Online custom report management method and system combined with ERP system

Abstract

The present invention discloses an online custom report management method and system combined with an ERP system, and relates to the technical field of enterprise data management, including: determining at least one report management task; custom editing at least one report data processing function corresponding to one report management task based on each report management task; determining the data update status of the ERP system based on the data history update log of the ERP system, wherein the data update status at least includes the data object type and the data update amount; setting an execution node for each report management task based on the data update status of the ERP system; obtaining the data update of the ERP system in real time, and automatically executing at least one report management task to automatically update the report; and using distributed database technology to store the report. The advantages of the present invention are: realizing direct docking with the ERP system, automatically obtaining and updating data from the ERP, and automatically adjusting the update tasks of the distributed report, thereby realizing efficient report management.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise data management, and in particular to an online custom report management method and system combined with an ERP system. Background Art

[0002] Reports are used to dynamically display data in the form of tables, charts, etc. Report management systems are used to help users display their own input data. More often, they display the data in the database in the way the customer wants. The existing technology usually uses an online spreadsheet system for collaborative reporting, which provides online spreadsheet editing and collaboration functions, and users can edit and share spreadsheets in real time.

[0003] However, the existing technology does not have the function of obtaining data from ERP, usually does not have the function of directly integrating with the ERP system, cannot automatically obtain and update data from ERP, does not support complex task distribution and authority management, and does not have the function of automatically summarizing task data. It is impossible to dynamically adjust the update tasks of the report according to real-time data, and it is difficult to achieve efficient report management. Summary of the invention

[0004] In order to solve the above technical problems, an online custom report management method and system combined with an ERP system are provided. The technical solution solves the problem that the above-mentioned prior art does not have the function of obtaining data from ERP, usually does not have the function of directly integrating with the ERP system, cannot automatically obtain and update data from ERP, does not support complex task distribution and authority management, does not have the function of automatically summarizing task data, cannot dynamically adjust the update tasks of the report according to real-time data, and is difficult to achieve efficient report management.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] An online custom report management method combined with an ERP system, comprising:

[0007] Based on the custom report management requirements, determine at least one report management task;

[0008] Customize and edit at least one report data processing function corresponding to each report management task based on each report management task;

[0009] Determine the data update status of the ERP system based on the data history update log of the ERP system, wherein the data update status includes at least the data object type and the data update amount;

[0010] Based on the data update status of the ERP system, set the execution node for each report management task;

[0011] Obtain data updates from the ERP system in real time, and automatically execute at least one report management task based on the data updates from the ERP system to automatically update the report;

[0012] Use distributed database technology to store reports.

[0013] Preferably, the data update status of the ERP system is based on which the execution node is set for each report management task, specifically including:

[0014] Determine the computational performance of all execution nodes;

[0015] Based on the report data processing function corresponding to each report management task, determine the baseline performance requirements for each report management task;

[0016] Determine the actual performance requirements of each report management task based on the data update status of the ERP system;

[0017] Determine the relevance between report management tasks based on the historical execution of report management tasks;

[0018] Based on the computing performance of all execution nodes and the actual performance requirements of each report management task, determine at least one report management task setting execution plan that meets the computing performance constraints of the execution nodes, which is recorded as the initial planning plan;

[0019] Based on the correlation between various report management tasks, the task-related indicators of each initial planning scheme are calculated through the task allocation algorithm;

[0020] The initial planning scheme with the smallest task-related indicators is selected as the optimal planning scheme, and the execution node is set for each report management task according to the optimal planning scheme.

[0021] Preferably, the determining of the actual performance requirements of each report management task based on the data update status of the ERP system specifically includes:

[0022] Determine the data object type for each report management task;

[0023] Based on the data update status of the ERP system, determine the data standard update amount for each data object type;

[0024] Determine the data standard update amount for the data object type targeted by the report management task, multiply the baseline performance requirement of the report management task by the data standard update amount for the data object type, and obtain the actual performance requirement of the report management task.

