Operation component permission isolation processing method, system, equipment and medium

Abstract

The invention discloses an operation component permission isolation processing method, system and device and a medium, and the method specifically comprises the steps: dividing an operation platform function into a plurality of independent operation components according to the characteristics of a game operation scene; establishing a first corresponding relationship between different operation roles and different operation components by analyzing the relationship between the operation roles and the operation capability, and binding a corresponding namespace for each operation role; when any operation role initiates an access request to any operation component, determining whether the operation role has the authority to access the operation component through the namespace corresponding to the operation role; and acquiring a preset gray release strategy and information of the newly developed operation component, and smoothly opening the newly developed operation component based on the gray release strategy and feedback data of the plurality of operation roles on the newly developed operation component. According to the invention, the fine management of the operation capability is realized, and the safety and reliability of the platform are improved.

Description

Technical Field

[0001] The present invention relates to the field of game operation technology, and in particular to a processing method, system, device and medium for isolating operation component permissions. Background Art

[0002] With the rapid development of mobile Internet, the acceleration of business pace and the sharp increase in user scale, platform systems are facing unprecedented challenges. In order to maintain the competitiveness of products, platform systems need to continuously iterate functions to cover complex and diverse scenarios and provide users with diversified interaction methods, so as to continuously maintain and enhance the connection between users and platforms. However, this rapid iteration and upgrade of functions also brings new problems.

[0003] With the continuous expansion of corporate organizational structures and the refinement of personnel division of labor, how to effectively manage the permissions of business systems has become a common problem that needs to be solved urgently. Permission management is not only related to the security of the system, but also directly affects the user experience and the security of platform assets. Once there is a loophole in permission management, it may cause user operation accidents outside the permission, resulting in platform asset losses and a decline in user experience.

[0004] This problem is particularly prominent in the user operation scenarios in the vertical field of games. Different games and different operation roles can apply different operation scenarios and reach capabilities. For example, the permission to reach users through SMS is usually only granted to the platform operation role, the permission to issue rewards in the user game gift package scenario is only limited to the project operation role, and the permission for the game marketing micro-maintenance scenario belongs only to the marketing GS role. This complex permission allocation method makes permission management more difficult.

[0005] Traditional permission management methods often adopt a one-size-fits-all approach and fail to allocate permissions in a refined manner based on the actual needs of operational roles. This not only leads to inefficient permission management, but may also cause permission abuse or insufficient permissions. Summary of the invention

[0006] The purpose of the present invention is to provide a processing method, system, device and medium for isolating the permissions of operation components, so as to realize the refined management of operation capabilities, effectively prevent operation accidents, improve the security and reliability of the platform, and solve at least one of the above-mentioned prior art problems.

[0007] In a first aspect, the present invention provides a method for processing permission isolation of an operation component, the method specifically comprising:

[0008] According to the characteristics of the game operation scenario, the operation platform functions are divided into multiple independent operation components;

[0009] By analyzing the relationship between operation roles and operation capabilities, the first correspondence between different operation roles and different operation components is established, and the corresponding namespace is bound to each operation role;

[0010] When any operation role initiates an access request to any operation component, the namespace corresponding to the operation role is used to determine whether the operation role has the authority to access the operation component;

[0011] Obtaining a preset grayscale release strategy and information about newly developed operation components, and smoothly opening the newly developed operation components based on the grayscale release strategy and feedback data from several operation roles on the newly developed operation components;

[0012] By monitoring the real-time performance indicator data of each operation component, operation components with abnormal status will be isolated and processed.

[0013] In a second aspect, the present invention provides a processing system for isolating permissions of operation components, and the system specifically includes:

[0014] The first processing module is used to divide the operation platform functions into multiple independent operation components according to the characteristics of the game operation scenario;

[0015] The second processing module is used to establish a first correspondence between different operation roles and different operation components by analyzing the relationship between the operation roles and the operation capabilities, and bind a corresponding namespace to each operation role;

[0016] The third processing module is used to determine whether any operation role has the authority to access any operation component through the namespace corresponding to the operation role when any operation role initiates an access request to the operation component;

[0017] The fourth processing module is used to obtain a preset grayscale release strategy and information of a newly developed operation component, and smoothly open the newly developed operation component based on the grayscale release strategy and feedback data of several operation roles on the newly developed operation component;

[0018] The fifth processing module is used to monitor the real-time performance indicator data of each operation component and isolate the operation components in abnormal state.

[0019] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor and a computer program stored in the memory, and when the computer program is executed on the processor, a processing method for isolating the permissions of operation components as described in any of the above methods is implemented.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the processing method for isolating the permissions of operation components as described in any of the above methods is implemented.

[0021] Compared with the prior art, the present invention has at least one of the following technical effects:

[0022] 1. The present invention realizes the refined management of operational capabilities, effectively prevents operational accidents, and improves the safety and reliability of the platform.

[0023] 2. The present invention divides the operation platform functions into multiple independent operation components, establishes a first corresponding relationship based on the relationship between the operation role and the operation capability, and binds a corresponding namespace to each operation role. This can not only achieve permission isolation between different operation roles, but also flexibly allocate permissions according to actual needs.

