A data operation integration management system based on regional cash processing center

Abstract

The present invention discloses a data operation fusion management system based on a regional cash processing center, which belongs to the field of electronic digital data processing technology, including: analyzing to obtain the information processing frequency coefficient of each information item, obtaining computing resource parameters, analyzing to obtain the computing resource activity, and thereby analyzing to obtain a resource allocation judgment result; analyzing to obtain a pre-calculation scale judgment index based on the information processing frequency coefficient and computing resource activity of each information item; screening to obtain high-frequency access data, and performing data pre-calculation on the high-frequency access data based on the pre-calculation scale judgment index to obtain a pre-calculation result; obtaining an information query matching result and transmitting it to a user display interface, thereby solving the problem in the prior art of slow data query response due to the complex cross-branch data aggregation calculation, which causes a serious load on the computer system.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a data operation fusion management system based on a regional cash processing center. Background Art

[0002] With the accelerated advancement of digital transformation in the financial industry, regional cash processing centers, as the core hubs of cash flow, have seen exponential growth in business complexity and data scale. In addition, there is a massive amount of heterogeneous data (such as cross-branch transaction records and real-time inventory status) that requires collaborative processing and integrated analysis through multiple systems.

[0003] For example, the operator multi-data fusion method, system, electronic device and computer storage medium announced with announcement number: CN116415206B includes: using different data sources for each task, and using different data fusion algorithms for different data sources, which greatly improves the accuracy and efficiency of data fusion. In addition, after splitting the tasks, the complex fusion process can be decomposed into simple subtasks, which is easier to manage and schedule. It can also support parallel computing and increase the speed of fusion, which is suitable for large-scale, complex multi-source data fusion problems. In addition, the integrated system application selects a suitable adaptive streaming rule engine according to business needs, performs relevant configurations, and achieves flexible deployment.

[0004] For example, the announcement number is: CN112667677B, which announces a digital operation method and system for a data middle platform, including: retrieving operation data from the data middle platform and caching the operation data; processing the operation data according to pre-established data processing rules to generate a data map; generating a business wide table according to the data map, deploying the business wide table on the data middle platform, and publishing the business wide table as a data interface service; building an interactive management module for the data middle platform, and the interactive management module includes complaint and reporting items, power outage items in substations, equipment defects items, and fault repair items.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In the existing technology, a fixed node resource allocation mechanism is usually adopted. When the regional cash processing center faces large-scale urgent data queries, it is unable to analyze multiple branch data according to the query processing needs and provide timely feedback. Therefore, there is a problem of slow data query response due to the complex cross-branch data aggregation calculation, which causes serious computer system load. Summary of the Invention

[0007] The embodiment of the present application solves the problem of slow data query response in the prior art due to complex cross-branch data aggregation calculations, which causes serious computer system load, by providing a data operation fusion management system based on a regional cash processing center, and achieves rapid response to query data.

[0008] An embodiment of the present application provides a data operation fusion management system based on a regional cash processing center, including: a resource occupancy determination module, which is used to obtain user search behavior parameters of each information item of the regional cash processing center, analyze and obtain the information processing frequency coefficient of each information item, obtain computing resource parameters, analyze and obtain the computing resource activity, and thereby analyze and obtain a resource allocation determination result; a pre-calculation scale determination module, which is used to determine whether to perform data pre-calculation based on the resource allocation determination result, and if data pre-calculation is performed, obtain a pre-calculation scale determination index based on the information processing frequency coefficient and computing resource activity of each information item; a pre-calculation execution analysis module, which is used to screen and obtain high-frequency access data based on the information processing frequency coefficient of each information item, and perform data pre-calculation on the high-frequency access data based on the pre-calculation scale determination index, obtain pre-calculation results and store them in a pre-calculation database; a pre-calculation result matching module, which is used to match pre-calculation results based on user query keywords when a user performs an information query, obtain information query matching results and transmit them to a user display interface.

[0009] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0010] 1. The present invention provides a data operation and fusion management system based on a regional cash processing center. By acquiring user search behavior parameters and computing resource parameters for each information item, the system analyzes the information processing frequency coefficient and computing resource activity of each information item to determine whether to perform data pre-computation. The system also pre-computes high-frequency access data based on a pre-computation scale determination indicator, thereby achieving rapid response to query data. This effectively addresses the existing problem of slow data query response caused by complex cross-branch data aggregation calculations, which results in heavy computer system load.

[0011] 2. The present invention analyzes the matching analysis parameters based on the user's query keywords to obtain the pre-calculated feature first fitness, and matches it with the pre-calculated feature first fitness threshold to determine whether to provide the pre-calculated results to the user or to perform a deep information query in real time based on the user's query keywords, thereby achieving accurate matching and rapid acquisition of query results;

[0012] 3. By obtaining the pre-calculation result call execution parameters and data update parameters, the data call value of each pre-calculation data item is analyzed and compared with the pre-calculation adjustment threshold to determine whether to perform the pre-calculation execution standard adjustment, and then perform pre-calculation adjustment based on the pre-calculation execution adjustment standard, thereby realizing dynamic optimization of pre-calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a structural diagram of the data operation fusion management system based on the regional cash processing center provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] The embodiment of the present application solves the problem of slow data query response in the prior art due to complex cross-branch data aggregation calculations, which causes serious computer system load, by providing a data operation fusion management system based on a regional cash processing center, thereby achieving rapid response to query data.

