execution.

Among them, downtime refers to the phenomenon that the operating system cannot recover from a serious system error, or there is a problem at the system hardware level, so that the system does not respond for a long time, and the computer has to be restarted. The first node device may correspond to an interface converter adapter, which may be an independent hardware interface device, allowing the hardware or electronic interface to be connected with other hardware or electronic interfaces, and may also be an information interface. For example, the adapter may be one of the following: a power adapter, a tripod base adapter, a USB and serial port adapter, etc., which are not limited here. In a specific implementation, when the first node device detects that the first node device is in a down state, a preset compensation mechanism may be added to the interface converter of the first node device, so as to obtain each second node through the preset compensation mechanism. Regarding the execution of the pre-commissioning operation of the node device, the above-mentioned preset compensation mechanism may be a transaction compensation mechanism, which may be set by the user or the system defaults.

In a specific implementation, the first node device may add some mechanisms to the adapter to complete the compensation mechanism of the transaction. Since the adapter has the certificates of all organizations, it can send transaction requests to the peers of each organization.

Optionally, the preset compensation mechanism can perform compensation according to certain conditions, for example, according to the level, according to the difficulty of the task, etc. In the specific implementation, the mapping relationship between the attribute information of the node device and the compensation parameter can be preset, based on the mapping. The relationship can determine compensation parameters corresponding to different second node devices, compensate the second node devices based on the compensation parameters, and acquire the pre-commissioning operation execution status of each second node device.

Optionally, in the above step 102, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution status of each second node device through the preset compensation mechanism, which may include the following: step:

21. Determine the N successful response messages that have been received by the first node device, where N is a natural number;

22. Determine, according to the N successful response messages, M second node devices that have not received the successful response message by the first node device, where M is a positive integer;

23. Obtain attribute information of the M second node devices, and obtain M attribute information;

24. Determine a preset compensation mechanism of the interface converter according to the M pieces of attribute information;

25. A preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution status of each second node device through the preset compensation mechanism.

In specific implementation, the attribute information may be at least one of the following: level, task difficulty, priority, processing efficiency, network speed, etc., which are not limited herein. The first node device may determine that the first node device has received the N success response messages, where N is a natural number, and may also determine that the first node device has not received M success response messages based on the N success response messages. The second node device, M is a positive integer, and N+M=the number of the second node device. Further, the attribute information of M second node devices can be obtained, and M pieces of attribute information can be obtained. Specifically, M pieces of attribute information can be obtained respectively through the adapter. The attribute information of each node device in the second node device is obtained, and M pieces of attribute information are obtained, and further, the mapping relationship between the attribute information and the compensation parameters can be stored in the adapter in advance, and then, according to the mapping relationship, the corresponding M pieces of attribute information are determined. Compensation parameters, determining a preset compensation mechanism of the interface converter based on the compensation parameters corresponding to the M pieces of attribute information, for example, respectively compensating the corresponding second node device based on the compensation parameters corresponding to the M pieces of attribute information, through the first node device A preset compensation mechanism is added to the interface converter of the device, so as to obtain the execution status of the pre-commissioning operation of each second node device through the preset compensation mechanism. In this way, when the first node device is down, it is possible to smoothly understand the completion of each second node device. The case for pre-debugging operations.

Optionally, in the above step 21, determining the N successful response messages that have been received by the first node device is implemented as follows:

When the probability that the first node device is in the down state is greater than a preset threshold, push a target message to the interface converter, where the target message includes the N pieces of the first node device that have been received. Successful response message.

The preset threshold may be set by the user or the system defaults. When the probability that the first node device is in a down state is greater than the preset threshold, push a target message to the interface converter, where the target message includes the N successful response messages that have been received by the first node device, that is, the first node device Before the downtime occurs, the adapter can know the successful response message it has received.

Optionally, further, acquiring the probability that the first node device occurs in the down state may include the following steps:

A1. Obtain the CPU load curve of the first node device within a preset time period;

A2. Sampling the CPU load curve to obtain multiple CPU load values;

A3. Perform an average calculation according to the plurality of CPU load values to obtain a first average CPU load value;

A4. Determine the target CPU load level corresponding to the first average CPU load value;

A5. According to the mapping relationship between the preset CPU load level and the first evaluation value, determine the target first evaluation value corresponding to the target CPU load level;

A6. Perform mean square error calculation according to the plurality of CPU load values to obtain a first mean square error;

A7. According to the mapping relationship between the preset mean square error and the second evaluation value, determine the target second evaluation value corresponding to the first mean square error;

A8. According to the preset mapping relationship between the CPU load level and the weight pair, determine the target weight pair corresponding to the target CPU load level, where the target weight pair includes the target first weight and the target second weight value, the first weight of the target is the first weight corresponding to the first evaluation value, and the second target weight is the second weight corresponding to the second evaluation value;

A9. Perform a weighted operation according to the first evaluation value of the target, the second evaluation value of the target, the first weight of the target and the second weight of the target to obtain the final evaluation value;

A10. Determine the probability of the downtime state corresponding to the final evaluation value according to the mapping relationship between the preset evaluation value and the downtime probability.

