Business supervision method and device and computer equipment

Abstract

The invention relates to a business supervision method and device and computer equipment. The method comprises the steps of performing attribute clustering processing on a plurality of to-be-supervised enterprises according to enterprise data of the plurality of to-be-supervised enterprises to obtain a clustering result, determining supervision priorities of the to-be-supervised enterprises according to the clustering result under the condition that a measurement index value of the clustering result reaches the standard, and determining the supervision priorities of the to-be-supervised enterprises according to the clustering result and the supervision priorities. And supervising the target resource business of each enterprise to be supervised. By adopting the method, the supervision priority of each to-be-supervised enterprise can be determined according to the clustering result under the condition that the measurement index value of the attribute clustering result of the plurality of to-be-supervised enterprises reaches the standard, so that the determined supervision priority of each to-be-supervised enterprise is more accurate; and furthermore, the target resource business of each to-be-supervised enterprise is effectively supervised based on the obtained supervision priorities of the different to-be-supervised enterprises, so that the timeliness of supervising the target resource business of the different to-be-supervised enterprises can be improved.

Description

Technical Field

[0001] The present application relates to the field of business management technology, and in particular to a business supervision method, device and computer equipment. Background Art

[0002] With the development of the Internet, big data technology has been widely used in various types of corporate businesses, such as finance, communications, e-commerce, construction and other businesses.

[0003] Taking financial business as an example, the financial business of each enterprise may have certain risks. Therefore, in order to reduce the risk rate, it is particularly important to supervise the financial services of each enterprise. In related technologies, the financial business of each enterprise is mainly supervised through the financial big data of each enterprise to improve the supervision ability of financial business.

[0004] However, in the related technologies, there is a problem of untimely supervision when supervising the financial business of each enterprise. Summary of the invention

[0005] Based on this, it is necessary to provide a business supervision method, device and computer equipment to address the above technical issues, so as to timely supervise the financial business of each enterprise.

[0006] In a first aspect, an embodiment of the present application provides a service supervision method, the method comprising:

[0007] According to the enterprise data of multiple enterprises to be regulated, attribute clustering processing is performed on the multiple enterprises to be regulated to obtain clustering results;

[0008] When the measurement index value of the clustering result meets the standard, the supervision priority of each enterprise to be supervised is determined according to the clustering result;

[0009] Based on the clustering results and various regulatory priorities, the target resource business of each enterprise to be regulated is supervised.

[0010] The technical solution in the embodiment of the present application performs attribute clustering processing on multiple enterprises to be regulated based on the enterprise data of multiple enterprises to be regulated to obtain clustering results. When the measurement index value of the clustering result meets the standard, the supervision priority of each enterprise to be regulated is determined according to the clustering result, and the target resource business of each enterprise to be regulated is supervised according to the clustering result and the supervision priority. The above method can determine the supervision priority of each enterprise to be regulated according to the clustering result only when the measurement index value of the attribute clustering result of multiple enterprises to be regulated meets the standard, so that the determined supervision priority of each enterprise to be regulated is more accurate, and further supervises each enterprise to be regulated based on the obtained supervision priorities of different enterprises to be regulated. Effective supervision of target resource businesses can improve the timeliness of supervision of target resource businesses of different regulated enterprises; in addition, the above method does not require additional operations, and can promptly handle the target resource businesses of different regulated enterprises based on the supervision priorities of different regulated enterprises, thereby reducing the cost of supervision of target resource businesses; furthermore, the above method can also conduct real-time supervision of the target resource businesses of each regulated enterprise according to the supervision priorities of different regulated enterprises, and can prevent risks from occurring in each regulated enterprise in advance, so as to reduce the risk rate of risks occurring in each regulated enterprise; at the same time, the above method has no limit on the range of changes in enterprise data, thereby increasing the application scenarios of target resource business supervision.

[0011] In one embodiment, based on the enterprise data of the multiple enterprises to be regulated, attribute clustering processing is performed on the multiple enterprises to be regulated to obtain clustering results, including:

[0012] The enterprise data of multiple enterprises to be regulated are input into the second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results.

[0013] The technical solution in the embodiment of the present application inputs the enterprise data of multiple enterprises to be regulated into a second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results; the above method can perform attribute clustering processing on multiple enterprises to be regulated to obtain clustering results, so as to provide reference information for the subsequent accurate determination of the regulatory priority of each enterprise to be regulated; at the same time, the second-order clustering model in the above method can be suitable for scenarios with large amounts of data. The above process realizes attribute clustering processing through the second-order clustering model, which can not only improve the efficiency of attribute clustering processing, but also the second-order clustering model is more robust to data, which can improve the accuracy of clustering results.

[0014] In one embodiment, the enterprise data of multiple enterprises to be regulated are input into a second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results, including:

[0015] Based on multiple enterprise operation evaluation indicators, the enterprise data of each enterprise to be regulated is clustered to obtain the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators;

[0016] Based on multiple different enterprise attribute parameters, the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators are subjected to secondary clustering processing to obtain the clustering results.

