College multi-business platform collaborative service management system

Abstract

The invention discloses a college multi-business platform collaborative service management system, and belongs to the technical field of service data management. Processing data of different aspects corresponding to different types of services are sorted and combined to obtain a corresponding processing identifier combination sequence, and data processing and analysis are carried out on operation reliability corresponding to the different types of services by utilizing the processing identifier combination sequence obtained by earlier-stage processing. Different types of businesses are dynamically marked according to a processing analysis result, and targeted risk management scheme prompt is performed on the college multi-business platform collaborative service, so that modular risk supervision and analysis are performed on the college multi-business platform collaborative service process; and carrying out targeted risk management scheme prompt on the high-risk service platform obtained through analysis. The method and the device are used for solving the technical problem of poor risk active mining analysis and risk prevention processing effects of the abnormal data of the multi-service cooperative service in the colleges and universities in the existing scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of service data management, and particularly relates to a collaborative service management system for multiple business middle platforms in colleges and universities. Background Art

[0002] As an important base for knowledge innovation and talent cultivation, colleges and universities have complex business processes. During the process of business digitization, problems such as business islands and data islands gradually emerge, resulting in low resource utilization efficiency, difficulty in sharing in isolation, etc. During the process of educational digital transformation, based on the data middle platform, the business middle platform has also emerged. The business middle platform is responsible for abstracting and precipitating core business capabilities, providing standardized, modular, and reusable services to support the rapid innovation and iteration of front-end businesses. The data middle platform is responsible for integrating and managing the school's data assets to achieve data sharing and application. Based on the "business middle platform - data middle platform" architecture, the efficient integration and reuse of the business capabilities and data capabilities of colleges and universities can be realized, thereby better enhancing the smart campus ecosystem.

[0003] When implementing the existing collaborative service management solutions for multiple business middle platforms in colleges and universities, for the abnormal service data that appears, it basically still stays at the level of single data recording and processing. It does not conduct diversified active supervision and risk assessment on different abnormal service data that appears, and does not adaptively conduct targeted risk control according to the evaluation results to avoid greater potential hazards generated by the abnormal situations with different degrees of influence not being processed in a timely manner. There are problems with poor active mining and analysis of risks and poor risk prevention and handling effects for the abnormal data of the collaborative services of multiple business middle platforms in colleges and universities. Summary of the Invention

[0004] The purpose of the present invention is to provide a collaborative service management system for multiple business middle platforms in colleges and universities, which is used to solve the technical problem of poor active mining and analysis of risks and poor risk prevention and handling effects for the abnormal data of the collaborative services of multiple business middle platforms in colleges and universities in the existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A collaborative service management system for multiple business middle platforms in colleges and universities includes a business middle platform service supervision module, which is used to supervise and digitally process different abnormalities that occur in the service processes of different types of services in the business middle platform, and obtain an abnormal digital sequence set corresponding to different types of services and a type business abnormality identifier; A multi-dimensional risk assessment module, which is used to conduct active supervision and risk assessment on different types of services in terms of service stability and service impact according to the abnormal digital sequence set, and respectively conduct dynamic marking on different types of services according to the evaluation results in different aspects, as well as a combination of a stability processing identifier and an impact processing identifier, to obtain a processing identifier combination sequence corresponding to different types of services; 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 solution prompts for the collaborative services of multiple business platforms in universities.

[0006] 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; The occurrence time point, resolution time point and resolution type of the exception corresponding to 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 their occurrence 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.

[0007] Preferably, the exception resolution type includes a short-term resolution type and a long-term resolution type.

[0008] 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; If the abnormal flag of the type of service 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 and obtained.

[0009] Preferably, if the stable state value is less than 0, the service of the corresponding type is marked as the second stable service, and its corresponding stable processing flag is set to W1; Otherwise, the service of the corresponding type is marked as the third stable service, and its corresponding stable processing flag is set to W2.

[0010] Preferably, when active supervision and risk assessment of service impact are performed on 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 first abnormal impact values ​​y1 corresponding to different second stable businesses and third stable businesses are calculated in turn; And, the second abnormal impact values ​​y2 corresponding to different second stable services and third stable services are calculated in sequence.