[0024] 3. The present invention introduces a grayscale release strategy, which can ensure that the newly developed operation components are fully tested before going online to avoid potential problems from causing negative impact on users.

[0025] 4. The present invention can timely discover and handle operational components in abnormal states through a real-time performance indicator monitoring mechanism to ensure the stability and security of the system.

[0026] 5. The present invention can realize the minimum authority convergence function between game projects and operation roles, thereby improving the efficiency and security of authority management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 It is a flowchart of a processing method for isolating permissions of operation components provided by an embodiment of the present invention;

[0029] Figure 2 It is a structural diagram of a processing system for isolating permissions of operation components provided by an embodiment of the present invention;

[0030] Figure 3 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0032] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0033] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0034] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0035] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0036] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0037] In the embodiment of the present application, the execution subject of the process includes a terminal device, which includes but is not limited to: a server, a computer, a smart phone, a tablet computer, and other devices capable of executing the method disclosed in the present application. Figure 1 A flowchart of a method for isolating permissions of an operation component disclosed in an embodiment of the present invention is shown, and is described in detail as follows:

[0038] S101, according to the characteristics of the game operation scenario, the operation platform functions are divided into multiple independent operation components.

[0039] In this embodiment, the operation scenarios of the game are first analyzed, including user registration and login, in-game activity management, virtual store management, player data analysis, version update and maintenance, etc. According to the analysis results, the core operation functions are determined as user registration and login, in-game activity management, and virtual store management. These functions are directly related to the operation efficiency and user experience of the game. Non-core operation functions include player data analysis, version update and maintenance, etc. Although these functions are also very important, they usually do not have a direct impact on the daily operation of the game. Based on the determination of core operation functions and non-core operation functions, the company has formulated an operation function division strategy. The core of the strategy is to divide the core operation functions into independent operation components for refined management and optimization. After completing the division of the operation components, the company conducted collaborative testing on multiple independent operation components to ensure compatibility and stability between them. According to the test results, the company adjusted and optimized the division of the operation components to ensure that each component can operate efficiently and stably.

[0040] In this embodiment, by dividing the operation platform functions into multiple independent operation components, the company can manage and optimize each component more finely, thereby improving operation efficiency. Independent operation components can reduce the coupling between systems and reduce the risk of fault propagation, thereby enhancing the stability of the system. The refined division of operation components can enable each component to better meet user needs, thereby improving user experience.

[0041] In some embodiments, in the above step S101, the operation platform function is divided into multiple independent operation components according to the characteristics of the game operation scenario, specifically including:

[0042] Obtaining the type of the game operation scenario, and determining a number of core operation functions and a number of non-core operation functions according to the type;

[0043] Determine an operation function division strategy for the operation platform function according to the core operation function and the non-core operation function, and divide the operation platform function into a plurality of independent operation components according to the operation function division strategy;

[0044] By conducting collaborative testing on multiple independent operation components, the division of the operation components of the operation platform functions is adjusted and optimized.

[0045] In this embodiment, the type information of the game operation scene is obtained, and the core operation function and non-core operation function corresponding to the scene type are determined according to the preset correspondence between the scene type and the function; according to the determined core operation function and non-core operation function, the operation function is clustered by using a clustering algorithm to obtain the function clustering result, and the operation platform function division strategy is formulated according to the clustering result; according to the formulated operation platform function division strategy, the operation platform function is divided into multiple independent operation components by using a graph division algorithm to form a component division scheme; for each divided independent operation component, the function weight of the component is determined according to the core operation function and non-core operation function contained in it, and the components are prioritized by using a sorting algorithm according to the function weight; according to the priority ranking result of the component, the deployment priority of each component is determined, and the corresponding computing resources and storage resources are allocated to each component according to the priority; after the component deployment is completed, the load balancing algorithm is used to monitor the load of each component in real time, and when the load of a component is too high, additional computing resources are dynamically allocated to ensure the smooth operation of the component; the game operation data is continuously monitored, and when the operation data changes, the above steps are re-executed to dynamically adjust the operation platform function division strategy and component division scheme to achieve adaptive optimization of the operation platform.

[0046] Develop a collaborative testing plan for each independent operational component, and clarify the test objectives, test scenarios, and test data; deploy independent operational components on the operational platform, and perform tests according to the collaborative testing plan, collecting the operation logs and performance data of each component; analyze the collaborative testing result data, evaluate the rationality of the current component division plan, and identify the optimization direction of component division; based on the optimization direction of component division, use clustering algorithms to re-divide the operational components and adjust the boundaries and responsibilities of the components; conduct a secondary collaborative test on the adjusted component division plan, and compare it with the test results of the initial plan to evaluate the optimization effect; continuously iterate the component division and collaborative testing process until the preset optimization goals are achieved, forming a final operational component division and collaborative plan.