[0017] The technical solution in the embodiment of the present application performs a clustering process on the enterprise data of each enterprise to be regulated based on multiple enterprise operation evaluation indicators to obtain indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators, and performs a secondary clustering process on the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators based on multiple different enterprise attribute parameters to obtain clustering results; the above method simplifies complex problems through two clustering processes, and can provide reference information for the subsequent accurate determination of the supervision priority of each enterprise to be regulated; at the same time, the above method does not require the participation of complex algorithms, and the processing process is relatively simple, thereby increasing the complexity of the second-order clustering process, and improving the speed and efficiency of the second-order clustering process; in addition, the above method can flexibly adjust the enterprise operation evaluation indicators and enterprise attribute parameters, so that the flexibility of the use scenarios of attribute clustering processing is higher.

[0018] In one embodiment, the method further comprises:

[0019] Obtaining a measurement index value of a clustering result; the measurement index value includes a performance measurement index value of a clustering model used in attribute clustering processing and / or a quality measurement index value of a clustering result;

[0020] If the performance metric value is less than a first preset threshold, and / or the quality metric value is greater than a second preset threshold, it is determined that the metric value of the clustering result meets the standard.

[0021] The technical solution in the embodiment of the present application obtains the performance measurement index value of the clustering model used for attribute clustering processing and / or the quality measurement index value of the clustering result. If the performance measurement index value is less than a first preset threshold, and / or the quality measurement index value is greater than a second preset threshold, then it is determined that the measurement index value of the clustering result meets the standard. The above method does not require the participation of complex algorithms, and the processing process is relatively simple. It can increase the speed of determining whether the measurement index value of the clustering result meets the standard, and on this basis, it can provide a reference for the subsequent accurate determination of the regulatory priority of each enterprise to be regulated.

[0022] In one embodiment, the clustering result includes multiple clustering categories; determining the supervision priority of each enterprise to be supervised according to the clustering result includes:

[0023] Get the number of enterprises with at least one enterprise to be regulated in each cluster category;

[0024] Sort the number of each enterprise to obtain the sorting result;

[0025] Based on the ranking results, the regulatory priority of each enterprise to be regulated is determined; among them, the regulatory priorities of different enterprises to be regulated in the same cluster category are the same.

[0026] The technical solution in the embodiment of the present application obtains the number of at least one enterprise to be regulated in each clustering category in the clustering results, sorts the number of each enterprise to obtain a sorting result, and determines the regulatory priority of each enterprise to be regulated according to the sorting result, wherein the regulatory priority of different enterprises to be regulated in the same clustering category is the same; the above method does not require the participation of complex algorithms, the processing process is relatively simple, and can improve the speed of determining the regulatory priority of each enterprise to be regulated, and on this basis, can provide a reference for the subsequent timely supervision of the target resource business of each enterprise to be regulated.

[0027] In one embodiment, the regulatory priority of each enterprise to be regulated is determined according to the ranking result, including:

[0028] According to the order of the number of enterprises in the sorting results from large to small, the corresponding enterprises to be regulated are assigned regulatory priorities from high to low.

[0029] The technical solution in this embodiment assigns regulatory priorities from high to low to the corresponding enterprises to be regulated in the order of the number of enterprises in the sorting results from large to small; the processing process in the above method is relatively simple and requires human participation, which can improve the speed and efficiency of assigning regulatory priorities, and can also reduce human participation errors and improve the accuracy of the assigned regulatory priorities.

[0030] In one embodiment, according to the clustering results and the supervision priorities, the target resource business of each enterprise to be supervised is supervised, including:

[0031] The target resource business of each regulated enterprise shall be supervised in order from high to low regulatory priority.

[0032] The technical solution in the embodiment of the present application supervises the target resource business of each enterprise to be supervised in order of supervision priority from high to low; the above method can supervise the target resource business of each enterprise to be supervised in a timely and effective manner according to the supervision priority of each enterprise to be supervised.

[0033] In one embodiment, before performing attribute clustering processing on the multiple enterprises to be regulated based on the enterprise data of the multiple enterprises to be regulated, the method further includes:

[0034] Obtaining original enterprise data of multiple enterprises to be regulated;

[0035] Preprocessing is performed on a plurality of original enterprise data to obtain enterprise data of a plurality of enterprises to be supervised; the preprocessing includes at least one of abnormal data compensation processing, data type conversion and normalization processing.

[0036] The technical solution in the embodiment of the present application obtains original enterprise data of multiple enterprises to be regulated, pre-processes the multiple original enterprise data, and obtains enterprise data of multiple enterprises to be regulated, wherein the pre-processing includes at least one of abnormal data compensation processing, data type conversion and normalization processing; the above method can obtain the enterprise data obtained after pre-processing to provide dependency information for the subsequent precise implementation of attribute clustering data; at the same time, the pre-processing process in the above method does not require the participation of complex algorithms, thereby reducing the complexity of pre-processing and improving the accuracy of pre-processing results.

