Data operation fusion management system based on regional cash processing center

Abstract

The invention discloses a data operation fusion management system based on a regional cash processing center, which belongs to the technical field of electric digital data processing, and comprises the following steps: analyzing to obtain an information processing frequency coefficient of each information item, obtaining a computing resource parameter, analyzing to obtain computing resource activeness, and analyzing to obtain a resource allocation judgment result; based on the information processing frequency coefficient of each information item and calculation resource activeness analysis, obtaining a pre-calculation scale determination index; screening to obtain high-frequency access data, and performing data pre-calculation on the high-frequency access data based on a pre-calculation scale judgment index to obtain a pre-calculation result; according to the information query method and device, the problem that in the prior art, due to the fact that cross-branch data aggregation calculation is complex, the load of a computer system is serious, and data query response is slow is solved.

Description

Technical Field

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

[0002] With the accelerating advancement of the digital transformation of the financial industry, as the core hub of cash flow, the business complexity and data scale of the regional cash processing center have increased exponentially, and there is a need to process and fuse analyze a large amount of heterogeneous data (such as cross-branch transaction records, real-time inventory status) through multi-system collaboration.

[0003] For example, the operator multi-data fusion method, system, electronic device and computer storage medium announced in the patent with the publication 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. At the same time, it also supports parallel computing to improve the fusion speed, and is applicable to large-scale and complex multi-source data fusion problems. In addition, for the integrated system application, according to the business requirements, a suitable adaptive streaming rule engine is selected for relevant configuration to achieve flexible deployment.

[0004] For example, a digital operation method and system of a data middle platform announced in the patent with the publication number CN112667677B includes: 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 in the data middle platform, and publishing the business wide table as a data interface service; constructing an interactive management module of the data middle platform, and the interactive management module includes complaint reporting items, power outage items in the substation area, equipment defect items and fault repair items.

[0005] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: In the prior art, a fixed node resource allocation mechanism is usually adopted. When the regional cash processing center faces large-scale emergency data queries, it cannot analyze multiple branch data according to the requirements of query processing and give timely feedback. Therefore, there is a problem of slow data query response caused by the complex cross-branch data aggregation calculation and the serious load of the computer system. Summary of the Invention

[0006] Embodiments of the present application provide a data operation integration management system based on a regional cash processing center, which solves the problem in the prior art that due to the complex cross-branch data aggregation calculation, the computer system load is serious, resulting in slow data query response, and realizes fast response of query data.

[0007] Embodiments of the present application provide a data operation integration management system based on a regional cash processing center, including: a resource occupancy determination module, configured to obtain user search behavior parameters of each information item in the regional cash processing center, analyze to obtain an information processing frequency coefficient of each information item, obtain a computing resource parameter, analyze to obtain computing resource activity, and thus analyze to obtain a resource allocation determination result; a pre-computation scale determination module, configured to determine whether to perform data pre-computation based on the resource allocation determination result, and if data pre-computation is to be performed, analyze to obtain a pre-computation scale determination index based on the information processing frequency coefficient and computing resource activity of each information item; a pre-computation execution analysis module, configured to screen out high-frequency access data based on the information processing frequency coefficient of each information item, perform data pre-computation on the high-frequency access data based on the pre-computation scale determination index, obtain a pre-computation result and store it in a pre-computation database; a pre-computation result matching module, configured to, when a user performs an information query, perform pre-computation result matching based on the user query keyword, obtain an information query matching result and transmit it to a user display interface.

[0008] One or more technical solutions provided in embodiments of the present application have at least the following technical effects or advantages: 1. A data operation integration management system based on a regional cash processing center provided by the present invention analyzes to obtain an information processing frequency coefficient and computing resource activity of each information item by obtaining user search behavior parameters and computing resource parameters of each information item, thereby determining whether to perform data pre-computation, and performing data pre-computation on high-frequency access data based on the pre-computation scale determination index, and further realizes fast response of query data, effectively solving the problem in the prior art that due to the complex cross-branch data aggregation calculation, the computer system load is serious, resulting in slow data query response; 2. The present invention analyzes based on a matching analysis parameter of a user query keyword to obtain a first adaptation degree of pre-computation features, and matches it with a first adaptation degree threshold of pre-computation features, thereby determining whether to provide a pre-computation result to the user or perform in-depth information query in real time according to the user query keyword, and further realizes accurate matching and fast acquisition of query results; 3. By obtaining a pre-computation result call execution parameter and a data update parameter, analyzing to obtain a data call value of each pre-computation data item, and comparing it with a pre-computation adjustment threshold, determining whether to perform a pre-computation execution standard adjustment, and thus performing pre-computation adjustment based on the pre-computation execution adjustment standard, and further realizes dynamic optimization of pre-computation. Description of the Drawings

[0009] Figure 1 The figure is a schematic structural diagram of a data operation integration management system based on a regional cash processing center provided by an embodiment of the present application. Detailed Embodiment

[0010] By providing a data operation integration management system based on a regional cash processing center, the embodiment of the present application solves the problem in the prior art that due to the complex cross-branch data aggregation calculation, the computer system load is serious, resulting in slow data query response, and achieves a fast response to query data.

