Resource data fluctuation analysis processing method and device, storage medium and program product

Abstract

The invention relates to the technical field of resource processing, provides a resource data fluctuation analysis processing method and device, a storage medium and a program product, and can automatically perform resource data fluctuation analysis processing. The method comprises the following steps: in response to a data extraction trigger event, extracting first resource data and corresponding first service volume data of each service in an analysis period from a multi-service system; classifying the first resource data of each service and the corresponding first service volume data according to preset multi-class resource inspection tasks to obtain second resource data and corresponding second service volume data under each class of resource inspection tasks; for each type of resource inspection task, acquiring fluctuation information of the second resource data and the corresponding second business volume data under each preset resource analysis dimension and / or each resource analysis dimension combination, and obtaining a fluctuation analysis result under the resource inspection task according to the fluctuation information; and triggering creation of a corresponding resource processing task according to a fluctuation analysis result.

Description

Technical Field

[0001] This application relates to the field of resource processing technology, and in particular to a resource data fluctuation analysis and processing method, computer equipment, storage medium, and computer program product. Background Technology

[0002] Fluctuations in resource data can be considered one of the key indicators of enterprise resource management. They can reflect the changes in the composition of resources invested in various businesses across different analysis periods, thereby reflecting the efficiency and anomalies in the use of enterprise resources.

[0003] Currently, resource data fluctuation analysis mainly relies on manual processing, requiring relevant personnel to export massive amounts of data from multiple business systems and calculate resource data fluctuations in a short period of time. This processing method requires significant manual labor costs. Summary of the Invention

[0004] Therefore, it is necessary to provide a resource data fluctuation analysis and processing method, computer equipment, storage medium, and computer program product to address the above-mentioned technical problems.

[0005] This application provides a method for analyzing and processing resource data fluctuations, the method comprising:

[0006] In response to a data extraction trigger event, the system extracts the first resource data and the corresponding first business volume data for each business in the analysis period from the multi-business system; the data extraction trigger event is generated when the analysis period is met at the current time.

[0007] According to the preset multi-type resource inspection tasks, the first resource data and the corresponding first business volume data of each business are classified to obtain the second resource data and the corresponding second business volume data under each type of resource inspection task;

[0008] For each type of resource inspection task, the fluctuation information of the second resource data and the corresponding second business volume data under each preset resource analysis dimension and / or combination of resource analysis dimensions is obtained, and the fluctuation analysis result under the resource inspection task is obtained based on the fluctuation information.

[0009] The creation of corresponding resource processing tasks is triggered based on the fluctuation analysis results.

[0010] This application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method.

[0011] This application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method.

[0012] This application provides a computer program product having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method.

[0013] The aforementioned resource data fluctuation analysis and processing method, computer equipment, storage medium, and computer program product, in response to a data extraction trigger event, extract the first resource data and corresponding first business volume data of each business in the analysis period from the multi-business system; the data extraction trigger event is generated when the analysis period is met at the current time; the first resource data and corresponding first business volume data of each business are classified according to preset multi-type resource inspection tasks to obtain the second resource data and corresponding second business volume data under each type of resource inspection task; for each type of resource inspection task, the fluctuation information of the second resource data and corresponding second business volume data under preset resource analysis dimensions and / or combinations of resource analysis dimensions is obtained, and the fluctuation analysis result under the resource inspection task is obtained based on the fluctuation information; the creation of the corresponding resource processing task is triggered based on the fluctuation analysis result. This solution can automatically perform resource data fluctuation analysis in response to data extraction trigger events that occur when the analysis cycle is met at the current time. It can automatically extract the first resource data and corresponding second business volume data for each business within the analysis cycle from multiple business systems, eliminating the need for manual export and reducing reliance on manual labor, thus saving labor costs. Next, it can automatically categorize the first resource data and corresponding first business volume data for each business according to preset resource inspection tasks, eliminating the need for manual classification and automatically obtaining the second resource data and corresponding second business volume data for each type of resource inspection task. For each type of resource inspection task, it can obtain the fluctuation information of the second resource data and corresponding second business volume data under preset resource analysis dimensions and / or combinations of resource analysis dimensions. Based on the fluctuation information, it obtains the fluctuation analysis results for the resource inspection task, thus providing technical support for multi-dimensional analysis, avoiding errors that are prone to occur in manual multi-dimensional analysis, and improving the accuracy of resource data fluctuation analysis. After obtaining the fluctuation analysis results, it can trigger the creation of corresponding resource processing tasks, enabling timely subsequent processing. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1This is an application environment diagram of the resource data fluctuation analysis and processing method in one embodiment;

[0016] Figure 2 This is a flowchart illustrating a resource data fluctuation analysis and processing method in one embodiment;

[0017] Figure 3 This is a flowchart illustrating the data classification process in one embodiment;

[0018] Figure 4 This is another flowchart illustrating the resource data fluctuation analysis and processing method in one embodiment;

[0019] Figure 5 This is a schematic diagram of the data extraction process in one embodiment;

[0020] Figure 6 This is another flowchart illustrating data classification in one embodiment;

[0021] Figure 7 This is a flowchart illustrating the multidimensional fluctuation calculation process in one embodiment;

[0022] Figure 8 This is a schematic diagram of the processing flow after obtaining the fluctuation analysis results in one embodiment;

[0023] Figure 9 This is another flowchart illustrating the resource data fluctuation analysis and processing method in one embodiment;

[0024] Figure 10 This is a structural block diagram of a resource data fluctuation analysis and processing device in one embodiment;

[0025] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0027] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various objects, but these objects are not limited by these terms. These terms are only used to distinguish the first object from the second object. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions, or any combination of multiple solutions.