[0015] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0016] like Figure 1 As shown, it is a structural diagram of the data operation fusion management system based on the regional cash processing center provided by the embodiment of the present application. The data operation fusion management system based on the regional cash processing center provided by the embodiment of the present application includes: a resource occupancy determination module, which is used to obtain user search behavior parameters of each information item of the regional cash processing center, analyze and obtain the information processing frequency coefficient of each information item, obtain computing resource parameters, analyze and obtain computing resource activity, and thereby analyze and obtain a resource allocation determination result; a pre-calculation scale determination module, which is used to determine whether to perform data pre-calculation based on the resource allocation determination result. If data pre-calculation is performed, a pre-calculation scale determination index is obtained based on the information processing frequency coefficient and computing resource activity of each information item; a pre-calculation execution analysis module, which is used to screen and obtain high-frequency access data based on the information processing frequency coefficient of each information item, and perform data pre-calculation on the high-frequency access data based on the pre-calculation scale determination index to obtain a pre-calculation result and store it in a pre-calculation database; a pre-calculation result matching module, which is used to match the pre-calculation result based on the user query keyword when the user performs an information query, obtain the information query matching result and transmit it to the user display interface.

[0017] In this embodiment, it should be noted that the computing resource parameters are obtained and the computing resource activity is obtained by analysis. The specific steps include: obtaining computing resource parameters, which include memory occupancy, disk read and write times per second, message queue length, storage delay and GPU memory usage; obtaining a preset computing resource reference set in the database, and analyzing it with the computing resource parameters to obtain computing resource activity; the computing resource reference set includes total physical memory capacity, maximum disk read and write speed, queue design load limit, maximum allowable delay and GPU memory usage reference value.

[0018] The resource allocation determination result is obtained by analysis, and the specific steps include: obtaining an information processing frequency threshold and a computing resource activity threshold preset in a database; comparing the information processing frequency coefficient of each information item with the information processing frequency threshold, and comparing the computing resource activity with the computing resource activity threshold to obtain a resource allocation determination result; the resource allocation determination result includes resource allocation conflict and normal resource allocation; if the information processing frequency coefficient of a certain information item is above the information processing frequency threshold and the computing resource activity is above the computing resource activity threshold, then the resource allocation determination result is a resource allocation conflict, otherwise the resource allocation determination result is normal resource allocation.

[0019] By setting thresholds for information processing frequency and computing resource activity, we can accurately identify which information items have high access frequencies and whether computing resources are currently scarce. Using this data, we automatically determine resource allocation conflicts and resource allocation compliance, thereby determining whether to perform pre-calculations. This prevents excessive resource usage and ensures that resources are properly allocated to the most needed data processing tasks.

[0020] It can effectively prevent blind large-scale pre-computation when computing resources are already highly strained, thereby avoiding performance degradation or even crash due to resource overload, and ensuring the stability and reliability of the system.

[0021] Get the computing resource activity. The specific method is:

[0022] ;

[0023] Where, Indicates the activity of computing resources, Indicates memory usage. Indicates the total physical memory capacity, Indicates the number of disk reads and writes per second. Indicates the maximum number of disk read and write times per second. Indicates the message queue length, Indicates the designed load limit of the queue. Indicates storage delay, Indicates the maximum allowed delay, Indicates the GPU memory usage. Indicates the reference value of GPU memory usage. Indicates the memory usage impact weight, Indicates the disk impact weight, Indicates the message queue impact weight, Indicates the storage delay impact weight, Indicates the impact weight of GPU memory usage.

[0024] It should be noted that the memory occupancy impact weight, disk impact weight, message queue impact weight, storage delay impact weight and GPU memory usage impact weight can be obtained from the database. For example, the memory occupancy impact weight can be obtained by obtaining the historical memory occupancy stored in the database, and the memory occupancy impact weight corresponding to the historical memory occupancy, thereby constructing a memory occupancy mapping set, wherein there is a one-to-one or many-to-one correspondence in the mapping set. The memory occupancy impact weight can be obtained by inputting the memory occupancy data to be used into the memory occupancy mapping set. The acquisition method of other impact weights such as the disk impact weight, message queue impact weight, storage delay impact weight and GPU memory usage impact weight is the same as the memory occupancy impact weight, and can all be matched in the corresponding mapping set, wherein the disk impact weight corresponds to the disk mapping set, the message queue impact weight corresponds to the message queue mapping set, the storage delay impact weight corresponds to the storage delay mapping set, and the GPU memory usage impact weight corresponds to the GPU memory usage mapping set.

[0025] It's also important to note that obtaining user search behavior parameters for various information items at regional cash processing centers requires data preprocessing, including cleaning to remove duplicate, erroneous, or invalid data. For example, there may be search keywords entered by users incorrectly, or abnormal search records due to system failures. These data must be removed to ensure the accuracy of subsequent analysis. User search behavior parameters must also be correlated with other information items at the regional cash processing center. For example, a user search for "cash shortage at a certain branch" can be correlated with the branch's actual cash inventory data and cash processing records to more fully understand the user's search intent and the relevant business situation.

