Monitoring view generation method, system, device and equipment and storage medium

Abstract

The invention provides a monitoring view generation method, system and device, equipment and a storage medium. The method and the device are used for rapidly monitoring and identifying state abnormity of service nodes and effectively preventing and controlling service abnormity. According to the specific scheme, the method comprises the following steps: obtaining multi-dimensional target data of each service node, and determining a state quantized value of each service node under a plurality of preset state monitoring dimensions; mapping the state quantized value of each dimension into a state level, wherein the higher the state level is, the higher the contribution degree to negative feedback is; converting each dimension state grade into a state score and weighting to obtain a comprehensive state score, wherein the lower the score is, the better the overall operation state of the service node is; and finally, a graphical node state monitoring view is generated based on the comprehensive state score, so that efficient monitoring and anomaly recognition of the node state of the resource advanced return service are realized.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of big data, and in particular to a monitoring view generation method, system, device, equipment and storage medium. BACKGROUND

[0002] Large enterprises often contain a large number of subordinate institutions distributed in different regions, which handle various types of businesses in parallel. Due to the objective differences in regions, resource allocation, management efficiency, etc. of each subordinate institution, their performance in business processing is very different, therefore, an effective monitoring mechanism needs to be established to achieve accurate management.

[0003] Taking a bank as an example, a large number of branches under the head office will handle high-frequency and sensitive businesses such as "loan prepayment". Improper handling of such businesses is prone to customer complaints, affecting customer relationships, and greatly affecting the operating efficiency of the branch. In order to effectively monitor the execution status of the prepayment business of each branch, a method is needed to monitor the execution status of the prepayment business of each subordinate business node in real time and comprehensively, so as to quickly identify abnormalities and effectively prevent and control business abnormalities. SUMMARY

[0004] Embodiments of the present application provide a monitoring view generation method, system, device, equipment and storage medium for quickly monitoring and identifying state abnormalities of business nodes and effectively preventing and controlling business abnormalities.

[0005] To achieve the above purpose, embodiments of the present application adopt the following technical solutions: In a first aspect, a monitoring view generation method is provided, which comprises: obtaining target data associated with a resource prepayment business for each business node; the target data includes business data generated by the business node in executing the resource prepayment business, negative feedback data received by the business node in executing the resource prepayment business, statistical data related to the cause of initiation of the resource prepayment business, and environmental data related to the execution strategy of the business node for the resource prepayment business; For each business node, according to the target data, determine the state quantization value of the business node in a plurality of preset state monitoring dimensions; the size of the state quantization value is related to the pros and cons of the running state in the preset state monitoring dimension; For each preset state monitoring dimension, map the state quantization values of each business node in the preset state monitoring dimension to determine the state level of each business node in the preset state monitoring dimension; the higher the state level, the higher the contribution of the preset state monitoring dimension to the negative feedback received by the business node for the resource prepayment business; For each business node, the status level of the business node under each preset status monitoring dimension is converted into a corresponding status score. Based on preset weight information, the status scores of the business node under each preset status monitoring dimension are weighted to obtain the comprehensive status score of the business node. The comprehensive status score represents the overall operational status of the business node related to the early return of resources. The status level is positively correlated with the status score. The lower the comprehensive status score, the better the overall operational status of the business node in executing the early return of resources. The comprehensive status score corresponding to each business node is sent to the preset display platform, so that the preset display platform can graphically encode the node status of each business node based on the comprehensive status score corresponding to each business node and generate a node status monitoring view.

[0006] This embodiment of the application provides a comprehensive data foundation for monitoring the status of resource early return services by acquiring multi-dimensional target data covering business data, negative feedback data, statistical data, and environmental data. For each business node, the target data is transformed into quantitative values ​​for multiple preset status monitoring dimensions, achieving an objective and quantitative description of the business node's operational status. For each dimension, the quantitative value is mapped to a status level, with higher levels contributing more to negative feedback, thus accurately identifying the key dimensions leading to negative feedback. The status levels of each dimension are converted into scores and weighted to obtain a comprehensive status score, achieving a fusion assessment of multi-dimensional statuses. Furthermore, by defining low scores as corresponding to good statuses, the assessment results are made more intuitive. Finally, the comprehensive status score is sent to a display platform for graphical encoding to generate a monitoring view, enabling the overall operational status of a large number of business nodes to be presented in real-time and intuitively, thereby significantly improving the efficiency of resource early return service monitoring and the accuracy of anomaly identification.

[0007] In one possible implementation of the first aspect, for each preset state monitoring dimension, the state quantification value of each business node under the preset state monitoring dimension is mapped to determine the state level of each business node under the preset state monitoring dimension, including: For each preset status monitoring dimension, the status quantification values ​​of each business node under the preset status monitoring dimension are sorted according to the sorting rules corresponding to the preset status monitoring dimension, the ordinal position of the status quantification value of each business node is determined, and the status level of each business node under the preset status monitoring dimension is determined based on the preset second mapping relationship and the ordinal position of the status quantification value of each business node; the second mapping relationship is the mapping relationship between the ordinal position of the status quantification value and the status level. Alternatively, based on the preset third mapping relationship and the state quantification value of each business node under the preset state monitoring dimension, the state level of each business node under the preset state monitoring dimension can be determined; the third mapping relationship is the mapping relationship between the magnitude of the state quantification value corresponding to the preset state monitoring dimension and the state level.

[0008] Based on the aforementioned technical content, two methods for determining state levels are provided: ordinal ranking or direct numerical mapping, making the state level division process standardized and configurable. This approach enhances the flexibility and adaptability of the evaluation system, enabling the selection of appropriate mapping methods based on the characteristics of data in different dimensions, while ensuring the objectivity and consistency of state level determination and avoiding subjective judgment bias.

[0009] In one possible implementation of the first aspect, the state levels include a first level, a second level, and a third level; the first level is higher than the second level, and the second level is higher than the third level. The status levels of business nodes under each preset status monitoring dimension are converted into corresponding status scores, including: If the state level under the preset state monitoring dimension is the first level, then the corresponding state score is determined to be the first value; If the state level under the preset state monitoring dimension is the second level, then the corresponding state score is determined to be the second value; If the state level under the preset state monitoring dimension is the third level, then the corresponding state score is determined to be the third value; where the first value is greater than the second value, and the second value is greater than the third value.

[0010] Here, a standardized conversion rule from level to score is established by clearly defining the correspondence between different state levels and specific numerical scores. This conversion rule is simple and clear, ensuring consistency in the calculation of state scores across different dimensions and nodes, avoiding arbitrariness in the conversion process, and providing a reliable foundation for the subsequent weighted calculation of the comprehensive state score.

[0011] In one possible implementation of the first aspect, in the node status monitoring view, each business node has a first display area and a second display area; the first display area is used to display the target data of the business node under multiple preset time dimensions, the status quantification value data of the business node under each preset status monitoring dimension under multiple preset time dimensions, and the change trend information of the target data of the business node within a preset historical period; the second display area is used to display the node status of the subordinate business nodes corresponding to the business node.

[0012] Specifically, by clearly defining the correspondence between different state levels and specific numerical scores, a standardized conversion rule from level to score was established. This conversion rule is simple and clear, ensuring consistency in the calculation of state scores across different dimensions and nodes, avoiding arbitrariness in the conversion process, and providing a reliable foundation for the subsequent weighted calculation of the comprehensive state score.