[0028] The resource data fluctuation analysis and processing method provided in this application can be applied to, for example... Figure 1 In the application environment shown, multiple business systems can communicate with the Big Data Platform (BDP) via a network. The Big Data Platform can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0029] When performing resource data fluctuation analysis, related technologies primarily rely on personnel exporting the necessary data from multiple business systems. This requires personnel to calculate resource data fluctuations based on the exported data within a short timeframe, resulting in high costs. For example, resource data fluctuation analysis can include expense fluctuation analysis. In enterprise expense fluctuation analysis, the required data is scattered across various business systems, such as financial systems, supplier settlement systems, and expense reimbursement systems. This data volume is substantial, requiring finance personnel to export massive amounts of data from multiple business systems and manually categorize and calculate expense fluctuations, incurring significant labor costs.

[0030] In response to this, the resource data fluctuation analysis and processing method of this application embodiment can automatically perform resource data fluctuation analysis and processing in response to the data extraction trigger event generated when the analysis cycle is met at the current time. It can automatically extract the first resource data and the corresponding second business volume data of each business in the analysis cycle from the multi-business system without manual export, which can reduce reliance on manual labor and save labor costs. It can automatically classify the first resource data and the corresponding first business volume data of each business according to preset multi-class resource inspection tasks without manual classification, and can automatically obtain the second resource data and the corresponding second business volume data under each type of resource inspection task. For each type of resource inspection task, it can obtain the fluctuation information of the second resource data and the corresponding second business volume data of the resource inspection task under preset resource analysis dimensions and / or combinations of resource analysis dimensions, and obtain the fluctuation analysis result under the resource inspection task based on the fluctuation information, thereby providing technical support for multi-dimensional analysis, avoiding the errors that are prone to occur in manual multi-dimensional analysis, and improving the accuracy of resource data fluctuation analysis and processing. After obtaining the fluctuation analysis result, it can trigger the creation of corresponding resource processing tasks based on the fluctuation analysis result, so that subsequent processing can be carried out in a timely manner.

[0031] In one exemplary embodiment, such as Figure 2 As shown, a method for analyzing and processing resource data fluctuations is provided, which can be applied to... Figure 1 In a big data platform, the method may include the following steps:

[0032] Step S201: In response to the data extraction trigger event, extract the first resource data and the corresponding first business volume data of each business in the analysis period from the multi-business system.

[0033] In this step, the data extraction trigger event is generated when the analysis period is met at the current time. The specific duration of the analysis period and the time when the analysis period is met can be set according to actual needs. For example, the analysis period can be set to one month, and correspondingly, the beginning of each month (such as the 1st to the 3rd) can be set as the time when the analysis period is met at the current time. When the analysis period is met at the current time, the data extraction trigger event can be generated. The big data platform can then perform passive or active trigger collection based on this event. It can extract resource data (which can be called first resource data) and corresponding business volume data (which can be called first business volume data) from multiple business systems during the analysis period. Among them, the resource data can include the resource volume data invested by the corresponding business, and the business volume data can include the business volume data generated by the corresponding business.

[0034] As an example, in passively triggered data collection, when the current time meets the analysis cycle, relevant personnel can trigger a data extraction event by clicking a front-end button or performing other actions. In response to this data extraction trigger event, the big data platform can extract the first resource data and corresponding first business volume data for each business within the analysis cycle from multiple business systems.

[0035] As another example, in proactive triggering data collection, the big data platform can use a timer to determine whether the current time meets the analysis cycle. When the current time meets the analysis cycle, the big data platform can automatically generate a data extraction trigger event. Then, it can extract the first resource data and the corresponding first business volume data of each business in the analysis cycle from the database of the multi-business system, or it can call the interface of the multi-business system to extract the first resource data and the corresponding first business volume data of each business in the analysis cycle.

[0036] Step S202: According to the preset multi-type resource inspection tasks, the first resource data and the corresponding first business volume data of each business are classified to obtain the second resource data and the corresponding second business volume data under each type of resource inspection task.

[0037] In this step, the primary resource data and corresponding primary business volume data for each business can be categorized according to preset resource inspection tasks. Multiple primary resource data items within the same type of resource inspection task can be grouped into secondary resource data under that same task, and vice versa. This step allows for task-based aggregation of primary resource data and corresponding primary business volume data according to the resource inspection tasks to which the business belongs. This lays the foundation for subsequent analysis of resource data fluctuations from a task perspective.

[0038] Step S203: For each type of resource inspection task, obtain the fluctuation information of the second resource data and the corresponding second business volume data under each preset resource analysis dimension and / or combination of resource analysis dimensions, and obtain the fluctuation analysis result under the resource inspection task based on the fluctuation information.

[0039] In this step, for each type of resource inspection task, the second resource data and the corresponding second business volume data for that task can be obtained. Next, the fluctuation information of the second resource data and the corresponding second business volume data under the same resource analysis dimension can be obtained, and / or, the fluctuation information of the second resource data and the corresponding second business volume data under the same combination of resource analysis dimensions can be obtained. Thus, the fluctuation information corresponding to the second resource data and the fluctuation information corresponding to the second business volume data can be obtained. The fluctuation information corresponding to the second resource data can be used to characterize the fluctuation of the amount of resources invested under that resource inspection task, and the fluctuation information corresponding to the second business volume data can be used to characterize the fluctuation of the business volume generated under that resource inspection task. Normally, if the amount of resources invested is greater than before, the corresponding business volume generated will also be greater than before. Based on this, the fluctuation analysis results under that resource inspection task can be determined.

[0040] Step S204: Trigger the creation of corresponding resource processing tasks based on the fluctuation analysis results.

