Cross-domain data collaborative statistical method and device based on message driving

Abstract

The invention discloses a cross-domain data collaborative statistical method and device based on message driving, belongs to the technical field of distributed computing and data processing, and is used for solving the technical problem of how to efficiently realize statistical analysis of cross-domain data on the premise of not converging data to ensure data security. The method comprises the steps of obtaining a task request of a cross-domain data statistics task, wherein the task request at least comprises at least one of a task type, a task priority and a task target node area; judging whether the cross-domain data statistical task is a detachable task or not, and if yes, splitting the cross-domain data statistical task into a plurality of sub-tasks according to the task target node; distributing the plurality of sub-tasks to corresponding task target node areas, so that the task target node areas execute the sub-tasks locally; and receiving task execution results returned by the task target node area, and summarizing the task execution results to generate a statistical result of the cross-domain data statistical task.

Description

Technical Field

[0001] The present application relates to the technical field of distributed computing and data processing, and particularly relates to a message-driven cross-domain data collaborative statistics method and device. Background Art

[0002] In modern information society, cross-domain data collaborative statistics has become an important requirement in many industries, especially in the fields of finance, government affairs, and healthcare. However, in practical applications, since data is distributed in isolated networks of multiple node areas and due to considerations of data security and privacy protection, it is impossible to gather all data together for unified processing. Moreover, this scenario poses the following challenges to cross-domain data interaction:

[0003] Data security: The data in each node area may contain sensitive information, and direct aggregation may lead to the risk of data leakage.

[0004] Statistical efficiency: In a distributed environment, how to efficiently complete multi-condition grouping and complex rule calculations is a difficult problem.

[0005] System performance: In a large-scale data scenario, how to ensure the balance of task scheduling and resource allocation is a key issue.

[0006] In the prior art, it usually relies on centralized processing or point-to-point transmission, but these statistical methods cannot meet the dual requirements of data security and statistical efficiency in an isolated network environment. Summary of the Invention

[0007] The present application provides a message-driven cross-domain data collaborative statistics method and device, which are used to solve the following technical problem: how to efficiently implement the statistical analysis of cross-domain data without aggregating data to ensure data security.

[0008] The present application adopts the following technical solutions:

[0009] On the one hand, the present application provides a message-driven cross-domain data collaborative statistics method, and the method includes: obtaining a task request for a cross-domain data statistical task, where the task request includes at least one of at least one of a task type, a task priority, and a task target node area; determining whether the cross-domain data statistical task is a splittable task, and if so, splitting the cross-domain data statistical task into several subtasks according to the task target node; distributing the several subtasks to corresponding task target node areas so that the task target node areas execute the subtasks locally; receiving the task execution results returned by the task target node areas, and generating a statistical result of the cross-domain data statistical task by summarizing the task execution results.

[0010] In a possible implementation manner of the present application, before distributing the several subtasks to the corresponding task target node area, the method further includes: verifying whether the task target node area is an authorized trusted node area; and when the task target node area passes the verification, distributing the several subtasks to the task target node area.

[0011] In a possible implementation manner of the present application, before the task target node area returns the task execution result, the method further includes: determining that there is sensitive information in the task execution result, where the sensitive information at least includes personal identity information and personal privacy information; performing desensitization processing on the sensitive information; encrypting the task execution result after desensitization processing by using a preset encryption algorithm, and returning the encrypted task execution result.

[0012] In a possible implementation manner of the present application, after receiving the task execution result returned by the task target node area, the method further includes: performing cleaning, conversion, and integration processing on the task execution results from different task target node areas; and providing a visualization tool for the user to view the processing process of the task execution result.

[0013] In a possible implementation manner of the present application, generating the statistical result of the cross-domain data statistics task through summarizing the task execution result includes: obtaining the service rule corresponding to the cross-domain data statistics task, where the service rule is a user-defined rule and / or a user-predefined rule; parsing the service rule to generate data statistics logic according to the parsing result; and summarizing and / or grouping the task execution result according to the data statistics logic to obtain the statistical result corresponding to the cross-domain data statistics task.

