A multi-service middle platform collaborative service management system for colleges and universities

Abstract

The present invention discloses a collaborative service management system for multiple business middle stations in universities, which belongs to the technical field of service data management. The system sorts and combines processing data corresponding to different aspects of different types of businesses to obtain corresponding processing identification combination sequences, utilizes the processing identification combination sequences obtained in the early stage of processing to perform data processing and analysis on the reliable operation of different types of businesses, and dynamically marks different types of businesses according to the processing and analysis results, and implements targeted risk management scheme prompts for the collaborative service of multiple business middle stations in universities, thereby realizing modular risk supervision and analysis of the collaborative service process of multiple business middle stations in universities, and implementing targeted risk management scheme prompts for the high-risk business middle stations obtained through analysis. The present invention is used to solve the technical problems of poor risk active mining and analysis and risk prevention processing effects of abnormal data of collaborative services of multiple business middle stations in universities in existing schemes.

Description

Technical Field

[0001] The present invention relates to the technical field of service data management, and in particular to a multi-business middle-station collaborative service management system for universities. Background Art

[0002] As an important base for knowledge innovation and talent cultivation, colleges and universities have complex business processes. In the process of business digitalization, problems such as business islands and data islands gradually arise, resulting in low resource utilization efficiency and closed and difficult sharing. In the process of digital transformation of education, based on the data middle platform, the business middle platform has also come into being. The business middle platform is responsible for abstracting and precipitating core business capabilities, providing standardized, modular, and reusable services, and supporting rapid innovation and iteration of front-end business. The data middle platform is responsible for integrating and managing the school's data assets and realizing data sharing and application. Based on the "business middle platform-data middle platform" architecture, the efficient integration and reuse of college business capabilities and data capabilities can be achieved, thereby better improving the smart campus ecosystem.

[0003] When implementing the existing collaborative service management solutions for multiple business platforms in universities, the solutions basically remain at the stage of single data recording and processing for abnormal service data that occurs. There is no diversified active supervision and risk assessment of different abnormal service data that occurs, and no targeted risk management is carried out adaptively based on the assessment results to avoid greater hidden dangers caused by failure to handle abnormalities of different impact levels in a timely manner. The active risk mining and analysis and risk prevention and treatment of abnormal data from collaborative services of multiple business platforms in universities are not effective. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-business middle-office collaborative service management system for universities, which is used to solve the technical problems of poor risk active mining and analysis and risk prevention and treatment effects of abnormal data of multi-business middle-office collaborative services in universities in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A multi-business middle-office collaborative service management system for universities, including a business middle-office service supervision module, is used to supervise and digitally process different anomalies that occur in different types of business service processes in the business middle-office, and obtain abnormal digital sequence sets and type business anomaly identifiers corresponding to different types of business;

[0007] The multi-dimensional risk assessment module is used to proactively monitor and assess the service stability and service impact of different types of services based on abnormal digital sequence sets. Based on the assessment results of different aspects, different types of services are dynamically marked and combined with stability processing identifiers and impact processing identifiers to obtain the corresponding processing identifier combination sequences for different types of services.

[0008] The business reliability analysis management module is used to process and analyze the operational reliability of different types of businesses according to the processing identification combination sequence, dynamically mark different types of businesses according to the processing and analysis results, and implement targeted risk management plan prompts for the collaborative services of multiple business platforms in universities.

[0009] Preferably, when monitoring different exceptions that occur in different types of business service processes in the business platform, the occurrence time point, resolution time point and exception resolution type corresponding to the exception are obtained;

[0010] The occurrence time point, resolution time point, and resolution type of the exception are digitally processed and combined to obtain the exception digital sequence corresponding to the exception.

[0011] All abnormal digital sequences belonging to the same type of business are sorted and combined according to the order of the time points of occurrence to obtain the abnormal digital sequence set corresponding to the type of business, and the total number of all abnormal digital sequences in the abnormal digital sequence set is set as the type of business abnormality identifier.