[0041] Through the aforementioned steps, fluctuation analysis results under various resource inspection tasks can be obtained. The big data platform can determine whether the fluctuation analysis results under each type of resource inspection task indicate normal fluctuations. Based on the judgment results, the resource settlement system can be triggered to create corresponding resource processing tasks, thereby enabling the subsequent processing of resource data fluctuation analysis to proceed normally.

[0042] In the above-mentioned resource data fluctuation analysis and processing method, in response to the data extraction trigger event, the first resource data and the corresponding first business volume data of each business in the analysis period are extracted from the multi-business system; the data extraction trigger event is generated when the analysis period is met at the current time; the first resource data and the corresponding first business volume data of each business are classified according to the preset multi-type resource inspection tasks to obtain the second resource data and the corresponding second business volume data under each type of resource inspection task; for each type of resource inspection task, the fluctuation information of the second resource data and the corresponding second business volume data under the preset resource analysis dimensions and / or combinations of resource analysis dimensions is obtained, and the fluctuation analysis result under the resource inspection task is obtained based on the fluctuation information; the creation of the corresponding resource processing task is triggered based on the fluctuation analysis result. This solution can automatically perform resource data fluctuation analysis in response to data extraction trigger events that occur when the analysis cycle is met at the current time. It can automatically extract the first resource data and corresponding second business volume data for each business within the analysis cycle from multiple business systems, eliminating the need for manual export and reducing reliance on manual labor, thus saving labor costs. Next, it can automatically categorize the first resource data and corresponding first business volume data for each business according to preset resource inspection tasks, eliminating the need for manual classification and automatically obtaining the second resource data and corresponding second business volume data for each type of resource inspection task. For each type of resource inspection task, it can obtain the fluctuation information of the second resource data and corresponding second business volume data under preset resource analysis dimensions and / or combinations of resource analysis dimensions. Based on the fluctuation information, it obtains the fluctuation analysis results for the resource inspection task, thus providing technical support for multi-dimensional analysis, avoiding errors that are prone to occur in manual multi-dimensional analysis, and improving the accuracy of resource data fluctuation analysis. After obtaining the fluctuation analysis results, it can trigger the creation of corresponding resource processing tasks, enabling timely subsequent processing.

[0043] In an exemplary embodiment, step S201, extracting the first resource data and corresponding first service volume data of each service in the analysis period from the multi-service system, may include:

[0044] Identify the business orders generated by each business during the analysis period; for each business, extract data matching the order number from the first business system in the multi-business system to obtain the first resource data of the business, and extract data matching the order number from the second business system in the multi-business system to obtain the first business volume data of the business.

[0045] In this embodiment, the business can be, for example, but not limited to, facility operation and maintenance business, human resource management business, logistics and transportation management business, etc. As an example, the first resource data for facility operation and maintenance business can include, but is not limited to, water and electricity fees, maintenance fees, and property management fees; the first business volume data for facility operation and maintenance business can include, but is not limited to, the water and electricity used, and the number of devices maintained. As an example, the first resource data for human resource management business can include, but is not limited to, wage expenses; the first business volume data for human resource management business can include, but is not limited to, the number of items delivered by employees. As an example, the first resource data for logistics and transportation management business can include, but is not limited to, transportation costs; the first business volume data for logistics and transportation management business can include, but is not limited to, the number of express deliveries transported. The big data platform can obtain business orders generated by each business during the analysis period. Each business order has a corresponding order number. Based on this, the big data platform can use the order number as an index key to query multiple business systems to obtain the first resource data and the first business volume data for each business. The first resource data and the corresponding first business volume data for each business can be stored in different business systems. The business system used to store the first resource data can be called the first business system, and the business system used to store the first business volume data can be called the second business system. The big data platform can send the order number of a business order to the first business system. The first business system can query the data associated with the order number and return it to the big data platform, thus allowing the big data platform to obtain the business's initial resource data. The big data platform can also send the order number of a business order to a second business system. The second business system can query and return the data associated with the order number, thus allowing the big data platform to obtain the business's initial volume data.

[0046] In this embodiment, the first resource data and the corresponding first business volume data of a business can be retrieved simultaneously using the order number of the business order, thereby enabling synchronous data retrieval and laying the foundation for subsequent fluctuation analysis and calculation.

[0047] In one exemplary embodiment, such as Figure 3 As shown, step S202, which involves classifying the first resource data and corresponding first business volume data of each service according to preset multi-type resource inspection tasks to obtain the second resource data and corresponding second business volume data under each type of resource inspection task, may include:

[0048] Step S301: Based on the business classification configuration information, determine the resource inspection task to which each business belongs from the preset multi-type resource inspection tasks.

[0049] In this step, the business category configuration information can include configuration information about the resource inspection tasks to which each business belongs. This configuration information can be dynamically configured as needed by relevant personnel. When one or more businesses need to be reclassified, or when one or more new businesses are added, the business category configuration information can be directly adjusted. Subsequent applications can then use the adjusted business category configuration information without redevelopment, saving development costs. The big data platform can determine the resource inspection tasks to which each business belongs from a set of preset resource inspection tasks based on the business category configuration information.

[0050] Step S302: Based on the resource inspection tasks to which each business belongs, group the first resource data and the corresponding first business volume data of several businesses belonging to the same type of resource inspection task into the same category, and obtain the second resource data and the corresponding second business volume data under the corresponding category of resource inspection task.

[0051] In this step, the big data platform can group the first resource data of several businesses belonging to the same type of resource inspection task into the same category, obtaining the second resource data under that type of resource inspection task, based on the resource inspection tasks to which each business belongs. It can also group the first business volume data of several businesses belonging to the same type of resource inspection task into the same category, obtaining the second business volume data under that type of resource inspection task. Therefore, the solution in this embodiment can automatically classify the first resource data and the first business volume data through business classification configuration information, without manual intervention.