[0047] Exemplarily, the type of game operation scenario can be obtained by analyzing the game's gameplay, subject matter, audience and other dimensions, and using clustering algorithms such as K-means to divide the game into different types such as role-playing, casual puzzle, and competitive battle. According to the game type, the core operation functions include activity operation, social operation, numerical operation, etc., and the non-core operation functions include customer service operation, brand operation, etc. The operation function division strategy can adopt a network division algorithm based on functional relevance. By calculating the correlation coefficient between different operation functions, when the correlation coefficient is greater than 8, it can be divided into the same component, when it is less than 3, it can be divided into different components, and when it is between the two, it can be divided according to business characteristics. The operation platform function is divided into 6 independent components such as activity operation, social operation, numerical operation, and customer service operation. Through collaborative testing of different components, the A / B test method is adopted to randomly divide the same users into two groups, using different operation components respectively, and comparing the user's activity, retention rate and other indicators. When the difference is greater than 5%, it is necessary to adjust and optimize the division of components, such as adjusting the activity operation from a separate component to merge with social operation. Through iterative optimization, we will continuously improve the modularity, flexibility and coordination of the operation platform functions to better support game operations.

[0048] S102, by analyzing the relationship between the operation roles and the operation capabilities, establishing a first correspondence between different operation roles and different operation components, and binding a corresponding namespace to each operation role;

[0049] When any operation role initiates an access request to any operation component, the namespace corresponding to the operation role is used to determine whether the operation role has the authority to access the operation component.

[0050] In this embodiment, in order to more effectively manage the authority relationship between operation roles and operation components and improve operation efficiency and security, the relationship between operation roles and operation capabilities is analyzed, a first correspondence between different operation roles and different operation components is established, and a corresponding namespace is bound to each operation role.

[0051] First, we sorted out the existing operational roles, including platform operations, project operations, marketing GS, etc., and clarified the responsibilities and operational capabilities of each role. Then, we conducted a detailed analysis of the functions of each operational component and determined the core functions and operational permissions of each component.

[0052] Based on the results of the role and operation capability analysis, the decision tree algorithm was used to analyze the association rules between operation roles and operation capabilities, and the key features were obtained. According to the key features, a mapping matrix between operation roles and operation capabilities was constructed, and different operation roles and their corresponding operation capability ranges were clarified. Then, the operation components were logically isolated according to the mapping matrix, and the first correspondence between each operation role and each operation component was determined.

[0053] Based on the first correspondence, a separate namespace is created for each operation role. In the namespace, the operation components accessible to each operation role are determined, forming the second correspondence. In this way, each operation role is assigned a unique namespace that contains all the operation components that the role has access to.

[0054] When any operation role initiates an access request to any operation component, the system will first obtain the namespace of the operation role. Then, based on the second correspondence, the system will determine whether the target operation component is within the scope of the namespace corresponding to the operation role. If the target operation component is within the scope of the namespace, the system will allow the operation role to access the operation component; otherwise, the system will deny the access request of the operation role.

[0055] In this embodiment, by establishing a correspondence between operation roles and operation components, and binding a corresponding namespace to each operation role, the refinement of permission management is achieved, which helps to ensure that each operation role can only access the operation components within its scope of responsibility, thereby improving the security of the system. By adopting the design concept of namespace and componentization, the system has good scalability and flexibility. With the continuous changes in game operation scenarios and the continuous addition of new functions, the correspondence between operation roles and operation components can be easily adjusted to adapt to new operation needs.

[0056] In some embodiments, in the above step S102, the first correspondence between different operation roles and different operation components is established by analyzing the relationship between the operation roles and the operation capabilities, and a corresponding namespace is bound to each operation role, specifically including:

[0057] Use decision tree algorithm to analyze the association rules between operation roles and operation capabilities to obtain key features;

[0058] According to the key features, a mapping matrix between operation roles and operation capabilities is constructed, wherein the mapping matrix includes different operation roles and their corresponding operation capability ranges;

[0059] Logically isolating the operation components according to the mapping matrix, and determining a first corresponding relationship between each operation role and each operation component;

[0060] Based on the first corresponding relationship, an independent namespace is created for each operation role, and the accessible operation components in the namespace corresponding to each operation role are determined to form a second corresponding relationship.

[0061] In this embodiment, a decision tree algorithm is used to analyze the data of operational roles and operational capabilities, and key features are obtained through algorithm analysis. Based on the extraction of key features, a mapping matrix is ​​constructed to list in detail the range of operational capabilities corresponding to each operational role. Through the mapping matrix, the accuracy of the correspondence between the role definition and the capability is judged. If the mapping is accurate, the decision tree model parameters are further optimized to improve the accuracy of the analysis. If there is a deviation in the mapping, the data input is adjusted, and feature extraction and algorithm analysis are performed again to ensure the correct correspondence between the role and the capability. By continuously optimizing the algorithm parameters and data input, the accuracy and practicality of the mapping matrix are ensured, thereby effectively supporting the precise matching of operational roles and capabilities.

[0062] Traverse each operational role in the mapping matrix and create an independent namespace for each role. In the process of creating a namespace, determine the list of operational components that are accessible in the namespace based on the mapping matrix. Use the role-based access control (RBAC) mechanism to associate operational roles with their corresponding namespaces. On the basis of the RBAC mechanism, further refine the access control granularity and assign component-level access rights to the corresponding namespaces. Through the namespace isolation mechanism, ensure that data and operations between different operational roles are isolated from each other to avoid unauthorized access. Continuously monitor and audit access behaviors within the namespace. If any illegal operations are found, trigger the predefined security policy to block and record the illegal behaviors.