[0014] In a possible implementation manner of the present application, after summarizing the task execution result according to the data statistics logic, the method further includes: optimizing the data statistics logic according to the summarization result and / or the grouping result; re-summarizing and / or re-grouping the task execution result by using the optimized data statistics logic; generating the statistical result according to the result of re-summarization and / or the result of re-grouping, and outputting the statistical result in a structured manner.

[0015] In a possible implementation manner of the present application, before distributing the several subtasks to the corresponding task target node area, the method further includes: querying the resource usage of the task target node area, where the resource usage at least includes one of CPU utilization rate and memory occupancy rate; calculating the load index corresponding to the task target node area according to the resource usage; and when the load index is lower than a preset index threshold, distributing the several subtasks to the task target node area.

[0016] In a possible implementation manner of the present application, after obtaining the task request of the cross-domain data statistics task, the method further includes: determining that there is an isolated node area in the task target node area that is in a one-way isolation environment; deploying an intermediate proxy node at the isolation boundary of the isolated node area to implement data communication of the isolated node area through the intermediate proxy node.

[0017] In a possible implementation manner of the present application, the method further includes: when the intermediate proxy node receives a sub-task corresponding to the cross-domain data statistics task, and / or when the intermediate proxy node receives the task execution result returned by the isolated node area, performing integrity check and format verification on the sub-task and / or the task execution result, and encrypting the task execution result.

[0018] On the other hand, the present application further provides a cross-domain data collaborative statistics device based on message-driven. The device includes: at least one processor; and a memory connected to the at least one processor. The memory stores computer-executable instructions. When the computer-executable instructions are executed, the processor can be made to execute: obtaining a task request of a cross-domain data statistics task, where the task request includes at least one of a task type, a task priority, and a task target node area; determining whether the cross-domain data statistics task is a splittable task. If so, splitting the cross-domain data statistics task into several sub-tasks according to the task target node; distributing the several sub-tasks to the corresponding task target node areas so that the task target node areas locally execute the sub-tasks; receiving the task execution results returned by the task target node areas, and generating a statistical result of the cross-domain data statistics task by summarizing the task execution results.

[0019] The cross-domain data collaborative statistics method and device provided by the present application have the following beneficial effects:

[0020] By splitting the data statistics task, this application divides the task into multiple subtasks and distributes them to different target node areas correspondingly, achieving distributed task scheduling, which can significantly improve the execution efficiency of the data statistics task. At the same time, after receiving the task execution results returned by each target node area, it will generate data statistics logic according to business rules, and summarize and / or group the task execution results based on this data statistics logic, realizing the use of intelligent orchestration technology, significantly improving the efficiency of cross-domain data collaborative statistics, and being able to dynamically adjust the statistics logic, so that the data statistics method in this application can adapt to business scenarios of different scales and complexities. Moreover, during the process of subtask distribution and the transmission of task execution results, this application will encrypt the data, and before returning the task execution results, it will also desensitize the sensitive data therein. By introducing encryption technology and desensitization processing, it can ensure the security and privacy protection of data in a distributed environment, thereby significantly improving the efficiency of cross-domain data statistics without the need to centralize cross-domain data to ensure data security. Brief Description of the Drawings

[0021] To more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0022] Figure 1 It is a flowchart of a cross-domain data collaborative statistics method based on message-driven provided by this application;

[0023] Figure 2 It is a schematic structural diagram of a cross-domain data collaborative statistics device based on message-driven provided by this application. Detailed Embodiments

[0024] To enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in this application with reference to the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of them. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0025] The present application discloses a message-driven cross-domain data collaborative statistics method and system. By using a distributed message-driven architecture, task scheduling, and intelligent orchestration technologies, it realizes efficient statistical analysis of cross-domain data without data aggregation, while ensuring data security and privacy protection. The present application significantly improves the system performance and statistical efficiency, and is applicable to complex data collaboration scenarios in fields such as finance, government affairs, and healthcare.

[0026] The method in the present application will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 It is a flowchart of a message-driven cross-domain data collaborative statistics method provided by the present application. As Figure 1 shown, the cross-domain data collaborative statistics method in the present application at least includes the following execution steps:

[0028] Step 101: Obtain a task request for a cross-domain data statistics task.