[0052] In an exemplary embodiment, step S204, which triggers the creation of a corresponding resource processing task based on the fluctuation analysis result, may include: when the fluctuation analysis result indicates an abnormal fluctuation, triggering the resource settlement system to create a manual follow-up task that matches the resource inspection task and assigning the manual follow-up task to the target personnel according to a preset allocation rule.

[0053] The method provided in this application may further include: issuing a verification prompt message for business classification configuration information when the follow-up results reported by the target personnel indicate that the fluctuation is normal; the verification prompt message is used to remind relevant personnel to confirm whether the business classification configuration information is incorrect and to correct the business classification configuration information if it is incorrect.

[0054] In this embodiment, after obtaining the fluctuation analysis results for various resource inspection tasks, the big data platform can trigger the resource settlement system to issue an inspection anomaly notification and create a manual follow-up task matching the resource inspection task for resource inspection tasks whose fluctuation analysis results indicate abnormal fluctuations. After creating the manual follow-up task, the resource settlement system can assign the manual follow-up task to the target personnel according to the preset allocation rules. This allows the target personnel to analyze the second resource data and second business volume data of the resource inspection task to determine whether the resource inspection task really has abnormal fluctuations and to feed back the follow-up results to the big data platform through the front end, thereby realizing closed-loop follow-up processing.

[0055] When the follow-up results reported by the target personnel indicate that the fluctuations are normal, it suggests that the fluctuation analysis results of the resource inspection task obtained by the big data platform are incorrect. The reason for the error may be that a business that does not actually belong to the resource inspection task is incorrectly configured as belonging to the resource inspection task, resulting in incorrect business classification configuration information. At this time, the big data platform can issue a verification prompt message for the business classification configuration information. The verification prompt message can be used to remind relevant personnel to confirm whether the business classification configuration information is incorrect and to correct the business classification configuration information if it is incorrect. If the big data platform receives corrected business classification configuration information, it can redetermine the resource inspection tasks belonging to each business based on the corrected configuration information. It then reclassifies and re-analyzes these tasks, resulting in new fluctuation analysis results for each resource inspection task. The big data platform can determine whether the new fluctuation analysis results indicate normal fluctuations. If the new fluctuation analysis results indicate abnormal fluctuations, it means that the new fluctuation analysis results obtained based on the corrected business classification configuration information are inconsistent with the analysis results of the target personnel. This may be due to errors in other configuration information used for fluctuation analysis. In this case, the big data platform can issue a verification prompt message for other configuration information. This message can remind relevant personnel to confirm whether other configuration information used for fluctuation analysis (such as the fluctuation difference rate threshold of the resource inspection task in the resource analysis dimension) is incorrect and to correct it if errors are found. This allows for continuous optimization of the configuration information used for fluctuation analysis, improving the accuracy of subsequent fluctuation analysis results.

[0056] In an exemplary embodiment, step S204, which involves triggering the creation of a corresponding resource processing task based on the fluctuation analysis results, may include:

[0057] When the fluctuation analysis results indicate that the fluctuation is normal, the resource settlement system is triggered to create an automatic posting task that matches the resource inspection task and to perform automatic posting processing based on the automatic posting task.

[0058] In this embodiment, after obtaining the fluctuation analysis results under various resource inspection tasks, if the fluctuation analysis results under various resource inspection tasks all indicate normal fluctuations, the big data platform can trigger the resource settlement system to issue a normal inspection notification and create an automatic posting task matching the various resource inspection tasks. After creating the automatic posting task, the resource settlement system can automatically post the second resource data and second business volume data of various resource inspection tasks based on the automatic posting task, so that the data in the unposted state can be automatically posted without manual follow-up, saving labor costs.

[0059] In an exemplary embodiment, obtaining the fluctuation analysis results under the resource inspection task based on the fluctuation information in step S203 may include:

[0060] Based on the fluctuation information of the second resource data and the corresponding second business volume data under the same resource analysis dimension and / or the same combination of resource analysis dimensions, obtain the fluctuation difference rate under the corresponding resource analysis dimension and / or combination of resource analysis dimensions; obtain the fluctuation difference rate threshold corresponding to the resource analysis dimension and / or combination of resource analysis dimensions; and obtain the fluctuation analysis result under the resource inspection task based on the relative size between the fluctuation difference rate and the fluctuation difference rate threshold.

[0061] In this embodiment, the fluctuation information can specifically use volatility rate. Taking a certain resource analysis dimension as an example, the big data platform can determine the volatility rate of the second resource data under the resource inspection task in that resource analysis dimension, and it can also determine the volatility rate of the second business volume data under the resource inspection task in that resource analysis dimension, thereby obtaining the volatility difference rate of the resource inspection task in that resource analysis dimension. The big data platform can also obtain a preset volatility difference rate threshold corresponding to that resource analysis dimension, and can compare the relative size between the volatility difference rate of the resource inspection task in that resource analysis dimension and the volatility difference rate threshold. If the volatility difference rate exceeds the volatility difference rate threshold, it can be determined that the volatility analysis result of the resource inspection task in that resource analysis dimension is normal; if the volatility difference rate does not exceed the volatility difference rate threshold, it can be determined that the volatility analysis result of the resource inspection task in that resource analysis dimension is abnormal.