[0025] Preferably, the determining the correlation between the report management tasks based on the historical execution status of the report management tasks specifically includes:

[0026] The report management task whose correlation is to be calculated is respectively recorded as B x and B y

[0027] When determining the historical execution status of report management tasks, B x The number of times B appears y The number of times and simultaneous occurrences of B x and B y The number of times;

[0028] Then B x and B y The correlation between them is:

[0029]

[0030] In the formula, r xy For B x and B y The correlation between x B appears in the historical execution of the report management task x The number of times, N xy B appears in the historical execution of the report management task y The number of times, N xy B appears in the historical execution status of the report management task x and B y The number of times.

[0031] Preferably, the step of determining at least one report management task setting execution plan that satisfies the computing performance constraints of the execution nodes based on the computing performance of all execution nodes and the actual performance requirements of each report management task specifically includes:

[0032] Build performance constraints based on the computing performance of all execution nodes and the actual performance requirements of each report management task;

[0033] Determine the execution plans for all report management tasks that meet performance constraints, which are all preliminary planning plans;

[0034] The mathematical expression of the performance constraint condition is:

[0035]

[0036] In the formula, x ik is the actual performance requirement of the kth report management task assigned to the i-th execution node, y i is the computing performance of the i-th execution node, li is the number of report management tasks assigned to the i-th execution node, L is the total number of all report management tasks, and n is the total number of execution nodes.

[0037] Preferably, the task allocation algorithm is specifically as follows:

[0038] The report management tasks assigned to the execution nodes are randomly paired in pairs to obtain several report management task groups of the execution nodes;

[0039] Based on the task allocation formula, calculate the task-related indicators of each preliminary planning scheme;

[0040] The task allocation formula is specifically:

[0041]

[0042] In the formula, H eva is the task-related indicator of the initial planning scheme, mi is the total number of report management task groups assigned to the i-th execution node, is the sum of the actual performance requirements of the j-th report management task group assigned to the i-th execution node, Manages the relevance of the task group for the j-th report assigned to the i-th execution node.

[0043] Preferably, the real-time acquisition of data updates of the ERP system and the automatic execution of at least one report management task based on the data updates of the ERP system, wherein the automatic updating of the report specifically includes:

[0044] The data of the ERP system obtained last time is stored in the cache space;

[0045] Compare the currently acquired ERP system data with the previously acquired ERP system data, obtain the data object type with differences, record it as the updated data object type, and obtain the data update amount corresponding to the updated data object type;

[0046] Determine at least one report management task for updating a data object type, recorded as a report update management task;

[0047] The data update amount corresponding to the updated data object type is sent to the execution node corresponding to the report update management task, the report update management task is executed, and the report is automatically updated.

[0048] Preferably, the use of distributed database technology to store reports specifically includes:

[0049] Divide and slice the updated report data to obtain a number of report slice data, each of which contains at least one complete piece of information;

[0050] Store report slice data evenly on multiple distributed database nodes;

[0051] Set up data replication and redundant backup strategies in distributed databases, using master-slave replication or multi-master replication to replicate data to multiple distributed database nodes;

[0052] Use distributed transaction processing technology to achieve data consistency and synchronization in distributed databases;

[0053] Use data sharding routing method to implement load balancing and performance optimization strategies in distributed databases;

[0054] In a distributed database, a disaster recovery and fault recovery mechanism including fault detection and automatic switching is set up.

[0055] Among them, distributed storage technology is a mature data storage technology in this field and will not be elaborated here.

[0056] Furthermore, an online custom report management system combined with an ERP system is proposed, which is used to implement the online custom report management method combined with an ERP system as described above, including:

[0057] A report task setting module, the report task setting module is used to determine at least one report management task based on the customized report management requirements, and customize and edit at least one report data processing function corresponding to the report management task based on each report management task;

[0058] An ERP system docking module, which is integrated with the ERP system through an API interface to achieve automatic acquisition and synchronization of ERP data;

[0059] A management task allocation module, the management task allocation module is electrically connected to the ERP system docking module, and the management task allocation module is used to set an execution node for each report management task based on the data update status of the ERP system;

[0060] A report update module, the report update module is electrically connected to the ERP system docking module and the management task allocation module, the report update module is used to obtain data updates of the ERP system in real time, and automatically execute at least one report management task based on the data updates of the ERP system to automatically update the report;

[0061] A distributed storage management module is electrically connected to the report updating module, and is used to store reports using a distributed database technology.