[0029] A cross-domain data statistics task refers to the need to statistically analyze various data in different network environments. The traditional method is to centralize the data in these different network environments, but this method cannot guarantee data security and has low data statistics efficiency. Based on this, the present application proposes a new data statistics method.

[0030] First, a task request for a cross-domain data statistics task is received. The task request generally carries information such as the task type, task priority, and target task node area corresponding to the cross-domain data statistics task. The target task node area here refers to the node area where the data is located during the parameter data statistics process in the cross-domain data statistics task.

[0031] Step 102: Determine whether the cross-domain data statistics task is a splittable task. If so, split the cross-domain data statistics task into several subtasks according to the task target nodes.

[0032] Furthermore, a cross-domain data statistics task generally needs to statistically analyze data in multiple node areas in different environments. To improve the task execution efficiency and thus enhance the cross-domain data statistics efficiency, the present application adopts a distributed task execution method. Before that, the cross-domain data statistics task needs to be split into several subtasks. In an example, when splitting the cross-domain data statistics task, the task can be split according to the task target node area to which the data participating in the data statistics process belongs, so as to facilitate subsequent distribution of the split subtasks to different node areas.

[0033] Step 103: Distribute several subtasks to the corresponding task target node areas so that the task target node areas execute the subtasks locally.

[0034] In a possible implementation manner of the present application, a number of subtasks split from the cross-domain data statistics task are distributed to corresponding task target nodes, enabling the task target nodes to execute the subtasks locally, thereby achieving the effect that the original data does not leave the domain, which helps to maintain data security. However, before that, the present application will first verify whether the task target node area has obtained authorization. Only after the task target node area passes the verification will the subtasks be issued. This process can implement a strict access control policy between node areas to ensure that only authorized nodes can participate in data interaction, which is of positive significance for maintaining data security and privacy protection.

[0035] In a possible implementation manner of the present application, during the distribution process of the subtasks, tasks are allocated according to the task priority and resource load situation. Here, the task priority refers to the priority corresponding to the aforementioned cross-domain data statistics task, and the resource load situation refers to the load situation of each node area. Specifically, before issuing the subtasks, the resource usage situation of the task target node area will be queried first. The resource usage situation here includes at least one of the CPU occupancy rate and the memory occupancy rate. After that, the load index corresponding to the task target node area is calculated based on the obtained resource usage situation. When the load index meets the requirements, such as being lower than a preset index threshold, the subtasks are issued. This can ensure the execution efficiency of the subtasks and thus ensure the efficiency of cross-domain data statistics.

[0036] In an example, the process of issuing the subtasks can be implemented through a message queue, using the message queue to achieve asynchronous communication between different node areas, thereby avoiding direct network connection between node areas and enhancing data security and privacy protection.

[0037] Step 104: Receive the task execution results returned by the task target node area, and generate the statistical results corresponding to the cross-domain data statistics task by summarizing the task execution results.

[0038] After the task target node area receives the issued subtasks, it executes the subtasks locally to implement out-of-domain-free statistical analysis of the data, thereby being able to avoid the risk of data leakage and maintain data security and privacy protection.

[0039] In a possible implementation manner of the present application, after the task target node area executes the subtasks to generate task execution results, to further achieve data privacy protection, the present application will identify sensitive information in the task execution results and perform desensitization processing on the identified sensitive information. Here, the sensitive information includes at least one of personal identity information and personal privacy information, and the desensitization processing can be implemented through existing algorithms or methods, which will not be elaborated in the present application. For the desensitized task execution results, they are encrypted and then returned. Here, the encryption process can be implemented through encryption algorithms such as AES and RSA to prevent data leakage.

[0040] Further, after receiving the task execution results returned by the task target node area, the present application performs cleaning, transformation, and integration processing on multiple task execution results from different task target node areas. During this process, a visualization tool can be provided for the user to view the data processing process. After that, the processed task execution results are summarized to obtain the statistical results corresponding to the cross-domain data statistics task.