[0062] Taking a specific combination of resource analysis dimensions as an example, the big data platform can determine the volatility of the second resource data under the resource inspection task within that combination of resource analysis dimensions, and it can also determine the volatility of the second business volume data under the resource inspection task within that combination of resource analysis dimensions, thereby obtaining the volatility difference rate of the resource inspection task within that combination of resource analysis dimensions. The big data platform can also obtain a preset volatility difference rate threshold corresponding to that combination of resource analysis dimensions, and can compare the relative magnitude between the volatility difference rate of the resource inspection task within that combination of resource analysis dimensions and the volatility difference rate threshold. If the volatility difference rate exceeds the volatility difference rate threshold, the volatility analysis result of the resource inspection task within that combination of resource analysis dimensions can be determined to be normal; if the volatility difference rate does not exceed the volatility difference rate threshold, the volatility analysis result of the resource inspection task within that combination of resource analysis dimensions can be determined to be abnormal.

[0063] For each type of resource inspection task, the big data platform can perform multi-dimensional fluctuation analysis, such as combining one resource analysis dimension with another, or combining one resource analysis dimension with another. Thus, the big data platform can obtain the multi-dimensional fluctuation analysis results for the resource inspection task. The platform can determine whether the fluctuation analysis results for each dimension indicate normal fluctuation. If so, the fluctuation analysis result for the resource inspection task is considered normal. If the fluctuation analysis result for at least one dimension indicates abnormal fluctuation, the fluctuation analysis result for the resource inspection task is considered abnormal.

[0064] In this embodiment, fluctuation difference rate thresholds can be configured for each resource analysis dimension and / or combination of resource analysis dimensions for the resource inspection task. When making comparisons later, the corresponding fluctuation difference rate thresholds can be used, thereby improving the accuracy of fluctuation analysis results.

[0065] In an exemplary embodiment, obtaining the fluctuation difference rate under the corresponding resource analysis dimension and / or resource analysis dimension combination based on the fluctuation information of the second resource data and the corresponding second business volume data under the same resource analysis dimension and / or the same resource analysis dimension combination may include:

[0066] In response to the dimension value configuration operation for the resource analysis dimension and / or the combination of resource analysis dimensions, the target dimension value is obtained; based on the target dimension value, the third resource data and the corresponding third business volume data are determined from the second resource data and the corresponding second business volume data; based on the fluctuation information of the third resource data and the corresponding third business volume data, the fluctuation difference rate corresponding to the target dimension value is obtained.

[0067] In this embodiment, each resource analysis dimension has several values ​​(which can be called dimension values). As an example, relevant personnel can determine a dimension value from among the several dimension values ​​of a resource analysis dimension; this dimension value can be considered as the target dimension value obtained by configuring the dimension value for the resource analysis dimension. Similarly, relevant personnel can determine a dimension value from among the several dimension values ​​of a combination of resource analysis dimensions; this dimension value can also be considered as the target dimension value obtained by configuring the dimension value for the combination of resource analysis dimensions. After obtaining the target dimension value, the big data platform can determine resource data matching the target dimension value in the second resource data of the resource inspection task; this resource data can be called third resource data. The big data platform can also determine business volume data matching the target dimension value in the second business volume data of the resource inspection task; this business volume data can be called third business volume data. The big data platform can calculate the fluctuation information of the third resource data and the fluctuation information of the third business volume data. Based on the difference between the fluctuation information of the third resource data and the fluctuation information of the third business volume data, the fluctuation difference rate corresponding to the target dimension value can be obtained. Therefore, the solution in this embodiment can lay the foundation for subsequent multi-dimensional fluctuation analysis, thereby enabling multi-dimensional fluctuation analysis and achieving more accurate fluctuation analysis.

[0068] In an exemplary embodiment, a method for analyzing and processing resource data fluctuations is also provided. As an example, this method can be applied to the analysis of cost fluctuations in enterprises. Cost fluctuations can be considered one of the key indicators of an enterprise's financial costs, reflecting the changes in the cost composition of various business operations across different analysis periods, and demonstrating the health of the enterprise's operations from a financial perspective. The financial shared service center can be responsible for the enterprise's cost settlement and accounting, and also for the analysis of relevant financial indicators. The data required for cost fluctuation analysis is scattered across various business systems, such as the financial system, supplier settlement system, and expense reimbursement system. The solution in this embodiment can automatically export the data required for cost fluctuation analysis from various business systems through a big data platform, automatically classify, process, and summarize it, thereby calculating the cost amount fluctuation rate. By comparing and analyzing this with the business volume fluctuation rate, it can be determined whether the fluctuation is abnormal. In the event of abnormal fluctuations, relevant systems can be triggered to create manual follow-up tasks to instruct relevant personnel to conduct closing checks. These manual follow-up tasks can be added to a task pool for allocation and follow-up closure.

[0069] like Figure 4 As shown, the method may include the following steps:

[0070] Step S401: In response to the data extraction trigger event, determine the business orders generated by each business during the analysis period; the data extraction trigger event is generated when the analysis period is met at the current time.

[0071] As an example, the beginning of each month (e.g., the 1st to the 3rd) can be set as the time period that meets the analysis cycle. When the current time meets the analysis cycle, relevant personnel can trigger a data extraction event by clicking a button on the front end. The big data platform responds to this data extraction trigger event and can determine the business orders generated by various business processes during the analysis cycle.

[0072] Step S402: For each business, extract data matching the order number of the business order from the first business system in the multi-business system to obtain the first resource data of the business, and extract data matching the order number of the business order from the second business system in the multi-business system to obtain the first business volume data of the business.

[0073] Each business order has a corresponding order number. The big data platform can send the order number to the first business system. The first business system can query the data associated with the order number and return it to the big data platform, thus allowing the big data platform to obtain the business's primary resource data. The big data platform can also send the order number to the second business system. The second business system can query and return the data associated with the order number, thus allowing the big data platform to obtain the business's primary business volume data. For example, the primary resource data for each business may include expense amount data. For example, the data collected by the big data platform may include, but is not limited to, supplier pre-accrual data, settlement data, and detailed business volume data; employee expense reimbursement settlement details and detailed business volume data; pre-accrual data and detailed business volume data from the financial system's own and outsourced HR departments; offline pre-accrual data and detailed business volume data, etc. Data required for expense fluctuation analysis can be synchronized to the big data platform for management.