[0013] In one possible implementation of the first aspect, based on the target data, the state quantification values ​​of the business node in multiple preset state monitoring dimensions are determined, including: Based on business data, determine the state quantification value of business nodes in terms of business load and the state quantification value of business nodes in terms of business processing efficiency. Based on negative feedback data, determine the quantitative value of the business node's status in the dimension of business processing satisfaction; Based on statistical data, determine the quantitative values ​​of the business nodes in terms of business cost differences and user return capabilities. Based on environmental data, determine the quantitative value of the business node's status in the dimension of business competitive pressure.

[0014] Based on the aforementioned technical aspects, different types of target data are mapped to six specific status monitoring dimensions, establishing a clear data-dimensional mapping relationship. This approach ensures that each monitoring dimension has targeted data sources to support it, enabling status assessments to comprehensively cover key aspects such as business load, processing efficiency, processing satisfaction, cost differences, user return capability, and competitive pressure, thereby improving the representativeness of status quantification values ​​and the comprehensiveness of assessment results.

[0015] In one possible implementation of the first aspect, the business data includes the business request volume data for the resource early return business in the current statistical period and the previous statistical period, as well as the processing delay time of the resource early return business in multiple consecutive time periods. Based on business data, determine the state quantification values ​​of business nodes in terms of business load and business node state quantification values ​​in terms of business processing efficiency, including: Based on the business request volume data, calculate the rate of change of business request volume in the current statistical period relative to the previous statistical period, and use it as the state quantification value of the business node in the business load dimension. The maximum value among the processing delays of resource early return services within multiple consecutive time periods is used as the state quantification value of the business node in the dimension of business processing efficiency.

[0016] Here, by using the rate of change in business request volume as a quantitative value for the business load dimension, abnormal fluctuations in business volume can be sensitively captured; by using the maximum processing latency as a quantitative value for the processing efficiency dimension, performance bottlenecks in the business processing flow can be effectively exposed. These two calculation methods quantify from two key perspectives—change trends and extreme performance—making the status assessment more targeted and providing more early warning value.

[0017] In one possible implementation of the first aspect, the negative feedback data includes the number of negative customer feedback received through multiple data receiving channels during the execution of the resource early return business in the current statistical period, and the number of negative customer feedback received through multiple data receiving channels during the execution of the resource early return business in the previous statistical period. Based on negative feedback data, determine the quantitative value of the business node's status in the dimension of business processing satisfaction, including: Based on preset weighting coefficients, the number of negative feedback received by each data receiving channel in the current statistical period is weighted and summed to determine the first comprehensive number of negative feedback in the current statistical period. Based on the preset path weighting coefficients, the number of negative feedback received by each data receiving path in the previous statistical period is weighted and summed to determine the second comprehensive negative feedback number in the previous statistical period. Determine the rate of change of the first comprehensive negative feedback quantity relative to the second comprehensive negative feedback quantity, and use the rate of change as the quantitative value of the business node's status in the dimension of business processing satisfaction.

[0018] In this embodiment, by assigning weights to the number of negative feedback sources from different data reception channels and performing a weighted summation, a comprehensive negative feedback quantity that more accurately reflects the severity of the problem is calculated. Furthermore, the rate of change of the comprehensive negative feedback quantity between adjacent statistical periods is calculated as a quantitative value for the business processing satisfaction dimension. This method not only considers the absolute quantity of negative feedback but also emphasizes its changing trend, enabling the satisfaction dimension assessment to provide earlier and more sensitive warnings of potential service quality risks.

[0019] In one possible implementation of the first aspect, the statistical data includes the agreed fee rate data for existing business, the agreed fee rate data for new business, and the average return capability value of customer groups in the business nodes; Based on statistical data, determine the quantified status values ​​of business nodes in terms of business cost differences and user return capabilities, including: Determine the difference between the agreed fee rate data for existing services and the agreed fee rate data for new services, and use the difference as a quantitative value of the status of the business node in the dimension of business cost difference; The average return capability value is used as a quantitative value of the business node's status in the dimension of user return capability.

[0020] Environmental data includes the business increment ranking of business nodes; based on the environmental data, the quantitative value of the business node's status in the dimension of business competitive pressure is determined, including: The ranking of business incremental growth is used as a quantitative value of the business node's status in terms of business competitive pressure.

[0021] In this embodiment, by calculating the difference between the agreed-upon rates for existing and new business as a state quantification value for the business cost difference dimension, the risk of business demand fluctuations caused by changes in pricing strategies can be directly exposed. Such demand fluctuations significantly increase the processing pressure on business nodes, and improper handling can easily trigger negative feedback. By using the average return capability of the customer group as a state quantification value for the user return capability dimension, customer cluster nodes with strong return intentions can be identified. These nodes are more sensitive to business processing efficiency and service quality, and are more likely to generate negative feedback due to service not meeting expectations. By using the business increment ranking as a state quantification value for the business competitive pressure dimension, the situation of nodes in the external market environment can be assessed. Nodes with high competitive pressure may experience slow business processing due to conservative strategies or insufficient resources, thus becoming potential sources of negative feedback. These quantification methods accurately capture the core business drivers of negative feedback, enabling the state assessment results to proactively warn of risks and providing a key basis for precise management.

[0022] Secondly, a monitoring view generation system is provided, which includes a data processing device and a display device: Data processing equipment for performing the following steps: Obtain the target data associated with each business node and the resource early return business; the target data includes the business data generated by the business node in executing the resource early return business, the negative feedback data received by the business node in executing the resource early return business, the statistical data related to the reason for initiating the resource early return business, and the environmental data related to the execution strategy of the business node for the resource early return business. For each business node, based on the corresponding target data, determine the state quantification value of the business node under multiple preset state monitoring dimensions; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions. For each preset status monitoring dimension, the status quantification value of each business node under the preset status monitoring dimension is mapped to determine the status level of each business node under the preset status monitoring dimension; the higher the status level, the higher the contribution of the preset status monitoring dimension to the negative feedback received by the business node and the early return of resources. For each business node, the status level of the business node under each preset status monitoring dimension is converted into a corresponding status score. Based on preset weight information, the status scores of business nodes under each preset status monitoring dimension are weighted to obtain the comprehensive status score of the business node. The comprehensive status score represents the overall operational status of the business node related to the early return of resources. The status level is positively correlated with the status score. The lower the comprehensive status score, the better the overall operational status of the business node in executing the early return of resources. Send the comprehensive status score corresponding to each business node to the display device; The display device is used to graphically encode the node status of each business node based on the comprehensive status score corresponding to each business node, and generate a node status monitoring view.

[0023] Thirdly, a monitoring view generation apparatus is provided, the apparatus comprising: The acquisition unit is used to acquire target data associated with the resource early return business for each business node. The target data includes business data generated by the business node in executing the resource early return business, negative feedback data received by the business node in executing the resource early return business, statistical data related to the reason for initiating the resource early return business, and environmental data related to the execution strategy of the business node for the resource early return business. The first processing unit is used to determine the state quantification value of each business node under multiple preset state monitoring dimensions based on the target data; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions. The second processing unit is used to map the state quantification value of each business node under each preset state monitoring dimension to determine the state level of each business node under the preset state monitoring dimension; the higher the state level, the higher the contribution of the preset state monitoring dimension to the negative feedback received by the business node and the early return of resources. The third processing unit is used to convert the status level of each business node under each preset status monitoring dimension into a corresponding status score, and to perform weighted processing on the status scores of the business node under each preset status monitoring dimension according to preset weight information to obtain the comprehensive status score of the business node. The comprehensive status score represents the overall operating status of the business node related to the early return of resources. The status level is positively correlated with the status score. The lower the comprehensive status score, the better the overall operating status of the business node in executing the early return of resources. The sending unit is used to send the comprehensive status score corresponding to each business node to the preset display platform, so that the preset display platform can graphically encode the node status of each business node based on the comprehensive status score corresponding to each business node and generate a node status monitoring view.