[0041] In a possible implementation manner of the present application, to improve the efficiency of data statistics, an intelligent orchestration engine is introduced during the process of generating statistical results. The intelligent orchestration engine can support multi-condition grouping and complex rule calculations, and can also automatically adjust the statistical logic according to predefined business rules and generate accurate statistical results. Specifically, an initial statistical logic is generated according to the business rules corresponding to the cross-domain data statistics task. Here, the business rules can be rules predefined by the user or rules set by the user (which can be changed at any time). The task execution results from multiple different task target node areas are summarized and / or grouped according to the initial statistical logic, the initial statistical logic is optimized according to the summary result and / or grouping result, and the summary and / or grouping are re-performed using the optimized statistical logic to obtain the final statistical result, that is, the execution of the cross-domain data statistics task is completed.

[0042] In a possible implementation manner of the present application, some task target node areas may be isolated node areas in a one-way isolation environment. At this time, the present application will deploy a data diode or hardware isolation device as an intermediate proxy node at the isolation boundary of the isolation node area. The intermediate proxy node replaces the isolation node area for data interaction and communication. Specifically, when the intermediate proxy node receives the subtask corresponding to the cross-domain data statistics task, it performs integrity check and format verification on the subtask to achieve data verification and ensure the security of data communication. And when the intermediate proxy node receives the task execution result returned by the isolation node area, it also performs integrity check and format verification on the task execution result. At the same time, to further ensure data security and privacy protection, the task execution result will also be encrypted and the encrypted execution result will be uploaded to prevent data leakage.

[0043] In an example, the intermediate proxy node deployed at the isolation boundary is responsible for data reception, processing, and forwarding. It has the following functions: data verification, performing integrity check and format verification on the received data; data encryption, encrypting sensitive data to prevent data leakage; data caching, temporarily storing the data in the local buffer area and forwarding it when the isolation node area is available.

[0044] That is to say, the cross-domain data statistics method proposed in the present application includes but is not limited to the following key technical points:

[0045] 1) Distributed message-driven architecture

[0046] This application uses a message queue as the core component to build a message-driven architecture for cross-domain data interaction. Each node area communicates asynchronously through the message queue, avoiding security risks brought by direct network connections, and helping to maintain data security and privacy protection. Moreover, the message queue in this application supports multiple protocols, can communicate with multiple node areas, and can also dynamically adjust the queue capacity and throughput according to business requirements to adapt to different business scenarios.

[0047] 2) Distributed task scheduling

[0048] This application designs a distributed task scheduling algorithm based on priority and load balancing, which dynamically allocates tasks according to factors such as task type and resource load. The algorithm mainly includes the following steps:

[0049] Task splitting: Split complex statistical tasks into multiple subtasks, and each subtask only involves local data.

[0050] Task distribution: Distribute subtasks to the corresponding node areas through the message queue to ensure the localization of task execution.

[0051] Result aggregation: Collect the results of subtasks from each node area, and generate the final statistical result through the intelligent orchestration engine.

[0052] 3) Data security and privacy protection

[0053] In the distributed architecture, this application ensures data security and privacy protection through the following measures:

[0054] Data encryption: Protect the data in transmission using encryption algorithms such as AES and RSA to prevent data leakage.

[0055] Data desensitization: Desensitize the information involving privacy (such as personal identity information) to ensure data compliance.

[0056] Zero-trust architecture: Implement strict access control policies between each node area to ensure that only authorized nodes can participate in data interaction.

[0057] Data does not leave the domain: All statistical analyses are completed in the local node area, and the original data will not leave its affiliated area.

[0058] 4) Intelligent orchestration engine

[0059] This application supports multi-condition grouping and complex rule calculation by introducing an intelligent orchestration engine. The engine can automatically adjust the statistical logic according to predefined business rules and generate accurate statistical results. The main functions include:

[0060] Rule parsing: Parse the user-defined business rules to generate the initial statistical logic.

[0061] Dynamic adjustment: Dynamically optimize the statistical logic according to the running results and feedback information to improve the statistical accuracy.

[0062] Result output: Output the final statistical results in a structured form, supporting multiple formats (such as JSON, CSV, Excel, etc.).