In this embodiment of the present application, the above-mentioned preset time period may be set by the user or the system defaults. The first node device may pre-store the mapping relationship between the preset CPU load level and the first evaluation value, the mapping relationship between the preset mean square error and the second evaluation value, and the preset CPU load level and weight. The mapping relationship between pairs. The weight pair may include a first weight of the first evaluation value and a second weight of the second evaluation value, and the sum of the first weight and the first and second weights may be 1. Of course, the higher the CPU load level , the larger the first weight is, the lower the CPU load level is, and the smaller the first weight is.

In a specific implementation, the first node device may obtain a CPU load curve of the first node device within a preset time period, and sample the CPU load curve to obtain multiple CPU load values. The specific sampling method may be every preset time period. Sampling at intervals or random sampling is not limited here, and the preset time interval can be set by the user or the system defaults.

Further, the first node device may perform an average calculation according to multiple CPU load values to obtain a first average CPU load value, and the first node device may also pre-store a mapping relationship between the CPU load value and the CPU load level, and further, The target CPU load level corresponding to the first average CPU load value can be determined according to the mapping relationship, and the target first evaluation value corresponding to the target CPU load level can be determined according to the above-mentioned mapping relationship between the preset CPU load level and the first evaluation value. In addition, the mean square error is calculated according to multiple CPU load values, and the first mean square error is obtained. The mean square error reflects the fluctuation of CPU load to a certain extent. Of course, the smaller the mean square error, the better the CPU stability. The larger the mean square error, the worse the CPU stability. The first node device may determine the target second evaluation value corresponding to the first mean square error according to the above-mentioned mapping relationship between the preset mean square error and the second evaluation value.

Further, the first node device may determine a target weight pair corresponding to the target CPU load according to the above-mentioned preset mapping relationship between the CPU load and the weight pair, where the target weight pair includes the target first weight and the target first weight. Two weights, the first weight of the target is the first weight corresponding to the first evaluation value, and the second target weight is the second weight corresponding to the second evaluation value. The second evaluation value, the first weight of the target and the second weight of the target are weighted to obtain the final evaluation value, that is, the specific formula is as follows:

Final evaluation value = first evaluation value of target * first weight of target + second evaluation value of target * second weight of target

Finally, the mapping relationship between the preset evaluation value and the downtime probability can also be pre-stored in the adapter, and the downtime state corresponding to the final evaluation value can be determined according to the mapping relationship between the preset evaluation value and the downtime probability. The probability.

In this way, in the embodiment of the present application, not only the CPU load curve within a period of time is selected, but also the average CPU load value and the mean square error are determined based on the CPU load curve to determine the evaluation value, one of which can reflect the stability of the CPU within a period of time, Second, the CPU load value reflects the connection stability. The larger the CPU load value, the more stable the CPU is. The mean square error reflects the CPU stability. The smaller the mean square error, the more stable the CPU is. Third, in the CPU load evaluation process , the weight corresponding to the CPU load value and the weight corresponding to the mean square error can be dynamically adjusted, so that the probability of occurrence of the down state can be accurately evaluated.

103. When the execution status of the pre-debugging operation shows that each second node device in the plurality of second node devices successfully executes the pre-debugging operation, execute the next stage of operation.

In a specific implementation, the first node device may show that each second node device in the plurality of second node devices has successfully performed the pre-commissioning operation in the pre-debugging operation execution status, and then the next-stage operation may be performed, and the next-stage operation may be It is a commit operation or a rollback operation.

In the specific implementation, if the consensus mechanism is the soul core of the blockchain, then. For blockchains, especially consortium chains and private chains, cross-chain technology is the key to realizing the value network. It is a good medicine to save consortium chains from being scattered and isolated islands, and it is the expansion and connection of blockchains to the outside world. bridge. In order to achieve data consistency in cross-chain operations, the underlying chain provides two-phase transaction submission. However, in the two-stage transaction processing, in the second stage, the coordinator does not send a commit/rollback request to the participants, the participating resources will always be occupied and cannot be unlocked. The adapter is used for transaction compensation, and the first solution is to solve the problem. The resources of the participants are locked. Second, there is no need to add other services, which makes the network architecture simple, saves resources, and reduces the complexity of the architecture.

Optionally, after the above step 102, the following steps may also be included:

When the execution status of the pre-debugging operation shows that at least one second node device among the plurality of second node devices fails to perform the pre-debugging operation successfully, the rollback mechanism operation of the interface converter is invoked through a preset page .

The preset page may be a preset web page, and the preset web page may be preset or system default. In a specific implementation, the first node device may display that at least one second node device among the plurality of second node devices fails to perform the pre-debugging operation when the pre-debugging operation is performed, and then the rollback mechanism of the interface converter is invoked through a preset page Operation rollback.

Optionally, the above step 103, performing the next stage operation, may be implemented as follows:

The interface converter is invoked through a preset page to perform preset operations.

Among them, different operations correspond to different interfaces, and the interfaces can be provided by the peers of the underlying blockchain. The interfaces include query interfaces for all unfinished transactions, for example, the commit interface in the second stage of the adapter transaction, and the rollback interface in the second stage. . In the specific implementation, the commit operation of the adapter can be called through the web page to execute the commit operation, or the rollback interface of the adapter can also be called through the web page to execute the rollback operation.

By way of example, the embodiment of the present application may include the following steps:

1. In the first stage, the coordinator sends a precommit operation to the participant, and the participant executes the precommit operation, locks the relevant resources, and returns a successful response to the coordinator.