[0037] In a second aspect, an embodiment of the present application provides a service supervision device, the device comprising:

[0038] A clustering processing module is used to perform attribute clustering processing on multiple enterprises to be regulated based on their enterprise data to obtain clustering results;

[0039] A priority determination module is used to determine the supervision priority of each enterprise to be supervised according to the clustering results when the measurement index value of the clustering results meets the standard;

[0040] The supervision module is used to supervise the target resource business of each enterprise to be supervised according to the clustering results and each supervision priority.

[0041] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of any one of the embodiments of the first aspect when executing the computer program.

[0042] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method of any embodiment in the above-mentioned first aspect are implemented.

[0043] In a fifth aspect, an embodiment of the present application further provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method of any embodiment of the first aspect above.

[0044] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 An application environment diagram of a business supervision method in an embodiment;

[0046] Figure 2 A flowchart of a business supervision method in an embodiment;

[0047] Figure 3 A flowchart of a business supervision method in another embodiment;

[0048] Figure 4 A flowchart of a business supervision method in another embodiment;

[0049] Figure 5 A flowchart of a business supervision method in another embodiment;

[0050] Figure 6 A flowchart of a business supervision method in another embodiment;

[0051] Figure 7 is a structural block diagram of a service supervision device in an embodiment;

[0052] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] In the financial field, the financial business of each enterprise may have certain risks. Therefore, in order to reduce the risk incidence, it is particularly important to supervise the financial services of each enterprise. In the related technology, the financial business of each enterprise is mainly supervised through the financial big data of each enterprise to improve the financial business supervision ability. However, in the related technology, there is a problem of untimely supervision when supervising the financial business of each enterprise. Based on this, the embodiment of the present application provides a business supervision method, which can supervise the financial business of each enterprise in a timely manner and improve the effect of financial business supervision.

[0055] The service supervision method provided in the embodiment of the present application can be applied to Figure 1The business supervision system shown. The business supervision system includes computer equipment and management equipment for the financial data of each enterprise. Optionally, the computer equipment and each management system can be connected by communication, and the communication method can be Wi-Fi, mobile network or Bluetooth connection. Optionally, the computer equipment can be but not limited to personal computers, desktop computers, laptop computers, iPads and other electronic devices with data processing functions, and each management device can be but not limited to personal computers, desktop computers, laptop computers, iPads, databases and other electronic devices with data storage functions. Among them, Figure 1 In the example, a business supervision system includes three management devices (management device 1, management device 2 and management device 3). In the following, the embodiment of the present application takes a computer device as the execution subject of the business supervision method to introduce the specific process of the business supervision method.

[0056] like Figure 2 FIG. 1 is a flow chart of a service supervision method provided in an embodiment of the present application, and the method can be implemented by the following steps:

[0057] S100. Perform attribute clustering processing on the multiple enterprises to be regulated based on the enterprise data of the multiple enterprises to be regulated to obtain clustering results.

[0058] In practical applications, the above-mentioned enterprises to be regulated may be enterprises of any category, such as communication enterprises, transportation enterprises, construction enterprises, medical and health enterprises, wholesale and retail enterprises, financial-related enterprises, etc. Among them, the computer device may obtain the enterprise data of each enterprise to be regulated in the historical time period. Optionally, the above-mentioned period of time may be the time corresponding to the current time and a continuous period of time before the current time.

[0059] Optionally, the enterprise data of the above-mentioned enterprises to be regulated may include the employee insurance information, credit information, various sub-information on qualifications such as business licenses, case information such as disputes, flow and transaction information, tax credit information and recognition information of the enterprises to be regulated; the above-mentioned employee insurance information may be but not limited to the employee's name, ID number, insurance information, insurance time, insurance status, insurance institution, and unified social credit code of the insurance institution; the above-mentioned credit information may be but not limited to the unified credit code, credit rating, credit score, penalty information, etc. of the enterprise to be regulated; the above-mentioned various sub-information on qualifications such as business licenses may be but not limited to business licenses, business scope, business hours, and unified social credit codes; the above-mentioned case information such as disputes may be but not limited to dispute records of financial cases in the enterprises to be regulated, court prosecutions, administrative penalties, case types, and penalty information; the above-mentioned recognition information may be but not limited to enterprise type, business scope, and unified social credit code.

[0060] In practical applications, the computer device can perform attribute clustering processing on multiple enterprises to be regulated based on the enterprise data of multiple enterprises to be regulated to obtain clustering results. Optionally, the attributes of the above-mentioned enterprises to be regulated can be business scope, enterprise type, business industry type, development stage, etc. In the embodiment of the present application, the above-mentioned clustering results can be represented in the form of a summary graph.

[0061] In one embodiment, a computer device may pre-train an algorithm model, and then input the enterprise data of each enterprise to be regulated into the algorithm model. The algorithm model performs attribute clustering processing on each enterprise to be regulated based on the enterprise data of each enterprise to be regulated and outputs a clustering result.

[0062] In another embodiment, the computer device may perform matching processing, comparison processing, classification processing and other operations based on the enterprise data of each enterprise to be regulated, so as to complete attribute clustering processing of each enterprise to be regulated and obtain clustering results.