[0024] Fourthly, an electronic device is provided, the method comprising: a memory, a transceiver, and at least one processor. The memory and transceiver are communicatively connected to the processor. The memory is used to store computer program code, the computer program code including computer instructions. When the processor executes the computer instructions, it causes the electronic device to perform the method of the first aspect and any possible implementation thereof.

[0025] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer instructions. When executed by a processor, these computer instructions are used to implement the method as described in the first aspect and any possible implementation thereof.

[0026] Sixthly, embodiments of this application provide a computer program product that, when running on a computer / executed by the computer's processor, implements the method described in the first aspect and any possible design thereof. The computer may be an electronic device as described in the fourth aspect and any possible implementation thereof.

[0027] Understandably, the beneficial effects achieved by the monitoring view generation apparatus of the second aspect, the electronic device of the fourth aspect, the computer-readable storage medium of the fifth aspect, and the computer program product of the sixth aspect provided above can be referred to as the beneficial effects of the first aspect and any possible implementation thereof, which will not be repeated here. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a monitoring view generation method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a monitoring view generation system provided in an embodiment of this application; Figure 3 This is a schematic diagram of a data processing flow provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a monitoring view generation device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this embodiment, unless otherwise stated, "a plurality of" means two or more.

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0031] The technical solutions provided in this application, including the collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data, comply with relevant laws and regulations and do not violate public order and good morals.

[0032] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0033] Large enterprises often comprise numerous subsidiaries located in different regions, which handle various business operations in parallel. Due to objective differences in geographical location, resource allocation, and management efficiency among these subsidiaries, their performance in business processing varies greatly. Therefore, an effective monitoring mechanism is needed to achieve precise management.

[0034] In one example, taking a bank as an example, numerous branches under the head office handle high-frequency and sensitive transactions such as "loan early repayment." Improper handling of this type of transaction can easily lead to customer complaints, affecting customer relationships and significantly impacting the branch's operational efficiency. To effectively monitor the execution status of early repayment transactions at each branch, a method is urgently needed to monitor the execution status of subordinate business nodes in this area in real time and comprehensively, enabling rapid identification and effective prevention of business anomalies. In another example, in the supply chain and customer service management of large enterprises, widely distributed warehousing, distribution, and service nodes handle various critical business processes in parallel. Taking the typical process of "customer return processing" as an example, its execution efficiency and quality directly affect customer satisfaction, inventory turnover, and operating costs. If the return processing cycle of a core warehouse is too long, it will cause logistics bottlenecks and customer backlogs; if the execution standards of a regional service center deviate from the headquarters' policies, it may lead to inconsistent service experiences and damage brand reputation. Therefore, a method is needed to monitor the execution status of subordinate business nodes in real time and comprehensively, enabling rapid identification and effective prevention of business anomalies.

[0035] To quickly monitor and identify abnormal statuses of business nodes and effectively prevent business anomalies, the embodiments of this application acquire multi-dimensional target data of each business node to determine its status quantification value under multiple preset status monitoring dimensions; map the status quantification value of each dimension to a status level, where a higher status level indicates a greater contribution to negative feedback; convert the status levels of each dimension into status scores and weight them to obtain a comprehensive status score, where a lower score indicates a better overall operating status of the business node; finally, generate a graphical node status monitoring view based on the comprehensive status score, thereby achieving efficient monitoring and anomaly identification of the status of business nodes that return resources in advance.

[0036] This application provides a monitoring view generation method that can be applied to electronic devices. The electronic device can be a single server or a server cluster consisting of multiple servers, or a cloud computing platform or edge computing device with data processing capabilities, or a chip or a device with computing capabilities. This application does not limit the specific form of the electronic device.

[0037] Figure 1 This is a flowchart illustrating a monitoring view generation method provided in an embodiment of this application. Figure 1 As shown, the method in the embodiments of this application may include: S101. Obtain the target data associated with each business node and the early return of resources business.

[0038] The target data includes business data generated by business nodes in executing resource early return services, negative feedback data received by business nodes in executing resource early return services, statistical data related to the reasons for initiating resource early return services, and environmental data related to the execution strategies of business nodes for resource early return services.

[0039] For example, this embodiment collects multi-dimensional target data by accessing the business system database, customer feedback platform, statistical reporting system, and external data interfaces. Business data includes business processing volume, processing time, etc.; negative feedback data may include the number of complaints, the proportion of negative evaluations, the rate of decline in customer satisfaction, etc.; statistical data may include the distribution of reasons for business initiation, changes in customer group characteristics, etc.; environmental data may include market interest rate fluctuations, changes in capital costs, and adjustments in regulatory policies, etc.

[0040] For example, in the financial sector, in the scenario of loan prepayment, the business node can be each branch, and the target data can specifically include the number of prepayment transactions, the number of related complaints, the proportion of different reasons for prepayment (such as loan refinancing, sufficient funds), and the local market interest rate level, etc.

[0041] This embodiment can provide comprehensive data support for subsequent status monitoring by acquiring relevant data from multiple dimensions.

[0042] S102. For each business node, based on the target data, determine the state quantification value of the business node under multiple preset state monitoring dimensions; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions.

[0043] For example, the embodiments pre-set multiple status monitoring dimensions, such as business processing efficiency, number of negative customer feedback, rationality of business structure, external environment, etc.

[0044] For each dimension, this embodiment extracts or calculates the corresponding state quantization value from the target data.

[0045] The magnitude of the state quantification value is related to the quality of the operating status under the preset state monitoring dimensions. This can include a larger state quantification value indicating a worse or better operating status under the preset state monitoring dimensions, and the correlation between the state quantification value and the operating status of the business node under different preset state monitoring dimensions may vary.

[0046] This embodiment sets up multiple preset status monitoring dimensions, quantifies the status characteristics of business nodes under multiple preset status monitoring dimensions into status quantification values, which can make the status comparison between different business nodes more objective and accurate.

[0047] S103. For each preset status monitoring dimension, map the status quantification value of each business node under the preset status monitoring dimension to determine the status level of each business node under the preset status monitoring dimension.

[0048] In this context, a higher status level indicates a greater contribution of the preset status monitoring dimension to negative feedback received by the business node regarding the early return of resources. This can be understood as: a higher status level means a more prominent problem in that dimension, and a greater potential contribution to triggering negative customer feedback.

[0049] For example, this embodiment pre-sets a mapping relationship between the numerical range of the state quantification value and the state level for each state monitoring dimension. This mapping relationship is used to determine the state level of each service node under the preset state monitoring dimension.

[0050] This embodiment can also sort the state quantification values ​​of each business node under the preset state monitoring dimension in the same dimension, and determine the state level of each business node under the preset state monitoring dimension based on the determined order and the pre-set mapping relationship between the order and the state level.

[0051] This embodiment uses preset mapping rules to convert continuous values ​​into discrete level indicators, highlighting the risk level of each dimension. It can quickly identify prominent dimensions and issues, enabling managers to focus on the most critical risk factors and improve the targeted nature of problem-solving.

[0052] S104. For each business node, the status level of the business node under each preset status monitoring dimension is converted into a corresponding status score. Based on the preset weight information, the status scores of the business node under each preset status monitoring dimension are weighted to obtain the comprehensive status score of the business node.

[0053] Among them, the comprehensive status score represents the overall operational status of the business node related to the early return of resources; the status level is positively correlated with the status score; the lower the comprehensive status score, the better the overall operational status of the business node in executing the early return of resources.