[0012] Preferably, the exception resolution type includes a short-term resolution type and a long-term resolution type.

[0013] Preferably, when actively supervising and assessing the service stability of different types of services based on the abnormal digital sequence set, data analysis is performed on the type of service abnormality identifiers associated with the different types of services;

[0014] If the type of service exception flag is 0, the type of service is marked as the first stable service, and its corresponding stable processing flag is set to 0;

[0015] Otherwise, the stable state value corresponding to the type of business is calculated and obtained.

[0016] Preferably, if the stable state value is less than 0, the service type is marked as a second stable service, and its corresponding stable processing flag is set to W1;

[0017] Otherwise, the service type is marked as the third stable service, and its corresponding stable processing flag is set to W2.

[0018] Preferably, when performing active supervision and risk assessment on the service impact of all marked second stable businesses and third stable businesses, the total number of short-term solution types and the total number of long-term solution types in the second stable businesses and third stable businesses are counted in sequence, and the first abnormal impact value y1 corresponding to different second stable businesses and third stable businesses is calculated in sequence;

[0019] Furthermore, the second abnormal impact values y2 corresponding to different second stable services and third stable services are calculated in sequence.

[0020] Preferably, if y1-1≤0 and y2-1≤0, the second stable service or the third stable service is marked as the first impact service, and its corresponding impact processing flag is set to 0;

[0021] If y1-1>0 and y2-1≤0, the second stable service or the third stable service is marked as the second impact service, and its corresponding impact processing flag is set to Y1;

[0022] If y1-1>0 and y2-1>0, the second stable service or the third stable service is marked as the third impact service, and its corresponding impact processing flag is set to Y2;

[0023] The stable processing identifiers and impact processing identifiers obtained by processing corresponding to different types of business are sequentially sorted and combined to obtain a processing identifier combination sequence corresponding to different types of business.

[0024] Preferably, when processing and analyzing the operation reliability corresponding to different types of services according to the processing identification combination sequence, the processing identification combination sequence of different types of services is sequentially calculated to obtain the corresponding operation reliability;

[0025] Conduct data analysis on operational reliability and dynamically mark the type of business as operationally reliable based on the analysis results.

[0026] Preferably, if the operation reliability is 1, the business of the type concerned is marked as a completely reliable business;

[0027] If the operation reliability is greater than 0 and less than 1, the business type is marked as partially reliable business;

[0028] If the operation reliability is less than or equal to 0, the service of the corresponding type will be marked as unreliable service.

[0029] Preferably, a first risk management solution is implemented for the collaborative service of multiple business platforms in universities based on the marked partially reliable businesses, and a second risk management solution is implemented for the collaborative service of multiple business platforms in universities based on the marked unreliable businesses.

[0030] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0031] The present invention obtains the corresponding processing identifier combination sequence by sorting and combining the processing data corresponding to different aspects of different types of businesses, thereby realizing multi-dimensional and multi-level data processing of different anomalies occurring in different types of businesses, and improving the risk active mining and analysis effect of abnormal data of collaborative services of multiple business platforms in universities.

[0032] The present invention utilizes the processing identification combination sequence obtained in the early stage of processing to perform data processing and analysis on the reliable operation of different types of businesses, and dynamically marks different types of businesses according to the processing and analysis results, as well as implements targeted risk management program prompts for the collaborative services of multiple business middle stations in colleges and universities, thereby realizing modular risk supervision and analysis of the collaborative service process of multiple business middle stations in colleges and universities, and implementing targeted risk management program prompts for the high-risk business middle stations obtained through analysis, so that different types of risk prevention processing prompts can be carried out in a timely and efficient manner to avoid greater hidden dangers in the future, thereby improving the risk prevention processing effect of abnormal data of the collaborative services of multiple business middle stations in colleges and universities. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below with reference to the accompanying drawings.