[0011] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the drawings in the specification and specific embodiments.

[0012] As Figure 1 shown, the figure is a schematic structural diagram of a data operation integration management system based on a regional cash processing center provided by an embodiment of the present application. The data operation integration management system based on a regional cash processing center provided by an embodiment of the present application includes: a resource occupancy determination module, which is used to obtain the user search behavior parameters of each information item in the regional cash processing center, analyze to obtain the information processing frequency coefficient of each information item, obtain the computing resource parameters, analyze to obtain the computing resource activity, and thus analyze to obtain the resource allocation determination result; a pre-computation scale determination module, which is used to judge whether to execute data pre-computation based on the resource allocation determination result. If data pre-computation is to be executed, the pre-computation scale determination index is analyzed based on the information processing frequency coefficient and the computing resource activity of each information item; a pre-computation execution analysis module, which is used to screen out the high-frequency access data based on the information processing frequency coefficient of each information item, and perform data pre-computation on the high-frequency access data based on the pre-computation scale determination index, obtain the pre-computation result and store it in the pre-computation database; a pre-computation result matching module, which is used to perform pre-computation result matching based on the user query keyword when the user performs information query, obtain the information query matching result and transmit it to the user display interface.

[0013] In this embodiment, it should be noted that the steps of obtaining the computing resource parameters and analyzing to obtain the computing resource activity specifically include: obtaining the computing resource parameters, and the computing resource parameters include memory occupancy rate, disk read and write times per second, message queue length, storage delay, and GPU video memory usage; obtaining the preset computing resource reference set in the database, and analyzing the computing resource activity with the computing resource parameters; the computing resource reference set includes the total physical memory capacity, the maximum disk read and write speed, the queue design bearing upper limit, the maximum allowable delay, and the GPU video memory usage reference value.

[0014] The resource allocation determination result is obtained through analysis. The specific steps include: obtaining the preset information processing frequency threshold and computing resource activity threshold in the 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 the resource allocation determination result; the resource allocation determination result includes resource allocation conflict and normal resource allocation; if there is an information item whose information processing frequency coefficient is above the information processing frequency threshold and the computing resource activity is above the computing resource activity threshold, the resource allocation determination result is resource allocation conflict, otherwise the resource allocation determination result is normal resource allocation.

[0015] By setting the information processing frequency threshold and the computing resource activity threshold, it is possible to accurately identify which information items have a high access frequency and whether the current computing resources are tight. Based on these data, the resource allocation determination results of resource allocation conflict and normal resource allocation are automatically determined, thereby determining whether to perform pre-computation, avoiding excessive pre-computation from occupying computing resources, and ensuring that resources can be reasonably allocated to the most needed data processing tasks.

[0016] It can effectively prevent blindly performing large-scale pre-computation when the computing resources are already highly tense, thereby avoiding performance degradation or even collapse due to resource overload, and ensuring the stability and reliability of the system.

[0017] The computing resource activity is obtained through the following specific method: ; In the formula, represents the computing resource activity, represents the memory occupancy rate, represents the total physical memory capacity, represents the number of disk reads and writes per second, represents the maximum number of disk reads and writes per second, represents the message queue length, represents the designed upper limit of the queue capacity, represents the storage delay, represents the maximum allowable delay, represents the GPU video memory usage, represents the reference value of the GPU video memory usage, represents the influence weight of the memory occupancy rate, represents the disk influence weight, represents the message queue influence weight, represents the storage delay influence weight, represents the GPU video memory usage influence weight.

[0018] It should be noted that the memory occupancy impact weight, disk impact weight, message queue impact weight, storage latency impact weight, and GPU video memory usage impact weight can be obtained from the database. For example, the memory occupancy impact weight can be obtained by retrieving the historical memory occupancy stored in the database and the corresponding memory occupancy impact weight, thereby constructing a memory occupancy mapping set. There is a one-to-one or many-to-one correspondence in this mapping set. By inputting the memory occupancy data to be used into the memory occupancy mapping set, the memory occupancy impact weight can be obtained. The acquisition methods of other impact weights, such as disk impact weight, message queue impact weight, storage latency impact weight, and GPU video memory usage impact weight, are the same as that of the memory occupancy impact weight and can all be obtained by matching in the corresponding mapping sets. Among them, the disk impact weight corresponds to the disk mapping set, the message queue impact weight corresponds to the message queue mapping set, the storage latency impact weight corresponds to the storage latency mapping set, and the GPU video memory usage impact weight corresponds to the GPU video memory usage mapping set.

[0019] It also should be noted that the user search behavior parameters of each information item of the regional cash processing center need to be preprocessed, including cleaning to remove duplicate, incorrect, or invalid data. For example, there may be search keywords misentered by users or abnormal search records generated due to system failures, and these data need to be excluded to ensure the accuracy of subsequent analysis. The user search behavior parameters also need to be associated and analyzed with other information items of the regional cash processing center. For example, the user's search for "cash shortage situation at a certain branch" is associated with the actual cash inventory data and cash processing records of that branch to more comprehensively understand the user's search intent and related business conditions.