[0062] Optionally, the management task allocation module includes:

[0063] A task analysis unit, the task analysis unit is used to determine the baseline performance requirements of each report management task based on the report data processing function corresponding to each report management task, and determine the actual performance requirements of each report management task based on the data update status of the ERP system;

[0064] A node analysis unit, the node analysis unit is used to determine the computing performance of all execution nodes;

[0065] A correlation analysis unit, the correlation analysis unit is used to determine the correlation between the report management tasks based on the historical execution status of the report management tasks;

[0066] An initial planning unit, the initial planning unit is used to determine at least one report management task setting execution plan that meets the computing performance constraints of the execution nodes based on the computing performance of all execution nodes and the actual performance requirements of each report management task, which is recorded as the initial planning plan;

[0067] A solution analysis unit is used to calculate the task-related indicators of each initial planning solution based on the correlation between each report management task through a task allocation algorithm, and to select the initial planning solution with the smallest task-related indicators as the optimal planning solution, and to set an execution node for each report management task according to the optimal planning solution.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The present invention proposes an online custom report management solution combined with an ERP system, which realizes real-time docking with the ERP system, can directly obtain updated data from the ERP system, and automatically distribute and execute report management tasks based on the updated data obtained, and intelligently update the content of the report;

[0070] The present invention combines the computing power requirements of different report management tasks and the computing performance of different task execution nodes to intelligently allocate the report management tasks executed by different execution nodes. In this way, the multi-threaded processing capability of the report management system for report management tasks is guaranteed, and the balanced load of the task execution nodes is achieved, thus avoiding the problem of data update delay caused by a surge in the load of a single task execution node when a single report data is updated. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A flow chart of the online custom report management method combined with the ERP system proposed by the present invention;

[0072] Figure 2 A flow chart of a method for setting an execution node for each report management task in the present invention;

[0073] Figure 3 A flow chart of a method for determining the actual performance requirements of each report management task in the present invention;

[0074] Figure 4 A flow chart of a method for determining at least one report management task to set an execution plan under the computing performance constraints of an execution node in the present invention;

[0075] Figure 5 A method flow chart of the task allocation algorithm in the present invention;

[0076] Figure 6 A flow chart of a method for automatically updating a report in the present invention;

[0077] Figure 7 This is a flow chart of the method for storing reports using distributed database technology in the present invention. DETAILED DESCRIPTION

[0078] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0079] Reference Figure 1 As shown, an online custom report management method combined with an ERP system includes:

[0080] Based on the custom report management requirements, determine at least one report management task;

[0081] Customize and edit at least one report data processing function corresponding to each report management task based on each report management task;

[0082] Based on the data history update log of the ERP system, determine the data update status of the ERP system, where the data update status at least includes the data object type and the data update amount;

[0083] Based on the data update status of the ERP system, set the execution node for each report management task;

[0084] Obtain data updates from the ERP system in real time, and automatically execute at least one report management task based on the data updates from the ERP system to automatically update the report;

[0085] Use distributed database technology to store reports.

[0086] Reference Figure 2 As shown in the figure, based on the data update status of the ERP system, the execution nodes set for each report management task include:

[0087] Determine the computational performance of all execution nodes;

[0088] Based on the report data processing function corresponding to each report management task, determine the baseline performance requirements for each report management task;

[0089] Determine the actual performance requirements of each report management task based on the data update status of the ERP system;

[0090] Determine the relevance between report management tasks based on the historical execution of report management tasks;

[0091] Based on the computing performance of all execution nodes and the actual performance requirements of each report management task, determine at least one report management task setting execution plan that meets the computing performance constraints of the execution nodes, which is recorded as the initial planning plan;

[0092] Based on the correlation between various report management tasks, the task-related indicators of each initial planning scheme are calculated through the task allocation algorithm;

[0093] The initial planning scheme with the smallest task-related indicators is selected as the optimal planning scheme, and the execution node is set for each report management task according to the optimal planning scheme.