[0011] 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 the 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 the 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 the corresponding impact processing flag is set to Y2; The stable processing identifiers and the influencing processing identifiers obtained by processing corresponding to different types of businesses are sequentially sorted and combined to obtain processing identifier combination sequences corresponding to different types of businesses.

[0012] 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 sequences of different types of services are calculated in turn to obtain the corresponding operation reliability; Perform data analysis on operational reliability and dynamically mark the type of business as operationally reliable based on the analysis results.

[0013] Preferably, if the operation reliability is 1, the service of the corresponding type is marked as a completely reliable service; If the operation reliability is greater than 0 and less than 1, the business of the corresponding type is marked as a partially reliable business; If the operation reliability is less than or equal to 0, the service of the corresponding type is marked as unreliable service.

[0014] 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.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: 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 effect of active risk mining and analysis of abnormal data of collaborative services of multiple business platforms in universities.

[0016] The present invention utilizes the processing identification combination sequence obtained in the early stage to reliably process and analyze the data corresponding to the 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 solution prompts for the collaborative services of multiple business platforms in colleges and universities, thereby realizing modular risk supervision and analysis of the collaborative service process of multiple business platforms in colleges and universities, and implementing targeted risk management solution prompts for the high-risk business platforms 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 collaborative services of multiple business platforms in colleges and universities. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 This is a module block diagram of a multi-business middle-office collaborative service management system for universities according to the present invention.

[0019] 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.

[0020] Figure 3 This is a principle block diagram of the operation of a multi-business middle-station collaborative service management system for universities according to the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0022] like Figures 1 to 3 As shown, the present invention is a multi-business middle-office collaborative service management system for colleges and universities, including a business middle-office service supervision module, a multi-dimensional risk assessment module and a business reliability analysis management module; 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 sets and type business exception identifiers corresponding to different types of businesses; including: 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; different types of businesses included in different business middle platforms can be customized according to the application requirements of actual application scenarios, which is not limited here; When supervising different exceptions that occur during the service processes of different types of services in the business middle platform, obtain the occurrence time point, resolution time point, and exception resolution type corresponding to the exception; the exception resolution type includes a short-term resolution type and a long-term resolution type; Among them, the units of the occurrence time point and the resolution time point are both accurate to minutes, which is convenient for subsequent operation and maintenance to query and count; in addition, the short-term resolution type means that the affiliated exception cannot be completely resolved and may occur again later. For example, due to design reasons or logical reasons, it can only be temporarily resolved; The long-term resolution type means that the affiliated exception is completely resolved and will not occur again later; The determination of the short-term resolution type and the long-term resolution type is classified and marked by the operation and maintenance personnel who resolve the exception, so as to provide a reliable target for subsequent operation and maintenance management; It should be noted that the impact of exceptions of the short-term resolution type is greater than that of exceptions of the long-term resolution type; Digitally process and combine the occurrence time point, resolution time point, and exception resolution type corresponding to the exception to obtain the exception digital sequence corresponding to the affiliated exception; Among them, when digitally processing the occurrence time point and the resolution time point, the numerical extraction and combination can be carried out in the order of date / hour / minute; the digital processing of the exception resolution type can be set according to the 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; Sort and combine all the exception digital sequences belonging to the same type of business according to the order of the occurrence time point to obtain the exception digital sequence set corresponding to the type of business, and set the total number of all the exception digital sequences in the exception digital sequence set as the type business exception identifier; In the embodiments of the present invention, by supervising and digitally processing different exceptions that occur during the service processes of different types of services in the business middle platform, reliable digital data support can be provided for subsequent risk supervision and assessment of different aspects corresponding to different types of services.