[0020] After data preprocessing, the system resources of the regional cash processing center can be reasonably allocated according to the processing results of the user search behavior parameters. For example, during peak search demand periods, the processing capacity of the server is increased to ensure response speed and stability. Personalized services can also be provided to users to understand their search preferences and needs. For example, based on the user's historical search records, relevant cash processing information and data analysis reports are pushed to the user. By analyzing user search behavior, problems and deficiencies in the business process of the regional cash processing center can be discovered. For example, if a large number of users frequently search for information on a specific link, it indicates that this link is complex to operate.

[0021] Further, obtain the user search behavior parameters of each information item in the regional cash processing center, and analyze to obtain the information processing frequency coefficients of each information item. The specific steps include: obtaining the user search behavior parameters of each information item in the regional cash processing center. 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 residence time per query, the cross-module jump frequency, and the priority coefficient of each information item. After performing differential processing on the number of searches, the average residence time per query, and the cross-module jump frequency of each information item with the average number of searches, the average residence time per query, and the average cross-module jump frequency of all information items, and combining the influence weights and the priority coefficients of each information item, obtain the information processing frequency coefficients of each information item. The user search benchmark set includes the average number of searches, the average residence time per query, and the average cross-module jump frequency of all information items. The information processing frequency coefficients of each information item are used to perform differential comparison on the number of searches, the average residence time per query, and the cross-module jump frequency of each information item with the average number of searches, the average residence time per query, and the average cross-module jump frequency of all information items, introduce the corresponding influence weights, and perform joint analysis on the operation results and the priority coefficients to obtain the information processing frequency coefficients.

[0022] In this embodiment, by obtaining user search behavior parameters to analyze information items, it is possible to deeply understand the user's concerns and needs for each information item in the regional cash processing center, avoid users from looking for the required content in a large amount of irrelevant data, and thus improve the efficiency and satisfaction of users in obtaining information. For example, when a user searches for cash allocation information, relevant allocation plans, time arrangements, etc. can be directly pushed according to the search behavior parameters.

[0023] Based on the analysis results of user search behavior parameters, the processing method of information items can be adjusted. For example, if it is found that users often search for cash flow data of a certain specific time period, more efficient storage and indexing can be performed on this type of data to respond to similar search requests faster and improve information processing efficiency.

[0024] Obtaining the information processing frequency coefficients by analyzing the number of searches, the average residence time per query, the cross-module jump frequency of each information item within a preset time period, and the priority coefficients of each information item takes into account the mutual influence relationships between these parameters. For example, the more the number of searches and the longer the average residence time per query, the higher the frequency of access to this information item and the more important it is. The longer the residence time indicates that the information provided can be browsed by users more, and the higher the satisfaction. The higher the cross-module jump frequency indicates that the data content of this information item involves richer content and higher correlation with multiple types of information.

[0025] It should be noted that the priority coefficients of each information item are obtained by analyzing the historical call times of each information item and the CPU occupancy rate of the computer system when processing the information item. The calculation method of the priority coefficient of each information item is as follows: ; In the formula, represents the priority coefficient of the i-th information item, i represents the information item number, , represents the total number of information items, represents the CPU occupancy rate of the computer system when processing the i-th information item, represents the historical call times of the i-th information item.

[0026] The information processing frequency coefficients of each information item are obtained. The specific steps include: ; In the formula, represents the information processing frequency coefficient of the i-th information item, i represents the information item number, , represents the total number of information items, represents the number of search times of the i-th information item, the average residence time per person for querying the i-th information item, the cross-module jump frequency of the i-th information item, represents the search influence weight, represents the jump frequency influence weight.

[0027] It should be noted that the search influence weight and the jump frequency influence weight can be obtained from the database. For example: the search influence weight can be obtained by obtaining the historical searches stored in the database and the corresponding search influence weights, thereby constructing a search mapping set, in which there is a one-to-one or many-to-one correspondence relationship in the mapping set. By inputting the search data to be used into the search mapping set, the search influence weight can be obtained. The acquisition method of the jump frequency influence weight is the same as that of the search influence weight, and it can also be obtained by matching in the corresponding mapping set, where the jump frequency influence weight corresponds to the jump frequency mapping set.

[0028] Further, a pre - calculation scale determination index is obtained based on the information processing frequency coefficients of each information item and the activity of computing resources. The specific steps are as follows: Determine 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 perform data pre - calculation; if the resource allocation determination result is normal resource allocation, then do not perform data pre - calculation. If data pre - calculation is to be performed, then a pre - calculation scale determination index is obtained based on the information processing frequency coefficients of each information item and the activity of computing resources. The pre - calculation scale determination index is used to obtain the pre - calculation scale determination index by jointly analyzing the information processing frequency coefficients of each information item and the activity of computing resources and introducing their corresponding influence weights.

[0029] In this embodiment, the method for obtaining the pre - calculation scale determination index is as follows: ; In the formula, represents the pre - calculation scale determination index, represents the activity of computing resources, represents the information processing frequency coefficient of the i - th information item, where i represents the information item number, , represents the total number of information items, represents the computing resource influence weight, represents the information item influence weight.