[0034] Figure 1 This is a module block diagram of a multi-business middle-station collaborative service management system for universities according to the present invention.

[0035] Figure 2 This is a flowchart of the operation of a multi-business middle-station collaborative service management system for universities according to the present invention.

[0036] Figure 3 This is a block diagram of the principles of the operation of a multi-business middle-station collaborative service management system for universities according to the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] like Figures 1 to 3 As shown, the present invention is a multi-business middle-office collaborative service management system for universities, including a business middle-office service supervision module, a multi-dimensional risk assessment module and a business reliability analysis management module;

[0039] The business middle platform service supervision module is used to supervise and digitally process different exceptions that occur in different types of business service processes in the business middle platform, and obtain the exception digital sequence set and type business exception identification corresponding to different types of business; including:

[0040] The multi-business middle platform includes but is not limited to the identity middle platform, process / light application middle platform, to-do middle platform, biometric middle platform and access middle platform. The different types of businesses contained in different business middle platforms can be customized according to the application requirements of the actual application scenario, which is not limited here.

[0041] When monitoring different exceptions that occur in different types of business service processes in the business platform, obtain the corresponding exception occurrence time point, resolution time point and exception resolution type; exception resolution types include short-term resolution type and long-term resolution type;

[0042] The occurrence and resolution time are both accurate to the minute, facilitating subsequent O&M query and statistics. A "temporary resolution" type indicates that the anomaly cannot be completely resolved and may recur later, perhaps due to design or logic reasons, requiring only a temporary resolution.

[0043] Long-term resolution type means that the anomaly is completely resolved and will not reappear in the future;

[0044] The determination of short-term and long-term resolution types is done by the operations and maintenance personnel who resolve the anomaly and categorizes them to provide reliable targets for subsequent operations and maintenance management.

[0045] It is important to note that the abnormal impact of the short-term solution type is greater than the abnormal impact of the long-term solution type;

[0046] The occurrence time point, resolution time point, and resolution type of the exception are digitally processed and combined to obtain the exception digital sequence corresponding to the exception.

[0047] When digitizing the occurrence time point and the resolution time point, the numerical values can be extracted and combined in the order of date / hour / minute. The digitization of the exception resolution type can be set according to a preset digital identifier, for example, the digital identifier corresponding to the short-term resolution type is 0; the digital identifier corresponding to the long-term resolution type is 1.

[0048] All abnormal digital sequences belonging to the same type of business are sorted and combined according to the order of the time points at which they appear, to obtain an abnormal digital sequence set corresponding to the type of business, and the total number of all abnormal digital sequences in the abnormal digital sequence set is set as the type of business abnormality identifier;

[0049] In an embodiment of the present invention, by supervising and digitally processing different anomalies that occur in different types of business service processes in the business platform, reliable digital data support can be provided for subsequent risk supervision and assessment of different aspects of different types of businesses.

[0050] The multi-dimensional risk assessment module is used to proactively monitor and assess the service stability and service impact of different types of services based on abnormal digital sequence sets. Based on the assessment results of different aspects, different types of services are dynamically marked and combined with stability processing identifiers and impact processing identifiers to obtain the corresponding processing identifier combination sequences for different types of services. This module includes:

[0051] When proactively monitoring and assessing service stability for different types of services based on abnormal digital sequence sets, data analysis is performed on the abnormal business identifiers associated with different types of services;

[0052] If the abnormal flag of the type of business is 0, the business of the type is marked as the first stable business, and its corresponding stable processing flag is set to 0; the first stable business indicates that the operation of the business of the type has been normal and stable;

[0053] Otherwise, through the formula Calculate and obtain the stable state value w corresponding to the type of service; where n is the type of service anomaly identifier value corresponding to the type of service; n´ is the median value of the type of service anomaly identifiers associated with all types of services; and n´´ is the anomaly occurrence alert value corresponding to the type of service. This value can be determined based on the operational design data of the type of service or based on the preliminary operational test data of the type of service. The specific value is not limited.

