A software operation management system based on intelligent collaboration

Abstract

The application discloses a kind of software operation management systems based on intelligent cooperation, it is related to software operation management technical field, including data acquisition layer, data fusion layer, intelligent cooperation engine layer, core business operation layer, adaptive process engine layer, multi-role cooperation layer, decision support layer and system support layer.The software operation management system based on intelligent cooperation, the automation execution of the cooperation of each module and operation process is realized by intelligent cooperation engine layer, cross-module operation task can be completed without manual intervention, reduces manual operation link, reduces labor cost;And the efficient cooperation mechanism of multi-role cooperation layer, the processing period of work task is shortened, and the operation efficiency is further improved;Through the fusion prediction model and multi-objective optimization algorithm of intelligent resource allocation module, accurately predict resource demand, generate optimal resource allocation scheme;Combined with resource dynamic adjustment unit realizes the elasticity of resource, avoids the situation of resource excess or deficiency.

Description

Technical Field

[0001] This invention relates to the field of software operation management technology, specifically to a software operation management system based on intelligent collaboration. Background Technology

[0002] With the deepening of digital transformation, the operational scale of software products continues to expand, and the operational processes are becoming increasingly complex, encompassing multiple dimensions such as user management, resource scheduling, troubleshooting, performance monitoring, data analysis, and strategy optimization. Currently, most mainstream software operation and management systems adopt a modular architecture design, with each module undertaking specific operational functions, such as independent user operation modules, resource management modules, and monitoring modules.

[0003] In existing technologies, some software operation management systems introduce data statistics functions to achieve basic aggregation and display of operational data, providing preliminary data support for operational decisions; other systems use preset process templates to achieve semi-automated processing of some operational processes, reducing the intensity of manual operation.

[0004] Patent publication number CN114816723A discloses a load balancing system, method, and computer-readable storage medium. The load balancing system comprises a first scaling module and a second scaling module, each including at least two load balancers. It adjusts the number of load balancers based on the amount of data to be processed and allocates the data to be processed to the second scaling module. By flexibly adjusting the number of load balancers, the system improves the performance of processing capabilities.

[0005] Patent publication number CN115271896A discloses a method for verifying computerized accounting calculations. This method uses a BP neural network to process and securely input computerized accounting data twice. It effectively improves the efficiency of computerization while achieving complete electronic data entry. By utilizing relevant accounting functions to constrain the input of enterprise accounting data, it enhances the functionality of the accounting system. Furthermore, by leveraging the unique optimization function of neural networks, the system can automatically filter out erroneous or intrusive data, thereby ensuring the security of financial information.

[0006] In the aforementioned disclosed patent, the system method can flexibly process and transmit information and automate information processing. However, the system method in this patent still has the following problems: low accuracy of resource allocation, serious resource waste, existing systems cannot quickly respond to sudden user access demands, resulting in system response delays; during off-peak periods, redundant resources cannot be recovered in time, resulting in idle resources; poor flexibility and insufficient adaptability of the operation process; the operation process for e-commerce software differs greatly from that for industrial control software, and existing systems cannot quickly adapt to the operation needs of the two different scenarios; low efficiency of multi-role collaboration, unclear division of responsibilities; software operation involves multiple roles such as product, technology, operation, and customer service, and existing systems lack a unified role collaboration platform.

[0007] To address the aforementioned issues, there is an urgent need for innovative design based on the existing management system. Summary of the Invention

[0008] The purpose of this invention is to provide a software operation and management system based on intelligent collaboration to solve the problems mentioned in the background art, such as low accuracy of resource allocation, serious resource waste, poor flexibility of operation process, insufficient adaptability, low efficiency of multi-role collaboration, and unclear division of rights and responsibilities.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a software operation and management system based on intelligent collaboration, comprising a data acquisition layer, a data fusion layer, an intelligent collaboration engine layer, a core business operation layer, an adaptive process engine layer, a multi-role collaboration layer, a decision support layer, and a system support layer; The data acquisition layer is used to collect multi-dimensional data throughout the entire software operation lifecycle, including system operation data, user behavior data, business transaction data, resource usage data, fault alarm data, and external environment data. The data fusion layer connects to the data acquisition layer and is used to perform multi-dimensional fusion processing on the raw data, including data cleaning, data association, data desensitization, data standardization, and data storage, forming a unified operational data center.

[0010] The intelligent collaboration engine layer serves as the core collaboration unit of the system, connecting to the data fusion layer and being used to operate the data in the data center.