[0063] S200. When the measurement index value of the clustering result meets the standard, the supervision priority of each enterprise to be supervised is determined according to the clustering result.

[0064] Among them, the achievement of the measurement index value of the above clustering result can be understood as the number of clustering categories of the clustering result being equal to the target number.

[0065] Specifically, when the measurement index value of the above clustering results meets the standard, the supervision priority of each enterprise to be regulated can be determined according to the clustering results.

[0066] In one embodiment, the clustering results may be subjected to analysis, sorting, and / or comparison to obtain the supervision priority of each enterprise to be supervised.

[0067] S300: supervise the target resource business of each enterprise to be supervised according to the clustering results and each supervision priority.

[0068] In practical applications, the computer device can pre-train a business supervision model, and then input the clustering results and each supervision priority into the business supervision model, and the business supervision model supervises the target resource business of each enterprise to be supervised in turn to improve the timeliness of supervising the target resource business of different enterprises to be supervised. In the embodiment of the present application, the above-mentioned target resource business can be a financial business.

[0069] The technical solution in the embodiment of the present application performs attribute clustering processing on multiple enterprises to be regulated based on the enterprise data of multiple enterprises to be regulated to obtain clustering results. When the measurement index value of the clustering result meets the standard, the supervision priority of each enterprise to be regulated is determined according to the clustering result, and the target resource business of each enterprise to be regulated is supervised according to the clustering result and the supervision priority. The above method can determine the supervision priority of each enterprise to be regulated according to the clustering result only when the measurement index value of the attribute clustering result of multiple enterprises to be regulated meets the standard, so that the determined supervision priority of each enterprise to be regulated is more accurate, and further supervises each enterprise to be regulated based on the obtained supervision priorities of different enterprises to be regulated. Effective supervision of target resource businesses can improve the timeliness of supervision of target resource businesses of different regulated enterprises; in addition, the above method does not require additional operations, and can promptly handle the target resource businesses of different regulated enterprises based on the supervision priorities of different regulated enterprises, thereby reducing the cost of supervision of target resource businesses; furthermore, the above method can also conduct real-time supervision of the target resource businesses of each regulated enterprise according to the supervision priorities of different regulated enterprises, and can prevent risks from occurring in each regulated enterprise in advance, so as to reduce the risk rate of risks occurring in each regulated enterprise; at the same time, the above method has no limit on the range of changes in enterprise data, thereby increasing the application scenarios of target resource business supervision.

[0070] The following is an explanation of the process of performing attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results based on the enterprise data of the multiple enterprises to be regulated. In one embodiment, the step in S100 may include: inputting the enterprise data of the multiple enterprises to be regulated into a second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results.

[0071] In an embodiment of the present application, the computer device may input the enterprise data of each enterprise to be regulated into a second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain a clustering result.

[0072] Optionally, the second-order clustering model can be implemented by at least one of a convolutional neural network model, a fully connected neural network model, a recurrent neural network model, a long short-term memory neural network model, and a residual neural network model. In addition, the second-order clustering model can also be an algorithm model constructed based on a second-order clustering algorithm.

[0073] It should be noted here that the above-mentioned second-order clustering model can realize secondary clustering processing, two-step clustering processing or two-layer clustering processing.

[0074] The technical solution in the embodiment of the present application inputs the enterprise data of multiple enterprises to be regulated into a second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results; the above method can perform attribute clustering processing on multiple enterprises to be regulated to obtain clustering results, so as to provide reference information for the subsequent accurate determination of the regulatory priority of each enterprise to be regulated; at the same time, the second-order clustering model in the above method can be suitable for scenarios with large amounts of data. The above process realizes attribute clustering processing through the second-order clustering model, which can not only improve the efficiency of attribute clustering processing, but also the second-order clustering model is more robust to data, which can improve the accuracy of clustering results.

[0075] In one embodiment, if Figure 3 As shown, the step of inputting the enterprise data of multiple enterprises to be regulated into the second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results can be achieved in the following ways:

[0076] S110. Based on multiple enterprise operation evaluation indicators, clustering processing is performed on the enterprise data of each enterprise to be regulated to obtain indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators.

[0077] Among them, the above-mentioned multiple enterprise operation evaluation indicators may include production capacity, market sales capacity, operation-related indicators, etc. In the embodiment of the present application, the above-mentioned multiple enterprise operation evaluation indicators may include indicators such as enterprise service scope, enterprise financial strength, enterprise technical level, enterprise service quality and enterprise risk control ability.

[0078] Specifically, for any enterprise to be regulated, the computer equipment can use the operation evaluation indicators of each enterprise as the clustering object of a clustering, and perform similarity matching, classification, comparison and other processing on the enterprise data of the enterprise to be regulated to complete a clustering process and obtain the indicator data corresponding to different enterprise operation evaluation indicators of the enterprise to be regulated.

[0079] S120. Based on a plurality of different enterprise attribute parameters, secondary clustering processing is performed on the indicator data of different enterprise operation evaluation indicators of each enterprise to be regulated to obtain a clustering result.