[0074] In this step, such as Figure 5 As shown, big data platforms can use ETL for triggered data extraction, obtaining the first resource data and first business volume data of various business systems from multiple business systems, and automatically completing the data, which can improve the timeliness and accuracy of massive data collection. ETL is a tool for data warehousing and data integration.

[0075] Step S403: Based on the business classification configuration information, determine the resource inspection task to which each business belongs from the preset multi-type resource inspection tasks.

[0076] In this step, the business category configuration information can include configuration information about the resource inspection tasks to which each business belongs. This configuration information can be dynamically configured as needed by relevant personnel. When one or more businesses need to be reclassified, or when one or more new businesses are added, the business category configuration information can be directly adjusted. Subsequent applications can then use the adjusted configuration information without redevelopment, saving development costs. The big data platform can determine the resource inspection tasks to which each business belongs from a set of preset resource inspection tasks based on the business category configuration information. For example, resource inspection tasks can include, but are not limited to, property management tasks (also known as property prepayment fee inspection and posting tasks), transportation capacity tasks, vehicle tasks, and manpower tasks.

[0077] Step S404: Based on the resource inspection tasks to which each business belongs, group the first resource data and the corresponding first business volume data of several businesses belonging to the same type of resource inspection task into the same category, and obtain the second resource data and the corresponding second business volume data under the corresponding category of resource inspection task.

[0078] As examples, business operations can include, but are not limited to, facility operation and maintenance, human resource management, and logistics and transportation management. The primary resource data for facility operation and maintenance may include, but is not limited to, water and electricity fees, maintenance fees, and property management fees; the primary business volume data may include, but is not limited to, water and electricity consumption, and the number of devices being maintained. The primary resource data for human resource management may include, but is not limited to, payroll expenses; the primary business volume data may include, but is not limited to, the number of items delivered by employees. The primary resource data for logistics and transportation management may include, but is not limited to, transportation costs; the primary business volume data may include, but is not limited to, the number of express deliveries transported. Resource inspection tasks may include, but are not limited to, property management tasks, transportation capacity tasks, vehicle tasks, and manpower tasks.

[0079] like Figure 6 As shown, the big data platform can support dynamically configured data cleaning conditions, such as data cleaning conditions based on document status, business text, system code, service master data, service short text, and other dimensions and combinations. Through data cleaning conditions, the first resource data and first business volume data of several business items belonging to the same resource inspection task can be identified, thus enabling aggregation. For example, water and electricity fees, maintenance fees, and property management fees can be uniformly classified as property tasks, and other business items such as manpower and transportation capacity can be classified in the same way. This lays the foundation for subsequent analysis of cost fluctuations from a task perspective.

[0080] Step S405: Determine the fluctuation analysis results for each type of resource inspection task.

[0081] As an example, such as Figure 7 As shown, the big data platform can combine Python technology to calculate cost fluctuations using multi-dimensional combinations, including but not limited to "subject + branch," "branch + settlement type," or "subject + branch + settlement type." Python is a high-level, interpreted, interactive, object-oriented general-purpose programming language.

[0082] As an example, for each type of resource inspection task, the volatility of the cost amount data (belonging to the second resource data) and business volume data (belonging to the second business volume data) of the resource inspection task under the same resource analysis dimension and / or combination of resource analysis dimensions can be calculated, thereby obtaining the cost amount volatility and business volume volatility. For example, relevant personnel select a resource analysis dimension combination, which can be "subject + branch". Relevant personnel can select a dimension value from the multiple dimension values ​​of "subject" (such as the first subject) and a dimension value from the multiple dimension values ​​of "branch" (such as the first branch). The big data platform can determine the cost amount data (belonging to the third resource data) matching the first subject and the first branch from the cost amount data of the resource inspection task, and can determine the business volume data (belonging to the third business volume data) matching the first subject and the first branch from the business volume data of the resource inspection task. The big data platform can obtain the cost amount volatility of the first subject and the first branch corresponding to the resource inspection task based on the cost amount data matching the first subject and the first branch, combined with the cost amount volatility calculation formula. As an example, the formula for calculating the volatility of expense amount can be: Expense amount volatility = (Amount accrued in the current month + Amount settled in the current period - Amount settled in the current month - Amount accrued in the current month for the previous month) / (Amount settled in the current month for the previous month + Amount accrued in the current month for the previous month).

[0083] The big data platform can obtain the business volume volatility of the first subject and the first branch corresponding to the resource inspection task based on the business volume data matched with the first subject and the first branch, and in combination with the business volume volatility calculation formula. As an example, the business volume volatility calculation formula can be: Business volume volatility = (Current month's pre-accrued business volume + Current month's settled business volume - Current month's settled previous month's business volume - Current month's cumulative pre-accrued previous month's business volume) / (Current month's settled previous month's business volume + Current month's cumulative pre-accrued previous month's business volume).

[0084] After obtaining the fluctuation rates of the expense amount and business volume for the first subject and the first branch corresponding to the resource inspection task, the big data platform can use the expense fluctuation rate difference calculation formula to obtain the fluctuation difference rate for the first subject and the first branch corresponding to the resource inspection task. As an example, the expense fluctuation rate difference calculation formula can be: Expense Fluctuation Difference Rate = ABS(|Expense Fluctuation Rate| - |Business Volume Fluctuation Rate|), where ABS represents taking the absolute value.