[0011] The core business operation layer connects to the intelligent collaboration engine layer and includes multiple business operation modules. The adaptive process engine layer connects the intelligent collaboration engine layer and the core business operation layer. The multi-role collaboration layer connects the core business operation layer and the decision support layer. The decision support layer connects the data fusion layer and the intelligent collaboration engine layer. The system support layer provides basic support services for the above layers.

[0012] Preferably, the data acquisition layer includes a multi-source data acquisition module and a data preprocessing module; the multi-source data acquisition module adopts a distributed acquisition architecture, including a system operation data acquisition unit, a user behavior data acquisition unit, a business transaction data acquisition unit, a resource usage data acquisition unit, a fault alarm data acquisition unit, and an external environment data acquisition unit; the data preprocessing module includes a data filtering unit, a data format conversion unit, a data deduplication unit, and a data completion unit; the data filtering unit is used to filter out noisy data and abnormal data in the acquired data.

[0013] Preferably, the data fusion layer includes a data cleaning module, a data association module, a data desensitization module, a data standardization module, and a distributed data storage module. The data cleaning module is used to perform deep cleaning on the standardized raw data, including processing missing values, outliers, and inconsistent data in the data. The data association module is used to establish association relationships between different types of data based on preset association rules.

[0014] Preferably, the data anonymization module is used to anonymize data involving user privacy and trade secrets, and the anonymization methods include data encryption, data replacement, data masking, and custom data anonymization rules. The data standardization module is used to perform unified standardization processing on the cleaned and associated data, including data dimension standardization, data unit standardization, and data encoding standardization. The distributed data storage module adopts a hybrid storage architecture of "relational database + non-relational database + time-series database".

[0015] Preferably, the intelligent collaboration engine layer is the core innovative unit of this invention, including a collaborative scheduling module, an intelligent resource configuration module, a dynamic state perception module, and an intelligent algorithm model library. The collaborative scheduling module, as the core hub for collaborative work among various modules, adopts an "event-driven + rule engine" collaborative scheduling mechanism to realize collaborative work among various business operation modules. The intelligent resource configuration module is used to operate the data center data and realizes precise configuration and dynamic adjustment of resources through intelligent algorithm models. The dynamic state perception module is used to perceive the overall status of software operation in real time, including system running status, business execution status, user experience status, and resource usage status. The intelligent algorithm model library is used to store and manage various intelligent algorithm models required by the system.

[0016] Preferably, the core business operation layer includes multiple independent but collaborative business operation modules. Each module interacts with the intelligent collaboration engine layer and the adaptive process engine layer through standardized interfaces to achieve data interaction and command response. Specifically, it includes a user operation module, a system monitoring module, a resource management module, a fault operation and maintenance module, a business operation module, a data analysis module, and a strategy optimization module. Based on the evaluation results, the operation strategy can be dynamically adjusted and iteratively optimized to ensure the effectiveness and adaptability of the operation strategy.

[0017] Preferably, the adaptive process engine layer includes a process modeling module, a process configuration module, a process execution module, a process monitoring module, and a process optimization module. The process modeling module provides a visual process modeling tool, supporting users to build operational process models through drag-and-drop and component-based configuration. The process configuration module is used to configure the constructed process model in detail, including the execution logic of process nodes, jump rules between nodes, process triggering conditions, process timeout handling mechanisms, and role permission configuration. The process execution module is used to receive collaborative scheduling instructions from the intelligent collaboration engine layer and drive each business operation module to execute operational tasks according to the configured process model. The process monitoring module is used to monitor the execution status of the process in real time. The process optimization module uses the monitoring data from the process monitoring module and the operational data from the data fusion layer to optimize the operational process.

[0018] Preferably, the multi-role collaboration layer includes a role management module, a permission configuration module, a collaborative work platform, and a message notification module. The role management module is used to uniformly manage various roles involved in software operation. The permission configuration module adopts a permission management mechanism based on the RBAC (Role-Based Access Control) model to achieve fine-grained configuration of permissions for each role. The collaborative work platform provides a unified work entry and collaborative work interface for each role. The message notification module is used to realize real-time information transmission between roles and supports multiple notification methods.