[0054] It should be explained that the stable state value is used to process and calculate the abnormal data corresponding to the type of business and different standard data to digitally represent the stable state corresponding to the type of business. At the same time, the stable state value can also provide reliable data support for the subsequent dynamic marking of the stable state corresponding to the type of business. The larger the stable state value, the worse the corresponding stable state, and thus the worse the user experience when providing services.

[0055] If the stable state value is less than 0, the service type is marked as the second stable service, and its corresponding stable processing flag is set to W1;

[0056] Otherwise, the service type is marked as the third stable service, and its corresponding stable processing flag is set to W2;

[0057] It should be noted that by actively supervising and assessing the service stability of different types of businesses, we can not only obtain the stable status and digital data corresponding to different types of businesses, but also provide reliable screening data support for the risk supervision analysis of the service impact of all the second stable businesses and third stable businesses obtained, thereby improving the data processing and analysis effect of the collaborative service of different businesses in the university's multi-business middle platform in terms of service stability.

[0058] In addition, when conducting active supervision and risk assessment on the service impact of all marked second stable businesses and third stable businesses, the total number of short-term solution types and the total number of long-term solution types in the second stable businesses and third stable businesses are counted in turn, and the total number of long-term solution types is calculated in turn through the formula Calculate and obtain the first abnormal impact value y1 corresponding to different second stable services and third stable services; where m is the total number of long-term solution types corresponding to the second stable services or third stable services; m0 is the first solution alert value corresponding to the long-term solution type, which can be determined based on the operational design data of the long-term solution type or based on the preliminary operational test data of the long-term solution type. The specific value is not limited.

[0059] The first abnormal impact value is used to process and calculate the regulatory data of the long-term solution types corresponding to different stable businesses, so as to digitally represent the abnormalities in the long-term solution types;

[0060] And, through the formula Calculate and obtain the second abnormal impact value y2 corresponding to different second stable services and third stable services; where m2 is the total number of temporary resolution types corresponding to the second stable services or third stable services; m1 is the second resolution alert value corresponding to the temporary resolution type, which can be determined based on the operational design data of the temporary resolution type or based on the preliminary operational test data of the temporary resolution type. The specific value is not limited; max() indicates obtaining the maximum value among different real numbers.

[0061] The second abnormal impact value is used to process and calculate the regulatory data of the short-term resolution types corresponding to different stable businesses, so as to digitally represent the abnormalities in the short-term resolution types;

[0062] It should be noted that unlike existing technical solutions that do not classify different exception types and handle them independently, resulting in poor reliability of subsequent exception impact analysis, the embodiments of the present invention effectively improve the diversity and reliability of data processing and analysis of different types of business exceptions by separately supervising and processing data from the perspectives of long-term resolution types and short-term resolution types.

[0063] If y1-1≤0 and y2-1≤0, the second stable service or the third stable service is marked as the first impact service, and its corresponding impact processing flag is set to 0;

[0064] If y1-1>0 and y2-1≤0, the second stable service or the third stable service is marked as the second impact service, and its corresponding impact processing flag is set to Y1;

[0065] If y1-1>0 and y2-1>0, the second stable service or the third stable service is marked as the third impact service, and its corresponding impact processing flag is set to Y2;

[0066] In other cases, custom settings can be made based on the actual application needs of the actual application scenario;

[0067] The stable processing identifiers and impact processing identifiers obtained from the processing of different types of business are sequentially sorted and combined to obtain a processing identifier combination sequence corresponding to the different types of business; the processing identifier combination sequence is used to provide different aspects of digital data support for the subsequent reliable operation analysis of the type of business;

[0068] In the embodiment of the present invention, by sorting and combining the processing data corresponding to different aspects of different types of businesses, a corresponding processing identifier combination sequence is obtained, which realizes multi-dimensional and multi-level data processing of different anomalies occurring in different types of businesses, and improves the risk active mining and analysis effect of abnormal data of collaborative services of multiple business platforms in universities.