[0085] After obtaining the volatility difference rate of the first subject and the first branch corresponding to the resource inspection task, the big data platform can determine the volatility difference threshold corresponding to "subject + branch" and judge whether the volatility difference rate exceeds the volatility difference threshold. If it does, the volatility analysis result can be determined to be abnormal volatility; if it does not exceed the threshold, the volatility analysis result can be determined to be normal volatility.

[0086] Step S406: When the fluctuation analysis result indicates that the fluctuation is normal, the resource settlement system is triggered to create an automatic posting task that matches the resource inspection task and to perform automatic posting processing based on the automatic posting task.

[0087] The resource settlement system can include a monthly closing collaboration unit, which can focus on financial monthly closing management, providing finance with one-stop full-process monthly closing management of operations, inspections, and tasks. Based on the needs of automated financial monthly closing scenarios, it can also provide a monthly closing data inspection platform, a closing dashboard, and a shared operation platform.

[0088] In this step, such as Figure 8 As shown, the fluctuation analysis results under each resource inspection task indicate that when the fluctuation is normal, the big data platform can trigger the resource settlement system to send a normal inspection notification and automatically post the data. This enables the automatic posting of unposted data and other subsequent execution logic without manual intervention.

[0089] Step S407: When the fluctuation analysis results indicate abnormal fluctuations, the resource settlement system is triggered to create a manual follow-up task that matches the resource inspection task and to assign the manual follow-up task to the target personnel according to the preset allocation rules.

[0090] In this step, such as Figure 8 As shown, if the fluctuation analysis results of one or more types of resource inspection tasks indicate abnormal fluctuations, the big data platform can trigger the resource settlement system to issue an inspection anomaly notification and create a manual follow-up task matching the resource inspection task. After creating the manual follow-up task, the resource settlement system can assign the task to the target personnel according to the preset allocation rules. This allows the target personnel to analyze the second resource data and second business volume data of the resource inspection task to determine whether the resource inspection task truly has abnormal fluctuations and to report the follow-up results to the big data platform through the front end, thereby achieving closed-loop follow-up processing.

[0091] Step S408: When the follow-up results reported by the target personnel indicate that the fluctuation is normal, a verification prompt message for the business classification configuration information is issued.

[0092] In this step, the verification prompt message can be used to remind relevant personnel to confirm whether the business category configuration information is incorrect and to correct the business category configuration information if there is an error.

[0093] like Figure 9 As shown, the solution in this embodiment can achieve triggered massive data collection, cleaning, calculation, follow-up closed loop and automatic posting through big data technology, which can improve processing efficiency.

[0094] The solution in this embodiment can be applied to the scenario of calculating cost fluctuations in enterprises. Through big data technology, scattered data can be classified, cleaned and calculated. Combined with Python technology, cost fluctuations can be calculated dynamically and in multiple dimensions, reducing the time-consuming and labor-intensive work of finance, thereby improving work efficiency.

[0095] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0096] Based on the same inventive concept, this application also provides a resource data fluctuation analysis and processing apparatus for implementing the resource data fluctuation analysis and processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more resource data fluctuation analysis and processing apparatus embodiments provided below can be found in the limitations of the resource data fluctuation analysis and processing method described above, and will not be repeated here.

[0097] In one exemplary embodiment, such as Figure 10 As shown, a resource data fluctuation analysis and processing device is provided, comprising:

[0098] The data extraction module 1001 is used to extract the first resource data and the corresponding first business volume data of each business in the analysis period from the multi-business system in response to a data extraction trigger event; the data extraction trigger event is generated when the analysis period is met at the current time.

[0099] The classification module 1002 is used to classify the first resource data and the corresponding first business volume data of each business according to the preset multi-class resource inspection tasks, so as to obtain the second resource data and the corresponding second business volume data under each type of resource inspection task.

[0100] The fluctuation analysis module 1003 is used to obtain the fluctuation information of the second resource data and the corresponding second business volume data under preset resource analysis dimensions and / or combinations of resource analysis dimensions for each type of resource inspection task, and to obtain the fluctuation analysis result under the resource inspection task based on the fluctuation information.

[0101] Trigger module 1004 is used to trigger the creation of corresponding resource processing tasks based on the fluctuation analysis results.

[0102] In one exemplary embodiment, the data extraction module 1001 is configured to:

[0103] Identify the business orders generated by each business during the analysis period; for each business, extract data matching the order number of the business order from the first business system in the multi-business system to obtain the first resource data of the business, and extract data matching the order number of the business order from the second business system in the multi-business system to obtain the first business volume data of the business.

[0104] In one exemplary embodiment, the classification module 1002 is used for:

[0105] Based on the business classification configuration information, the resource inspection task to which each business belongs is determined among the preset multiple types of resource inspection tasks; based on the resource inspection task to which each business belongs, the first resource data and the corresponding first business volume data of several businesses belonging to the same type of resource inspection task are grouped into the same category to obtain the second resource data and the corresponding second business volume data under the resource inspection task of the corresponding category.

[0106] In an exemplary embodiment, the triggering module 1004 is configured to: when the fluctuation analysis result indicates an abnormal fluctuation, trigger the resource settlement system to create a manual follow-up task that matches the resource inspection task and assign the manual follow-up task to the target personnel according to a preset allocation rule.

[0107] The device further includes a correction processing module, used to: when the follow-up results reported by the target personnel indicate that the fluctuation is normal, issue a verification prompt message for the business classification configuration information; the verification prompt message is used to remind relevant personnel to confirm whether the business classification configuration information is incorrect and to correct the business classification configuration information if it is incorrect.

[0108] In one exemplary embodiment, the trigger module 1004 is configured to:

[0109] When the fluctuation analysis result indicates that the fluctuation is normal, the resource settlement system is triggered to create an automatic posting task that matches the resource inspection task and to perform automatic posting processing based on the automatic posting task.