[0094] It can realize real-time docking with the ERP system, directly obtain updated data from the ERP system, and automatically distribute and execute report management tasks based on the updated data, and intelligently update the content of the report. At the same time, it combines the computing power requirements of different report management tasks and the computing performance of different task execution nodes to intelligently allocate report management tasks executed by different execution nodes. In this way, the multi-threaded processing capability of the report management system for report management tasks is guaranteed, and the balanced load of task execution nodes is achieved, avoiding the problem of data update delay caused by a surge in the load of a single task execution node when a single report data is updated.

[0095] Reference Figure 3 As shown in the figure, based on the data update status of the ERP system, the actual performance requirements for each report management task are determined to include:

[0096] Determine the data object type for each report management task;

[0097] Based on the data update status of the ERP system, determine the data standard update amount for each data object type;

[0098] Determine the data standard update amount for the data object type targeted by the report management task, multiply the baseline performance requirement of the report management task by the data standard update amount for the data object type, and obtain the actual performance requirement of the report management task.

[0099] The standard update amount of data object type is the average value of data update amount for all single data updates. It can be understood that due to the different data tags faced by report management tasks, the calculation amount of task functions called by report management tasks is different. Different report management tasks require different computing power for unit data volume. Based on this, this solution combines the data update status of the ERP system to estimate the data processing amount of a single data update of different report management tasks, and then evaluates the actual performance requirements of each report management task in a single data update.

[0100] Based on the historical execution of report management tasks, determine the relevance between report management tasks, including:

[0101] The report management task whose correlation is to be calculated is respectively recorded as B x and B y

[0102] When determining the historical execution status of report management tasks, B x The number of times B appears y The number of times and simultaneous occurrences of B x and B y The number of times;

[0103] Then B x and B y The correlation between them is:

[0104]

[0105] In the formula, r xy For B x and B y The correlation between x B appears in the historical execution of the report management task x The number of times, N xy B appears in the historical execution of the report management task y The number of times, N xy B appears in the historical execution status of the report management task x and B y The number of times.

[0106] It can be understood that, in report management tasks, the correlation between any two is the probability index that two report management tasks are executed simultaneously in the same data update. Based on this, the present solution adopts the method of calculating the correlation between any two report management tasks in the historical execution of report management tasks by the ratio of the number of cases where any two report management tasks appear simultaneously to the number of cases where a single report management task appears alone. In the present embodiment, the greater the calculated correlation is, the greater the probability that the two report management tasks are executed simultaneously is.

[0107] Reference Figure 4 As shown, based on the computing performance of all execution nodes and the actual performance requirements of each report management task, determining at least one report management task setting execution plan that meets the computing performance constraints of the execution nodes specifically includes:

[0108] Build performance constraints based on the computing performance of all execution nodes and the actual performance requirements of each report management task;

[0109] Determine the execution plans for all report management tasks that meet performance constraints, which are all preliminary planning plans;

[0110] The mathematical expression of the performance limit condition is:

[0111]

[0112] In the formula, x ik is the actual performance requirement of the kth report management task assigned to the i-th execution node, y i is the computing performance of the i-th execution node, li is the number of report management tasks assigned to the i-th execution node, L is the total number of all report management tasks, and n is the total number of execution nodes.

[0113] Through performance constraints, it is ensured that under normal conditions, in a single update task, the report management tasks assigned to each execution node will not exceed the performance of the execution node itself, thereby effectively ensuring the efficiency of report data updates.

[0114] Reference Figure 5 As shown in the figure, the task allocation algorithm is as follows:

[0115] The report management tasks assigned to the execution nodes are randomly paired in pairs to obtain several report management task groups of the execution nodes;

[0116] Based on the task allocation formula, calculate the task-related indicators of each preliminary planning scheme;

[0117] The task allocation formula is as follows:

[0118]

[0119] In the formula, H eva is the task-related indicator of the initial planning scheme, mi is the total number of report management task groups assigned to the i-th execution node, is the sum of the actual performance requirements of the j-th report management task group assigned to the i-th execution node, Manages the relevance of the task group for the j-th report assigned to the i-th execution node.

[0120] In this solution, the rationality of task allocation is comprehensively evaluated based on the performance requirements of the report management tasks assigned to each node and the correlation between the report management tasks. That is, report management tasks with high performance requirements and a high probability of occurring at the same time are assigned to different execution nodes for execution, so as to achieve balanced load on the task execution nodes and avoid the problem of data update delay caused by a surge in the load of a single task execution node when a single report data is updated.