[0054] For example, in this embodiment, the status levels are converted into status scores based on a positive correlation. For instance, level 1 corresponds to 1 point, level 2 corresponds to 2 points, and so on, with level 5 corresponding to 5 points. Subsequently, preset weights are assigned according to the importance of each dimension to the overall business, and the status scores of each dimension are weighted and summed to obtain the comprehensive status score of the business node.

[0055] This embodiment generates a single quantitative indicator reflecting the overall operational status of a node by weighted fusion of multi-dimensional evaluation results. It considers both the independent performance of each dimension and the differences in importance between them, providing a basis for resource allocation and management decisions.

[0056] S105. Send the comprehensive status score corresponding to each business node to the preset display platform so that the preset display platform can graphically encode the node status of each business node based on the comprehensive status score corresponding to each business node and generate a node status monitoring view.

[0057] For example, in this embodiment, the calculated comprehensive status score of each business node is pushed to a preset visualization display platform. The display platform graphically encodes the node status according to the score, for example, using visual elements such as color intensity (dark red represents high score / poor status, light green represents low score / good status), icon shape or size, and marker style on the map. Finally, a node status monitoring view is generated, which centrally displays the overall status distribution of all monitored business nodes and the changing trend of each node over time on a single interface, thereby providing intuitive and efficient support for management decisions.

[0058] This embodiment transforms complex numerical evaluation results into intuitive visual presentations, enabling rapid understanding and transmission of monitoring information. It significantly improves the efficiency of processing massive amounts of monitoring information, allowing managers to quickly grasp the overall situation, promptly identify anomalies, and support efficient decision-making.

[0059] In summary, this embodiment provides a comprehensive data foundation for monitoring the status of resource early repayment services by acquiring multi-dimensional target data covering business data, negative feedback data, statistical data, and environmental data. For example, in the scenario of loan early repayment, the target data covers various data types such as complaint volume, number of transactions, and interest rate spread. For each business node, the target data is converted into quantitative values ​​of multiple preset status monitoring dimensions, achieving an objective and quantitative description of the operational status of the business node. For each dimension, the quantitative value of the status is mapped to a status level, and the higher the level, the greater the contribution to negative feedback, thereby accurately identifying the key dimensions that lead to negative feedback. For example, the node with the largest change rate in complaint volume is identified as a high-risk node. The status levels of each dimension are converted into scores and weighted to obtain a comprehensive status score, achieving a fusion evaluation of multi-dimensional status. By defining low scores as corresponding to good status, the evaluation results are made more intuitive. Finally, the comprehensive status score is sent to the display platform for graphical encoding to generate a monitoring view, enabling the overall operational status of a large number of business nodes to be presented in real time and intuitively, thereby significantly improving the efficiency of monitoring resource early repayment services, such as loan early repayment services, and the accuracy of anomaly identification.

[0060] In one possible implementation, S102 may include the following steps: Based on business data, determine the state quantification value of business nodes in terms of business load and business node state quantification value in terms of business processing efficiency.

[0061] Based on negative feedback data, determine the quantitative value of the business node's status in the dimension of business processing satisfaction.

[0062] Based on statistical data, determine the quantitative values ​​of the business nodes in terms of business cost differences and user return capabilities.

[0063] Based on environmental data, determine the quantitative value of the business node's status in the dimension of business competitive pressure.

[0064] For example, this embodiment establishes a precise mapping relationship between data and dimensions, systematically transforming four types of target data into quantitative indicators of six core monitoring dimensions.

[0065] This embodiment targets four types of data, each corresponding to one of six specific status monitoring dimensions, establishing a clear data-dimensional mapping relationship. For example, business data is mapped to the business load and efficiency dimensions, negative feedback data to the satisfaction dimension, statistical data to the cost difference and user return capability dimensions, and environmental data to the competitive pressure dimension. This approach ensures that each monitoring dimension is supported by targeted data sources, enabling status assessment to comprehensively cover key aspects and improving the representativeness of status quantification values ​​and the comprehensiveness of assessment results.

[0066] The business data includes the volume of business requests for resource early return services in the current and previous statistical periods, as well as the processing latency of resource early return services within multiple consecutive time periods. Based on the business data, the state quantification values ​​of business nodes in terms of business load and business node state quantification values ​​in terms of business processing efficiency are determined, including: Based on the business request volume data, calculate the rate of change of business request volume in the current statistical period relative to the previous statistical period, and use it as the state quantification value of the business node in terms of business load.

[0067] The maximum value among the processing delays of resource early return services within multiple consecutive time periods is used as the state quantification value of the business node in the dimension of business processing efficiency.

[0068] In the scenario of early repayment of mortgage loans, the change rate of business request volume can be obtained by calculating the month-on-month growth rate of the number of early repayment applications this week compared to the number of applications last week. For example, a growth rate of more than 10% indicates a significant increase in business load.

[0069] The processing delay duration can be calculated by taking the maximum number of delay days between the customer's scheduled repayment date and the actual processing date within the past three days (the current day, the previous day, and the two days prior), thus revealing information about processing efficiency bottlenecks.

[0070] This embodiment captures the fluctuation trend of business volume through the rate of change and quantifies the processing efficiency through the maximum value of business processing delay, so that the state quantification value can sensitively reflect the load pressure and processing capacity of the business node.

[0071] This embodiment uses the rate of change in business request volume as a quantitative value for the business load dimension, which can sensitively capture abnormal fluctuations in business volume, such as a sudden increase in early repayment business volume. By using the maximum processing latency as a quantitative value for the processing efficiency dimension, it can effectively expose performance bottlenecks in the business processing flow, such as repayment processing delays. These two calculation methods quantify from two key perspectives—change trends and extreme performance—making the status assessment more targeted and providing more early warning value.

[0072] In one example, negative feedback data includes the number of negative customer feedback received through multiple data receiving channels during the resource early return business execution process in the current statistical period, and the number of negative customer feedback received through multiple data receiving channels during the resource early return business execution process in the previous statistical period. Based on the negative feedback data, a quantitative value for the business node's status in the business processing satisfaction dimension is determined, including: Based on preset weighting coefficients, the number of negative feedback received by each data receiving channel in the current statistical period is weighted and summed to determine the first comprehensive number of negative feedback in the current statistical period.

[0073] Based on preset path weighting coefficients, the number of negative feedback received by each data receiving path in the previous statistical period is weighted and summed to determine the second comprehensive negative feedback quantity in the previous statistical period.

[0074] Determine the rate of change of the first comprehensive negative feedback quantity relative to the second comprehensive negative feedback quantity, and use the rate of change as the quantitative value of the business node's status in the dimension of business processing satisfaction.

[0075] For example, a business node can receive negative customer feedback data through multiple data receiving channels, such as web pages, telephone calls, and emails. Different channels can be preset with a corresponding weight coefficient.

[0076] In the scenario of monitoring complaints about early mortgage repayments, business nodes receive customer complaints through regulatory channels, green channels, and customer service hotlines. This embodiment sets the regulatory weight at 40%, the green channel weight at 30%, and the customer service hotline weight at 30%. Based on this, the total number of negative feedbacks this week is calculated as: (Number of regulatory complaints × 40%) + (Number of green channel complaints × 30%) + (Number of customer service hotline complaints × 30%). The same method is used to calculate the total number of negative feedbacks last week, and then the week-on-week change rate is calculated as the status quantification value. For example, if a branch's total number of negative feedbacks this week is 85, and last week it was 70, then the change rate is 21.4%.