[0026] After data preprocessing, system resources within the regional cash processing center can be rationally allocated based on the results of analyzing user search behavior parameters. For example, during peak search demand periods, server processing capacity can be increased to ensure responsiveness and stability. Personalized services can also be provided to users by understanding their search preferences and needs. For example, relevant cash processing information and data analysis reports can be pushed to users based on their historical search history. By analyzing user search behavior, problems and deficiencies within the regional cash processing center's business processes can be identified. For example, if a large number of users frequently search for information on a specific process, this indicates operational complexity within that process.

[0027] Furthermore, the user search behavior parameters of each information item of the regional cash processing center are obtained, and the information processing frequency coefficient of each information item is obtained by analysis. The specific steps include: obtaining the user search behavior parameters of each information item of the regional cash processing center, and the user search behavior parameters of each information item include: the number of searches for each information item within a preset time period, the average length of stay per query, the frequency of cross-module jumps, and the priority coefficient of each information item; based on the difference between the number of searches for each information item, the average length of stay per query, and the frequency of cross-module jumps, and the average number of searches for the total number of information items, the average length of stay per query, and the average frequency of cross-module jumps After the information processing, the information processing frequency coefficient of each information item is obtained by combining the influence weight and the priority coefficient of each information item; the user search benchmark set includes the average number of searches for the total information items, the average average query average stay time and the average cross-module jump frequency; the information processing frequency coefficient of each information item is used to differentiate the number of searches for each information item, the average query average stay time and the average cross-module jump frequency of the total information items, and then introduce its corresponding influence weight after comparing it with the average number of searches for the total information items, the average query average stay time and the average cross-module jump frequency, and jointly analyze the calculation result with the priority coefficient to obtain the information processing frequency coefficient.

[0028] In this embodiment, by analyzing information items using user search behavior parameters, we can gain a deeper understanding of users' interests and needs for various information items related to regional cash processing centers, preventing them from searching for what they need amidst a large amount of irrelevant data. This improves user information acquisition efficiency and satisfaction. For example, when a user searches for cash allocation information, relevant information such as allocation plans and schedules can be directly delivered based on the search behavior parameters.

[0029] Based on the analysis of user search behavior parameters, we can adjust how we process information items. For example, if we find that users frequently search for cash flow data for a specific time period, we can store and index this data more efficiently to respond to similar search requests more quickly and improve information processing efficiency.

[0030] The information processing frequency coefficient is obtained by analyzing the number of searches for each information item within a preset time period, the average length of stay per query, the frequency of cross-module jumps, and the priority coefficient of each information item. This takes into account the mutual influence between these parameters. For example, the more searches and the longer the average length of stay per query, the more frequently the information item is accessed and the more important it is. The longer the stay time, the more likely the information provided is to be browsed by users and the higher the satisfaction level. The higher the frequency of cross-module jumps, the richer the data content of the information item and the higher the correlation with multiple information.

[0031] It should be noted that the priority coefficient of each information item is obtained by analyzing the historical call times of each information item and the CPU usage of the computer system when processing the information item. The priority coefficient of each information item is calculated as follows:

[0032] ;

[0033] Where, Indicates the priority coefficient of the i-th information item, i represents the number of the information item, , Indicates the total number of information items, represents the CPU occupancy rate of the computer system when processing the i-th information item, Indicates the historical call count of the i-th information item.

[0034] The information processing frequency coefficient of each information item is obtained, and the specific steps include:

[0035] ;

[0036] Where, represents the information processing frequency coefficient of the i-th information item, i represents the number of the information item, , Indicates the total number of information items, represents the number of searches for the i-th information item, The average length of stay of the query of the i-th information item, The cross-module jump frequency of the i-th information item, Indicates the search influence weight, Indicates that the jump frequency affects the weight.

[0037] It should be noted that the search impact weight and the jump frequency impact weight can be obtained from the database. For example, the search impact weight can be obtained by obtaining the historical searches stored in the database, and the search impact weights corresponding to the historical searches, thereby constructing a search mapping set, where there is a one-to-one or many-to-one correspondence in the mapping set. The search impact weight can be obtained by inputting the search data to be used into the search mapping set. The jump frequency impact weight is obtained in the same way as the search impact weight, and can also be obtained by matching in the corresponding mapping set, where the jump frequency impact weight corresponds to the jump frequency mapping set.

[0038] Furthermore, a pre-calculation scale determination index is obtained based on the information processing frequency coefficient and computing resource activity analysis of each information item, and the specific steps include: judging whether to perform data pre-calculation based on the resource allocation determination result, if the resource allocation determination result is a resource allocation conflict, then performing data pre-calculation, if the resource allocation determination result is that the resource allocation is normal, then not performing data pre-calculation; if data pre-calculation is performed, then a pre-calculation scale determination index is obtained based on the information processing frequency coefficient and computing resource activity analysis of each information item; the pre-calculation scale determination index is used to obtain the pre-calculation scale determination index by jointly analyzing the information processing frequency coefficient and computing resource activity of each information item and introducing their corresponding influence weights.

[0039] In this embodiment, the pre-calculation scale determination index is obtained by:

[0040] ;

[0041] Where, Indicates the pre-calculation scale determination index, Indicates the activity of computing resources, represents the information processing frequency coefficient of the i-th information item, i represents the number of the information item, , Indicates the total number of information items, Indicates the impact weight of computing resources, Represents the influence weight of the information item.