[0121] Reference Figure 6 As shown, real-time data updates of the ERP system are obtained, and at least one report management task is automatically executed based on the data updates of the ERP system. The automatic update of the report specifically includes:

[0122] The data of the ERP system obtained last time is stored in the cache space;

[0123] Compare the currently acquired ERP system data with the previously acquired ERP system data, obtain the data object type with differences, record it as the updated data object type, and obtain the data update amount corresponding to the updated data object type;

[0124] Determine at least one report management task for updating a data object type, recorded as a report update management task;

[0125] The data update amount corresponding to the updated data object type is sent to the execution node corresponding to the report update management task, the report update management task is executed, and the report is automatically updated.

[0126] This solution achieves seamless integration with the ERP system and can automatically obtain and update data from ERP, greatly improving the efficiency and accuracy of data management.

[0127] Reference Figure 7 As shown in the figure, using distributed database technology to store reports specifically includes:

[0128] Divide and slice the updated report data to obtain a number of report slice data, each of which contains at least one complete piece of information;

[0129] Store report slice data evenly on multiple distributed database nodes;

[0130] Set up data replication and redundant backup strategies in distributed databases, using master-slave replication or multi-master replication to replicate data to multiple distributed database nodes;

[0131] Use distributed transaction processing technology to achieve data consistency and synchronization in distributed databases;

[0132] Use data sharding routing method to implement load balancing and performance optimization strategies in distributed databases;

[0133] In a distributed database, a disaster recovery and fault recovery mechanism including fault detection and automatic switching is set up.

[0134] The use of distributed storage technology can improve the fault tolerance of storage, thereby avoiding the damage of report data when unexpected situations occur, thereby effectively improving the security performance of the report management system.

[0135] Further, based on the same inventive concept as the above-mentioned online custom report management method combined with the ERP system, this solution proposes an online custom report management system combined with the ERP system, including:

[0136] A report task setting module, the report task setting module is used to determine at least one report management task based on the customized report management requirements, and customize and edit at least one report data processing function corresponding to the report management task based on each report management task;

[0137] ERP system docking module: The ERP system docking module is integrated with the ERP system through the API interface to realize automatic acquisition and synchronization of ERP data;

[0138] A management task allocation module, which is electrically connected to the ERP system docking module. The management task allocation module is used to set an execution node for each report management task based on the data update status of the ERP system;

[0139] A report update module, the report update module is electrically connected to the ERP system docking module and the management task allocation module, and the report update module is used to obtain data updates of the ERP system in real time, and automatically execute at least one report management task based on the data updates of the ERP system to automatically update the report;

[0140] The distributed storage management module is electrically connected to the report updating module, and is used for storing reports by adopting distributed database technology.

[0141] The management task allocation module includes:

[0142] Task analysis unit: The task analysis unit is used to determine the baseline performance requirements of each report management task based on the report data processing function corresponding to each report management task, and to determine the actual performance requirements of each report management task based on the data update status of the ERP system;

[0143] A node analysis unit, the node analysis unit is used to determine the computational performance of all execution nodes;

[0144] A correlation analysis unit, which is used to determine the correlation between various report management tasks based on the historical execution status of the report management tasks;

[0145] The initial planning unit is used to determine at least one report management task setting execution plan that meets the calculation performance limit of the execution node based on the calculation performance of all execution nodes and the actual performance requirement of each report management task, which is recorded as the initial planning plan;

[0146] The solution analysis unit is used to calculate the task-related indicators of each initial planning solution based on the correlation between various report management tasks through the task allocation algorithm, and screen out the initial planning solution with the smallest task-related indicators as the optimal planning solution, and set the execution node for each report management task according to the optimal planning solution.