[0063] Exemplarily, a decision tree algorithm is used to analyze the association rules between operation roles and operation capabilities. By constructing the operation role feature vector and the operation capability feature vector, the ID3 algorithm is used to calculate the information gain, and the feature with the largest information gain is selected as the key feature, such as operation experience, professional background, etc. A mapping matrix between operation roles and operation capabilities is constructed based on the key features. Each element in the matrix represents the score of a certain operation role in a certain operation capability, and the score range is 0-10 points. The mapping matrix is ​​clustered using a clustering algorithm, and operation roles with similar scores are divided into the same category. The operation components are logically isolated, and each type of operation role is associated with a specific operation component to form a first correspondence between roles and components. Based on the first correspondence, an independent namespace is created for each operation role. By setting an access control list, the accessible operation components in each namespace are determined to generate a second correspondence. For example, the activity operation role is mapped to the activity operation component, and the social operation role is mapped to the social operation component. Through identity authentication and permission management, the isolation of the namespaces corresponding to different roles is achieved to avoid unauthorized access. A role-based access control model is adopted to dynamically adjust the access policy according to the mapping matrix to improve system security. By tracking the behavior logs of operation roles and using association rule mining algorithms to analyze the operation modes of different roles, when the number of abnormal operations of a role exceeds 5 times, its authority level will be automatically lowered to avoid the risk of misoperation.

[0064] In some embodiments, in the above step S102, when any operation role initiates an access request to any operation component, determining whether the operation role has the authority to access the operation component through the namespace corresponding to the operation role specifically includes:

[0065] Obtain a first access request initiated by a first operation role to a target operation component;

[0066] Based on the second corresponding relationship, determining whether the target operation component is within the scope of the namespace corresponding to the first operation role;

[0067] If the judgment is yes, the first operation role is allowed to access the target operation component; otherwise, the first operation role is denied to access the target operation component.

[0068] In this embodiment, when the operation role initiates an access request to the target operation component, the system first obtains the relevant information of the request, including the source IP address of the request, the target URL of the request, the method of the request (such as GET, POST, etc.) and the parameters carried by the request. Then, the system determines whether the target operation component of the request is within the namespace corresponding to the operation role according to the second corresponding relationship. Specifically, the system obtains the access rights of the namespace to which the operation role belongs by querying the access control list, and matches the target URL of the request with the URL pattern of the namespace using the string matching algorithm. If the matching degree exceeds 90%, the request is considered legal. If the judgment result is that access is allowed, the system calls the interface of the target operation component, passes the request parameters, and returns the response result of the component to the operation role. If the judgment result is that access is denied, the system records the log information of the illegal access, including timestamp, request source IP, request target URL, etc., identifies suspicious access patterns through log aggregation and correlation analysis, and automatically adds it to the blacklist when the number of illegal accesses of a certain IP exceeds the threshold (such as 10 times) to limit its access frequency. At the same time, the system can also combine machine learning algorithms to analyze historical access logs, establish a characteristic model of abnormal access behavior, detect the access behavior of operation roles in real time, and issue alarms for identified abnormal behaviors, further improving the system's security protection capabilities.

[0069] S103, obtaining a preset grayscale release strategy and information of a newly developed operation component, and smoothly opening the newly developed operation component based on the grayscale release strategy and feedback data of several operation roles on the newly developed operation component.

[0070] In this embodiment, when formulating the gray release strategy, it is necessary to comprehensively consider factors such as the characteristics of the operating components, the user group, and the risk tolerance. For example, you can decide to open the new version to a small number of core users or users in a specific region to observe the performance of the new version and collect user feedback. Then, by configuring the traffic distribution rules, gradually introduce the new version into the production environment. For example, you can set the traffic share of the new version to 20% and gradually increase it to 100%.

[0071] According to the different responsibilities and expertise of the operation team, the operation roles are divided into product managers, data analysts, customer service representatives, etc. Through user behavior data analysis and other methods, feedback data on newly developed operation components from different operation roles is collected. The feedback data includes information on functional experience, performance, ease of use, user satisfaction, etc.

[0072] Conduct in-depth analysis of the collected feedback data to identify problems and potential improvements in the new version. Adjust the grayscale release strategy based on the analysis results. For example, if the new version is found to have performance issues in certain user groups, the traffic share of this user group can be reduced, and monitoring and testing of this part of users can be increased. During the grayscale release period, continue to pay attention to the performance of the new version and user feedback, fix problems in a timely manner, and optimize functions. When the new version performs stably and user satisfaction is high, its coverage can be gradually expanded until it is fully released.

[0073] In this embodiment, through the grayscale release strategy, the new version can be gradually tested and optimized without affecting a large number of users, which helps to timely discover and fix potential problems and reduce the risk of software updates. Based on the feedback data of the operation role, the enterprise can more accurately understand user needs and pain points, so as to carry out targeted optimization of the new version, which helps to improve user experience and satisfaction and increase user stickiness.