[0042] It should be noted that the computing resource impact weight and the information item impact weight can be obtained from the database. For example, the computing resource impact weight can be obtained by obtaining the historical computing resources stored in the database, and the computing resource impact weights corresponding to the historical computing resources, thereby constructing a computing resource mapping set, wherein there is a one-to-one or many-to-one correspondence in the mapping set. The computing resource impact weight can be obtained by inputting the computing resource data to be used into the computing resource mapping set, wherein the information item impact weight corresponds to the information item mapping set.

[0043] Furthermore, high-frequency access data is obtained based on the information processing frequency coefficient of each information item. The specific steps include: obtaining an information processing frequency threshold preset in the database, and comparing it with the information processing frequency coefficient of each information item. If there is an information item whose information processing frequency coefficient is greater than the information processing frequency threshold, the information item is marked as high-frequency access data.

[0044] In this embodiment, the decision to perform data precomputation is based on the resource allocation determination result. This ensures that precomputation is only executed if the resource allocation determination result indicates a resource allocation conflict. This avoids the waste of resources and system burden caused by blind precomputation, making the precomputation process more rational and targeted. The information processing frequency coefficient and computing resource activity of each information item are jointly analyzed, and their corresponding impact weights are introduced to obtain the precomputation scale determination index. This enables refined control of the precomputation scale. Through a comprehensive analysis based on the information processing frequency coefficient and computing resource activity, the investment in precomputation resources is ensured to match the importance and access frequency of the data, thereby improving resource utilization efficiency.

[0045] Reasonable pre-calculation scale determination can effectively improve the system's query response speed. By allocating appropriate pre-calculation resources to frequently accessed data, pre-calculation results can be quickly provided when users query, reducing the complexity and time consumption of real-time calculations. The pre-calculation strategy can be flexibly adjusted according to different resource conditions and data access patterns. When the resource allocation determination result is normal, you can choose not to perform pre-calculation and directly perform real-time queries, avoiding unnecessary pre-calculation. By comprehensively considering factors such as the information processing frequency coefficient and the activity of computing resources, it is possible to more accurately determine which data requires pre-calculation and the scale of pre-calculation, ensuring a high degree of correlation between pre-calculation results and actual query requirements, and improving the accuracy and effectiveness of data processing.

[0046] Furthermore, data pre-calculation is performed on high-frequency access data based on the pre-calculation scale determination index to obtain pre-calculation results and store them in a pre-calculation database. The specific steps include: obtaining each pre-calculation scale determination index interval preset in the database and the branch data item limited time reference window length corresponding to each pre-calculation scale determination index interval, and comparing them with the pre-calculation scale determination index; if the pre-calculation scale determination index is in a certain preset pre-calculation scale determination index interval, obtaining the branch data item limited time reference window length corresponding to the interval as the branch data item limited time window length; performing data pre-calculation based on the branch data item limited time window length to obtain a pre-calculation result, and the pre-calculation result includes a number of data information.

[0047] In this embodiment, it should be noted that precalculating data based on a limited time window length for a branch data item refers to calculating the branch data item data within the limited time window length. The precalculation result includes several data information, for example, the total amount of a certain enterprise on January 1, 2025 is a certain yuan.

[0048] By setting the information processing frequency threshold and comparing it with the information processing frequency coefficient of each information item, it is possible to accurately identify which data is high-frequency access data, which helps to pre-calculate and process these data in a targeted manner, thereby improving the efficiency of data processing, and providing a clear screening basis for the pre-calculation execution analysis module to ensure that only high-frequency access data is selected for pre-calculation. This avoids unnecessary pre-calculation of low-frequency data, saves computing resources and storage space, and makes the pre-calculation process more efficient and targeted. After the high-frequency access data is pre-calculated and stored, when the user queries this data, the pre-calculated results can be quickly provided without the need for complex real-time calculations, significantly shortening the response time of user queries and improving query efficiency and user experience.

[0049] Furthermore, pre-calculated result matching is performed based on user query keywords, and the specific steps include: obtaining matching analysis parameters of user query keywords, which include query requirement time interval, query requirement area set and query requirement business dimension set; obtaining each pre-calculated data item under the limited time window length of the branch data item, and comparing them with the matching analysis parameters of the user query keyword to obtain the feature fitness of each pre-calculated data item and the user query keyword, and marking the pre-calculated data item corresponding to the maximum feature fitness as the pre-calculated feature first fitness; the feature fitness of each pre-calculated data item and the user query keyword is used to perform differential analysis on the matching analysis parameters of the user query keyword and the limited time window length of the branch data item, and introduce its corresponding influence weight to obtain the feature fitness of each pre-calculated data item and the user query keyword.

[0050] In this embodiment, each pre-calculated data item within a time window length limited by the branch data item is obtained, and each pre-calculated data item within the time window length limited by the branch data item includes a pre-calculated time interval, a pre-calculated region set, and a pre-calculated business dimension set of each pre-calculated data item;

[0051] The feature adaptability of each pre-calculated data item and the user query keyword is obtained by:

[0052] ;

[0053] Where, Indicates the feature adaptability between the jth pre-calculated data item and the user query keyword, j represents the number of the information item, , Indicates the total number of information items, Indicates the query demand time interval, represents the pre-calculation time interval of the j-th pre-calculated data item, Represents the query requirement area set, represents the set of precomputed regions for the j-th precomputed data item, Indicates the query requirement business dimension set, represents the set of pre-computed business dimensions for the j-th pre-computed data item, Indicates the time interval impact weight, represents the influence weight of the regional set, Indicates the impact weight of the business dimension set.