[0147] The process of using the above system is as follows:

[0148] Step 1: The report task setting module determines at least one report management task based on the customized report management requirements, and customizes and edits at least one report data processing function corresponding to the report management task based on each report management task;

[0149] Step 2: The management task allocation module obtains the data history update log of the ERP system through the ERP system docking module, and determines the data update status of the ERP system;

[0150] Step 3: The task analysis unit determines the baseline performance requirements of each report management task based on the report data processing function corresponding to each report management task, and determines the actual performance requirements of each report management task based on the data update status of the ERP system;

[0151] Step 4: The node analysis unit determines the computing performance of all execution nodes;

[0152] Step 5: The correlation analysis unit determines the correlation between the report management tasks based on the historical execution status of the report management tasks;

[0153] Step 6: The initial planning unit determines at least one report management task setting execution plan that meets the calculation performance constraints of the execution nodes based on the calculation performance of all execution nodes and the actual performance requirements of each report management task, which is recorded as the initial planning plan;

[0154] Step 7: Based on the correlation between the report management tasks, the solution analysis unit calculates the task-related index of each preliminary planning solution through the task allocation algorithm, selects the preliminary planning solution with the smallest task-related index as the optimal planning solution, and sets the execution node for each report management task according to the optimal planning solution;

[0155] Step 8: The report update module obtains data updates of the ERP system in real time through the ERP system docking module, and automatically executes at least one report management task based on the data updates of the ERP system to automatically update the report;

[0156] Step 9: The distributed storage management module uses distributed database technology to store reports.

[0157] In summary, the advantages of the present invention are: it realizes direct connection with the ERP system, can automatically obtain and update data from the ERP, and automatically adjusts the update tasks of the distribution report to achieve efficient report management.

[0158] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An online custom report management method combined with an ERP system, characterized in that: include: Based on the custom report management requirements, determine at least one report management task; Customize and edit at least one report data processing function corresponding to each report management task based on each report management task; Determine the data update status of the ERP system based on the data history update log of the ERP system, wherein the data update status includes at least the data object type and the data update amount; Based on the data update status of the ERP system, set the execution node for each report management task; Obtain data updates from the ERP system in real time, and automatically execute at least one report management task based on the data updates from the ERP system to automatically update the report; Use distributed database technology to store reports; The data update status of the ERP system is based on which the execution node is set for each report management task, specifically including: Determine the computational performance of all execution nodes; Based on the report data processing function corresponding to each report management task, determine the baseline performance requirements for each report management task; Determine the actual performance requirements of each report management task based on the data update status of the ERP system; Determine the relevance between report management tasks based on the historical execution of report management tasks; Based on the computing performance of all execution nodes and the actual performance requirements of each report management task, determine at least one report management task setting execution plan that meets the computing performance constraints of the execution nodes, which is recorded as the initial planning plan; Based on the correlation between various report management tasks, the task-related indicators of each initial planning scheme are calculated through the task allocation algorithm; The initial planning scheme with the smallest task-related indicators is selected as the optimal planning scheme, and the execution node is set for each report management task according to the optimal planning scheme.

2. According to claim 1, the online custom report management method combined with the ERP system is characterized in that: The actual performance requirements of each report management task determined based on the data update status of the ERP system specifically include: Determine the data object type for each report management task; Based on the data update status of the ERP system, determine the data standard update amount for each data object type; Determine the data standard update amount for the data object type targeted by the report management task, multiply the baseline performance requirement of the report management task by the data standard update amount for the data object type, and obtain the actual performance requirement of the report management task.

3. The online custom report management method combined with the ERP system according to claim 2 is characterized in that: The determining of the correlation between the report management tasks based on the historical execution status of the report management tasks specifically includes: The report management task whose correlation is to be calculated is respectively recorded as B x and B y When determining the historical execution status of report management tasks, B x The number of times B appears y The number of times and simultaneous occurrences of B x and B y The number of times; Then B x and B y The correlation between them is: In the formula, r xy For B x and B y The correlation between x B appears in the historical execution of the report management task x The number of times, N xy B appears in the historical execution of the report management task y The number of times, N xy B appears in the historical execution status of the report management task x and B y The number of times.

4. The online custom report management method combined with the ERP system according to claim 3 is characterized in that: The step of determining at least one report management task setting execution plan that satisfies the calculation performance limit of the execution nodes based on the calculation performance of all execution nodes and the actual performance requirement of each report management task specifically includes: Build performance constraints based on the computing performance of all execution nodes and the actual performance requirements of each report management task; Determine the execution plans for all report management tasks that meet performance constraints, which are all preliminary planning plans; The mathematical expression of the performance constraint condition is: In the formula, x ik is the actual performance requirement of the kth report management task assigned to the i-th execution node, y i is the computing performance of the i-th execution node, li is the number of report management tasks assigned to the i-th execution node, L is the total number of all report management tasks, and n is the total number of execution nodes.