[0074] In some embodiments, in the above step S103, the smooth opening of the newly developed operation component based on the phased release strategy and the feedback data of the newly developed operation component from several operation roles specifically includes:

[0075] According to the grayscale release strategy, the time range and target operation role of this grayscale release are obtained, and the newly developed operation component is grayscale released to the target operation role within the time range;

[0076] During the grayscale release process, according to the feedback data of the target operation role on the newly developed operation component, the parameters of the grayscale release strategy are dynamically adjusted using the naive Bayes algorithm;

[0077] Based on the operational data collected during the grayscale release, comprehensively evaluate the actual effects of the newly developed operational components to obtain evaluation results;

[0078] Based on the evaluation result, it is determined whether to perform full open processing on the newly developed operation component.

[0079] In this embodiment, the time range and target operation role information of this grayscale release are obtained from the grayscale release strategy and used as the basic parameters of the grayscale release. According to the time range of the grayscale release, determine whether the current time is within the release time range. If it is within the range, continue to execute the subsequent steps, otherwise wait until the release time range to execute. Get the newly developed operation component, and use the component permission control module to determine whether the component has the authority to release to the target operation role. If it has the authority, continue to execute, otherwise apply for permission or modify the component permission. Using a progressive release algorithm, the operation components are gradually released to the target operation role in a certain proportion, and the system stability and user feedback during the release process are monitored in real time. During the release process, the user behavior analysis algorithm is used to analyze the acceptance and usage of the new component by the target operation role in real time, and the release strategy is dynamically adjusted according to the analysis results, such as slowing down the release speed or rolling back the component version.

[0080] The actual operation data of the newly developed operation components collected during the grayscale release phase is obtained, and the data is preprocessed, including data cleaning and data integration, to obtain a data set that can be used for evaluation. According to the preset evaluation index system, the corresponding machine learning algorithm is used to model and analyze the data set to obtain the evaluation results that reflect the actual effect of the newly developed operation components. Common machine learning algorithms may include decision trees, support vector machines, and neural networks. The evaluation results are compared with the preset effect threshold. If the evaluation results are better than or reach the threshold, the newly developed operation component is judged to be effective and can be fully opened; otherwise, the component is considered to be ineffective and will not be opened or will be evaluated again after further optimization. If the newly developed operation component needs to be fully opened, it will be deployed to the production environment, and its operating status and key performance indicators will be monitored to ensure smooth operation. After the operation component is fully opened, its operating data will be continuously collected, and the effect will be evaluated regularly. According to the evaluation results and actual needs, the component will be optimized and upgraded to continuously improve its performance. Based on the collected operational data, machine learning algorithms such as incremental learning are used to update and optimize the prediction models of operational components in real time, so that they can adapt to business changes, provide the best business strategy, and feed the data generated by the operational components back to the algorithm model for retraining.

[0081] Exemplarily, according to the gray release strategy, the time range for this gray release is from May 1, 2023 to May 7, 2023, and the target operation role is a Class A operator. The system automatically releases the newly developed operation component X to Class A operators within this time range. During the gray release process, the target operation role's feedback data on the use of component X is collected, including indicators such as the response time and error rate of component X. The naive Bayes algorithm is used to analyze these feedback data, and it is found that the average response time of component X is 5 seconds, the error rate is 1%, and the user satisfaction is 95%. Based on the above data analysis results, the parameters of the gray release strategy are dynamically adjusted, such as extending the gray release time to May 14, 2023, and expanding the target operation role to Class B operators. During the gray release process, the operation data of component X is continuously collected, including business indicators such as PV, UV, order volume, and transaction amount. The weighted average algorithm was used to comprehensively evaluate these operational data, and the actual effect of component X was obtained: the average daily PV increased by 10%, the average daily UV increased by 8%, the average daily order volume increased by 15%, and the average daily transaction volume increased by 20%. Based on the above evaluation results, it was determined that component X had a significant effect on improving business indicators, so it was decided to fully open component X and open it to all operational roles.

[0082] S104, by monitoring the real-time performance indicator data of each operation component, the operation component in abnormal state is isolated.

[0083] In this embodiment, key performance indicators (KPIs) that need to be monitored are determined, such as CPU usage, memory usage, disk I / O, network throughput, response time, etc. Professional monitoring tools, such as Zabbix, Prometheus, Grafana, etc., are used to monitor the real-time performance indicators of the operation components. These tools can collect, store and analyze performance data in real time, and provide a visual monitoring interface and alarm function. Then, a monitoring strategy is formulated, including data collection frequency, data storage method, alarm threshold, etc., to ensure the real-time and accuracy of the monitoring data, so as to promptly detect and handle abnormal conditions.

[0084] The alarm function provided by the monitoring tool can detect abnormal status of operation components in real time. When the performance indicator of an operation component exceeds the preset alarm threshold, the alarm signal is triggered. Once an abnormal state is detected, the isolation processing mechanism is immediately initiated, including: temporarily shutting down or simplifying non-core functions of operation components with performance bottlenecks to release resources and ensure the normal operation of core businesses; service isolation: isolating abnormal operation components from the system to prevent their failures from spreading to other components or affecting the stability of the entire system; traffic control: by adjusting the traffic distribution rules, limiting access requests to abnormal operation components to reduce their load and prevent the expansion of failures.