[0077] This embodiment calculates a comprehensive negative feedback quantity that more accurately reflects the severity of the problem by assigning weights to the number of negative feedback items from different data reception channels and performing a weighted summation. For example, in a loan prepayment scenario, different weights are assigned to regulatory complaints, green channel complaints, and customer service hotline complaints. Furthermore, the rate of change in the comprehensive negative feedback quantity over adjacent statistical periods is calculated as a quantitative value for the business processing satisfaction dimension. This method not only considers the absolute quantity of negative feedback but also emphasizes its changing trend, enabling the satisfaction dimension assessment to provide earlier and more sensitive warnings of potential service quality risks.

[0078] In one feasible implementation, the statistical data includes agreed-upon fee rate data for existing services, agreed-upon fee rate data for new services, and the average return capability value of customer groups within the service nodes. Based on the statistical data, the status quantification value of the service nodes in terms of business cost difference and user return capability is determined, including: Determine the difference between the agreed fee rate data for existing services and the agreed fee rate data for new services, and use the difference as a quantitative value of the status of the business node in the dimension of business cost difference.

[0079] The average return capability value is used as a quantitative value of the business node's status in the dimension of user return capability.

[0080] For example, in the scenario of monitoring complaints about early repayment of mortgage loans, the state quantification value of the business cost difference dimension can be obtained by calculating the interest rate spread between the weighted average interest rate of existing mortgage loans and the weighted average interest rate of new mortgage loans.

[0081] For example, if the interest rate for existing mortgage loans at a certain branch is 5.2%, and the interest rate for new mortgage loans in the past three months is 4.5%, then the interest rate spread is 0.7%.

[0082] In this embodiment, the average repayment capability value can be the customer's income-to-loan ratio. The customer's income-to-loan ratio is used as a quantitative value of the user's repayment capability dimension. Here, the customer's income-to-loan ratio can be the average or median value of the ratio of income to repayment amount for all customers.

[0083] The environmental data includes the business increment ranking of business nodes; based on the environmental data, the quantitative value of the business node's status in the dimension of business competitive pressure is determined, including: The ranking of business incremental growth is used as a quantitative value of the business node's status in terms of business competitive pressure.

[0084] For example, in the scenario of monitoring complaints about early repayment of mortgage loans, the business growth ranking can be the ranking of the business growth of this branch compared with other banks, where the business growth can be the number and amount of new mortgage loans.

[0085] This embodiment uses the interest rate spread between existing and new mortgage loans as a quantitative value for the business cost difference dimension. For example, the interest rate spread between historical mortgage rates and newly issued mortgage rates can directly expose the risk of a surge in customer prepayment demand due to the widening interest rate spread. Such a surge in demand in the short term can greatly impact the branch's processing capacity, and if the response is not timely, it can easily lead to customer complaints. This embodiment also uses the customer's income-to-loan ratio as a quantitative value for the user's repayment ability dimension. For example, the lower the repayment ratio, the stronger the customer's repayment ability, and the higher the willingness to prepay may be. This can identify branches with a high concentration of high-potential prepayment customers. These customers have higher requirements for the convenience and timeliness of the repayment process. If the branch's processing process is lengthy, it is more likely to cause dissatisfaction and complaints. By using the branch's ranking in new mortgage loans in the local market as a quantitative value for the business competitive pressure dimension, for example, branches with lower rankings can assess their market situation. Branches with poor rankings may set obstacles or handle prepayment business passively for fear of losing customers. Such strategic delays will directly lead to a decline in customer experience and generate complaints. These quantitative methods accurately capture the core business drivers behind loan prepayment complaints, enabling the status assessment results to proactively warn of complaint risks and provide crucial and specific insights for management intervention.

[0086] In one feasible implementation, S103 may include the following steps: For each preset status monitoring dimension, the status quantification values ​​of each business node under the preset status monitoring dimension are sorted according to the sorting rules corresponding to the preset status monitoring dimension to determine the ordinal position of the status quantification value of each business node; and based on the preset second mapping relationship and the ordinal position of the status quantification value of each business node, the status level of each business node under the preset status monitoring dimension is determined; the second mapping relationship is the mapping relationship between the ordinal position of the status quantification value and the status level; the higher the status level, the higher the contribution of the preset status monitoring dimension to the negative feedback received by the business node and the early return of resources.

[0087] Alternatively, based on the preset third mapping relationship and the state quantification value of each business node under the preset state monitoring dimension, the state level of each business node under the preset state monitoring dimension can be determined; the third mapping relationship is the mapping relationship between the magnitude of the state quantification value corresponding to the preset state monitoring dimension and the state level.

[0088] For example, this embodiment uses a sorting mapping rule for dimensions such as business processing satisfaction, business load, business cost difference, user return capability, and business competitive pressure.

[0089] Taking business processing satisfaction as an example, this embodiment sorts the weekly comprehensive score of multiple, such as 37 first-level branches, from high to low, and then maps the top 10 (about the top 27%) to red (first status level, i.e. high risk level), the middle 17 (about the middle 46%) to yellow (second status level, i.e. medium risk level), and the bottom 10 (about the bottom 27%) to green (third status level, i.e. low risk level).

[0090] As for the processing efficiency dimension, a direct numerical mapping rule can be adopted: if the maximum processing delay of the resource early return business in multiple consecutive time periods is greater than 10 days, it is mapped as a red light; if it is between 6 and 10 days, it is mapped as a yellow light; and if it is less than or equal to 5 days, it is mapped as a green light.

[0091] This embodiment provides two methods for determining state levels: ordinal ordering or direct numerical mapping, making the state level division process standardized and configurable. This method enhances the flexibility and adaptability of the evaluation system, enabling the selection of appropriate mapping methods based on the characteristics of data in different dimensions, while ensuring the objectivity and consistency of state level determination and avoiding subjective judgment bias.

[0092] In one example, in S104 above, the status level may include a first level, a second level, and a third level; the first level is greater than the second level, and the second level is greater than the third level; S204 includes the following steps: if the status level under the preset status monitoring dimension is the first level, then determine the corresponding status score as the first value; If the state level under the preset state monitoring dimension is the second level, then the corresponding state score is determined to be the second value; If the state level under the preset state monitoring dimension is the third level, then the corresponding state score is determined to be the third value; where the first value is greater than the second value, and the second value is greater than the third value.

[0093] In one example, Table 1 is a multi-dimensional scoring and weighting table. Table 1 Multi-dimensional scoring and weighting table

[0094] Referring to Table 1, this embodiment establishes a standard mapping relationship between status levels and status scores. For example, a red light (first level) corresponds to 100 points, a yellow light (second level) corresponds to 50 points, and a green light (third level) corresponds to 0 points.

[0095] For example, if a branch receives a red light for customer complaint volume, it will receive 100 points in that dimension; a yellow light for business processing efficiency, it will receive 50 points; and a green light for user return capability, it will receive 0 points. This linear mapping relationship is simple and clear, facilitating subsequent weighted calculations and result interpretation.

[0096] This embodiment establishes a standardized conversion rule from level to score by defining the correspondence between different state levels and specific numerical scores. This conversion rule is simple and clear, ensuring consistency in the calculation of state scores across different dimensions and nodes, avoiding arbitrariness in the conversion process, and providing a reliable foundation for subsequent weighted calculation of the comprehensive state score.

[0097] Referring to Table 1, this embodiment also sets the weight of the status score corresponding to each dimension based on importance: for example, the business processing satisfaction dimension corresponds to a weight of 30%, the business load dimension corresponds to a weight of 20%, the business processing efficiency dimension corresponds to a weight of 20%, the business cost difference dimension corresponds to a weight of 20%, the user return capability dimension corresponds to a weight of 5%, and the business competition pressure dimension corresponds to a weight of 5%.