2. Before sending the second phase, the coordinator goes down. Phase 2 operations cannot be sent. At this point, the resources on the participant cannot be released due to locking. affect subsequent operations.

3. Add some mechanism to the adapter to complete the compensation mechanism of the transaction. Since the adapter has all the organizations' certificates, it can send transaction requests to each organization's peers.

4. Call the adapter transaction query interface through the web page, and the adapter calls the peer's unfinished transaction query interface to view the execution of the first stage of the unfinished transaction.

5. If the first stage of all participants is completed, the second stage operation of the adapter can be called through the web page, that is, the commit or rollback operation can be performed. The specific implementation situation needs to be determined with the specific business scenario. If the first stage of a participant is not successfully executed, the rollback operation of the second stage of the adapter is called through the web page.

In a specific implementation, the coordinator can receive the response message returned by each executor.

Optionally, before the above step 101, the following steps may be included:

B1. Obtain the target vein image;

B2. Determine the target image quality evaluation value of the target vein image;

B3, when the target image quality evaluation value is greater than the preset evaluation value, matching the target vein image with the preset vein template;

B4. Step 101 is performed when the target vein image is successfully matched with the preset vein template.

Wherein, the preset vein template and the preset evaluation value may be stored in the first node device in advance, and the preset evaluation value may be set by the user or the system defaults. The evaluation value can be obtained by processing the target vein image based on artificial intelligence technology. In a specific implementation, the first node device may acquire the target vein image, and use at least one image quality evaluation index to evaluate the target vein image to obtain the target image quality evaluation value, and the image quality evaluation index may be at least one of the following: information entropy, Mean square error, sharpness, average gradient, etc., are not limited here. Further, the first node device may match the target vein image with the preset vein template when the target image quality evaluation value is greater than the preset evaluation value, and when the target vein image and the preset vein template are successfully matched, perform step 101, In this way, the vein identification efficiency can be improved.

Further, in the above step B2, determining the target image quality evaluation value of the target vein image may include the following steps:

B21. Perform multi-scale feature decomposition on the target vein image to obtain low-frequency feature components and high-frequency feature components;

B22. Divide the low-frequency characteristic component into multiple regions;

B23. Determine the information entropy corresponding to each area in the multiple areas, and obtain multiple information entropies;

B24. Determine the average information entropy and the target mean square error according to the plurality of information entropies;

B25. Determine the target adjustment coefficient corresponding to the target mean square error;

B26, adjusting the average information entropy according to the target adjustment coefficient to obtain a target information entropy;

B27, according to the mapping relationship between preset information entropy and evaluation value, determine the first evaluation value corresponding to described target information entropy;

B28, acquiring target shooting parameters corresponding to the target vein image;

B29, determining the target low-frequency weight corresponding to the target shooting parameter according to the mapping relationship between the preset shooting parameters and the low-frequency weight, and determining the target high-frequency weight according to the target low-frequency weight;

B30. Determine the distribution density of target feature points according to the high-frequency feature components;

B31. Determine a second evaluation value corresponding to the distribution density of the target feature points according to a preset mapping relationship between the distribution density of feature points and the evaluation value;

B32. Perform a weighting operation according to the first evaluation value, the second evaluation value, the target low frequency weight and the target high frequency weight to obtain a target image quality evaluation value of the target vein image.

In a specific implementation, the first node device can use a multi-scale decomposition algorithm to perform multi-scale feature decomposition on the target vein image to obtain low-frequency feature components and high-frequency feature components, and the multi-scale decomposition algorithm can be at least one of the following: pyramid transformation algorithm, wavelet Transformation, contourlet transformation, shearlet transformation, etc., are not limited here. Further, the low-frequency feature component can be divided into multiple regions, and the area of each region is the same or different. The low-frequency feature components reflect the main features of the image, and the high-frequency feature components reflect the details of the image.

Further, the first node device can determine the information entropy corresponding to each of the multiple areas, obtain multiple information entropies, and determine the average information entropy and the target mean square error according to the multiple information entropies, and the information entropy reflects the image to a certain extent. The amount of information, the mean square error can reflect the stability of the image information. The mapping relationship between the preset mean square error and the adjustment coefficient may be pre-stored in the first node device, and further, the target adjustment coefficient corresponding to the target mean square error may be determined according to the mapping relationship. In the embodiment of the present application, the value range of the adjustment coefficient It can be -0.15 to 0.15.

Further, the first node device may adjust the average information entropy according to the target adjustment coefficient to obtain the target information entropy, where the target information entropy=(1+target adjustment coefficient)*average information entropy. A preset mapping relationship between information entropy and evaluation value may be pre-stored in the first node device, and further, a first evaluation value corresponding to the target information entropy may be determined according to the preset mapping relationship between information entropy and evaluation value .

In addition, the first node device may acquire target shooting parameters corresponding to the target vein image, and the target shooting parameters may be at least one of the following: ISO, exposure duration, white balance parameters, focusing parameters, etc., which are not limited herein. The first node device may also pre-store the mapping relationship between the preset shooting parameters and the low-frequency weights, and further, the target low-frequency weights corresponding to the target shooting parameters may be determined according to the mapping relationship between the preset shooting parameters and the low-frequency weights. , the target high frequency weight is determined according to the target low frequency weight, and the target low frequency weight + the target high frequency weight=1.