[0085] While isolating and processing, troubleshoot abnormal operation components. Analyze performance data, log files, etc. to determine the cause and location of the fault. Take appropriate recovery measures based on the results of the troubleshooting. For example, fix code vulnerabilities, optimize system configuration, increase hardware resources, etc., to restore the normal operation of the operation component.

[0086] In this embodiment, by monitoring the real-time performance indicators of the operation components and detecting abnormal states, potential faults are discovered and handled in a timely manner, thereby improving the stability and reliability of the system. By isolating and handling the operation components in abnormal states, the failures are prevented from affecting the user experience. At the same time, through measures such as service degradation and flow control, the normal operation of the core business is ensured, and user satisfaction and loyalty are improved. Through automated monitoring and alarm functions, the time cost of manual intervention and troubleshooting is reduced. At the same time, through measures such as service isolation and flow control, the business losses and maintenance costs caused by failures are reduced.

[0087] In some embodiments, in the above step S103, the monitoring of the real-time performance indicator data of each operation component to isolate the operation component in an abnormal state specifically includes:

[0088] Acquire real-time performance indicator data of all operation components, and obtain key indicator data by processing the real-time performance indicator data;

[0089] According to the preset abnormality severity assessment rules, each operation component is assessed through the key indicator data to determine whether an abnormal state occurs, and the operation component that is in an abnormal state is identified as an abnormal operation component;

[0090] The abnormal severity score of each abnormal operation component is calculated, and the corresponding processing scheme is determined from a preset abnormal processing mapping table according to the abnormal severity score.

[0091] In this embodiment, the real-time performance indicator data of all operation components are obtained, and the obtained real-time performance indicator data are stored in a specified database. The stored real-time performance indicator data is cleaned and preprocessed, and abnormal values ​​and invalid values ​​are eliminated to obtain an indicator data set that can be used for analysis. According to the pre-set key indicator extraction rules, the key indicator data reflecting the operating status of the operation component is extracted from the indicator data set. The extracted key indicator data is input into the anomaly detection model, and the key indicator data is analyzed by using the isolation forest algorithm to determine whether the operating status of each operation component is abnormal. If the anomaly detection model determines that the key indicator data of a certain operation component is abnormal, the operation component is marked as a suspected abnormal operation component, and an abnormal alarm information is output.

[0092] Count the number of abnormal indicators in each abnormal operation component, and calculate the abnormal severity score of the abnormal operation component according to the ratio of the number of abnormal indicators to the total number of operating parameter indicators. Obtain a preset abnormal processing mapping table, which includes the correspondence between multiple abnormal severity score intervals and corresponding processing solutions. Match the abnormal severity score of the abnormal operation component with the multiple abnormal severity score intervals in the abnormal processing mapping table to determine the abnormal severity score interval it falls into. Obtain the processing solution corresponding to the abnormal severity score interval it falls into, and determine the processing solution as the processing solution for the abnormal operation component. If there are multiple abnormal operation components, perform the above steps for each abnormal operation component to obtain their respective processing solutions. According to the determined processing solution, use corresponding technical means to process the abnormal operation component to achieve abnormal repair or isolation and ensure the overall operational stability of the system.

[0093] Exemplarily, the system collects performance indicator data of all operating components in real time, including response time, error rate, throughput, etc. The collected raw data is preprocessed using a data cleaning algorithm, invalid data is removed, and missing values ​​are interpolated to obtain a standardized performance indicator data set. Then the principal component analysis (PCA) algorithm is applied to reduce the dimension of the data set to extract the key indicators that have the greatest impact on component performance, such as average response time, maximum error rate, etc. The system presets a set of abnormal severity evaluation rules, including indicator thresholds and weight coefficients. The key indicator data of each component is compared with the threshold, and the abnormal severity score is calculated in combination with the weight coefficient. For example, the average response time of component A is 2 seconds, which exceeds the threshold by 5 seconds, and the weight coefficient is 3; the maximum error rate is 5%, which exceeds the threshold by 3%, and the weight coefficient is 7. According to the weighted average formula, the abnormal severity score of component A is 3*2 / 5+7*5 / 3=57. The system sets components with a severity score greater than 1 as abnormal components. For abnormal components, the system will query the preset abnormal processing mapping table and determine the corresponding processing plan according to the abnormal severity score. For example, if component A scores 57, the corresponding solution is "downgrade service, limit call frequency, and issue an alarm to notify maintenance personnel." The system automatically executes this solution, triggers the work order flow, and notifies maintenance personnel to follow up on problem diagnosis and recovery. Through the closed-loop process of real-time monitoring, intelligent analysis, and automatic processing, the high availability and stability of the operation components are guaranteed.

[0094] Furthermore, the method further comprises:

[0095] Record the key operation behaviors of each operation component to form an operation log;

[0096] Associating the operation log with each abnormal operation component to determine the operation characteristics corresponding to the abnormal event occurring in each abnormal operation component;

[0097] The abnormal events occurring in each abnormal operation component and their corresponding operation characteristics are used as input to train a decision tree model and generate an abnormal operation component detection model, which is used to identify and analyze the abnormal patterns of operation components in abnormal states.