[0054] The time interval impact weight, regional set impact weight and business dimension set impact weight can be obtained from the database. For example, the time interval impact weight can be obtained by obtaining the historical time intervals stored in the database, and the time interval impact weights corresponding to the historical time intervals, thereby constructing a time interval mapping set, where there is a one-to-one or many-to-one correspondence in the mapping set. The time interval impact weight can be obtained by inputting the time interval data to be used into the time interval mapping set. The acquisition method of other impact weights such as regional set impact weight and business dimension set impact weight is the same as the acquisition method of time interval impact weight, and can all be matched in the corresponding mapping set, where the regional set impact weight corresponds to the regional set mapping set, and the business dimension set impact weight corresponds to the business dimension set mapping set.

[0055] Furthermore, the information query matching result is obtained and transmitted to the user display interface. The specific steps include: obtaining the pre-calculated feature first fitness threshold preset in the database, and comparing it with the pre-calculated feature first fitness; if the pre-calculated feature first fitness is above the pre-calculated feature first fitness threshold, then providing the pre-calculated data item corresponding to the pre-calculated feature first fitness as the pre-calculated feature matching result to the user; if the pre-calculated feature first fitness is less than the pre-calculated feature first fitness threshold, performing a deep information query in real time according to the user query keyword, and outputting the real-time deep information query result to the user; and jointly marking the pre-calculated feature matching result and the real-time deep information query result as the information query matching result.

[0056] In this embodiment, it should be noted that deep information query is achieved by matching user query keywords with all the data in the database using the jieba library in Python.

[0057] By judging the pre-calculated feature's first fitness, it's possible to quickly determine whether the user's query matches the pre-calculated result. If the pre-calculated feature's first fitness is above the pre-calculated feature's first fitness threshold, the pre-calculated result is provided to the user, avoiding the complex process of real-time calculation, significantly shortening the user's query response time and improving query efficiency. If the pre-calculated feature's first fitness is less than the pre-calculated feature's first fitness threshold, a real-time deep information query is performed based on the user's query keywords.

[0058] By judging the first fitness of pre-calculated features, it is possible to quickly determine whether the user query matches the pre-calculated results. If a match is successful, the pre-calculated results are directly provided, avoiding the complex process of real-time calculation, significantly shortening the response time of the user query and improving query efficiency. When the first fitness of the pre-calculated features is less than the first fitness threshold, a real-time deep information query is performed based on the user's query keywords, rather than blindly providing pre-calculated results. This ensures the rational allocation and efficient use of computing resources while ensuring the correctness of the user's query information.

[0059] When querying, users can quickly obtain results that closely match their needs, reducing wait times, improving query accuracy and satisfaction, and enhancing the user experience. By flexibly adjusting response strategies based on different query scenarios, pre-computed results can be quickly provided for queries with a high degree of match, while queries with a low degree of match are calculated in real time, ensuring flexibility and adaptability to diverse query needs. A threshold matching mechanism allows for rapid determination of whether to use pre-computed results, avoiding complex real-time calculations and data processing.

[0060] Furthermore, it also includes a pre-calculation execution standard adjustment module, which is used to obtain the pre-calculation result call execution parameter analysis to obtain the data call value of each pre-calculation data item, thereby adjusting the pre-calculation execution standard, and the specific steps include: obtaining the pre-calculation result call execution parameter, the pre-calculation result call execution parameter includes the number of calls, call hit rate, call waiting time and call adaptability of each pre-calculation data item within the second preset time period; obtaining the call execution reference set preset in the database, and analyzing it with the pre-calculation result call execution parameter to obtain the data call value of each pre-calculation data item; obtaining the pre-calculation execution adjustment based on the data call value analysis of each pre-calculation data item. The adjustment standard is pre-calculated thereby; the call execution reference set includes a lower limit value for the number of calls, a lower limit value for the call hit rate, an upper limit value for the call waiting time, and a lower limit value for the call adaptability; the data call value of each pre-calculated data item is used to compare the number of calls, the call hit rate, the call waiting time, and the call adaptability of each pre-calculated data item within the second preset time period with the lower limit value for the number of calls, the lower limit value for the call hit rate, the upper limit value for the call waiting time, and the lower limit value for the call adaptability and introduce corresponding influence weights to obtain the data call value of each pre-calculated data item; the pre-calculation execution standard is adjusted thereby according to the data call value of each pre-calculated data item.

[0061] In this embodiment, it should be noted that the calling fitness of each pre-calculated data item is obtained by obtaining the fitness of each pre-calculated data item when it is output as the first fitness of the pre-calculated feature and displayed to the user, and converting it into the display fitness of each pre-calculated data item. The calling fitness of each pre-calculated data item can be obtained by averaging the display fitness of each pre-calculated data item.

[0062] Obtain the data call value of each pre-calculated data item. The specific steps include:

[0063] ;

[0064] Where, Represents the data call value of the j-th pre-calculated data item, j represents the number of the information item, , Indicates the total number of information items, represents the feature adaptability between the jth pre-calculated data item and the user query keyword, Indicates the lower limit of calling fitness. represents the number of calls for the j-th precomputed data item, Indicates the lower limit of the number of calls. represents the call hit rate of the jth pre-calculated data item, Indicates the lower limit of the call hit rate. Indicates the waiting time for calling the j-th pre-calculated data item, Indicates the upper limit of the call waiting time. Indicates the influence weight of feature fitness, Indicates the call impact weight, Indicates that the call hit rate affects the weight, Indicates that the call waiting time affects the weight.