Further, the first node device may determine the distribution density of the target feature points according to the high-frequency feature components, where the target feature point distribution density=the total number of feature points of the high-frequency feature components/area area. The first node device may also store a preset mapping relationship between the distribution density of feature points and the evaluation value in advance, and further, according to the preset mapping relationship between the distribution density of feature points and the evaluation value, determine the distribution of target feature points. The second evaluation value corresponding to the density, and finally, according to the first evaluation value, the second evaluation value, the target low-frequency weight and the target high-frequency weight, a weighted operation is performed to obtain the target image quality evaluation value of the target vein image, specifically as follows:

Target image quality evaluation value = first evaluation value * target low frequency weight + second evaluation value * target high frequency weight

In this way, the image quality evaluation can be performed based on the two dimensions of the low-frequency component and the high-frequency component of the vein image, and an evaluation parameter suitable for the shooting environment, that is, the target image quality evaluation value, can be accurately obtained.

It can be seen that the blockchain-based data processing method described in the embodiments of this application is applied to the first node device in the blockchain system, and the blockchain system includes the first node device and a plurality of second node devices , sending a pre-debugging operation instruction to each second node device in the plurality of second node devices, and instructing each second node device to perform a pre-debugging operation corresponding to the pre-debugging operation instruction, so as to The resource is locked, and a successful response message indicating that the pre-debugging operation is successfully performed is returned to the first node device. When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device. Obtain the pre-commissioning operation execution status of each second node device through the preset compensation mechanism. If the pre-commissioning operation execution status shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, execute the following: One-stage operation. In this way, transaction compensation is realized through the interface converter, which solves the problem of locked resources, and does not need to add other services, which makes the network architecture concise, saves resources, reduces the complexity of the architecture, and can also ensure cross-chain operations. Data consistency.

Please refer to FIG. 2. FIG. 2 is a schematic flowchart of a blockchain-based data processing method provided by an embodiment of the present application. The method is applied to a first node device in a blockchain system. The blockchain system includes For the first node device and multiple second node devices, as shown in the figure, this blockchain-based data processing method includes:

201. Send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to The resource corresponding to the pre-commissioning operation is locked, and a success response message indicating that the pre-commissioning operation is performed successfully is returned to the first node device.

202. When the first node device is in a down state, add a preset compensation mechanism to the interface converter of the first node device, so as to obtain the pre-compensation of each second node device through the preset compensation mechanism Operation execution.

203. When the execution status of the pre-debugging operation shows that each second node device in the plurality of second node devices successfully executes the pre-debugging operation, execute the next stage of operation.

204. When the execution status of the pre-debugging operation shows that at least one second node device among the plurality of second node devices fails to perform the pre-debugging operation, invoke the rollback of the interface converter through a preset page mechanism operation.

For the specific description of the above steps 201 to 204, reference may be made to the corresponding steps described in the above FIG. 1 , which will not be repeated here.

It can be seen that the blockchain-based data processing method described in the embodiments of this application realizes transaction compensation through the interface converter, solves the problem of locked resources, and does not need to add other services, which makes the network architecture concise and saves money resources, reduce the complexity of the architecture, and ensure the data consistency of cross-chain operations.

Consistent with the foregoing embodiments, please refer to FIG. 3 , which is a schematic structural diagram of a first node device provided by an embodiment of the present application, where the first node device includes a processor and a memory. Optionally, the first node device may further include a communication interface. For example, as shown, the first node device includes a processor, a memory, a communication interface, and one or more programs, the one or more programs are stored in the memory, and are configured to be executed by the processor, applying In the blockchain system, the blockchain system includes the first node device and a plurality of second node devices. In the embodiment of the present application, the above program includes instructions for executing the following steps:

Send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to perform a pre-debug operation on the Locking the resource corresponding to the pre-commissioning operation, and returning a success response message indicating that the pre-commissioning operation is successful to the first node device;

When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism condition;

When the execution status of the pre-commissioning operation shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, the next stage of operation is performed.

It can be seen that the first node device described in the embodiment of the present application, the blockchain system includes the first node device and multiple second node devices, and sends the data to each second node device in the multiple second node devices. Pre-debug operation instruction, and instruct each second node device to perform pre-debug operation corresponding to the pre-debug operation instruction, so as to lock the resources corresponding to the pre-debug operation, and return a success response message that the pre-debug operation is successful For the first node device, when the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism If the pre-debugging operation execution status shows that each second node device in the plurality of second node devices successfully performs the pre-debugging operation, the next stage operation is performed. In this way, transaction compensation is realized through the interface converter, which solves the problem of resource The problem of being locked, and no need to add other services, makes the network architecture simple, saves resources, reduces the complexity of the architecture, and can also ensure data consistency in cross-chain operations.

Optionally, the above program also includes instructions for performing the following steps:

When the execution status of the pre-debugging operation shows that at least one second node device among the plurality of second node devices fails to perform the pre-debugging operation successfully, the rollback mechanism operation of the interface converter is invoked through a preset page .

Optionally, in terms of performing the next stage operation, the above program includes instructions for performing the following steps:

The interface converter is invoked through a preset page to perform preset operations.