[0098] In this embodiment, the key operation behavior data of each operation component is obtained and recorded in the operation log, which contains information such as operation time, operation type, and operation object. The operation status of the operation component is detected in real time by the monitoring system to determine whether there is an abnormal operation component. If there is an abnormal component, it is marked as an abnormal state. The operation log is associated with the abnormal operation component, and the operation record of the corresponding component in the time period is searched in the operation log according to the abnormal occurrence time of the abnormal component. The operation record of the abnormal component found is analyzed, and the operation characteristics are extracted, including operation frequency, operation duration, operation type distribution, etc., to construct the abnormal component operation feature vector. The clustering algorithm is used to perform cluster analysis on the operation feature vector of the abnormal component, and the abnormal component is divided into different abnormal event categories according to the clustering results. The operation characteristics of the abnormal components in each abnormal event category are statistically analyzed to obtain the distribution of the operation characteristics of the abnormal event of this type, which is used as the operation feature label of the abnormal event. The abnormal component is associated with its corresponding abnormal event operation feature label to form a mapping relationship between the abnormal component-abnormal event-operation feature.

[0099] The dataset that forms the mapping relationship between abnormal components, abnormal events, and operation features is used as input, and the decision tree algorithm is used for model training. The abnormal operation component detection model is constructed by recursively partitioning the feature space. In the process of building the decision tree model, the optimal partitioning features are selected by indicators such as information gain or Gini index, and the feature subspace is continuously refined until the preset stop condition is met. The constructed abnormal operation component detection model is evaluated and optimized, and the generalization performance of the model is evaluated by methods such as cross-validation, and the model is optimized by pruning and other technologies. The optimized abnormal operation component detection model is deployed in the actual operation environment to perform anomaly detection and analysis on real-time operation component data. According to the output results of the abnormal operation component detection model, it is judged whether the operation component is in an abnormal state. If an abnormality is detected, the corresponding abnormal handling process is triggered to identify and analyze the abnormal pattern, and take corresponding disposal measures.

[0100] Exemplarily, the system will record the key operation behaviors of each operation component. For example, for a certain operation component A, the system records its 100 key operations in the past 24 hours, including login, configuration modification, restart, etc., forming the operation log of the operation component. When the system detects that the operation component is in an abnormal state, it will associate its abnormal state with the operation log for analysis, and find that within 1 hour before the abnormality occurs, the operation component has performed 10 configuration modification operations, which is much higher than the average modification frequency of once per hour under normal circumstances. It is determined that the abnormal state is related to the operation characteristics of frequent configuration modification. The system will use the abnormal events of the abnormal operation component and the corresponding operation characteristics as training samples, input them into the decision tree model for training, and continuously optimize the model parameters. When other operation components have similar frequent configuration modification operations in the subsequent operation process, the abnormal operation component detection model can timely identify and analyze its abnormal pattern, predict that the operation component may be abnormal, and take preventive measures in advance to avoid the expansion of the abnormality. Through the continuous learning and optimization of the abnormal operation component detection model, the system can continuously improve the intelligent analysis and processing capabilities of the abnormal state of the operation component to ensure the stable operation of the entire system.

[0101] Reference Figure 2 An embodiment of the present invention provides a processing system 2 for isolating permissions of operation components, and the system 2 specifically includes:

[0102] The first processing module 201 is used to divide the operation platform function into multiple independent operation components according to the characteristics of the game operation scenario;

[0103] The second processing module 202 is used to establish a first correspondence between different operation roles and different operation components by analyzing the relationship between the operation roles and the operation capabilities, and bind a corresponding namespace to each operation role;

[0104] The third processing module 203 is used to determine whether any operation role has the authority to access the operation component through the namespace corresponding to the operation role when any operation role initiates an access request to any operation component;

[0105] The fourth processing module 204 is used to obtain a preset grayscale release strategy and information of a newly developed operation component, and smoothly open the newly developed operation component based on the grayscale release strategy and feedback data of several operation roles on the newly developed operation component;

[0106] The fifth processing module 205 is used to monitor the real-time performance indicator data of each operation component and isolate the operation components in abnormal state.

[0107] It is understandable that if Figure 1The contents of the processing method embodiment of the operation component permission isolation shown in the figure are applicable to the processing system embodiment of the operation component permission isolation. The functions specifically implemented by the processing system embodiment of the operation component permission isolation are similar to those of the embodiment of the operation component permission isolation. Figure 1 The processing method for isolating the permissions of the operation components shown in the embodiment is the same as that of the embodiment shown in the embodiment, and the beneficial effects achieved are the same as those of the embodiment shown in the embodiment. Figure 1 The beneficial effects achieved by the illustrated embodiment of the processing method for isolating the permissions of the operational components are also the same.

[0108] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0109] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0110] Reference Figure 3 The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a processing method for isolating the permissions of the operation components as described in any one of the above methods is implemented.

[0111] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0112] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0113] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as the program code of the computer program, etc. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0114] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the processing method for isolating the permissions of the operation components as described in any of the above methods is implemented.