[0030] It should be noted that the computing resource influence weight and the information item influence weight can be obtained from the database. For example, the computing resource influence weight can be obtained by acquiring the historical computing resources stored in the database and the corresponding computing resource influence weights of the historical computing resources, thereby constructing a computing resource mapping set, where there is a one - to - one or many - to - one correspondence in this mapping set. By inputting the computing resource data to be used into the computing resource mapping set, the computing resource influence weight can be obtained, and the information item influence weight corresponds to the information item mapping set.

[0031] Further, high - frequency access data is screened based on the information processing frequency coefficients of each information item. The specific steps are as follows: Obtain the preset information processing frequency threshold in the database and compare it with the information processing frequency coefficients of each information item. If there is an information item whose information processing frequency coefficient is greater than the information processing frequency threshold, then mark this information item as high - frequency access data.

[0032] In this embodiment, whether to perform data pre-computation is determined based on the resource allocation determination result, ensuring that pre-computation is only performed when the resource allocation determination result is a resource allocation conflict, avoiding resource waste and system burden caused by blind pre-computation, and making the pre-computation process more reasonable and targeted. The information processing frequency coefficients and computing resource activity levels of each information item are jointly analyzed, and their corresponding influence weights are introduced to obtain the pre-computation scale determination index, achieving refined control of the pre-computation scale. Through comprehensive analysis based on the information processing frequency coefficients and computing resource activity levels, it is ensured that the input of pre-computation resources matches the importance and access frequency of the data, improving the efficiency of resource utilization.

[0033] Reasonable pre-computation scale determination can effectively improve the query response speed of the system. By allocating appropriate pre-computation resources to frequently accessed data, the pre-computation results can be quickly provided during user queries, reducing the complexity and time consumption of real-time computing. The pre-computation strategy can be flexibly adjusted according to different resource conditions and data access patterns. When the resource allocation determination result is normal resource allocation, it is possible to choose not to perform pre-computation and directly perform real-time queries, avoiding unnecessary pre-computation. By comprehensively considering factors such as information processing frequency coefficients and computing resource activity levels, it is possible to more accurately determine which data needs to be pre-computed and the scale of pre-computation, ensuring a high degree of relevance between the pre-computation results and the actual query requirements, and improving the accuracy and effectiveness of data processing.

[0034] Furthermore, based on the pre-computation scale determination index, data pre-computation is performed on frequently accessed data, and the pre-computation results are obtained and stored in the pre-computation database. The specific steps include: obtaining each preset pre-computation scale determination index interval in the database and the corresponding branch data item limited time reference window length for each pre-computation scale determination index interval, and comparing them with the pre-computation scale determination index. If the pre-computation scale determination index is within a certain preset pre-computation scale determination index interval, the corresponding branch data item limited time reference window length of this interval is obtained as the branch data item limited time window length; data pre-computation is performed based on the branch data item limited time window length to obtain the pre-computation results, and the pre-computation results include several data information.

[0035] In this embodiment, it should be noted that performing data pre-computation based on the branch data item limited time window length means calculating the branch data item data under this limited time window length. The pre-computation results include several data information. For example: The total amount of a certain enterprise's transactions on January 1, 2025 is a certain amount.

[0036] By setting an information processing frequency threshold and comparing it with the information processing frequency coefficients of each information item, it is possible to accurately identify which data are high-frequency accessed data, which helps to perform pre-computation processing on this data in a targeted manner, thereby improving the efficiency of data processing, and providing a clear screening basis for the pre-computation execution analysis module to ensure that only high-frequency accessed data will be selected for pre-computation. This avoids unnecessary pre-computation of low-frequency data, saves computing resources and storage space, and makes the pre-computation process more efficient and targeted. After the high-frequency accessed data is pre-computed and stored, when the user queries this data, the pre-computed result can be quickly provided without performing complex real-time calculations, significantly shortening the response time of the user query and improving the query efficiency and user experience.

[0037] Furthermore, perform pre-computed result matching based on the user's query keywords. The specific steps include: obtaining the matching analysis parameters of the user's query keywords, where the matching analysis parameters of the user's query keywords include the query demand time interval, the query demand area set, and the query demand business dimension set; obtaining each pre-computed data item under the time window length defined by the branch data item, and comparing it with the matching analysis parameters of the user's query keywords to obtain the feature adaptation degree between each pre-computed data item and the user's query keywords, and marking the pre-computed data item corresponding to the maximum feature adaptation degree as the first adaptation degree of pre-computed features; the feature adaptation degree between each pre-computed data item and the user's query keywords is used to perform differential analysis on the matching analysis parameters of the user's query keywords and the time window length defined by the branch data item, and introduce its corresponding influence weight to obtain the feature adaptation degree between each pre-computed data item and the user's query keywords.

[0038] In this embodiment, obtain each pre-computed data item under the time window length defined by the branch data item. Each pre-computed data item under the time window length defined by the branch data item includes the pre-computation time interval, the pre-computation area set, and the pre-computation business dimension set of each pre-computed data item; The method for obtaining the feature adaptation degree between each pre-computed data item and the user's query keywords is as follows: ; In the formula, represents the feature adaptation degree between the j-th pre-computed data item and the user's query keywords, j represents the information item number, , represents the total number of information items, represents the query demand time interval, represents the pre-computation time interval of the j-th pre-computed data item, represents the query demand area set, represents the pre-computation area set of the j-th pre-computed data item, represents the query demand business dimension set, represents the pre-computed service dimension set of the j-th pre-computed data item, represents the time interval impact weight, represents the region set impact weight, represents the service dimension set impact weight.