Optionally, in terms of adding a preset compensation mechanism to the interface converter of the first node device, so as to obtain the performance of the pre-debugging operation of each second node device through the preset compensation mechanism, the above program includes: Instructions to perform the following steps:

Determine the N successful response messages that have been received by the first node device, where N is a natural number;

According to the N successful response messages, it is determined that the first node device has not received M second node devices of the successful response message, where M is a positive integer;

acquiring attribute information of the M second node devices, and obtaining M attribute information;

determining a preset compensation mechanism of the interface converter according to the M pieces of attribute information;

A preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution status of each second node device through the preset compensation mechanism.

Optionally, in the aspect of determining the N success response messages that have been received by the first node device, the above program includes instructions for performing the following steps:

When the probability that the first node device is in the down state is greater than a preset threshold, push a target message to the interface converter, where the target message includes the N pieces of the first node device that have been received. Successful response message.

Optionally, the above program also includes instructions for performing the following steps:

Obtain the target vein image;

determining the target image quality evaluation value of the target vein image;

When the target image quality evaluation value is greater than a preset threshold, matching the target vein image with a preset vein template;

When the target vein image is successfully matched with the preset vein template, the step of sending a pre-commissioning operation instruction to each of the plurality of second node devices is performed.

Optionally, in terms of determining the target image quality evaluation value of the target vein image, the above program includes instructions for performing the following steps:

Perform multi-scale feature decomposition on the target vein image to obtain low-frequency feature components and high-frequency feature components;

dividing the low-frequency feature components into a plurality of regions;

Determine the information entropy corresponding to each area in the multiple areas to obtain multiple information entropies;

determining an average information entropy and a target mean square error according to the plurality of information entropies;

determining the target adjustment coefficient corresponding to the target mean square error;

Adjusting the average information entropy according to the target adjustment coefficient to obtain a target information entropy;

According to the preset mapping relationship between the information entropy and the evaluation value, determine the first evaluation value corresponding to the target information entropy;

acquiring target shooting parameters corresponding to the target vein image;

According to the mapping relationship between the preset shooting parameters and the low-frequency weights, the target low-frequency weights corresponding to the target shooting parameters are determined, and the target high-frequency weights are determined according to the target low-frequency weights;

determining the distribution density of target feature points according to the high-frequency feature components;

According to the preset mapping relationship between the distribution density of feature points and the evaluation value, the second evaluation value corresponding to the distribution density of the target feature point is determined;

A weighted operation is performed according to the first evaluation value, the second evaluation value, the target low-frequency weight and the target high-frequency weight, so as to obtain a target image quality evaluation value of the target vein image.

The foregoing mainly introduces the solutions of the embodiments of the present application from the perspective of the method-side execution process. It can be understood that, in order to implement the above-mentioned functions, the first node device includes corresponding hardware structures and/or software modules for performing each function. Those skilled in the art should easily realize that the present application can be implemented in hardware or in the form of a combination of hardware and computer software, in combination with the units and algorithm steps of each example described in the embodiments provided herein. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

In this embodiment of the present application, the first node device may be divided into functional units according to the foregoing method examples. For example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and other division methods may be used in actual implementation.

FIG. 4A is a block diagram of functional units of the blockchain-based data processing apparatus 400 involved in the embodiment of the present application. The blockchain-based data processing device 400 is applied to a first node device in a blockchain system, where the blockchain system includes the first node device and a plurality of second node devices, and the device 400 includes :

The sending unit 401 is configured to send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to execute the pre-debug corresponding to the pre-debug operation instruction operation, to lock the resource corresponding to the pre-debugging operation, and return a success response message that the pre-debugging operation is successfully executed to the first node device;

The compensation unit 402 is configured to add a preset compensation mechanism to the interface converter of the first node device when the first node device is in a down state, so as to obtain each second node through the preset compensation mechanism The implementation of pre-commissioning operations of the equipment;

The executing unit 403 is configured to execute the next stage operation when the execution status of the pre-debugging operation shows that each second node device in the plurality of second node devices successfully executes the pre-debugging operation.

It can be seen that the blockchain-based data processing device described in the embodiments of this application is applied to the first node device in the blockchain system, and the blockchain system includes the first node device and a plurality of second node devices , sending a pre-debugging operation instruction to each second node device in the plurality of second node devices, and instructing each second node device to perform a pre-debugging operation corresponding to the pre-debugging operation instruction, so as to The resource is locked, and a successful response message indicating that the pre-debugging operation is successfully performed is returned to the first node device. When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device. Obtain the pre-commissioning operation execution status of each second node device through the preset compensation mechanism. If the pre-commissioning operation execution status shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, execute the following: One-stage operation. In this way, transaction compensation is realized through the interface converter, which solves the problem of locked resources, and does not need to add other services, which makes the network architecture concise, saves resources, reduces the complexity of the architecture, and can also ensure cross-chain operations. Data consistency.

Optionally, the apparatus 400 is further configured to implement the following functions:

The execution unit 403 is configured to call the pre-debugging operation through a preset page when it is displayed that at least one second node device in the plurality of second node devices fails to perform the pre-debugging operation successfully. The interface converter's rollback mechanism operates.