[0063] 5) Adaptation to one-way isolation environment

[0064] For the one-way isolation network environment, this application designs a dedicated data transmission protocol and intermediate proxy nodes to ensure that data can complete interactions under strict isolation conditions. The specific implementation includes:

[0065] One-way transmission protocol: Realize one-way data transmission through an optical switch or hardware isolation device to ensure that data can only flow from the source area to the target area.

[0066] Intermediate proxy node: Deploy intermediate proxy nodes at the isolation boundary to be responsible for data reception, processing, and forwarding.

[0067] Based on the same inventive concept, this application also provides a message-driven cross-domain data collaborative statistics device, the structure of which is as Figure 2 shown.

[0068] Figure 2 It is a schematic diagram of the structure of a message-driven cross-domain data collaborative statistics device provided by this application. As Figure 2 shown, the message-driven cross-domain data collaborative statistics device 200 in this application specifically includes: at least one processor 201; and a memory 203 communicatively connected to the at least one processor 201 (connected through a bus 202), and computer-executable instructions are stored on the memory 203, and when the computer-executable instructions are executed, they can enable the processor 201 to execute a message-driven cross-domain data collaborative statistics method described in any of the above embodiments.

[0069] In a possible implementation manner of the present application, the foregoing processor is configured to execute: obtain a task request for a cross-domain data statistics task, where the task request includes at least one of a task type, a task priority, and a task target node area; determine whether the cross-domain data statistics task is a splittable task, and if so, split the cross-domain data statistics task into several subtasks according to the task target node; distribute the several subtasks to the corresponding task target node areas, so that the task target node areas execute the subtasks locally; receive the task execution results returned by the task target node areas, and generate a statistical result of the cross-domain data statistics task by summarizing the task execution results.

[0070] In addition, the method in the present application can also be provided in the form of a system, and the system architecture is as follows:

[0071] Message queue module: Responsible for message transmission and management of cross-domain data interaction. Supports multiple message queue technologies (such as RabbitMQ, Kafka, etc.), and can dynamically adjust queue configurations according to business requirements.

[0072] Task scheduling module: Allocate tasks according to task priorities and resource load conditions. This module is integrated with the message queue module through an API interface to achieve dynamic distribution and management of tasks. The specific steps include: query the resource usage conditions of each node area (such as CPU utilization rate, memory occupancy rate, etc.); calculate the load index of each node area, and select the node area with the lowest load as the task execution node; split the task into multiple subtasks, and send them to the corresponding node areas through the message queue; after each node area receives the subtasks, it starts to execute and returns the execution results to the task scheduling module; the task scheduling module summarizes the results of all subtasks and generates the final statistical result.

[0073] Data integration module: Clean, transform, and integrate data from different node areas. Supports multiple data formats (such as JSON, XML, CSV, etc.), and provides visualization tools to facilitate users to monitor the data processing process.

[0074] Intelligent orchestration module: Supports multi-condition grouping and complex rule calculations. Users can define business rules through a graphical interface and view statistical results in real time. It is mainly used to: receive business rule configurations, where the business rules can be predefined SQL query statements or custom scripts; analyze data characteristics and generate initial statistical logics, including data grouping rules, aggregation functions, etc.; dynamically adjust statistical logics according to operation results; for example, if it is found that the results of certain groups are abnormal, automatically adjust the grouping conditions to obtain more accurate results; output the final statistical results, and support export functions in multiple formats (such as JSON, CSV, Excel, etc.).

[0075] Security Control Module: Ensures security and privacy protection during data transmission, adopting a multi-level protection strategy, including data encryption, access control, and log auditing.

[0076] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the description in the method embodiment.

[0077] The system and method provided in this application correspond one by one. Therefore, the system also has beneficial technical effects similar to those of its corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system will not be elaborated here.

[0078] Those skilled in the art should understand that the embodiments of this application can be provided as methods, devices, systems, or computer program products. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0079] It should also be noted that the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, commodity, or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity, or device including the element.