[0039] The time interval impact weight, the region set impact weight, and the service dimension set impact weight can be obtained from the database. For example, the time interval impact weight can be obtained by retrieving the historical time intervals stored in the database and the corresponding time interval impact weights, thereby constructing a time interval mapping set. There is a one-to-one or many-to-one correspondence in this mapping set. By inputting the time interval data to be used into the time interval mapping set, the time interval impact weight can be obtained. The acquisition methods of other impact weights, such as the region set impact weight and the service dimension set impact weight, are the same as that of the time interval impact weight and can be obtained by matching in the corresponding mapping sets. The region set impact weight corresponds to the region set mapping set, and the service dimension set impact weight corresponds to the service dimension set mapping set.

[0040] Furthermore, obtaining the information query matching result and transmitting it to the user display interface, the specific steps include: obtaining the preset first adaptation degree threshold of the pre-computed feature in the database and comparing it with the first adaptation degree of the pre-computed feature. If the first adaptation degree of the pre-computed feature is above the first adaptation degree threshold of the pre-computed feature, then the pre-computed data item corresponding to the first adaptation degree of the pre-computed feature is provided to the user as the pre-computed feature matching result. If the first adaptation degree of the pre-computed feature is less than the first adaptation degree threshold of the pre-computed feature, then perform a deep information query in real time according to the user query keyword and output the real-time deep information query result to the user; jointly mark the pre-computed feature matching result and the real-time deep information query result as the information query matching result.

[0041] In this embodiment, it should be noted that the deep information query is to perform keyword matching on all the data in the database according to the user query keyword by using the jieba library in Python to achieve information query.

[0042] Through the judgment of the first adaptation degree of the pre-computed feature, it can be quickly determined whether the user query matches the pre-computed result. If the first adaptation degree of the pre-computed feature is above the first adaptation degree threshold of the pre-computed feature, then the pre-computed result is provided to the user, avoiding the complex process of real-time calculation, thereby significantly shortening the response time of the user query and improving the query efficiency. If the first adaptation degree of the pre-computed feature is less than the first adaptation degree threshold of the pre-computed feature, then perform a deep information query in real time according to the user query keyword.

[0043] By judging the first adaptation degree of the pre-computed features, it is possible to quickly determine whether the user query matches the pre-computed result. If the match is successful, the pre-computed result is directly provided, avoiding the complex process of real-time calculation, thus significantly shortening the response time of the user query, improving the query efficiency. When the first adaptation degree of the pre-computed features is less than the first adaptation degree threshold of the pre-computed features, in-depth information query will be performed in real time according to the user query keywords, rather than blindly providing the pre-computed result, ensuring the reasonable allocation and efficient utilization of computing resources while ensuring the correctness of the user query information.

[0044] When querying, users can quickly obtain results that highly match their needs, reducing waiting time, improving the accuracy and satisfaction of the query, and enhancing the user experience. By flexibly adjusting the response strategy according to different query scenarios, for queries with a high match degree to the pre-computed result, the pre-computed result can be quickly provided; for queries with a low match degree, real-time calculation is performed, ensuring flexibility and adaptability in the face of diverse query requirements. Through the threshold matching mechanism, it is possible to quickly judge whether to use the pre-computed result, avoiding complex real-time calculation and data processing processes.

[0045] Furthermore, it also includes a pre-computed execution standard adjustment module, which is used to obtain the data call values of each pre-computed data item by analyzing the execution parameters for calling the pre-computed result, and thus adjust the pre-computed execution standard. The specific steps include: obtaining the execution parameters for calling the pre-computed result, where the execution parameters for calling the pre-computed result include the call times, call hit rates, call waiting durations, and call adaptation degrees of each pre-computed data item within the second preset time period; obtaining the preset call execution reference set in the database, and analyzing the data call values of each pre-computed data item in combination with the execution parameters for calling the pre-computed result; analyzing the pre-computed execution adjustment standard based on the data call values of each pre-computed data item, and thus performing pre-computed adjustment; the call execution reference set includes the lower limit value of call times, the lower limit value of call hit rates, the upper limit value of call waiting durations, and the lower limit value of call adaptation degrees; the data call values of each pre-computed data item are used to compare the call times, call hit rates, call waiting durations, and call adaptation degrees of each pre-computed data item within the second preset time period with the lower limit value of call times, the lower limit value of call hit rates, the upper limit value of call waiting durations, and the lower limit value of call adaptation degrees and introduce corresponding influence weights to obtain the data call values of each pre-computed data item; according to the data call values of each pre-computed data item, the pre-computed execution standard is adjusted accordingly.

[0046] In this embodiment, it should be noted that to obtain the call fitness of each pre-computed data item, the specific method is to obtain the fitness when each pre-computed data item is output and displayed to the user as the first fitness of the pre-computed feature each time, and calculate it as the display fitness of each pre-computed data item. Then, perform a mean processing on the display fitness of each pre-computed data item to obtain the call fitness of each pre-computed data item.