[0065] It should be noted that the feature fitness influence weight, call influence weight, call hit rate influence weight and call waiting time influence weight can be obtained from the database. For example, the feature fitness influence weight can be obtained by obtaining the historical feature fitness stored in the database, and the feature fitness influence weight corresponding to the historical feature fitness, thereby constructing a feature fitness mapping set, wherein there is a one-to-one or many-to-one correspondence in the mapping set. The feature fitness influence weight can be obtained by inputting the feature fitness data to be used into the feature fitness mapping set. The method for obtaining the call influence weight is the same as the method for obtaining the feature fitness influence weight, and can also be matched in the corresponding mapping set, wherein the call influence weight corresponds to the call mapping set, the call hit rate influence weight corresponds to the call hit rate mapping set, and the call waiting time influence weight corresponds to the call waiting time mapping set.

[0066] By analyzing the pre-calculation result call execution parameters and data update parameters, the effectiveness of the pre-calculation strategy and the timeliness of the data can be dynamically evaluated. Based on the data call value of each pre-calculated data item, it is possible to determine whether the pre-calculation execution standard needs to be adjusted, thereby dynamically optimizing the pre-calculation strategy and ensuring that the pre-calculation results always match actual needs. The calculation of the data call value for each pre-calculated data item takes into account parameters such as the number of pre-calculation result calls, hit rate, and wait time, which reflect the actual use of the pre-calculation results. Analysis of these parameters enables rational resource allocation, avoiding unnecessary investment in inefficient or outdated pre-calculation results and improving the rationality of resource allocation. The introduction of data update parameters enables the system to determine whether pre-calculation results need to be updated based on the data update frequency and interval length. This ensures that pre-calculation results promptly reflect the latest data changes, enhances data timeliness and accuracy, and avoids pre-calculation deviations caused by outdated data.

[0067] Flexible adjustments to pre-calculation strategies based on different data update patterns and pre-calculation result usage can better adapt to data growth, changes in query patterns, and adjustments to business needs, improving overall adaptability. By properly adjusting pre-calculation strategies, query efficiency can be maintained while avoiding query delays or errors caused by inaccurate or outdated pre-calculation results, improving user query accuracy and satisfaction, and enhancing the user experience.

[0068] By analyzing the number of precomputed result calls, call hit rate, call wait time, and the first fitness of precomputed features, as well as the average update frequency and average update interval, we consider the interplay between these parameters. For example, a higher number of precomputed result calls indicates a higher frequency of user access to that parameter, a higher frequency of use of the precomputed result, and a greater impact on users. A higher precomputed feature first fitness leads to a higher call hit rate because the precomputed result closely matches the user's query requirements, allowing the required information to be directly obtained from the precomputed result without requiring additional real-time calculations. A high precomputed feature first fitness allows for faster data retrieval from the precomputed result, reducing call wait time and improving query response speed. On the other hand, a low precomputed feature first fitness requires more real-time calculations, increasing call wait time and impacting query efficiency. The average data update frequency influences the first fitness of the precomputed feature. Frequent data updates are associated with better timeliness, making the precomputed result more suitable for the current scenario. A higher precomputed feature first fitness and a longer average update interval indicate that the precomputed result has not been updated for a long time, resulting in poor timeliness.

[0069] Furthermore, the pre-calculation execution standard is adjusted, and the specific steps include: obtaining a pre-calculation adjustment threshold preset in the database, and comparing it with the data call value of each pre-calculation data item; if there is a pre-calculation data item whose data call value is above the pre-calculation adjustment threshold, retaining the information item and marking it as a remaining data item; if there is a pre-calculation data item whose data call value is less than the pre-calculation adjustment threshold, deleting the high-frequency access data of the information item; obtaining the data call value of each information item in the remaining data items, and performing mean processing on them to obtain the data call mean; obtaining each data call mean interval preset in the database and the pre-calculation reference adjustment data set corresponding to each data call mean interval; if there is a pre-calculation data item whose data call mean is within a preset data call mean interval, obtaining the pre-calculation reference adjustment data set corresponding to the interval as the pre-calculation execution adjustment standard, and performing pre-calculation adjustment based on the pre-calculation execution adjustment standard.

[0070] In this embodiment, the data call value of each pre-calculated data item is a key factor in determining whether to adjust the pre-calculation execution standard. If the data call value of each pre-calculated data item is above the pre-calculation adjustment threshold, the current pre-calculation strategy needs to be optimized, thereby triggering the adjustment of the pre-calculation execution standard. On the contrary, if the data call value of each pre-calculated data item is less than the pre-calculation adjustment threshold, the pre-calculation execution standard adjustment will not be performed. The data call value interval of each pre-calculated data item divides the data call value of each pre-calculated data item into different levels, and each interval corresponds to a pre-calculation reference adjustment data set. This ratio determines the degree of adjustment of the pre-calculation execution standard and reflects the urgency and direction of the adjustment of the pre-calculation strategy. When the data call value of each pre-calculated data item falls within a specific interval, the corresponding pre-calculation reference adjustment data set provides a specific quantitative basis for the adjustment, ensuring that the adjustment is neither excessive nor insufficient, so as to achieve the purpose of optimizing the pre-calculation strategy.