[0098] For a specific business node, assuming its status scores across the six dimensions mentioned above are: business processing satisfaction 100 points, business load 50 points, business processing efficiency 0 points, business cost difference 100 points, user return capability 0 points, and business competitive pressure 50 points, then the calculation process for its comprehensive status score is as follows: 100 × 30% + 50 × 20% + 0 × 20% + 100 × 20% + 0 × 5% + 50 × 5% = 30 + 10 + 0 + 20 + 0 + 2.5 = 62.5 points.

[0099] In this embodiment, the higher the overall status score, the worse the overall operating status of the business node, the higher the overall risk of triggering negative feedback such as customer complaints, and the more attention and intervention management personnel need to pay attention to it.

[0100] In one feasible implementation, in the node status monitoring view obtained in S105 above, each business node corresponds to a first display area and a second display area; the first display area is used to display the target data of the business node under multiple preset time dimensions, the status quantification value data of the business node under each preset status monitoring dimension under multiple preset time dimensions, and the change trend information of the target data of the business node within a preset historical period; the second display area is used to display the node status of the subordinate business nodes corresponding to the business node.

[0101] This embodiment can push the comprehensive status score of each business node, the corresponding data, trend information, and the node status of lower-level business nodes to the large-screen visualization platform.

[0102] The display platform uses multi-dimensional graphical encoding based on scores.

[0103] For example, the display platform can sort each business node according to the value of the comprehensive status score to obtain a node sequence, and divide the node sequence into N segments according to a preset ratio to determine the segment to which each business node belongs; N is a positive integer greater than 1.

[0104] Based on the pre-defined mapping relationship between the segment to which the business node belongs and the overall status level, and the segment to which each business node belongs, the overall status level corresponding to each business node is determined.

[0105] The overall status level can include a first level, a second level, and a third level. The higher the status score, the higher the overall status level of the node.

[0106] For example, in this embodiment, the overall status scores of the branches are sorted from high to low, and the top 30 are mapped to red lights (first status level, i.e., high risk level), the middle 40% are mapped to yellow lights (second status level, i.e. medium risk level), and the bottom 30% are mapped to green lights (third status level, i.e. low risk level).

[0107] The risk levels are then mapped using red, yellow, and green colors, and the node locations are marked on a geographic map based on the geographical location of each business node. The first display area shows detailed node data, including trend charts for the past 12 months, quantitative values ​​for each dimension, and historical comparison data. The second display area shows the risk distribution of the node's subordinate units through a tree diagram or hierarchical map.

[0108] The monitoring view supports a variety of interactive operations. For example, when the user places the mouse over the corresponding node in the view, the detailed information of the mortgage prepayment complaint for the corresponding business node can be displayed.

[0109] This embodiment sets up a first display area and a second display area in the node status monitoring view. The first display area shows detailed data, quantitative values, and historical trends of business nodes, such as the complaint trends and detailed indicators of a branch over the past 12 months. The second display area shows the status of its subordinate business nodes, such as showing the status of each branch from the head office's perspective, and the status of each sub-branch from the branch's perspective. This area division allows the monitoring view to provide both in-depth individual analysis and a macro-level structural relationship, improving the view's information capacity and analytical efficiency.

[0110] Figure 2 This is a schematic diagram of the monitoring view generation system provided in an embodiment of this application. The system includes a data processing device 201 and a display device 202. Data processing device 201 is used to perform the following steps: Obtain the target data associated with each business node and the early return of resources business; the target data includes the business data generated by the business node in executing the early return of resources business, the negative feedback data received by the business node in executing the early return of resources business, the statistical data related to the reasons for initiating the early return of resources business, and the environmental data related to the execution strategy of the business node for the early return of resources business.

[0111] For each business node, based on the corresponding target data, determine the state quantification value of the business node under multiple preset state monitoring dimensions; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions.

[0112] For each preset status monitoring dimension, the status quantification value of each business node under the preset status monitoring dimension is mapped to determine the status level of each business node under the preset status monitoring dimension; the higher the status level, the higher the contribution of the preset status monitoring dimension to the negative feedback received by the business node and the early return of resources.

[0113] For each business node, the status level of the business node under each preset status monitoring dimension is converted into a corresponding status score.

[0114] Based on preset weight information, the status scores of business nodes under each preset status monitoring dimension are weighted to obtain the comprehensive status score of the business node. The comprehensive status score represents the overall operational status of the business node related to the early return of resources. The status level is positively correlated with the status score. The lower the comprehensive status score, the better the overall operational status of the business node in executing the early return of resources.

[0115] The overall status score corresponding to each business node is sent to the display device.

[0116] Display device 202 is used to graphically encode the node status of each business node based on the comprehensive status score corresponding to each business node, and generate a node status monitoring view.

[0117] See Figure 3 , Figure 3 This is a schematic diagram of a data processing flow provided in an embodiment of this application, such as... Figure 3 As shown, the data processing device processes the business data generated by the business node in executing the resource early return business, the negative feedback data received by the business node in executing the resource early return business, the statistical data related to the reason for initiating the resource early return business, and the environmental data related to the execution strategy of the business node for the resource early return business. The processed data is then sent to the display device for data display.

[0118] Figure 4 This is a schematic diagram of a monitoring view generation device provided in an embodiment of this application. Figure 4 As shown, the monitoring view generation device includes an acquisition unit 401, a first processing unit 402, a second processing unit 403, a third processing unit 404, and a transmission unit 405.

[0119] The acquisition unit 401 is used to acquire target data associated with the resource early return business for each business node. The target data includes business data generated by the business node in executing the resource early return business, negative feedback data received by the business node in executing the resource early return business, statistical data related to the reason for initiating the resource early return business, and environmental data related to the execution strategy of the business node for the resource early return business.

[0120] The first processing unit 402 is used to determine the state quantification value of each business node under multiple preset state monitoring dimensions based on the target data; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions.

[0121] The second processing unit 403 is used to map the state quantification value of each business node under the preset state monitoring dimension for each preset state monitoring dimension, and determine the state level of each business node under the preset state monitoring dimension; the higher the state level, the higher the contribution of the preset state monitoring dimension to the negative feedback received by the business node and the early return of resources.

[0122] The third processing unit 404 is used to convert the status level of each business node under each preset status monitoring dimension into a corresponding status score for each business node, and to perform weighted processing on the status scores of the business node under each preset status monitoring dimension according to preset weight information to obtain the comprehensive status score of the business node. The comprehensive status score represents the overall operating status of the business node related to the early return of resources. The status level is positively correlated with the status score. The lower the comprehensive status score, the better the overall operating status of the business node in executing the early return of resources.

[0123] The sending unit 405 is used to send the comprehensive status score corresponding to each service node to the preset display platform, so that the preset display platform can graphically encode the node status of each service node based on the comprehensive status score corresponding to each service node and generate a node status monitoring view.

[0124] In other embodiments, the second processing unit 403 is specifically used for: For each preset status monitoring dimension, the status quantification values ​​of each business node under the preset status monitoring dimension are sorted according to the sorting rules corresponding to the preset status monitoring dimension, the ordinal position of the status quantification value corresponding to each business node is determined, and the status level of each business node under the preset status monitoring dimension is determined based on the preset second mapping relationship and the ordinal position of the status quantification value corresponding to each business node; the second mapping relationship is the mapping relationship between the ordinal position of the status quantification value and the status level.

[0125] Alternatively, based on the preset third mapping relationship and the state quantification value of each business node under the preset state monitoring dimension, the state level of each business node under the preset state monitoring dimension can be determined; the third mapping relationship is the mapping relationship between the magnitude of the state quantification value corresponding to the preset state monitoring dimension and the state level.