[0047] To obtain the data call value of each pre-computed data item, the specific steps include: ; In the formula, represents the data call value of the j-th pre-computed data item, j represents the information item number, , represents the total number of information items, represents the feature fitness between the j-th pre-computed data item and the user query keyword, represents the lower limit value of the call fitness, represents the call times of the j-th pre-computed data item, represents the lower limit value of the call times, represents the call hit rate of the j-th pre-computed data item, represents the lower limit value of the call hit rate, represents the call waiting duration of the j-th pre-computed data item, represents the upper limit value of the call waiting duration, represents the feature fitness impact weight, represents the call impact weight, represents the call hit rate impact weight, represents the call waiting duration impact weight.

[0048] It should be noted that the feature fitness impact weight, call impact weight, call hit rate impact weight, and call waiting duration impact weight can be obtained from the database. For example: the feature fitness impact weight can be obtained by obtaining the historical feature fitness stored in the database and the corresponding feature fitness impact weight, thereby constructing a feature fitness mapping set, where there is a one-to-one or many-to-one correspondence in this mapping set. By inputting the feature fitness data to be used into the feature fitness mapping set, the feature fitness impact weight can be obtained. The acquisition method of the call impact weight is the same as that of the feature fitness impact weight and can also be obtained by matching in the corresponding mapping set. Among them, the call impact weight corresponds to the call mapping set, the call hit rate impact weight corresponds to the call hit rate mapping set, and the call waiting duration impact weight corresponds to the call waiting duration mapping set.

[0049] By analyzing the pre-computation results to call the execution parameters and data update parameters, the effectiveness of the pre-computation strategy and the timeliness of the data can be dynamically evaluated. According to the data call values of each pre-computed data item, it can be decided whether to adjust the pre-computation execution standard, so as to achieve the dynamic optimization of the pre-computation strategy and ensure that the pre-computation results always match the actual requirements. The calculation of the data call values of each pre-computed data item takes into account parameters such as the call times, hit rate, and waiting duration of the pre-computation results, and these parameters reflect the actual usage of the pre-computation results. Through the analysis of these parameters, resources can be reasonably allocated, unnecessary resource investment in inefficient or outdated pre-computation results can be avoided, and the rationality of resource allocation is improved. The introduction of the data update parameter enables the system to judge whether the pre-computation results need to be updated according to the update frequency and interval duration of the data, ensuring that the pre-computation results can timely reflect the latest data changes, enhancing the timeliness and accuracy of the data, and avoiding biases in pre-computation caused by outdated data.

[0050] Flexibly adjusting the pre-computation strategy according to different data update modes and the usage of pre-computation results can better adapt to the growth of data volume, the change of query patterns, and the adjustment of business requirements, improving the overall adaptability. By reasonably adjusting the pre-computation strategy, while ensuring the query efficiency, it can avoid query delays or errors caused by inaccurate or outdated pre-computation results, improving the accuracy and satisfaction of user queries and enhancing the user experience.

[0051] By analyzing the pre-computation result call times, call hit rate, call waiting duration, and the first adaptation degree of pre-computation features, as well as the average update frequency and average update interval duration, the mutual influence relationship between these parameters is considered. For example: the more the pre-computation result call times, the higher the access frequency of this parameter by users, indicating that the usage frequency of this pre-computation result is higher and the impact on users is greater. A higher first adaptation degree of pre-computation features will have a higher call hit rate because the pre-computation result highly matches the user's query requirements and the required information can be directly obtained from the pre-computation result without additional real-time calculation. When the first adaptation degree of pre-computation features is high, data can be quickly obtained from the pre-computation result, reducing the call waiting duration and improving the query response speed. If the first adaptation degree of pre-computation features is low, more real-time calculations are required, resulting in an increase in the call waiting duration and affecting the query efficiency. The average update frequency of the data will affect the first adaptation degree of pre-computation features. If the data is updated frequently and has good timeliness, the pre-computation result is more suitable for the current scenario, and the higher the first adaptation degree of pre-computation features. A longer average update interval duration means that the pre-computation result has not been updated for a long time and the timeliness is poor.

[0052] Further, perform pre - calculation execution standard adjustment. The specific steps include: obtaining the preset pre - calculation adjustment threshold in the database and comparing it with the data call values of each pre - calculation data item. If there is a data call value of a certain pre - calculation data item above the pre - calculation adjustment threshold, retain this information item and mark it as the remaining data item. If there is a data call value of a certain pre - calculation data item less than the pre - calculation adjustment threshold, delete the high - frequency access data of this information item; obtaining the data call values of each information item in the remaining data items and performing mean processing on them to obtain the data call mean value; obtaining the preset data call mean value intervals in the database and the corresponding pre - calculation reference adjustment data sets for each data call mean value interval. If the data call mean value of a certain pre - calculation data item is within a certain preset data call mean value interval, obtain the corresponding pre - calculation reference adjustment data set as the pre - calculation execution adjustment standard and perform pre - calculation adjustment based on the pre - calculation execution adjustment standard.