[0080] Optionally, the above-mentioned different enterprise attribute parameters may be production capacity utilization, market share, market growth rate, etc. In the embodiment of the present application, the above-mentioned multiple different enterprise attribute parameters may include multiple different enterprise scales.

[0081] In actual applications, computer equipment can use the attribute parameters of each enterprise as the clustering object of secondary clustering, and perform similarity matching, classification, comparison and other processing on the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators, so as to complete the secondary clustering processing and obtain the indicator data of the enterprise to be regulated corresponding to different enterprise operation evaluation indicators.

[0082] The technical solution in the embodiment of the present application performs a clustering process on the enterprise data of each enterprise to be regulated based on multiple enterprise operation evaluation indicators to obtain indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators, and performs a secondary clustering process on the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators based on multiple different enterprise attribute parameters to obtain clustering results; the above method simplifies complex problems through two clustering processes, and can provide reference information for the subsequent accurate determination of the supervision priority of each enterprise to be regulated; at the same time, the above method does not require the participation of complex algorithms, and the processing process is relatively simple, thereby increasing the complexity of the second-order clustering process, and improving the speed and efficiency of the second-order clustering process; in addition, the above method can flexibly adjust the enterprise operation evaluation indicators and enterprise attribute parameters, so that the flexibility of the use scenarios of attribute clustering processing is higher.

[0083] The following is a description of how to determine whether the clustering result's measurement index value meets the standard. In one embodiment, before executing the above step S200, Figure 4 As shown, the above method may further include the following steps:

[0084] S210: Obtain a measurement index value of the clustering result, wherein the measurement index value includes a performance measurement index value of the clustering model used in the attribute clustering process and / or a quality measurement index value of the clustering result.

[0085] In practical applications, the measurement index value of the clustering result may include a performance measurement index value of the clustering model used in the attribute clustering process and / or a quality measurement index value of the clustering result. Optionally, the performance measurement index value may be a Schwarz Bayesian Criterion (BIC) value; and the quality measurement index value may be a silhouette coefficient value.

[0086] Specifically, the computer device may obtain a measurement index value of the clustering result. In one implementation, the number of cluster categories in the clustering result and the expected number of cluster categories may be compared to obtain the measurement index value of the clustering result. In another implementation, the clustering result may be verified, and then the measurement index value of the clustering result may be determined based on the verification result.

[0087] In the embodiment of the present application, after the second-order clustering model performs the attribute clustering process, the performance measurement index value and / or the quality measurement index value can be automatically obtained.

[0088] S220: If the performance measurement indicator value is less than the first preset threshold, and / or the quality measurement indicator value is greater than the second preset threshold, determine that the measurement indicator value of the clustering result meets the standard.

[0089] Specifically, the computer device can determine whether the performance measurement indicator value is less than a first preset threshold, and / or determine whether the quality measurement indicator value is greater than a second preset threshold. When the performance measurement indicator value is less than the first preset threshold, and / or the quality measurement indicator value is greater than the second preset threshold, it can be determined that the measurement indicator value of the clustering result meets the standard.

[0090] Optionally, the first preset threshold and the second preset threshold can be user-defined or determined based on historical experience. In the embodiment of the present application, the smaller the performance measurement index value, the higher the quality of the clustering result; the larger the quality measurement index value, the higher the quality of the clustering result.

[0091] The technical solution in the embodiment of the present application obtains the performance measurement index value of the clustering model used for attribute clustering processing and / or the quality measurement index value of the clustering result. If the performance measurement index value is less than a first preset threshold, and / or the quality measurement index value is greater than a second preset threshold, then it is determined that the measurement index value of the clustering result meets the standard. The above method does not require the participation of complex algorithms, and the processing process is relatively simple. It can increase the speed of determining whether the measurement index value of the clustering result meets the standard, and on this basis, it can provide a reference for the subsequent accurate determination of the regulatory priority of each enterprise to be regulated.

[0092] The following is an explanation of the process of determining the supervision priority of each enterprise to be supervised based on the clustering results. In one embodiment, the clustering results include multiple clustering categories; Figure 5 As shown, the steps in the above S200 may include:

[0093] S210: Obtain the number of at least one enterprise to be regulated in each cluster category.

[0094] In practical applications, the computer device may use a statistical method to count the number of all enterprises to be regulated in each cluster category in the clustering result. Optionally, the number of all enterprises to be regulated in each cluster category may be greater than or equal to 1.

[0095] S220. Sort the number of enterprises to obtain a sorting result.

[0096] Specifically, the computer device may sort the number of enterprises corresponding to each cluster category in an ascending order or a descending order to obtain a sorting result.

[0097] S230: Determine the supervision priority of each enterprise to be supervised according to the ranking result, wherein different enterprises to be supervised in the same cluster category have the same supervision priority.

[0098] In practical applications, computer equipment can pre-train an algorithm model, and then input the sorting results into the algorithm model, which outputs the regulatory priority of each enterprise to be regulated.

[0099] In addition, the computer equipment can also configure the strategy according to the preset priority and determine the supervision priority of each enterprise to be supervised according to the sorting results.