Optionally, in the aspect of executing the next stage operation, the executing unit 403 is specifically configured to:

The interface converter is invoked through a preset page to perform preset operations.

Optionally, in terms of adding a preset compensation mechanism to the interface converter of the first node device, so as to obtain the pre-debugging operation performance of each second node device through the preset compensation mechanism, the compensation Unit 402 is specifically used for:

Determine the N successful response messages that have been received by the first node device, where N is a natural number;

According to the N successful response messages, it is determined that the first node device has not received M second node devices of the successful response message, where M is a positive integer;

acquiring attribute information of the M second node devices, and obtaining M attribute information;

determining a preset compensation mechanism of the interface converter according to the M pieces of attribute information;

A preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution status of each second node device through the preset compensation mechanism.

Optionally, in the aspect of determining the N successful response messages that have been received by the first node device, the compensation unit is specifically configured to:

When the probability that the first node device is in the down state is greater than a preset threshold, push a target message to the interface converter, where the target message includes the N pieces of the first node device that have been received. Successful response message.

Optionally, as shown in FIG. 4B , FIG. 4B is another modified structure of the blockchain-based data processing apparatus shown in FIG. 4A , which, compared with FIG. 4A , may further include an acquisition unit 404 , a determination unit 405 and The matching unit 406 is as follows:

The obtaining unit 404 is used to obtain the target vein image;

The determining unit 405 is configured to determine the target image quality evaluation value of the target vein image;

The matching unit 406 is configured to match the target vein image with the preset vein template when the target image quality evaluation value is greater than a preset threshold;

When the target vein image is successfully matched with the preset vein template, the sending unit 401 executes the step of sending a pre-commissioning operation instruction to each of the plurality of second node devices .

Optionally, in the aspect of determining the target image quality evaluation value of the target vein image, the determining unit 405 is specifically configured to:

Perform multi-scale feature decomposition on the target vein image to obtain low-frequency feature components and high-frequency feature components;

dividing the low-frequency feature components into a plurality of regions;

Determine the information entropy corresponding to each area in the multiple areas to obtain multiple information entropies;

determining an average information entropy and a target mean square error according to the plurality of information entropies;

determining the target adjustment coefficient corresponding to the target mean square error;

Adjusting the average information entropy according to the target adjustment coefficient to obtain a target information entropy;

According to the preset mapping relationship between the information entropy and the evaluation value, determine the first evaluation value corresponding to the target information entropy;

acquiring target shooting parameters corresponding to the target vein image;

According to the mapping relationship between the preset shooting parameters and the low-frequency weights, the target low-frequency weights corresponding to the target shooting parameters are determined, and the target high-frequency weights are determined according to the target low-frequency weights;

determining the distribution density of target feature points according to the high-frequency feature components;

According to the preset mapping relationship between the distribution density of feature points and the evaluation value, the second evaluation value corresponding to the distribution density of the target feature point is determined;

A weighted operation is performed according to the first evaluation value, the second evaluation value, the target low-frequency weight and the target high-frequency weight, so as to obtain a target image quality evaluation value of the target vein image.

It can be understood that the functions of each program module of the blockchain-based data processing device in this embodiment can be specifically implemented according to the methods in the above method embodiments, and the specific implementation process can refer to the relevant descriptions of the above method embodiments. It is not repeated here.

Embodiments of the present application further provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program causes the computer to execute part or all of the steps of any method described in the above method embodiments , the above computer includes a first node device.

Optionally, the storage medium involved in this application may be a computer-readable storage medium. Further optionally, the storage medium involved in the present application, such as a computer-readable storage medium, may be non-volatile or volatile.

Embodiments of the present application further provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute any one of the method embodiments described above. some or all of the steps of the method. The computer program product may be a software installation package, and the above-mentioned computer includes a first node device. The computer program product may be a software installation package.

It should be noted that, for the sake of simple description, the foregoing method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence. Because in accordance with the present application, certain steps may be performed in other orders or concurrently. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical or other forms.

The units described above as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The above-mentioned integrated units, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art, or all or part of the technical solution, and the computer software product is stored in a memory, Several instructions are included to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above-mentioned methods in the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash disk , Read-only memory (English: Read-Only Memory, referred to as: ROM), random access device (English: Random Access Memory, referred to as: RAM), magnetic disk or optical disk, etc.

The embodiments of the present application have been introduced in detail above, and the principles and implementations of the present application are described in this paper by using specific examples. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for Persons of ordinary skill in the art, based on the idea of the present application, will have changes in the specific implementation manner and application scope. In summary, the contents of this specification should not be construed as limitations on the present application.