[0053] In this embodiment, the data call values of each pre - calculation data item are the key factors determining whether to perform pre - calculation execution standard adjustment. If the data call values of each pre - calculation data item are above the pre - calculation adjustment threshold, the current pre - calculation strategy needs to be optimized, thus triggering the adjustment of the pre - calculation execution standard. On the contrary, if the data call values of each pre - calculation data item are less than the pre - calculation adjustment threshold, the pre - calculation execution standard adjustment is not performed. The data call value intervals of each pre - calculation data item divide the data call values of each pre - calculation data item into different levels, and each interval corresponds to a pre - calculation reference adjustment data set. This ratio determines the degree of pre - calculation execution standard adjustment and reflects the urgency and direction of the pre - calculation strategy adjustment. When the data call values of each pre - calculation data item fall within a certain 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.

[0054] Further, perform pre - calculation adjustment based on the pre - calculation execution adjustment standard. The specific steps include: obtaining the pre - calculation execution standard, where the pre - calculation execution standard includes the adjustment proportional coefficient of the information processing frequency threshold and the pre - calculation update reference frequency; adjusting the information processing frequency threshold to decrease proportionally 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.

[0055] In this embodiment, by performing pre - calculation adjustment based on the pre - calculation execution adjustment standard, resources are released for other more important data processing tasks, optimizing resource allocation, avoiding wasting resources on unnecessary pre - calculations, enabling more query requirements to be met in the pre - calculation results, thus reducing the need for real - time calculations and improving the efficiency and speed of query responses.

[0056] By obtaining the data call value ranges of each pre-computed data item preset in the database and the pre-computed reference adjustment data sets corresponding to the data call value ranges of each pre-computed data item, comparing them with the data call values of each pre-computed data item, obtaining the pre-computed reference adjustment data set, and using this pre-computed reference adjustment data set as the pre-computed execution adjustment standard ratio for pre-computed execution standard adjustment, it is possible to flexibly adjust the pre-computed execution standard according to the actual pre-computed result usage and data update situation, and dynamically optimize the pre-computed strategy according to different requirements and data characteristics.

[0057] In summary, in this embodiment, by obtaining the user search behavior parameters and computing resource parameters of each information item, analyzing to obtain the information processing frequency coefficient and computing resource activity of each information item, thereby determining whether to perform data pre-computation, and performing data pre-computation on high-frequency accessed data based on the pre-computation scale determination index, the rapid response of query data is realized, and the problem of slow data query response caused by the complex cross-branch data aggregation calculation and the serious load of the computer system in the prior art is effectively solved.

[0058] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0060] These computer program instructions can 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 generate a manufactured article including an instruction device, and the instruction device implements the functions in the process Figure 1 one flow or multiple flows and / or blocksFigure 1 the functions specified in one or more boxes

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 one process or more processes and / or boxes Figure 1 the steps of the functions specified in one or more boxes

[0062] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A data operation integration management system based on a regional cash processing center, characterized in that Including: A resource occupancy determination module, which is used to obtain the user search behavior parameters of each information item in the regional cash processing center, analyze to obtain the information processing frequency coefficients of each information item, obtain the computing resource parameters, analyze to obtain the computing resource activity, and thus analyze to obtain the resource allocation determination result; A pre-computation scale determination module, which is used to determine whether to perform data pre-computation based on the resource allocation determination result. If data pre-computation is to be performed, analyze to obtain the pre-computation scale determination index based on the information processing frequency coefficients and computing resource activity of each information item; A pre-computation execution analysis module, which is used to screen out high-frequency access data based on the information processing frequency coefficients of each information item, perform data pre-computation on the high-frequency access data based on the pre-computation scale determination index, obtain the pre-computation result and store it in the pre-computation database; A pre-computation result matching module, which is used to perform pre-computation result matching based on the user query keyword when the user performs information query, obtain the information query matching result and transmit it to the user display interface.

2. The data operation integration management system based on the regional cash processing center according to claim 1, wherein: The specific steps for obtaining the user search behavior parameters of each information item in the regional cash processing center and analyzing to obtain the information processing frequency coefficients of each information item include: Obtain the user search behavior parameters of each information item in the regional cash processing center. 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 query stay duration per person, the cross-module jump frequency, and the priority coefficient of each information item; After performing differential processing on the number of searches, average query stay duration per person, and cross-module jump frequency of each information item with the average number of searches, average query stay duration per person, and average cross-module jump frequency of all information items, combine the influence weights and the priority coefficients of each information item to obtain the information processing frequency coefficients of each information item. The information processing frequency coefficients of each information item are used to perform differential comparison on the number of searches, average query stay duration per person, and cross-module jump frequency of each information item with the average number of searches, average query stay duration per person, and average cross-module jump frequency of all information items, introduce their corresponding influence weights, and perform joint analysis on the operation results with the priority coefficients to obtain the information processing frequency coefficients.