[0100] In one embodiment, the step of determining the supervision priority of each enterprise to be regulated according to the sorting results in the above S230 may include: assigning supervision priorities from high to low to each corresponding enterprise to be regulated in order of the number of enterprises from large to small in the sorting results.

[0101] The processing process of this embodiment is relatively simple and requires human participation, which can improve the speed and efficiency of assigning regulatory priorities, and can also reduce errors caused by human participation and improve the accuracy of assigned regulatory priorities.

[0102] The technical solution in the embodiment of the present application obtains the number of at least one enterprise to be regulated in each clustering category in the clustering results, sorts the number of each enterprise to obtain a sorting result, and determines the regulatory priority of each enterprise to be regulated according to the sorting result, wherein the regulatory priority of different enterprises to be regulated in the same clustering category is the same; the above method does not require the participation of complex algorithms, the processing process is relatively simple, and can improve the speed of determining the regulatory priority of each enterprise to be regulated, and on this basis, can provide a reference for the subsequent timely supervision of the target resource business of each enterprise to be regulated.

[0103] In one embodiment, the step of supervising the target resource business of each enterprise to be supervised according to the clustering results and each supervision priority in the above S300 may include: supervising the target resource business of each enterprise to be supervised in order of each supervision priority from high to low.

[0104] In actual applications, the target resource businesses of the enterprises to be supervised can be supervised in descending order of supervision priority. The supervision order of the target resource businesses of different enterprises to be supervised can be the same or different.

[0105] It should be noted here that when the regulatory priorities of different enterprises to be regulated are the same, the regulatory order of the target resource businesses of these enterprises to be regulated is the same.

[0106] The technical solution in the embodiment of the present application supervises the target resource business of each enterprise to be supervised in order of supervision priority from high to low; the above method can supervise the target resource business of each enterprise to be supervised in a timely and effective manner according to the supervision priority of each enterprise to be supervised.

[0107] In one embodiment, before executing the step in S100, Figure 6 As shown, the above method may further include the following steps:

[0108] S400. Obtain original enterprise data of multiple enterprises to be regulated.

[0109] In actual applications, the computer device can send enterprise data acquisition instructions to the management device of each enterprise to be regulated, and receive the original enterprise data sent by each management device. In addition, the computer device can also receive the original enterprise data sent by the management device of each enterprise to be regulated in real time. At the same time, the computer device can also obtain the original enterprise data of each enterprise to be regulated from storage locations such as local, cloud, disk, hard disk, database, etc. It should be noted here that the above-mentioned original enterprise data and enterprise data are of the same type.

[0110] In addition, the computer equipment may obtain the original enterprise data of each enterprise to be regulated in full or incremental manner.

[0111] S500: Preprocess multiple original enterprise data to obtain enterprise data of multiple enterprises to be supervised, wherein the preprocessing includes at least one of abnormal data compensation processing, data type conversion and normalization processing.

[0112] In practical applications, the above abnormal data compensation processing may include abnormal data elimination processing, missing data filling processing (such as mean, median, mode and other data filling processing), duplicate data deletion processing, etc. Among them, the missing data filling processing may be implemented by collecting other dimensional data to fill the missing data, or by filling the missing data according to the knowledge in this field, and of course, it may also be a manual supplement method or an automatic filling method.

[0113] It should be noted here that the normalization of each original enterprise data is to make the original enterprise data consistent at different scales and prevent inaccurate subsequent processing results.

[0114] In addition, before executing the data type conversion process, it can be determined whether the type of the original enterprise data is numeric or non-numeric, wherein the non-numeric data may include categorical data and non-categorical data (i.e., string data), and the categorical data may include ordinal data and categorical data. Further, when the original enterprise data is non-numeric, the non-numeric original enterprise data may be converted to numeric for subsequent processing.

[0115] Specifically, serial number coding can be used to convert the data type of the ordinal original enterprise data. For example, the enterprise credit rating is divided into three levels: intermediate, advanced, and low. The serial number coding can assign an ID to the ordinal enterprise credit rating according to the size level. If the intermediate level is represented as 1, the intermediate level is represented as 2, and the advanced level is represented as 3, the size of the enterprise credit rating is still retained after the data type conversion. In addition, unique hot coding can be used to convert the data type of the original enterprise data of a certain type. It should be noted that when there are many category values ​​in the original enterprise data, the original enterprise data can be represented by a sparse vector to save space. If the original enterprise data is non-categorical data, the data type conversion can be implemented in a corresponding manner according to different usage scenarios and the advantages and disadvantages of different types of conversion methods.

[0116] The technical solution in the embodiment of the present application obtains original enterprise data of multiple enterprises to be regulated, pre-processes the multiple original enterprise data, and obtains enterprise data of multiple enterprises to be regulated, wherein the pre-processing includes at least one of abnormal data compensation processing, data type conversion and normalization processing; the above method can obtain the enterprise data obtained after pre-processing to provide dependency information for the subsequent precise implementation of attribute clustering data; at the same time, the pre-processing process in the above method does not require the participation of complex algorithms, thereby reducing the complexity of pre-processing and improving the accuracy of pre-processing results.