[0126] In other embodiments, the state levels include a first level, a second level, and a third level; the first level is higher than the second level, and the second level is higher than the third level; the third processing unit 404 is specifically used for: If the state level under the preset state monitoring dimension is the first level, then the corresponding state score is determined to be the first value.

[0127] If the state level under the preset state monitoring dimension is the second level, then the corresponding state score is determined to be the second value.

[0128] If the state level under the preset state monitoring dimension is the third level, then the corresponding state score is determined to be the third value, where the first value is greater than the second value, and the second value is greater than the third value.

[0129] In other embodiments, in the node status monitoring view, each business node has a first display area and a second display area; the first display area is used to display the target data of the business node under multiple preset time dimensions, the status quantification value data of the business node under each preset status monitoring dimension under multiple preset time dimensions, and the change trend information of the target data of the business node within a preset historical period; the second display area is used to display the node status of the subordinate business nodes corresponding to the business node.

[0130] In other embodiments, the first processing unit 402 is specifically used for: Based on business data, determine the state quantification value of business nodes in terms of business load and business node state quantification value in terms of business processing efficiency.

[0131] Based on negative feedback data, determine the quantitative value of the business node's status in the dimension of business processing satisfaction.

[0132] Based on statistical data, determine the quantitative values ​​of the business nodes in terms of business cost differences and user return capabilities.

[0133] Based on environmental data, determine the quantitative value of the business node's status in the dimension of business competitive pressure.

[0134] In other embodiments, the business data includes business request volume data for resource early return services in the current statistical period and the previous statistical period, as well as the processing delay duration of resource early return services in multiple consecutive time periods; the first processing unit 402 is further configured to: Based on the business request volume data, calculate the rate of change of business request volume in the current statistical period relative to the previous statistical period, and use it as the state quantification value of the business node in terms of business load.

[0135] The maximum value among the processing delays of resource early return services within multiple consecutive time periods is used as the state quantification value of the business node in the dimension of business processing efficiency.

[0136] In other embodiments, the negative feedback data includes the number of negative customer feedback received through multiple data receiving channels during the execution of the resource early return service in the current statistical period, and the number of negative customer feedback received through multiple data receiving channels during the execution of the resource early return service in the previous statistical period; the first processing unit 402 is further configured to: Based on preset weighting coefficients, the number of negative feedback received by each data receiving channel in the current statistical period is weighted and summed to determine the first comprehensive number of negative feedback in the current statistical period.

[0137] Based on preset path weighting coefficients, the number of negative feedback received by each data receiving path in the previous statistical period is weighted and summed to determine the second comprehensive negative feedback quantity in the previous statistical period.

[0138] Determine the rate of change of the first comprehensive negative feedback quantity relative to the second comprehensive negative feedback quantity, and use the rate of change as the quantitative value of the business node's status in the dimension of business processing satisfaction.

[0139] In other embodiments, the statistical data includes agreed-upon fee rate data for existing services, agreed-upon fee rate data for new services, and the average return capability value of customer groups in the service nodes; the first processing unit 402 is further configured to: Determine the difference between the agreed fee rate data for existing services and the agreed fee rate data for new services, and use the difference as a quantitative value of the status of the business node in the dimension of business cost difference.

[0140] The average return capability value is used as a quantitative value of the business node's status in the dimension of user return capability.

[0141] Environmental data includes rankings of business increments at business nodes; the first processing unit 402 is also specifically used for: The ranking of business incremental growth is used as a quantitative value of the business node's status in terms of business competitive pressure.

[0142] The monitoring view generation device provided in this application embodiment can execute the method shown in the above method embodiment. Its implementation principle and beneficial effects can be referred to the relevant description in the method embodiment, and will not be repeated here.

[0143] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 5 As shown, the electronic device includes: a memory 501, a transceiver 502, and at least one processor 503.

[0144] The transceiver 502 is used to interact with other devices to send and receive data. For example, in this embodiment, the transceiver 502 can be used to obtain target data associated with the early return of resources for each service node, and send the comprehensive status score corresponding to each service node to a preset display platform.

[0145] The memory 501 is used to store computer program code, which includes computer instructions. These computer instructions run in the aforementioned electronic device to implement the method shown in the above-described method embodiments. For example, the memory may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, or a USB flash drive, portable hard drive, read-only memory, magnetic disk, or optical disk, etc.

[0146] Processor 503 can be a general-purpose processor, including a Central Processing Unit (CPU), a network processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. Processor 503 can also be other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0147] The memory 501, transceiver 502, and processor 503 are communicatively connected. For example, the memory 501 and transceiver 502 can be connected to the processor 503 via a system bus and communicate with each other. The system bus can be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, an industry standard architecture (ISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the figure, but this does not mean that there is only one bus or one type of bus.

[0148] Optionally, the memory 501 can be either standalone or integrated with the processor 503. When the memory 501 is set up independently, it is connected to the processor 503 via a system bus.

[0149] This application also provides a chip for executing instructions, which is used to execute the technical solution of the monitoring view generation method in the above embodiments.

[0150] This application also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed by a processor, they are used to implement the technical solution of the monitoring view generation method described in the above embodiments. Specifically, when the computer instructions are executed by a processor, the electronic device can perform the technical solution of the monitoring view generation method described in the above embodiments.

[0151] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the monitoring view generation method in the above embodiments.

[0152] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0153] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the computer-readable storage medium can exist as discrete components in an electronic control unit or main control device; this application does not limit this.

[0154] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0155] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0156] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0157] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0158] It should be understood that the steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0159] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for generating a monitoring view, characterized in that, The method includes: Obtain target data associated with each business node and the resource early return service; the target data includes business data generated by the business node in executing the resource early return service, negative feedback data received by the business node in executing the resource early return service, statistical data related to the reason for initiating the resource early return service, and environmental data related to the execution strategy of the business node for the resource early return service. For each business node, based on the target data, a state quantification value for the business node under multiple preset state monitoring dimensions is determined; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions. For each preset status monitoring dimension, the status quantification value of each business node under the preset status monitoring dimension is mapped to determine the status level of each business node under the preset status monitoring dimension; the higher the status level, the higher the contribution of the preset status monitoring dimension to the negative feedback received by the business node related to the early return of resources. For each business node, the status level of the business node under each preset status monitoring dimension is converted into a corresponding status score. Based on preset weight information, the status scores of the business node under each preset status monitoring dimension are weighted to obtain a comprehensive status score for the business node. The comprehensive status score represents the overall operational status of the business node related to the early resource return service. The status level is positively correlated with the status score. The lower the comprehensive status score, the better the overall operational status of the business node in executing the early resource return service. The comprehensive status score corresponding to each business node is sent to the preset display platform, so that the preset display platform can graphically encode the node status of each business node based on the comprehensive status score corresponding to each business node, and generate a node status monitoring view.

2. The monitoring view generation method according to claim 1, characterized in that, For each preset state monitoring dimension, the process of mapping the state quantification value of each business node under the preset state monitoring dimension to determine the state level of each business node under the preset state monitoring dimension includes: For each preset state monitoring dimension, the state quantification values ​​of each business node under the preset state monitoring dimension are sorted according to the sorting rules corresponding to the preset state monitoring dimension to determine the ordinal position of the state quantification value of each business node. Based on the preset second mapping relationship and the ordinal position of the state quantification value of each business node, the state level of each business node under the preset state monitoring dimension is determined. The second mapping relationship is the mapping relationship between the ordinal position of the state quantification value and the state level. Alternatively, based on a preset third mapping relationship and the state quantification value of each business node under the preset state monitoring dimension, the state level of each business node under the preset state monitoring dimension is determined; the third mapping relationship is the mapping relationship between the magnitude of the state quantification value corresponding to the preset state monitoring dimension and the state level.