[0069] The business reliability analysis and management module is used to process and analyze the operational reliability of different types of businesses based on the processing identifier combination sequence, dynamically mark different types of businesses based on the processing and analysis results, and implement targeted risk management solutions for the multi-business middle-end collaborative services of universities; including:

[0070] When analyzing the reliability of operations corresponding to different types of services according to the processing identification combination sequence, the processing identification combination sequence of different types of services is sequentially processed by the formula Calculate and obtain the corresponding operational reliability K; where a and b are different proportional coefficients, and 0<a<b<1; k is 1 or 2, Wk is W1 or W2; Yk is Y1 or Y2;

[0071] The operational reliability is used to process and calculate the abnormal impact data of different aspects of the corresponding business in the early stage, and digitally represent the corresponding operational reliability status;

[0072] If the operation reliability is 1, the business type is marked as fully reliable business;

[0073] If the operation reliability is greater than 0 and less than 1, the business type is marked as partially reliable business;

[0074] If the operation reliability is less than or equal to 0, the business type is marked as unreliable business;

[0075] Implementing a first risk management solution for the multi-service middle-office collaborative service of universities based on the marked reliable services, and implementing a second risk management solution for the multi-service middle-office collaborative service of universities based on the marked unreliable services;

[0076] The implementation of the first risk management solution may specifically include performing operational and maintenance improvements on the business and / or logic levels for only the marked reliable businesses;

[0077] Implement the second risk management plan, specifically by implementing comprehensive business risk prevention management on the business middle platform to which the marked unreliable business belongs, such as replacing the business service provider.

[0078] In an embodiment of the present invention, a processing identifier combination sequence obtained in the early stage of processing is used to perform data processing and analysis on the reliable operation of different types of businesses, and different types of businesses are dynamically marked according to the processing and analysis results, and targeted risk management solution prompts are implemented for the collaborative service of multiple business middle stations in colleges and universities. This realizes modular risk supervision and analysis of the collaborative service process of multiple business middle stations in colleges and universities, and implements targeted risk management solution prompts for the high-risk business middle stations obtained through analysis, so that different types of risk prevention processing prompts can be carried out in a timely and efficient manner to avoid greater hidden dangers in the future, thereby improving the risk prevention processing effect of abnormal data in the collaborative service of multiple business middle stations in colleges and universities.

[0079] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative. For example, the division of modules is only a logical function division, and other division methods may be used in actual implementation.

[0080] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0081] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or hardware plus software functional modules.