[0117] In one embodiment, the present application also provides a service supervision method, which is applied to a computer device, and the method includes the following process:

[0118] (1) Obtaining original enterprise data of multiple enterprises to be regulated;

[0119] (2) Preprocessing multiple original enterprise data to obtain enterprise data of multiple enterprises to be regulated; the preprocessing includes at least one of abnormal data compensation processing, data type conversion and normalization processing;

[0120] (3) Based on multiple enterprise operation evaluation indicators, cluster the enterprise data of each enterprise to be regulated to obtain the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators;

[0121] (4) Based on multiple different enterprise attribute parameters, secondary clustering processing is performed on the indicator data of different enterprise operation evaluation indicators of each enterprise to be regulated to obtain clustering results;

[0122] (5) Obtaining measurement index values ​​of clustering results; the measurement index values ​​include performance measurement index values ​​of the clustering model used in attribute clustering processing and / or quality measurement index values ​​of the clustering results;

[0123] (6) If the performance measurement index value is less than the first preset threshold value, and / or the quality measurement index value is greater than the second preset threshold value, it is determined that the measurement index value of the clustering result meets the standard;

[0124] (7) When the measurement index value of the clustering result meets the standard, obtain the number of enterprises that have at least one enterprise to be regulated in each cluster category;

[0125] (8) Sort the number of enterprises and obtain the ranking results;

[0126] (9) Assigning a regulatory priority from high to low to each of the enterprises to be regulated in the descending order of the number of enterprises in the ranking results; however, different enterprises to be regulated in the same cluster category have the same regulatory priority;

[0127] (10) Supervise the target resource business of each regulated enterprise in order of regulatory priority from high to low.

[0128] The execution process of the above (1) to (10) can be specifically referred to the description of the above embodiment. The implementation principle and technical effect are similar and will not be repeated here.

[0129] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0130] Based on the same inventive concept, the embodiment of the present application also provides a service supervision device for implementing the service supervision method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more service supervision device embodiments provided below can refer to the limitations on the service supervision method above, and will not be repeated here.

[0131] In one embodiment, Figure 7 This is a schematic diagram of the structure of a service supervision device in one embodiment of the present application. The service supervision device provided in the embodiment of the present application can be applied to a computer device. Figure 7As shown, the service supervision device of the embodiment of the present application may include: a cluster processing module 11, a priority determination module 12 and a supervision module 13, wherein:

[0132] The clustering processing module 11 is used to perform attribute clustering processing on the multiple enterprises to be regulated based on the enterprise data of the multiple enterprises to be regulated to obtain clustering results;

[0133] The priority determination module 12 is used to determine the supervision priority of each enterprise to be supervised according to the clustering result when the measurement index value of the clustering result meets the standard;

[0134] The supervision module 13 is used to supervise the target resource business of each enterprise to be supervised according to the clustering results and each supervision priority.

[0135] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0136] In one embodiment, the clustering processing module 11 includes: a second-order clustering unit, wherein:

[0137] The second-order clustering unit is used to input the enterprise data of multiple enterprises to be regulated into the second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain clustering results.

[0138] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0139] In one embodiment, the second-order clustering unit is specifically used for:

[0140] Based on multiple enterprise operation evaluation indicators, the enterprise data of each enterprise to be regulated is clustered to obtain the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators;

[0141] Based on multiple different enterprise attribute parameters, the indicator data of each enterprise to be regulated corresponding to different enterprise operation evaluation indicators are subjected to secondary clustering processing to obtain the clustering results.

[0142] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0143] In one embodiment, the service supervision device further includes: an indicator value acquisition module and an indicator value compliance determination module, wherein:

[0144] An index value acquisition module is used to obtain a measurement index value of a clustering result; the measurement index value includes a performance measurement index value of a clustering model used in attribute clustering processing and / or a quality measurement index value of a clustering result;

[0145] The indicator value reaching standard determination module is used to determine whether the indicator value of the clustering result reaches the standard when the performance indicator value is less than a first preset threshold and / or the quality indicator value is greater than a second preset threshold.

[0146] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0147] In one embodiment, the clustering result includes multiple clustering categories; the priority determination module 12 includes: an enterprise quantity acquisition unit, a sorting unit and a priority determination unit, wherein:

[0148] An enterprise quantity acquisition unit, used for acquiring the enterprise quantity of at least one enterprise to be regulated in each cluster category;

[0149] A sorting unit is used to sort the quantity of each enterprise and obtain a sorting result;

[0150] The priority determination unit is used to determine the supervision priority of each enterprise to be regulated according to the sorting result; wherein, the supervision priority of different enterprises to be regulated in the same cluster category is the same.

[0151] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0152] In one embodiment, the priority determination unit is specifically configured to:

[0153] According to the order of the number of enterprises in the sorting results from large to small, the corresponding enterprises to be regulated are assigned regulatory priorities from high to low.