[0023] The multi-dimensional risk assessment module is used to actively supervise and risk assess the service stability aspect and the service impact aspect of different types of services according to the exception digital sequence set, and respectively perform dynamic marking on different types of services according to the assessment results of different aspects, as well as combine the stability processing identifier and the impact processing identifier to obtain the processing identifier combination sequence corresponding to different types of services; including: When actively supervising and risk assessing the service stability aspect of different types of services according to the exception digital sequence set, perform data analysis on the type business exception identifier associated with different types of services; If the type service exception flag is 0, mark the service of the corresponding type as the first stable service and set its corresponding stable processing flag to 0; the first stable service indicates that the operation of the service of the corresponding type has been normal and stable; Otherwise, calculate and obtain the stable state value w corresponding to the service of the corresponding type through the formula where n is the numerical value of the type service exception flag corresponding to the service of the corresponding type; n´ is the median of the type service exception flags associated with all types of services; n´´ is the warning value for the occurrence of exceptions corresponding to the service of the corresponding type, which can be determined according to the operation design data of the corresponding service type or based on the previous operation test data of the corresponding service type, and the specific numerical value is not limited; It should be noted that the stable state value is used to process and calculate the abnormal data corresponding to the type service with different standard data to digitally represent the stable state of the service of the corresponding type; at the same time, the stable state value can also provide reliable data support for the subsequent dynamic marking of the stable state of the service of the corresponding type; the larger the stable state value, the worse the corresponding stable state, and thus the worse the user experience when providing services; If the stable state value is less than 0, mark the service of the corresponding type as the second stable service and set its corresponding stable processing flag to W1; Otherwise, mark the service of the corresponding type as the third stable service and set its corresponding stable processing flag to W2; It should be noted that through the active supervision and risk assessment of the service stability of different types of services, not only can the stable state and digital data corresponding to different types of services be obtained, but also reliable screening data support can be provided for the risk supervision analysis of the service impact corresponding to all the second stable services and third stable services obtained through analysis, improving the data processing and analysis effect of the collaborative service of multiple business platforms in a university for different services in terms of service stability.

[0024] In addition, when conducting active supervision and risk assessment of the service impact on all the marked second stable services and third stable services respectively, sequentially count the total number of short-term solution types and the total number of long-term solution types in the second stable services and third stable services, and calculate and obtain the first abnormal impact value y1 corresponding to different second stable services and third stable services through the formula where m is the total number of long-term solution types corresponding to the second stable service or the third stable service; m0 is the first solution warning value corresponding to the long-term solution type, which can be determined according to the operation design data of the long-term solution type or based on the previous operation test data of the long-term solution type, and the specific numerical value is not limited; The first abnormal impact value is used to process and calculate the regulatory data of different stable businesses corresponding to the long-term solution type, so as to digitally represent the abnormality of the long-term solution 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 short-term solution types corresponding to the second stable services or the third stable services; m1 is the second solution alert value corresponding to the short-term solution type, which can be determined based on the operation design data of the short-term solution type or based on the previous operation test data of the short-term solution type, and the specific value is not limited; max() means obtaining the maximum value among different real numbers; The second abnormal impact value is used to process and calculate the supervision data of the short-term solution types corresponding to different stable businesses, so as to digitally represent the abnormalities in the short-term solution types; It should be noted that, unlike the prior art solution, which does not classify different types of exceptions and handle them independently, resulting in poor reliability of subsequent abnormal impact analysis, in the embodiment of the present invention, by separately supervising and processing data from the perspectives of long-term solution types and short-term solution types, the diversity and reliability of data processing and analysis of different types of business exceptions can be effectively improved; 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 the 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 the 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 the corresponding impact processing flag is set to Y2; In other cases, custom settings can be made based on the actual application needs of the actual application scenario; The stable processing identifiers and the influencing processing identifiers obtained by processing corresponding to different types of services are sequentially sorted and combined to obtain processing identifier combination sequences corresponding to different types of services; the processing identifier combination sequences are used to provide different aspects of digital data support for subsequent reliable analysis of the operation of the type of services; 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, thereby improving the risk active mining and analysis effect of abnormal data of collaborative services of multiple business platforms in universities.

[0025] The business reliability analysis management module is used to process and analyze the operation 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 multi-business middle-end collaborative service of universities; including: When analyzing the operation reliability of different types of services according to the processing identification combination sequence, the processing identification combination sequence of different types of services is sequentially processed through the formula Calculate and obtain the corresponding operation 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; Among them, the operational reliability is used to process and calculate the abnormal impact data of different aspects of the corresponding business type in the early stage, so as to digitally represent the corresponding operational reliability status; If the operation reliability is 1, the business of the corresponding type is marked as a fully reliable business; If the operation reliability is greater than 0 and less than 1, the business of the corresponding type is marked as a partially reliable business; If the operation reliability is less than or equal to 0, the service of the corresponding type is marked as unreliable service; Implementing a first risk management solution for the university multi-business middle-office collaborative service based on the marked partially reliable services, and implementing a second risk management solution for the university multi-business middle-office collaborative service based on the marked unreliable services; The implementation of the first risk management solution may specifically include only performing operation and maintenance improvements on the business level and / or logic level for the marked reliable businesses; Implementing the second risk management plan may specifically involve implementing comprehensive business risk prevention management on the business middle station to which the marked unreliable business belongs, such as replacing the business service provider.