[0071] Furthermore, pre-calculation adjustment is performed based on the pre-calculation execution adjustment standard, and the specific steps include: obtaining the pre-calculation execution standard, the pre-calculation execution standard includes the adjustment proportional coefficient of the information processing frequency threshold and the pre-calculation update reference frequency; proportionally decreasing the information processing frequency threshold according to the adjustment proportional coefficient of the information processing frequency threshold, and adjusting the numerical value of the pre-calculation update frequency to the pre-calculation update reference frequency.

[0072] In this embodiment, pre-computation adjustments are performed based on pre-computation execution adjustment standards to free up resources for other more important data processing tasks, optimize resource allocation, avoid wasting resources on unnecessary pre-computation, and enable more query requirements to be met in the pre-computation results, thereby reducing the need for real-time calculations and improving the efficiency and speed of query responses.

[0073] By obtaining the data call value interval of each pre-calculated data item preset in the database and the pre-calculated reference adjustment data set corresponding to the data call value interval of each pre-calculated data item, and comparing them with the data call value of each pre-calculated data item, a pre-calculated reference adjustment data set is obtained, and the pre-calculated reference adjustment data set is used as the pre-calculated execution adjustment standard ratio to adjust the pre-calculated execution standard. The pre-calculated execution standard can be flexibly adjusted according to the actual usage of the pre-calculated results and data update conditions, and the pre-calculation strategy can be dynamically optimized according to different needs and data characteristics.

[0074] To summarize, this embodiment obtains the user search behavior parameters and computing resource parameters of each information item, analyzes the information processing frequency coefficient and computing resource activity of each information item, and thereby determines whether to perform data pre-computation, and performs data pre-computation on high-frequency access data based on the pre-computation scale judgment index, thereby achieving a rapid response to query data, and effectively solving the problem of slow data query response in the prior art due to the complex cross-branch data aggregation calculation, which causes serious computer system load.

[0075] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of computer program products for systems, devices (systems) according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0079] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0080] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A data operation integration management system based on a regional cash processing center, characterized by: include: The resource occupancy determination module is used to obtain user search behavior parameters for each information item of the regional cash processing center, analyze and obtain the information processing frequency coefficient of each information item, obtain computing resource parameters, analyze and obtain computing resource activity, and thereby obtain resource allocation determination results; A pre-calculation scale determination module is used to determine whether to perform data pre-calculation based on the resource allocation determination result. If data pre-calculation is performed, a pre-calculation scale determination indicator is obtained based on the information processing frequency coefficient and computing resource activity analysis of each information item; A pre-calculation execution analysis module is used to screen out high-frequency access data based on the information processing frequency coefficient of each information item, and perform data pre-calculation on the high-frequency access data based on the pre-calculation scale determination index, obtain pre-calculation results, and store them in a pre-calculation database; The pre-calculation result matching module is used to match the pre-calculation results based on the user's query keywords when the user performs an information query, obtain the information query matching results and transmit them to the user display interface; Based on the pre-calculation scale determination index, data pre-calculation is performed on the frequently accessed data, and the pre-calculation results are obtained and stored in the pre-calculation database. The specific steps include: Obtaining each pre-calculated scale determination index interval preset in the database and the branch data item time reference window length corresponding to each pre-calculated scale determination index interval, and comparing them with the pre-calculated scale determination index; if the pre-calculated scale determination index is within a certain pre-calculated scale determination index interval, obtaining the branch data item time reference window length corresponding to the interval as the branch data item time reference window length; Data pre-calculation is performed based on the limited time window length of the branch data item to obtain a pre-calculation result, which includes several data information.

2. The data operation integration management system based on the regional cash processing center according to claim 1, characterized in that: The steps of obtaining user search behavior parameters of each information item of the regional cash processing center and analyzing to obtain the information processing frequency coefficient of each information item include: Obtaining user search behavior parameters for each information item of the regional cash processing center, wherein the user search behavior parameters for each information item include: the number of searches for each information item within a preset time period, the average length of stay per query, the frequency of cross-module jumps, and the priority coefficient of each information item; The information processing frequency coefficient of each information item is obtained by performing differential processing based on the number of searches, average query average stay time, and cross-module jump frequency of each information item and the average number of searches, average query average stay time, and average cross-module jump frequency of all information items, combined with the influence weight and the priority coefficient of each information item; The information processing frequency coefficient of each information item is used to introduce the corresponding influence weight after making a differentiated comparison between the number of searches, average average stay time per query and cross-module jump frequency of each information item and the average number of searches, average average stay time per query and average cross-module jump frequency of the total information items, and jointly analyze the calculation result with the priority coefficient to obtain the information processing frequency coefficient.

3. The data operation integration management system based on the regional cash processing center as claimed in claim 1, characterized in that: The information processing frequency coefficient of each information item and the computing resource activity analysis are used to obtain the pre-calculation scale determination index, and the specific steps include: Determine whether to perform data pre-computation based on the resource allocation determination result. If the resource allocation determination result is a resource allocation conflict, then perform data pre-computation. If the resource allocation determination result is a normal resource allocation, then do not perform data pre-computation. If data pre-computation is performed, the pre-computation scale determination index is obtained based on the information processing frequency coefficient and computing resource activity analysis of each information item; The pre-calculation scale determination index is used to obtain the pre-calculation scale determination index by jointly analyzing the information processing frequency coefficient and computing resource activity of each information item and introducing their corresponding influence weights.