[0115] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0116] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0118] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for isolating permissions of operation components, characterized in that: The method specifically comprises: According to the characteristics of the game operation scenario, the operation platform functions are divided into multiple independent operation components; By analyzing the relationship between operation roles and operation capabilities, the first correspondence between different operation roles and different operation components is established, and the corresponding namespace is bound to each operation role; When any operation role initiates an access request to any operation component, the namespace corresponding to the operation role is used to determine whether the operation role has the authority to access the operation component; Obtaining a preset grayscale release strategy and information about newly developed operation components, and smoothly opening the newly developed operation components based on the grayscale release strategy and feedback data from several operation roles on the newly developed operation components; By monitoring the real-time performance indicator data of each operation component, operation components with abnormal status will be isolated and processed.

2. The method according to claim 1, characterized in that According to the characteristics of the game operation scenario, the operation platform functions are divided into multiple independent operation components, including: Obtaining the type of the game operation scenario, and determining a number of core operation functions and a number of non-core operation functions according to the type; Determine an operation function division strategy for the operation platform function according to the core operation function and the non-core operation function, and divide the operation platform function into a plurality of independent operation components according to the operation function division strategy; By conducting collaborative testing on multiple independent operation components, the division of the operation components of the operation platform functions is adjusted and optimized.

3. The method according to claim 1, characterized in that By analyzing the relationship between the operation roles and the operation capabilities, a first correspondence between different operation roles and different operation components is established, and a corresponding namespace is bound to each operation role, specifically including: Use decision tree algorithm to analyze the association rules between operation roles and operation capabilities to obtain key features; According to the key features, a mapping matrix between operation roles and operation capabilities is constructed, wherein the mapping matrix includes different operation roles and their corresponding operation capability ranges; Logically isolating the operation components according to the mapping matrix, and determining a first corresponding relationship between each operation role and each operation component; Based on the first corresponding relationship, an independent namespace is created for each operation role, and the accessible operation components in the namespace corresponding to each operation role are determined to form a second corresponding relationship.

4. The method according to claim 3, characterized in that When any operation role initiates an access request to any operation component, determining whether the operation role has the authority to access the operation component through the namespace corresponding to the operation role specifically includes: Obtain a first access request initiated by a first operation role to a target operation component; Based on the second corresponding relationship, determining whether the target operation component is within the scope of the namespace corresponding to the first operation role; If the judgment is yes, the first operation role is allowed to access the target operation component; otherwise, the first operation role is denied to access the target operation component.

5. The method according to claim 1, characterized in that The step of smoothly opening the newly developed operation components based on the grayscale release strategy and the feedback data of the newly developed operation components from several operation roles specifically includes: According to the grayscale release strategy, the time range and target operation role of this grayscale release are obtained, and the newly developed operation component is grayscale released to the target operation role within the time range; During the grayscale release process, according to the feedback data of the target operation role on the newly developed operation component, the parameters of the grayscale release strategy are dynamically adjusted using the naive Bayes algorithm; Based on the operational data collected during the grayscale release, comprehensively evaluate the actual effects of the newly developed operational components to obtain evaluation results; Based on the evaluation result, it is determined whether to perform full open processing on the newly developed operation component.

6. The method according to any one of claims 1 to 5, characterized in that By monitoring the real-time performance indicator data of each operation component, the operation component in abnormal state is isolated and processed, specifically including: Acquire real-time performance indicator data of all operation components, and obtain key indicator data by processing the real-time performance indicator data; According to the preset abnormality severity assessment rules, each operation component is assessed through the key indicator data to see whether an abnormal state occurs, and the operation component in the abnormal state is identified as an abnormal operation component; The abnormal severity score of each abnormal operation component is calculated, and the corresponding processing scheme is determined from a preset abnormal processing mapping table according to the abnormal severity score.

7. The method according to claim 6, characterized in that The method further comprises: Record the key operation behaviors of each operation component to form an operation log; Associating the operation log with each abnormal operation component to determine the operation characteristics corresponding to the abnormal event occurring in each abnormal operation component; The abnormal events occurring in each abnormal operation component and their corresponding operation characteristics are used as input to train a decision tree model and generate an abnormal operation component detection model, which is used to identify and analyze the abnormal patterns of operation components in abnormal states.

8. A processing system for isolating permissions of operation components, characterized in that: The system specifically comprises: The first processing module is used to divide the operation platform functions into multiple independent operation components according to the characteristics of the game operation scenario; The second processing module is used to establish a first correspondence between different operation roles and different operation components by analyzing the relationship between the operation roles and the operation capabilities, and bind a corresponding namespace to each operation role; The third processing module is used to determine whether any operation role has the authority to access any operation component through the namespace corresponding to the operation role when any operation role initiates an access request to the operation component; The fourth processing module is used to obtain a preset grayscale release strategy and information of a newly developed operation component, and smoothly open the newly developed operation component based on the grayscale release strategy and feedback data of a plurality of operation roles on the newly developed operation component; The fifth processing module is used to monitor the real-time performance indicator data of each operation component and isolate the operation components in abnormal state.

9. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory. When the computer program is executed on the processor, the processing method for isolating the permissions of the operation components as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the processing method for isolating the permissions of the operation components as described in any one of claims 1 to 7 is implemented.

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