[0082] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, but that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-business middle-station collaborative service management system for universities, characterized by: It includes a business middle-office service supervision module, which is used to supervise and digitally process different exceptions that occur in different types of business service processes in the business middle-office, and obtain the exception digital sequence set and type business exception identification corresponding to different types of business; The multi-dimensional risk assessment module is used to proactively monitor and assess the service stability and service impact of different types of services based on abnormal digital sequence sets. Based on the assessment results of different aspects, different types of services are dynamically marked and combined with stability processing identifiers and impact processing identifiers to obtain the corresponding processing identifier combination sequences for different types of services. When proactively monitoring and assessing service stability for different types of services based on abnormal digital sequence sets, data analysis is performed on the abnormal business identifiers associated with different types of services; If the type of service exception flag is 0, the type of service is marked as the first stable service, and its corresponding stable processing flag is set to 0; Otherwise, the stable state value corresponding to the type of business is calculated; If the stable state value is less than 0, the service type is marked as the second stable service, and its corresponding stable processing flag is set to W1; Otherwise, the service type is marked as the third stable service, and its corresponding stable processing flag is set to W2; When conducting active supervision and risk assessment on the service impact of all marked second stable businesses and third stable businesses, the total number of short-term solution types and the total number of long-term solution types in the second stable businesses and third stable businesses are counted in turn, and the total number of long-term solution types is calculated in turn through the formula Calculate and obtain the first abnormal impact value y1 corresponding to different second stable services and third stable services; where m is the total number of long-term resolution types corresponding to the second stable services or third stable services; m0 is the first resolution alert value corresponding to the long-term resolution type. The first abnormal impact value is used to process and calculate the regulatory data of the long-term resolution types corresponding to different stable services to digitally represent the abnormalities in the long-term resolution type. And, through the formula Calculate and obtain the second abnormal impact value y2 corresponding to different second stable services and third stable services; where m2 is the total number of temporary resolution types corresponding to the second stable services or third stable services; m1 is the second resolution alert value corresponding to the temporary resolution type. The second abnormal impact value is used to process and calculate the regulatory data of the temporary resolution types corresponding to different stable services to digitally represent the abnormalities in the temporary resolution types. Performing data analysis on the first abnormal impact value and the second abnormal impact value, dynamically marking the second stable service or the third stable service according to the analysis result, and dynamically setting the corresponding impact processing identifier; The business reliability analysis management module is used to process and analyze the operational reliability of different types of businesses according to the processing identification combination sequence, dynamically mark different types of businesses according to the processing and analysis results, and implement targeted risk management plan prompts for the collaborative services of multiple business platforms in universities.

2. A multi-business middle-station collaborative service management system for universities according to claim 1, characterized in that: When monitoring different exceptions that occur in different types of business service processes in the business platform, obtain the corresponding exception occurrence time point, resolution time point and exception resolution type; The occurrence time point, resolution time point, and resolution type of the exception are digitally processed and combined to obtain the exception digital sequence corresponding to the exception. All abnormal digital sequences belonging to the same type of business are sorted and combined according to the order of the time points of occurrence to obtain the abnormal digital sequence set corresponding to the type of business, and the total number of all abnormal digital sequences in the abnormal digital sequence set is set as the type of business abnormality identifier.

3. A multi-business middle-station collaborative service management system for universities according to claim 2, characterized in that: Exception resolution types include short-term resolution types and long-term resolution types.

4. A multi-business middle-station collaborative service management system for universities according to claim 1, characterized in that: If y1-1≤0 and y2-1≤0, the second stable service or the third stable service is marked as the first impact service, and its corresponding impact processing flag is set to 0; If y1-1>0 and y2-1≤0, the second stable service or the third stable service is marked as the second impact service, and its corresponding impact processing flag is set to Y1; If y1-1>0 and y2-1>0, the second stable service or the third stable service is marked as the third impact service, and its corresponding impact processing flag is set to Y2; The stable processing identifiers and impact processing identifiers obtained by processing corresponding to different types of business are sequentially sorted and combined to obtain a processing identifier combination sequence corresponding to different types of business.

5. A multi-business middle-station collaborative service management system for universities according to claim 1, characterized in that: When processing and analyzing the operation reliability corresponding to different types of services according to the processing identification combination sequence, the processing identification combination sequence of different types of services is calculated in sequence to obtain the corresponding operation reliability; Conduct data analysis on operational reliability and dynamically mark the type of business as operationally reliable based on the analysis results.

6. A multi-business middle-station collaborative service management system for universities according to claim 5, characterized in that: If the operation reliability is 1, the business type is marked as fully reliable business; If the operation reliability is greater than 0 and less than 1, the business type is marked as partially reliable business; If the operation reliability is less than or equal to 0, the service of the corresponding type will be marked as unreliable service.

7. A multi-business middle-station collaborative service management system for universities according to claim 6, characterized in that: Based on the marked partially reliable businesses, a first risk management plan is implemented for the collaborative service of multiple business middle platforms in colleges and universities, and based on the marked unreliable businesses, a second risk management plan is implemented for the collaborative service of multiple business middle platforms in colleges and universities.

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