3. The data operation integration management system based on the regional cash processing center according to claim 1, characterized in that: The specific steps for analyzing to obtain the pre-computation scale determination index based on the information processing frequency coefficients and computing resource activity of each information item include: Determine whether to perform data pre-computation based on the resource allocation determination result. If the resource allocation determination result is resource allocation conflict, perform data pre-computation. If the resource allocation determination result is normal resource allocation, do not perform data pre-computation; If data pre-computation is to be performed, analyze to obtain the pre-computation scale determination index based on the information processing frequency coefficients and computing resource activity of each information item; The pre-computation scale determination index is used to perform joint analysis on the information processing frequency coefficients and computing resource activity of each information item and introduce their corresponding influence weights to obtain the pre-computation scale determination index.

4. The data operation integration management system based on the regional cash processing center according to claim 1, wherein: The specific steps for screening out high-frequency access data based on the information processing frequency coefficients of each information item include: Obtain the preset information processing frequency threshold in the database, and compare it with the information processing frequency coefficients of each information item. If there is an information item whose information processing frequency coefficient is greater than the information processing frequency threshold, mark this information item as high-frequency access data.

5. The data operation integration management system based on the regional cash processing center according to claim 1, wherein Perform data pre-computation on the high-frequency access data based on the pre-computation scale determination index, obtain the pre-computation result and store it in the pre-computation database. The specific steps include: Obtain each preset pre-computation scale determination index interval in the database and the corresponding branch data item limited time reference window length for each pre-computation scale determination index interval, and compare them with the pre-computation scale determination index. If the pre-computation scale determination index is within a certain preset pre-computation scale determination index interval, obtain the corresponding branch data item limited time reference window length for this interval as the branch data item limited time window length; Perform data pre-computation based on the branch data item limited time window length to obtain the pre-computation result, and the pre-computation result includes several data information.

6. The data operation integration management system based on the regional cash processing center according to claim 1, characterized in that: The specific steps for performing pre-computation result matching based on the user query keyword include: Obtain the matching analysis parameters of the user query keyword, and the matching analysis parameters of the user query keyword include the query requirement time interval, the query requirement area set, and the query requirement business dimension set; Obtain each pre-computed data item under the branch data item limited time window length, and compare it with the matching analysis parameters of the user query keyword to obtain the feature adaptation degree of each pre-computed data item to the user query keyword, and mark the pre-computed data item corresponding to the maximum feature adaptation degree as the first pre-computed feature adaptation degree; The feature adaptation degree of each pre-computed data item to the user query keyword is used to perform differential analysis on the matching analysis parameters of the user query keyword and the branch data item limited time window length, and introduce its corresponding influence weight to obtain the feature adaptation degree of each pre-computed data item to the user query keyword.

7. The data operation integration management system based on the regional cash processing center according to claim 6, wherein: The specific steps for obtaining the information query matching result and transmitting it to the user display interface include: Obtain the preset first pre-computed feature adaptation degree threshold in the database, and compare it with the first pre-computed feature adaptation degree. If the first pre-computed feature adaptation degree is above the first pre-computed feature adaptation degree threshold, provide the pre-computed data item corresponding to this first pre-computed feature adaptation degree to the user as the pre-computed feature matching result. If the first pre-computed feature adaptation degree is less than the first pre-computed feature adaptation degree threshold, perform in-depth information query in real time according to the user query keyword, and output the real-time in-depth information query result to the user; Jointly mark the pre-computed feature matching result and the real-time in-depth information query result as the information query matching result.

8. The data operation integration management system based on the regional cash processing center according to claim 1, characterized in that: It also includes a pre-computation execution standard adjustment module, which is used to obtain the data call value of each pre-computed data item by analyzing the pre-computation result call execution parameters, and thus perform pre-computation execution standard adjustment. The specific steps include: Obtain the pre-computation result call execution parameters, and the pre-computation result call execution parameters include the call times, call hit rate, call waiting duration, and call adaptation degree of each pre-computed data item within the second preset time period; Obtain the preset call execution reference set in the database, and analyze it with the pre-computation result call execution parameters to obtain the data call values of each pre-computed data item; Analyze the data call values of each pre-computed data item to obtain the pre-computation execution adjustment standard, and thus perform pre-computation adjustment; The call execution reference set includes a lower limit value of the call frequency, a lower limit value of the call hit rate, an upper limit value of the call waiting duration, and a lower limit value of the call adaptability; The data call values of each pre-computed data item are used to compare the call frequency, call hit rate, call waiting duration, and call adaptability of each pre-computed data item within the second preset period with the lower limit value of the call frequency, the lower limit value of the call hit rate, the upper limit value of the call waiting duration, and the lower limit value of the call adaptability, and introduce the corresponding influence weights to obtain the data call values of each pre-computed data item; According to the data call values of each pre-computed data item, thus adjust the pre-computation execution standard.

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

10. The data operation integration management system based on the regional cash processing center according to claim 9, wherein: The specific steps for performing pre-computation adjustment based on the pre-computation execution adjustment standard include: Obtain the pre-computation execution standard, and the pre-computation execution standard includes the adjustment proportional coefficient of the information processing frequency threshold and the pre-computation update reference frequency; Reduce the information processing frequency threshold proportionally according to the adjustment proportional coefficient of the information processing frequency threshold, and adjust the numerical value of the pre-computation update frequency to the pre-computation update reference frequency.

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