Claims (20)

1. A blockchain-based data processing method, wherein, applied to a first node device in a blockchain system, the blockchain system comprising the first node device and a plurality of second node devices, the method include:

   Send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to perform a pre-debug operation on the Locking the resource corresponding to the pre-commissioning operation, and returning a success response message indicating that the pre-commissioning operation is successful to the first node device;

   When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism condition;

   When the execution status of the pre-commissioning operation shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, the next stage of operation is performed.
2. The method of claim 1, wherein the method further comprises:

   When the execution status of the pre-debugging operation shows that at least one second node device among the plurality of second node devices fails to perform the pre-debugging operation successfully, the rollback mechanism operation of the interface converter is invoked through a preset page .
3. The method according to claim 1 or 2, wherein said performing the next stage operation comprises:

   The interface converter is invoked through a preset page to perform preset operations.
4. The method according to claim 1 or 2, wherein a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-compensation of each second node device through the preset compensation mechanism Operational execution, including:

   Determine the N successful response messages that have been received by the first node device, where N is a natural number;

   According to the N successful response messages, it is determined that the first node device has not received M second node devices of the successful response message, where M is a positive integer;

   acquiring attribute information of the M second node devices, and obtaining M attribute information;

   determining a preset compensation mechanism of the interface converter according to the M pieces of attribute information;

   A preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution status of each second node device through the preset compensation mechanism.
5. The method according to claim 4, wherein the determining of the N successful response messages received by the first node device comprises:

   When the probability that the first node device is in the down state is greater than a preset threshold, push a target message to the interface converter, where the target message includes the N pieces of the first node device that have been received. Successful response message.
6. The method according to claim 1 or 2, wherein the method further comprises:

   Obtain the target vein image;

   determining the target image quality evaluation value of the target vein image;

   When the target image quality evaluation value is greater than a preset threshold, matching the target vein image with a preset vein template;

   When the target vein image is successfully matched with the preset vein template, the step of sending a pre-commissioning operation instruction to each of the plurality of second node devices is performed.
7. The method according to claim 6, wherein the determining the target image quality evaluation value of the target vein image comprises:

   Perform multi-scale feature decomposition on the target vein image to obtain low-frequency feature components and high-frequency feature components;

   dividing the low-frequency feature components into a plurality of regions;

   Determine the information entropy corresponding to each area in the multiple areas to obtain multiple information entropies;

   determining an average information entropy and a target mean square error according to the plurality of information entropies;

   determining the target adjustment coefficient corresponding to the target mean square error;

   Adjusting the average information entropy according to the target adjustment coefficient to obtain a target information entropy;

   According to the preset mapping relationship between the information entropy and the evaluation value, determine the first evaluation value corresponding to the target information entropy;

   acquiring target shooting parameters corresponding to the target vein image;

   According to the mapping relationship between the preset shooting parameters and the low-frequency weights, the target low-frequency weights corresponding to the target shooting parameters are determined, and the target high-frequency weights are determined according to the target low-frequency weights;

   determining the distribution density of target feature points according to the high-frequency feature components;

   According to the preset mapping relationship between the distribution density of feature points and the evaluation value, the second evaluation value corresponding to the distribution density of the target feature point is determined;

   A weighted operation is performed according to the first evaluation value, the second evaluation value, the target low-frequency weight and the target high-frequency weight, so as to obtain a target image quality evaluation value of the target vein image.
8. A data processing device based on blockchain, wherein it is applied to a first node device in a blockchain system, the blockchain system includes the first node device and a plurality of second node devices, the device include:

   a sending unit, configured to send a pre-debug operation instruction to each second node device in the plurality of second node devices, and instruct each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction , to lock the resource corresponding to the pre-debugging operation, and return a success response message that the pre-debugging operation is successfully executed to the first node device;

   a compensation unit, configured to add a preset compensation mechanism to the interface converter of the first node device when the first node device is in a down state, so as to obtain each second node device through the preset compensation mechanism Execution of pre-commissioning operations;

   The executing unit is configured to execute the next stage operation when the execution status of the pre-debugging operation shows that each second node device in the plurality of second node devices successfully executes the pre-debugging operation.
9. A node device, which includes a processor and a memory, the memory is used to store one or more programs, and is configured to be executed by the processor to implement a blockchain-based data processing method, the method includes :

   Sending a pre-debug operation instruction to each second node device in the plurality of second node devices, and instructing each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to perform a pre-debug operation on the pre-debug Locking the resource corresponding to the operation, and returning a success response message indicating that the pre-debugging operation is successful to the first node device;

   When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism condition;

   When the execution status of the pre-commissioning operation shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, the next stage of operation is performed.
10. The node device according to claim 9, further comprising:

    When the execution status of the pre-debugging operation shows that at least one second node device among the plurality of second node devices fails to perform the pre-debugging operation successfully, the rollback mechanism operation of the interface converter is invoked through a preset page .
11. The node device according to claim 9 or 10, wherein the adding a preset compensation mechanism to the interface converter of the first node device is performed, so as to obtain the information of each second node device through the preset compensation mechanism Pre-debug operation execution, including:

    Determine the N successful response messages that have been received by the first node device, where N is a natural number;

    According to the N successful response messages, it is determined that the first node device has not received M second node devices of the successful response message, where M is a positive integer;

    acquiring attribute information of the M second node devices, and obtaining M attribute information;

    determining a preset compensation mechanism of the interface converter according to the M attribute information;

    A preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution status of each second node device through the preset compensation mechanism.
12. The node device according to claim 11, wherein performing the determining of the N success response messages that have been received by the first node device comprises:

    When the probability that the first node device is in the down state is greater than a preset threshold, push a target message to the interface converter, where the target message includes the N pieces of the first node device that have been received. Successful response message.
13. The node device according to claim 9 or 10, further comprising:

    Obtain the target vein image;

    determining the target image quality evaluation value of the target vein image;

    When the target image quality evaluation value is greater than a preset threshold, matching the target vein image with a preset vein template;