5. The online custom report management method combined with the ERP system according to claim 4 is characterized in that: The task allocation algorithm is specifically as follows: The report management tasks assigned to the execution nodes are randomly paired in pairs to obtain several report management task groups of the execution nodes; Based on the task allocation formula, calculate the task-related indicators of each preliminary planning scheme; The task allocation formula is specifically: In the formula, H eva is the task-related indicator of the initial planning scheme, mi is the total number of report management task groups assigned to the i-th execution node, is the sum of the actual performance requirements of the j-th report management task group assigned to the i-th execution node, Manages the relevance of the task group for the j-th report assigned to the i-th execution node.

6. The online custom report management method combined with the ERP system according to claim 5 is characterized in that: The real-time acquisition of data updates of the ERP system and automatic execution of at least one report management task based on the data updates of the ERP system, and automatic updating of the report specifically include: The data of the ERP system obtained last time is stored in the cache space; Compare the currently acquired ERP system data with the previously acquired ERP system data, obtain the data object type with differences, record it as the updated data object type, and obtain the data update amount corresponding to the updated data object type; Determine at least one report management task for updating a data object type, recorded as a report update management task; The data update amount corresponding to the updated data object type is sent to the execution node corresponding to the report update management task, the report update management task is executed, and the report is automatically updated.

7. The online custom report management method combined with the ERP system according to claim 6 is characterized in that: The use of distributed database technology to store reports specifically includes: Divide and slice the updated report data to obtain a number of report slice data, each of which contains at least one complete piece of information; Store report slice data evenly on multiple distributed database nodes; Set up data replication and redundant backup strategies in distributed databases, using master-slave replication or multi-master replication to replicate data to multiple distributed database nodes; Use distributed transaction processing technology to achieve data consistency and synchronization in distributed databases; Use data sharding routing method to implement load balancing and performance optimization strategies in distributed databases; In a distributed database, a disaster recovery and fault recovery mechanism including fault detection and automatic switching is set up.

8. An online custom report management system combined with an ERP system, characterized in that: The method for implementing the online custom report management method combined with the ERP system as claimed in any one of claims 1 to 7 comprises: A report task setting module, the report task setting module is used to determine at least one report management task based on the customized report management requirements, and customize and edit at least one report data processing function corresponding to the report management task based on each report management task; An ERP system docking module, which is integrated with the ERP system through an API interface to achieve automatic acquisition and synchronization of ERP data; A management task allocation module, the management task allocation module is electrically connected to the ERP system docking module, and the management task allocation module is used to set an execution node for each report management task based on the data update status of the ERP system; A report update module, the report update module is electrically connected to the ERP system docking module and the management task allocation module, the report update module is used to obtain data updates of the ERP system in real time, and automatically execute at least one report management task based on the data updates of the ERP system to automatically update the report; A distributed storage management module is electrically connected to the report updating module, and is used to store reports using a distributed database technology.

9. The online custom report management system combined with the ERP system according to claim 8, characterized in that: The management task allocation module includes: A task analysis unit, the task analysis unit is used to determine the baseline performance requirements of each report management task based on the report data processing function corresponding to each report management task, and determine the actual performance requirements of each report management task based on the data update status of the ERP system; A node analysis unit, the node analysis unit is used to determine the computing performance of all execution nodes; A correlation analysis unit, the correlation analysis unit is used to determine the correlation between the report management tasks based on the historical execution status of the report management tasks; An initial planning unit, the initial planning unit is used to determine at least one report management task setting execution plan that meets the computing performance constraints of the execution nodes based on the computing performance of all execution nodes and the actual performance requirements of each report management task, which is recorded as the initial planning plan; A solution analysis unit is used to calculate the task-related indicators of each initial planning solution based on the correlation between each report management task through a task allocation algorithm, and to select the initial planning solution with the smallest task-related indicators as the optimal planning solution, and to set an execution node for each report management task according to the optimal planning solution.

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