3. The monitoring view generation method according to claim 2, characterized in that, The status levels include a first level, a second level, and a third level; the first level is higher than the second level, and the second level is higher than the third level. The step of converting the status level of the business node under each preset status monitoring dimension into a corresponding status score includes: If the state level under the preset state monitoring dimension is the first level, then the corresponding state score is determined to be the first value; If the state level under the preset state monitoring dimension is the second level, then the corresponding state score is determined to be the second value; If the state level under the preset state monitoring dimension is the third level, then the corresponding state score is determined to be the third value; wherein, the first value is greater than the second value, and the second value is greater than the third value.

4. The monitoring view generation method according to claim 1, characterized in that, In the node status monitoring view, each business node has a first display area and a second display area. The first display area is used to display the target data of the business node under multiple preset time dimensions, the status quantification value data of the business node under each preset status monitoring dimension under multiple preset time dimensions, and the change trend information of the target data of the business node within a preset historical period. The second display area is used to display the node status of the subordinate business nodes corresponding to the business node.

5. The monitoring view generation method according to any one of claims 1-4, characterized in that, The step of determining the state quantification value of the business node under multiple preset state monitoring dimensions based on the target data includes: Based on the business data, determine the state quantification value of the business node in the business load dimension and the state quantification value of the business node in the business processing efficiency dimension. Based on the negative feedback data, determine the quantitative value of the business node's status in the dimension of business processing satisfaction; Based on the statistical data, determine the state quantification value of the business node in the dimension of business cost difference and the state quantification value in the dimension of user return capability; Based on the environmental data, determine the state quantification value of the business node in the dimension of business competition pressure.

6. The monitoring view generation method according to claim 5, characterized in that, The business data includes the number of business requests for the resource early return business in the current statistical period and the previous statistical period, as well as the processing delay time of the resource early return business in multiple consecutive time periods. The step of determining the state quantification value of the business node in the business load dimension and the state quantification value of the business node in the business processing efficiency dimension based on the business data includes: Based on the business request volume data, calculate the rate of change of business request volume in the current statistical period relative to the previous statistical period, and use it as the state quantification value of the business node in the business load dimension. The maximum value among the processing delays of the resource early return service within multiple consecutive time periods is used as the state quantification value of the service node in the dimension of service processing efficiency.

7. The monitoring view generation method according to claim 5, characterized in that, The negative feedback data includes the number of negative customer feedback received through multiple data receiving channels during the execution of the resource early return service in the current statistical period, and the number of negative customer feedback received through multiple data receiving channels during the execution of the resource early return service in the previous statistical period. The step of determining the quantitative value of the business node's status in the business processing satisfaction dimension based on the negative feedback data includes: Based on preset weighting coefficients, the number of negative feedback received by each data receiving channel in the current statistical period is weighted and summed to determine the first comprehensive number of negative feedback in the current statistical period. Based on the preset path weighting coefficient, the number of negative feedback received by each data receiving path in the previous statistical period is weighted and summed to determine the second comprehensive negative feedback number in the previous statistical period. Determine the rate of change of the first comprehensive negative feedback quantity relative to the second comprehensive negative feedback quantity, and use the rate of change as the state quantification value of the business node in the dimension of business processing satisfaction.

8. The monitoring view generation method according to claim 5, characterized in that, The statistical data includes the agreed fee rate data for existing business, the agreed fee rate data for new business, and the average return capability value of the customer group in the business node. The step of determining the state quantification value of the business node in the dimension of business cost difference and the state quantification value in the dimension of user return capability based on the statistical data includes: Determine the difference between the agreed fee rate data of the existing business and the agreed fee rate data of the new business, and use the difference as the state quantification value of the business node in the dimension of business cost difference; The average return capability value is used as the quantified value of the status of the business node in the dimension of user return capability. The environmental data includes the business increment ranking of business nodes; determining the state quantification value of the business node in the dimension of business competition pressure based on the environmental data includes: The ranking of business increments is used as the state quantification value of the business node in the dimension of business competition pressure.

9. A monitoring view generation system, characterized in that, The system includes data processing equipment and display equipment: The data processing device is used to perform the following steps: Obtain target data associated with each business node and the resource early return service; the target data includes business data generated by the business node in executing the resource early return service, negative feedback data received by the business node in executing the resource early return service, statistical data related to the reason for initiating the resource early return service, and environmental data related to the execution strategy of the business node for the resource early return service. For each business node, based on the corresponding target data, the state quantification value of the business node under multiple preset state monitoring dimensions is determined; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions. For each preset status monitoring dimension, the status quantification value of each business node under the preset status monitoring dimension is mapped to determine the status level of each business node under the preset status monitoring dimension; the higher the status level, the higher the contribution of the preset status monitoring dimension to the negative feedback received by the business node related to the early return of resources. For each business node, the status level of the business node under each preset status monitoring dimension is converted into a corresponding status score. Based on preset weight information, the status scores of the business node under each preset status monitoring dimension are weighted to obtain the comprehensive status score of the business node. The comprehensive status score represents the overall operational status of the business node related to the early return of resources; the status level is positively correlated with the status score; the lower the comprehensive status score, the better the overall operational status of the business node in executing the early return of resources. The comprehensive status score corresponding to each business node is sent to the display device; The display device is used to graphically encode the node status of each business node based on the comprehensive status score corresponding to each business node, and generate a node status monitoring view.

10. A monitoring view generation device, characterized in that, The device includes: The acquisition unit is used to acquire target data associated with the resource early return service for each business node; the target data includes business data generated by the business node in executing the resource early return service, negative feedback data received by the business node in executing the resource early return service, statistical data related to the reason for initiating the resource early return service, and environmental data related to the execution strategy of the business node for the resource early return service. The first processing unit is used to determine the state quantification value of each business node under multiple preset state monitoring dimensions based on the target data; the magnitude of the state quantification value is related to the quality of the operating state under the preset state monitoring dimensions. The second processing unit is used to map the state quantification value of each business node under each preset state monitoring dimension to determine the state level of each business node under the preset state monitoring dimension; the higher the state level, the higher the contribution of the preset state monitoring dimension to the negative feedback received by the business node related to the early return of resources. The third processing unit is used to convert the status level of each business node under each preset status monitoring dimension into a corresponding status score, and to perform weighted processing on the status scores of the business node under each preset status monitoring dimension according to preset weight information to obtain the comprehensive status score of the business node; the comprehensive status score represents the overall operating status of the business node related to the resource early return business; the status level is positively correlated with the status score; the lower the comprehensive status score, the better the overall operating status of the business node in performing the resource early return business. The sending unit is used to send the comprehensive status score corresponding to each service node to the preset display platform, so that the preset display platform can graphically encode the node status of each service node based on the comprehensive status score corresponding to each service node and generate a node status monitoring view.

11. An electronic device, characterized in that, include: A transceiver, a memory, and at least one processor; the memory is communicatively connected to the processor; the memory is used to store computer program code, the computer program code including computer instructions; when the processor executes the computer instructions, the electronic device causes the electronic device to perform the monitoring view generation method as described in any one of claims 1-8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, are used to implement the monitoring view generation method as described in any one of claims 1-8.

13. A computer program product, characterized in that, When the computer program product is run on a computer / executed by the computer's processor, the monitoring view generation method as described in any one of claims 1-8 is implemented.

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