[0019] Preferably, the decision support layer includes a report generation module, a visualization module, a decision suggestion module, and an early warning and prediction module. The report generation module is used for the analysis results of the operation data center and data analysis module of the data fusion layer. The visualization module adopts big data visualization technology and provides a variety of visualization components. The early warning and prediction module is used for the status assessment results of the dynamic status perception module and the prediction model of the intelligent algorithm model library to realize early warning and trend prediction of operational risks. The decision suggestion module provides targeted intelligent suggestions for operational decisions based on the analysis results of the intelligent algorithm model library and the industry knowledge base.

[0020] Preferably, the system support layer includes a security protection module, a log management module, an interface management module, an authorization module, and a system monitoring module. The security protection module is used to ensure the secure operation of the system, including data security protection, network security protection, and application security protection. The log management module is used to log all operations and operating statuses of the system. The interface management module is used to uniformly manage the interfaces between various layers and modules within the system, as well as the interfaces between the system and external systems. The system monitoring module is used to monitor the operating status of the system itself in real time.

[0021] Compared with the prior art, the beneficial effects of the present invention are: (1) Improve operational efficiency and reduce labor costs: Through the intelligent collaboration engine layer, the deep collaboration of each module and the automated execution of the operation process can be realized. Complex cross-module operation tasks can be completed without human intervention, reducing manual operation links and reducing labor costs; and the efficient collaboration mechanism of the multi-role collaboration layer shortens the processing cycle of work tasks, further improving operational efficiency. (2) Achieve precise resource allocation and reduce resource costs: Through the fusion prediction model and multi-objective optimization algorithm of the intelligent resource allocation module, accurately predict resource demand and generate the optimal resource allocation scheme; Combined with the resource dynamic adjustment unit, realize the elastic scaling of resources and avoid resource surplus or shortage. (3) Improve the scientificity and accuracy of operational decision-making: The data fusion layer realizes the full-dimensional fusion of multi-source data and builds a unified operational data center; the decision support layer provides multi-dimensional operational analysis reports, accurate decision suggestions and early risk warnings based on the data of the operational data center and the analysis results of intelligent algorithm models, so that operational decision-making is transformed from "experience-driven" to "data-driven", which improves the scientificity and accuracy of decision-making and reduces operational risks. (4) Enhance the adaptability and scalability of the system: Through the visual process modeling and dynamic configuration function of the adaptive process engine layer, the system supports the custom adjustment of the operation process and can quickly adapt to the software operation needs of different industries and different scales; the core business operation module adopts a modular design and supports the flexible expansion of functions; the intelligent algorithm model library supports the access of custom models and can expand the intelligent functions of the system according to actual operation needs. (5) Improve user experience and business operation results: Through the accurate user profile and personalized operation strategy of the user operation module, combined with the rapid fault handling capabilities of the system monitoring module and the fault operation and maintenance module, the stability of the software system and user experience are improved; and based on the continuous optimization and iteration of the data analysis and strategy optimization module, the core operation indicators such as user retention rate and business conversion rate can be effectively improved, and the continuous development of software business can be promoted. (6) Ensuring system security and data security: The security protection module of the system support layer adopts multi-layer security protection technology to achieve all-round protection of data security, network security and application security; the authorization authentication module adopts a multi-factor authentication mechanism to ensure the legitimacy of user identity; the log management module realizes the full-process recording of system operation and running status, providing a basis for the tracing and handling of security incidents, and ensuring the safe and reliable operation of the system and data. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the internal layered platform structure of the system of the present invention; Figure 2 This is a schematic diagram of the system operation flow structure of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1: In a specific embodiment, the present invention provides the following technical solution: a software operation and management system based on intelligent collaboration, such as... Figure 1 - Figure 2 As shown, the specific operating procedures of the system are disclosed.