    When the target vein image is successfully matched with the preset vein template, the step of sending a pre-commissioning operation instruction to each of the plurality of second node devices is performed.
14. The node device according to claim 13, wherein performing the determining of the target image quality evaluation value of the target vein image comprises:

    Perform multi-scale feature decomposition on the target vein image to obtain low-frequency feature components and high-frequency feature components;

    dividing the low-frequency feature components into a plurality of regions;

    Determine the information entropy corresponding to each area in the multiple areas to obtain multiple information entropies;

    determining an average information entropy and a target mean square error according to the plurality of information entropies;

    determining the target adjustment coefficient corresponding to the target mean square error;

    Adjusting the average information entropy according to the target adjustment coefficient to obtain a target information entropy;

    According to the preset mapping relationship between the information entropy and the evaluation value, determine the first evaluation value corresponding to the target information entropy;

    acquiring target shooting parameters corresponding to the target vein image;

    According to the mapping relationship between the preset shooting parameters and the low-frequency weights, the target low-frequency weights corresponding to the target shooting parameters are determined, and the target high-frequency weights are determined according to the target low-frequency weights;

    determining the distribution density of target feature points according to the high-frequency feature components;

    According to the preset mapping relationship between the distribution density of feature points and the evaluation value, the second evaluation value corresponding to the distribution density of the target feature point is determined;

    A weighted operation is performed according to the first evaluation value, the second evaluation value, the target low-frequency weight and the target high-frequency weight, so as to obtain a target image quality evaluation value of the target vein image.
15. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processor to execute a blockchain-based The data processing method, the method includes:

    Sending a pre-debug operation instruction to each second node device in the plurality of second node devices, and instructing each second node device to perform a pre-debug operation corresponding to the pre-debug operation instruction, so as to perform a pre-debug operation on the pre-debug Locking the resource corresponding to the operation, and returning a success response message indicating that the pre-debugging operation is successful to the first node device;

    When the first node device is in a down state, a preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution of each second node device through the preset compensation mechanism condition;

    When the execution status of the pre-commissioning operation shows that each second node device in the plurality of second node devices successfully performs the pre-commissioning operation, the next stage of operation is performed.
16. The computer-readable storage medium of claim 15, further comprising:

    When the execution status of the pre-debugging operation shows that at least one second node device among the plurality of second node devices fails to perform the pre-debugging operation successfully, the rollback mechanism operation of the interface converter is invoked through a preset page .
17. The computer-readable storage medium according to claim 15 or 16, wherein the adding a preset compensation mechanism to the interface converter of the first node device is performed, so as to obtain each second compensation mechanism through the preset compensation mechanism Execution of pre-commissioning operations on node devices, including:

    Determine the N successful response messages that have been received by the first node device, where N is a natural number;

    According to the N successful response messages, it is determined that the first node device has not received M second node devices of the successful response message, and the M is a positive integer;

    acquiring attribute information of the M second node devices, and obtaining M attribute information;

    determining a preset compensation mechanism of the interface converter according to the M pieces of attribute information;

    A preset compensation mechanism is added to the interface converter of the first node device, so as to obtain the pre-debugging operation execution status of each second node device through the preset compensation mechanism.
18. The computer-readable storage medium of claim 17, wherein performing the determining of the N success response messages that the first node device has received comprises:

    When the probability that the first node device is in the down state is greater than a preset threshold, push a target message to the interface converter, where the target message includes the N pieces of the first node device that have been received. Successful response message.
19. The computer-readable storage medium of claim 15 or 16, further comprising:

    Obtain the target vein image;

    determining the target image quality evaluation value of the target vein image;

    When the target image quality evaluation value is greater than a preset threshold, matching the target vein image with a preset vein template;

    When the target vein image is successfully matched with the preset vein template, the step of sending a pre-commissioning operation instruction to each of the plurality of second node devices is performed.
20. The computer-readable storage medium of claim 19, wherein performing the determining of the target image quality evaluation value of the target vein image comprises:

    Perform multi-scale feature decomposition on the target vein image to obtain low-frequency feature components and high-frequency feature components;

    dividing the low-frequency feature components into a plurality of regions;

    Determine the information entropy corresponding to each area in the multiple areas to obtain multiple information entropies;

    determining an average information entropy and a target mean square error according to the plurality of information entropies;

    determining the target adjustment coefficient corresponding to the target mean square error;

    Adjusting the average information entropy according to the target adjustment coefficient to obtain a target information entropy;

    According to the preset mapping relationship between the information entropy and the evaluation value, determine the first evaluation value corresponding to the target information entropy;

    acquiring target shooting parameters corresponding to the target vein image;

    According to the mapping relationship between the preset shooting parameters and the low-frequency weights, the target low-frequency weights corresponding to the target shooting parameters are determined, and the target high-frequency weights are determined according to the target low-frequency weights;

    determining the distribution density of target feature points according to the high-frequency feature components;

    According to the preset mapping relationship between the distribution density of feature points and the evaluation value, the second evaluation value corresponding to the distribution density of the target feature point is determined;

    A weighted operation is performed according to the first evaluation value, the second evaluation value, the target low-frequency weight and the target high-frequency weight, so as to obtain a target image quality evaluation value of the target vein image.

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