[0080] The above are only the embodiments of this application and are not used to limit this application. For those skilled in the art, various changes and modifications can be made to this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A message-driven cross-domain data collaborative statistics method, characterized in that: The method comprises: Obtaining a task request for a cross-domain data statistics task, wherein the task request includes at least one of a task type, a task priority, and a task target node area; Determine whether the cross-domain data statistics task is a divisible task, and if so, divide the cross-domain data statistics task into a plurality of subtasks according to the task target node; Distributing the plurality of subtasks to corresponding task target node areas so that the task target node areas execute the subtasks locally; Receive the task execution result returned by the task target node area, and generate the statistical result of the cross-domain data statistics task by aggregating the task execution result.

2. According to claim 1, a message-driven cross-domain data collaborative statistics method is characterized in that: Before distributing the plurality of subtasks to corresponding task target node areas, the method further includes: Verify whether the task target node area is an authorized trusted node area; When the task target node area passes the verification, the plurality of subtasks are distributed to the task target node area.

3. According to the message-driven cross-domain data collaborative statistics method of claim 1, it is characterized in that: Before the task target node area returns the task execution result, the method further includes: Determining that there is sensitive information in the task execution result, wherein the sensitive information includes at least personal identity information and personal privacy information; Desensitizing the sensitive information; The desensitized task execution result is encrypted using a preset encryption algorithm, and the encrypted task execution result is returned.

4. The message-driven cross-domain data collaborative statistics method according to claim 1 is characterized in that: After receiving the task execution result returned by the task target node area, the method further includes: Clean, convert and integrate the task execution results from different task target node areas; A visualization tool is provided to enable the user to view the processing process of the task execution result.

5. The message-driven cross-domain data collaborative statistics method according to claim 1 is characterized in that: The statistical results of the cross-domain data statistics task are generated by aggregating the task execution results, including: Obtaining business rules corresponding to the cross-domain data statistics task, wherein the business rules are user-defined rules and / or user-predefined rules; Parsing the business rules to generate data statistical logic according to the parsing structure; The task execution results are aggregated and / or grouped according to the data statistical logic to obtain statistical results corresponding to the cross-domain data statistical task.

6. The message-driven cross-domain data collaborative statistics method according to claim 5 is characterized in that: After summarizing the task execution results according to the data statistical logic, the method further includes: Optimizing the data statistics logic according to the summary results and / or grouping results; Re-aggregating and / or re-grouping the task execution results using the optimized data statistical logic; The statistical results are generated according to the re-aggregation results and / or the re-grouping results, and the statistical results are output in a structured manner.

7. The message-driven cross-domain data collaborative statistics method according to claim 1 is characterized in that: Before distributing the plurality of subtasks to corresponding task target node areas, the method further includes: Querying resource usage of the task target node area, wherein the resource usage includes at least one of CPU utilization and memory occupancy; Calculate the load index corresponding to the task target node area according to the resource usage; When the load index is lower than a preset index threshold, the plurality of subtasks are distributed to the task target node area.

8. The message-driven cross-domain data collaborative statistics method according to claim 1 is characterized in that: After obtaining the task request of the cross-domain data statistics task, the method further includes: Determining that there is an isolated node area in a one-way isolation environment in the task target node area; An intermediate proxy node is deployed at the isolation boundary of the isolated node area to implement data communication of the isolated node area through the intermediate proxy node.

9. The message-driven cross-domain data collaborative statistics method according to claim 8 is characterized in that: The method further comprises: When the intermediate agent node receives the subtask corresponding to the cross-domain data statistics task, and / or when the intermediate agent node receives the task execution result returned by the isolation node area, the subtask and / or the task execution result are checked for integrity and format, and the task execution result is encrypted.

10. A message-driven cross-domain data collaborative statistics device, characterized in that: The device comprises: at least one processor; and a memory connected to the at least one processor, the memory storing computer executable instructions, the computer executable instructions, when executed, enabling the processor to perform: Obtaining a task request for a cross-domain data statistics task, wherein the task request includes at least one of a task type, a task priority, and a task target node area; Determine whether the cross-domain data statistics task is a divisible task, and if so, divide the cross-domain data statistics task into a plurality of subtasks according to the task target node; Distributing the plurality of subtasks to corresponding task target node areas so that the task target node areas execute the subtasks locally; Receive the task execution result returned by the task target node area, and generate the statistical result of the cross-domain data statistics task by aggregating the task execution result.

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