[0154] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0155] In one embodiment, the supervision module 13 is specifically used for:

[0156] The target resource business of each regulated enterprise shall be supervised in order from high to low regulatory priority.

[0157] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0158] In one embodiment, the business supervision device further includes: an enterprise data acquisition module and a pre-processing module, wherein:

[0159] An enterprise data acquisition module is used to acquire the original enterprise data of multiple enterprises to be regulated;

[0160] The preprocessing module is used to preprocess multiple original enterprise data to obtain enterprise data of multiple enterprises to be supervised; the preprocessing includes at least one of abnormal data compensation processing, data type conversion and normalization processing.

[0161] The business supervision device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned business supervision method embodiment of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0162] For the specific definition of the service supervision device, please refer to the definition of the service supervision method above, which will not be repeated here. Each module in the above service supervision device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0163] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide processing power. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the original enterprise data and enterprise data of each enterprise to be regulated. The network interface of the computer device is used to communicate with an external endpoint through a network connection. When the computer program is executed by the processor, a business supervision method is implemented.

[0164] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0165] In one embodiment, a computer device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the technical solution in the above-mentioned business supervision method embodiment of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0166] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the technical solution of the above-mentioned business supervision method of the present application is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0167] In one embodiment, a computer program product is also provided, including a computer program, which, when executed by a processor, implements the technical solution of the above-mentioned business supervision method of the present application. Its implementation principle and technical effect are similar and will not be repeated here.

[0168] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0169] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A business supervision method, characterized in that: The method comprises: performing attribute clustering processing on the multiple enterprises to be regulated based on the enterprise data of the multiple enterprises to be regulated to obtain clustering results; When the measurement index value of the clustering result meets the standard, determining the supervision priority of each of the enterprises to be supervised according to the clustering result; According to the clustering results and the supervision priorities, the target resource business of each of the enterprises to be supervised is supervised.

2. The method according to claim 1, characterized in that The step of performing attribute clustering on the plurality of enterprises to be regulated based on their enterprise data to obtain clustering results includes: The enterprise data of the multiple enterprises to be regulated are input into a second-order clustering model to perform attribute clustering processing on the multiple enterprises to be regulated to obtain the clustering results.

3. The method according to claim 2, characterized in that Inputting the enterprise data of the plurality of enterprises to be regulated into a second-order clustering model to perform attribute clustering processing on the plurality of enterprises to be regulated to obtain the clustering result includes: Based on multiple enterprise operation evaluation indicators, clustering processing is performed on the enterprise data of each of the enterprises to be regulated to obtain indicator data corresponding to different enterprise operation evaluation indicators of each enterprise to be regulated; Based on a plurality of different enterprise attribute parameters, secondary clustering processing is performed on the indicator data of the different enterprise operation evaluation indicators corresponding to each of the enterprises to be regulated to obtain the clustering results.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Obtaining a measurement index value of the clustering result; the measurement index value includes a performance measurement index value of a clustering model used in attribute clustering processing and / or a quality measurement index value of the clustering result; If the performance measurement indicator value is less than a first preset threshold, and / or the quality measurement indicator value is greater than a second preset threshold, it is determined that the measurement indicator value of the clustering result meets the standard.

5. The method according to any one of claims 1 to 3, characterized in that The clustering result includes a plurality of clustering categories; and determining the supervision priority of each of the enterprises to be supervised according to the clustering result includes: Obtain the number of enterprises that have at least one enterprise to be regulated in each cluster category; Sorting the number of each enterprise to obtain a ranking result; The regulatory priority of each of the enterprises to be regulated is determined according to the ranking results; wherein, the regulatory priorities of different enterprises to be regulated in the same cluster category are the same.

6. The method according to claim 5, characterized in that Determining the supervision priority of each of the enterprises to be supervised according to the ranking results includes: According to the order of the number of enterprises in the ranking results from large to small, the corresponding enterprises to be regulated are assigned regulatory priorities from high to low.

7. The method according to any one of claims 1 to 3, characterized in that The supervising target resource business of each of the enterprises to be supervised according to the clustering results and the supervision priorities includes: The target resource business of each of the enterprises to be supervised is supervised in order from high to low supervision priorities.

8. The method according to any one of claims 1 to 3, characterized in that Before performing attribute clustering processing on the multiple enterprises to be regulated based on their enterprise data, the method further includes: Obtaining original enterprise data of the plurality of enterprises to be regulated; The plurality of original enterprise data are pre-processed to obtain enterprise data of the plurality of enterprises to be supervised; the pre-processing includes at least one of abnormal data compensation processing, data type conversion and normalization processing.

9. A service supervision device, characterized in that: The device comprises: A clustering processing module, configured to perform attribute clustering processing on the multiple enterprises to be regulated based on their enterprise data to obtain clustering results; A priority determination module, configured to determine the supervision priority of each of the enterprises to be supervised according to the clustering results if the measurement index value of the clustering results meets the standard; A supervision module is used to supervise the target resource business of each of the enterprises to be supervised according to the clustering results and the supervision priorities.

10. A computer device comprising a processing chip, a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processing chip executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

Images