[0026] In the embodiment of the present invention, the processing identifier combination sequence obtained by the previous processing is used to perform data processing and analysis on the operation reliability corresponding to 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 platforms in universities. This realizes modular risk supervision and analysis of the collaborative service process of multiple business platforms in universities, and implements targeted risk management solution prompts for the high-risk business platforms 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 platforms in universities.

[0027] In several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the described embodiments of the invention are merely illustrative. For example, the division of modules is only a logical function division, and there can be other division methods in actual implementation.

[0028] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0029] In addition, the functional modules in each embodiment of the present invention can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0030] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the basic features of the present invention, the present invention can be implemented in other specific forms.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-business middle-office collaborative service management system for universities, characterized in that: It includes a business middle platform service supervision module, which is used to supervise and digitally process different anomalies that occur in different types of business service processes in the business middle platform, and obtain the abnormal digital sequence sets and type business anomaly identifiers corresponding to different types of businesses; The multi-dimensional risk assessment module is used to actively supervise and assess the service stability and service impact of different types of services based on the abnormal digital sequence set, and dynamically mark different types of services and combine stable processing identifiers and impact processing identifiers according to the evaluation results of different aspects to obtain the processing identifier combination sequence corresponding to different types of services; 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 solution prompts for the collaborative services of multiple business platforms in universities.

2. According to claim 1, a university multi-business middle-station collaborative service management system is characterized in that: When monitoring different exceptions that occur in different types of business service processes in the business platform, obtain the corresponding occurrence time point, resolution time point and exception resolution type of the exception; The occurrence time point, resolution time point and resolution type of the exception corresponding to 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 their occurrence 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.

3. According to claim 2, a university multi-business middle-station collaborative service management system is characterized in that: Exception resolution types include short-term resolution types and long-term resolution types.

4. According to claim 2, a university multi-business middle-station collaborative service management system is characterized in that: When actively supervising and assessing the service stability of different types of services based on abnormal digital sequence sets, data analysis is performed on the abnormal identification of different types of services associated with the services; If the abnormal flag of the type of service 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 and obtained.

5. According to claim 4, a university multi-business middle-station collaborative service management system is characterized in that: If the stable state value is less than 0, the service of the corresponding type is marked as the second stable service, and its corresponding stable processing flag is set to W1; Otherwise, the service of the corresponding type is marked as the third stable service, and its corresponding stable processing flag is set to W2.

6. A multi-business middle-office collaborative service management system for colleges and universities according to claim 5, characterized in that: When active supervision and risk assessment of service impact are performed on 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 first abnormal impact values ​​y1 corresponding to different second stable businesses and third stable businesses are calculated in turn; And, the second abnormal impact values ​​y2 corresponding to different second stable services and third stable services are calculated in sequence.

7. A multi-business middle-office collaborative service management system for universities according to claim 6, 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 the 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 the 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 the corresponding impact processing flag is set to Y2; The stable processing identifiers and the influencing processing identifiers obtained by processing corresponding to different types of businesses are sequentially sorted and combined to obtain processing identifier combination sequences corresponding to different types of businesses.

8. A multi-business middle-office collaborative service management system for universities according to claim 7, 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 turn to obtain the corresponding operation reliability; Perform data analysis on operational reliability and dynamically mark the type of business as operationally reliable based on the analysis results.

9. A multi-business middle-office collaborative service management system for universities according to claim 8, characterized in that: If the operation reliability is 1, the business of the corresponding type is marked as a fully reliable business; If the operation reliability is greater than 0 and less than 1, the business of the corresponding type is marked as a partially reliable business; If the operation reliability is less than or equal to 0, the service of the corresponding type is marked as unreliable service.

10. A multi-business middle-office collaborative service management system for universities according to claim 9, 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 offices 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 offices in colleges and universities.

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