4. The data operation integration management system based on the regional cash processing center as claimed in claim 1, characterized in that: The information processing frequency coefficient based on each information item is screened to obtain the high-frequency access data, and the specific steps include: The information processing frequency threshold preset in the database is obtained and compared with the information processing frequency coefficient of each information item. If the information processing frequency coefficient of an information item is greater than the information processing frequency threshold, the information item is marked as high-frequency access data.

5. The data operation integration management system based on the regional cash processing center as claimed in claim 1, characterized in that: The specific steps of matching pre-calculated results based on user query keywords include: Obtaining matching analysis parameters of user query keywords, wherein the matching analysis parameters of the user query keywords include a query demand time interval, a query demand region set, and a query demand business dimension set; Obtain each pre-calculated data item within the time window length limited by the branch data item, and compare it with the matching analysis parameters of the user query keyword to obtain the feature fitness of each pre-calculated data item and the user query keyword, and mark the pre-calculated data item corresponding to the maximum feature fitness as the first pre-calculated feature fitness; The feature adaptability of each pre-calculated data item and the user query keyword is used to perform differential analysis on the matching analysis parameters of the user query keyword and the limited time window length of the branch data item, and introduce their corresponding influence weights to obtain the feature adaptability of each pre-calculated data item and the user query keyword.

6. The data operation integration management system based on the regional cash processing center as claimed in claim 1, characterized in that: The steps of obtaining the information query matching result and transmitting it to the user display interface include: Obtain a preset pre-calculated feature first fitness threshold in the database and compare it with the pre-calculated feature first fitness; if the pre-calculated feature first fitness is above the pre-calculated feature first fitness threshold, provide the pre-calculated data item corresponding to the pre-calculated feature first fitness as a pre-calculated feature matching result to the user; if the pre-calculated feature first fitness is less than the pre-calculated feature first fitness threshold, perform a real-time depth information query based on the user query keyword, and output the real-time depth information query result to the user; The pre-computed feature matching results and the real-time depth information query results are jointly marked as the information query matching results.

7. The data operation integration management system based on the regional cash processing center as claimed in claim 1, characterized in that: The module also includes a pre-calculation execution standard adjustment module, which is used to obtain the pre-calculation result call execution parameter analysis to obtain the data call value of each pre-calculation data item, and adjust the pre-calculation execution standard accordingly. The specific steps include: Obtaining pre-calculation result call execution parameters, wherein the pre-calculation result call execution parameters include the number of calls, call hit rate, call waiting time and call adaptability of each pre-calculation data item within a second preset time period; Obtain the call execution reference set preset in the database, and analyze it with the pre-calculated result call execution parameters to obtain the data call value of each pre-calculated data item; Based on the data call value analysis of each pre-calculated data item, a pre-calculation execution adjustment standard is obtained, and the pre-calculation adjustment is performed accordingly; The call execution reference set includes a lower limit value for the number of calls, a lower limit value for the call hit rate, an upper limit value for the call waiting time, and a lower limit value for the call adaptability; The data call value of each pre-calculated data item is used to compare the number of calls, call hit rate, call waiting time and call fitness of each pre-calculated data item within the second preset time period with the lower limit value of the number of calls, the lower limit value of the call hit rate, the upper limit value of the call waiting time and the lower limit value of the call fitness and introduce corresponding influence weights to obtain the data call value of each pre-calculated data item; The pre-calculation execution standard is adjusted based on the data call value of each pre-calculated data item.

8. The data operation integration management system based on the regional cash processing center as claimed in claim 7, characterized in that: The specific steps of performing the pre-calculation execution standard adjustment include: Obtain the pre-calculated adjustment threshold preset in the database and compare it with the data call value of each pre-calculated data item. If the data call value of a pre-calculated data item is above the pre-calculated adjustment threshold, retain the information item and mark it as a remaining data item. If the data call value of a pre-calculated data item is less than the pre-calculated adjustment threshold, delete the high-frequency access data of the information item. Obtaining the data call value of each information item in the remaining data items, and performing mean processing on them to obtain the data call mean; Obtain the preset data call mean intervals in the database and the pre-calculated reference adjustment data sets corresponding to the data call mean intervals. If there is a pre-calculated data item whose data call mean is within a preset data call mean interval, obtain the pre-calculated reference adjustment data set corresponding to the interval as the pre-calculation execution adjustment standard, and perform pre-calculation adjustment based on the pre-calculation execution adjustment standard.

9. The data operation integration management system based on the regional cash processing center as claimed in claim 8, characterized in that: The pre-calculation adjustment is performed based on the pre-calculation execution adjustment standard, and the specific steps include: Acquiring a pre-calculation execution standard, wherein the pre-calculation execution standard includes an adjustment proportional coefficient of an information processing frequency threshold and a pre-calculation update reference frequency; The information processing frequency threshold is proportionally decreased according to the adjustment proportional coefficient of the information processing frequency threshold, and the numerical value of the pre-calculated update frequency is adjusted to the pre-calculated update reference frequency.

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