[0110] In one exemplary embodiment, the fluctuation analysis module 1003 is used for:

[0111] Based on the fluctuation information of the second resource data and the corresponding second business volume data under the same resource analysis dimension and / or the same combination of resource analysis dimensions, obtain the fluctuation difference rate under the corresponding resource analysis dimension and / or combination of resource analysis dimensions; obtain the fluctuation difference rate threshold corresponding to the resource analysis dimension and / or combination of resource analysis dimensions; and obtain the fluctuation analysis result under the resource inspection task based on the relative size between the fluctuation difference rate and the fluctuation difference rate threshold.

[0112] In one exemplary embodiment, the fluctuation analysis module 1003 is used for:

[0113] In response to a dimension value configuration operation for a resource analysis dimension and / or a combination of resource analysis dimensions, a target dimension value is obtained; based on the target dimension value, a third resource data and a corresponding third business volume data are determined from the second resource data and the corresponding second business volume data; based on the fluctuation information of the third resource data and the corresponding third business volume data, the fluctuation difference rate corresponding to the target dimension value is obtained.

[0114] Each module in the aforementioned resource data fluctuation analysis and processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0115] In one exemplary embodiment, a computer device is provided that can be used to implement a big data platform, and its internal structure diagram can be as follows: Figure 11As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores the data involved in the aforementioned methods. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a resource data fluctuation analysis and processing method.

[0116] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0117] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the various method embodiments described above.

[0118] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the various method embodiments described above.

[0119] In one exemplary embodiment, a computer program product is provided having a computer program stored thereon, the computer program being executed by a processor of the steps described in the various method embodiments above.

[0120] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0123] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing and processing resource data fluctuations, characterized in that, The method includes: In response to a data extraction trigger event, the system extracts the first resource data and the corresponding first business volume data for each business in the analysis period from the multi-business system; the data extraction trigger event is generated when the analysis period is met at the current time. According to the preset multi-type resource inspection tasks, the first resource data and the corresponding first business volume data of each business are classified to obtain the second resource data and the corresponding second business volume data under each type of resource inspection task; For each type of resource inspection task, the fluctuation information of the second resource data and the corresponding second business volume data under each preset resource analysis dimension and / or combination of resource analysis dimensions is obtained, and the fluctuation analysis result under the resource inspection task is obtained based on the fluctuation information. The creation of corresponding resource processing tasks is triggered based on the fluctuation analysis results.

2. The method according to claim 1, characterized in that, The extraction of the first resource data and corresponding first business volume data of each business in the analysis period from the multi-business system includes: Identify the business orders generated by each business segment during the analysis period; For each business, data matching the order number of the business order is extracted from the first business system in the multi-business system to obtain the first resource data of the business, and data matching the order number of the business order is extracted from the second business system in the multi-business system to obtain the first business volume data of the business.

3. The method according to claim 1, characterized in that, According to preset multi-category resource inspection tasks, the first resource data and corresponding first business volume data of each service are classified to obtain the second resource data and corresponding second business volume data under each category of resource inspection tasks, including: Based on the business classification configuration information, determine the resource inspection task to which each business belongs from the preset multiple types of resource inspection tasks; Based on the resource inspection tasks to which each of the aforementioned services belongs, the first resource data and the corresponding first service volume data of several services belonging to the same type of resource inspection task are grouped into the same category to obtain the second resource data and the corresponding second service volume data under the resource inspection task of the corresponding category.

4. The method according to claim 3, characterized in that, The step of triggering the creation of corresponding resource processing tasks based on the fluctuation analysis results includes: When the fluctuation analysis results indicate abnormal fluctuations, the resource settlement system is triggered to create a manual follow-up task that matches the resource inspection task and to assign the manual follow-up task to the target personnel according to the preset allocation rules. The method further includes: When the follow-up results reported by the target personnel indicate that the fluctuation is normal, a verification prompt message for the business classification configuration information is issued; the verification prompt message is used to remind relevant personnel to confirm whether the business classification configuration information is incorrect and to correct the business classification configuration information if it is incorrect.

5. The method according to claim 1, characterized in that, The step of triggering the creation of corresponding resource processing tasks based on the fluctuation analysis results includes: When the fluctuation analysis result indicates that the fluctuation is normal, the resource settlement system is triggered to create an automatic posting task that matches the resource inspection task and to perform automatic posting processing based on the automatic posting task.

6. The method according to any one of claims 1 to 5, characterized in that, The step of obtaining the fluctuation analysis results under the resource inspection task based on the fluctuation information includes: Based on the fluctuation information of the second resource data and the corresponding second business volume data under the same resource analysis dimension and / or the same combination of resource analysis dimensions, the fluctuation difference rate under the corresponding resource analysis dimension and / or the combination of resource analysis dimensions is obtained; Obtain the volatility difference rate threshold corresponding to the resource analysis dimension and / or the combination of resource analysis dimensions; The volatility analysis results under the resource inspection task are obtained based on the relative magnitude between the volatility difference rate and the volatility difference rate threshold.

7. The method according to claim 6, characterized in that, The step of obtaining the fluctuation difference rate under the corresponding resource analysis dimension and / or resource analysis dimension combination based on the fluctuation information of the second resource data and the corresponding second business volume data under the same resource analysis dimension and / or the same resource analysis dimension combination includes: In response to dimension value configuration operations for resource analysis dimensions and / or combinations of resource analysis dimensions, the target dimension value is obtained; Based on the target dimension value, determine the third resource data and the corresponding third business volume data from the second resource data and the corresponding second business volume data; Based on the fluctuation information of the third resource data and the corresponding third business volume data, the fluctuation difference rate corresponding to the target dimension value is obtained.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images