[0025] Data acquisition layer, data fusion layer, intelligent collaboration engine layer, core business operation layer, adaptive process engine layer, multi-role collaboration layer, decision support layer, and system support layer; The data acquisition layer is used to collect multi-dimensional data throughout the entire software operation lifecycle, including system operation data, user behavior data, business transaction data, resource usage data, fault alarm data, and external environment data. The data fusion layer and the data acquisition connection layer are used to perform multi-dimensional fusion processing on standardized raw data, including data cleaning, data association, data desensitization, data standardization and data storage, to form a unified operational data center; The intelligent collaboration engine layer, as the core collaboration unit of the system, connects to the data fusion layer and is used to operate the data in the data center; The core business operations layer and the intelligent collaboration engine layer include multiple business operations modules. The adaptive process engine layer connects the intelligent collaboration engine layer to the core business operations layer. The multi-role collaboration layer connects the core business operations layer to the decision support layer. The decision support layer connects the data fusion layer to the intelligent collaboration engine layer. The system support layer provides basic support services for all the above layers. The data acquisition layer includes a multi-source data acquisition module and a data preprocessing module. The multi-source data acquisition module adopts a distributed acquisition architecture, including system operation data acquisition units, user behavior data acquisition units, business transaction data acquisition units, resource usage data acquisition units, fault alarm data acquisition units, and external environment data acquisition units. The data preprocessing module includes a data filtering unit, a data format conversion unit, a data deduplication unit, and a data completion unit. The data filtering unit is used to filter noisy data and extraneous data from the acquired data. The data fusion layer comprises a data cleaning module, a data association module, a data anonymization module, a data standardization module, and a distributed data storage module. The data cleaning module performs deep cleaning on standardized raw data, including handling missing values, outliers, and inconsistent data. The data association module establishes relationships between different types of data based on preset association rules. The data anonymization module anonymizes data involving user privacy and trade secrets using methods such as data encryption, data replacement, data masking, and custom anonymization rules. The data standardization module performs unified standardization on the cleaned and associated data, including data dimension standardization, data unit standardization, and data encoding standardization. The distributed data storage module adopts a hybrid storage architecture of "relational database + non-relational database + time-series database".

[0026] The workflow of a software operation and management system based on intelligent collaboration is as follows: Data Acquisition and Preprocessing: The multi-source data acquisition module of the data acquisition layer uses a distributed acquisition architecture to collect multi-dimensional data from the entire lifecycle of software operation in real time, including system operation data, user behavior data, business transaction data, resource usage data, fault alarm data, and external environment data; the data preprocessing module filters, converts, deduplicates, and completes the collected data to obtain standardized raw data, which is then transmitted to the data fusion layer through a message queue; Data fusion and storage: The data fusion layer performs deep cleaning, data association, data anonymization, and data standardization on standardized raw data to construct a data association graph; the processed data is stored through a distributed data storage module of a hybrid storage architecture to form a unified operational data center; Intelligent collaborative scheduling and resource allocation: The dynamic status perception module of the intelligent collaborative engine layer uses data from the operation data center to perform real-time perception and quantitative evaluation of the overall status of software operation; the collaborative scheduling module generates collaborative scheduling instructions based on the status evaluation results and preset collaborative rules; the intelligent resource allocation module uses data from the operation data center to predict resource demand through a fusion prediction model, generates the optimal resource allocation scheme by combining constraints, and achieves precise resource allocation and elastic scaling through the resource dynamic adjustment unit. Operational process execution and business processing: The adaptive process engine layer drives each business operation module of the core business operation layer to execute specific operational tasks according to the configured process model based on the collaborative scheduling instructions of the intelligent collaboration engine layer; during the execution process, each business operation module collects business data in real time and uploads it to the data fusion layer to realize the closed-loop flow of data; Multi-role collaborative work: The multi-role collaboration layer provides each role with a dedicated work interface and permission management, and realizes task allocation, flow and collaboration through the collaborative work platform; the message notification module transmits work information in real time, ensuring information synchronization and efficient collaboration among roles; Decision Support and Optimization Iteration: Based on data from the operations data center and the business processing results from the core business operations layer, the decision support layer generates multi-dimensional operational analysis reports and enables real-time monitoring of operational status through a visualization module; the decision suggestion module generates targeted operational decision suggestions, and the early warning and prediction module enables early warning and trend prediction of operational risks; users adjust operational strategies and process configurations based on the output of the decision support layer, and drive each module to execute optimized operational tasks through the intelligent collaboration engine layer, achieving continuous optimization and iteration of software operation management.

[0027] Example 2: In one specific embodiment, such as Figure 1 - Figure 2 As shown, the process by which the system accurately schedules resources is disclosed.

[0028] The intelligent collaboration engine layer is the core innovative unit of this invention, comprising a collaborative scheduling module, an intelligent resource configuration module, a dynamic state perception module, and an intelligent algorithm model library. The collaborative scheduling module, as the core hub for collaborative work among various modules, adopts an "event-driven + rule engine" collaborative scheduling mechanism to achieve collaborative work between various business operation modules. The intelligent resource configuration module manages data in the operational data center, achieving precise resource configuration and dynamic adjustment through intelligent algorithm models. The dynamic state perception module is used to perceive the overall status of software operation in real time, including system running status, business execution status, user experience status, and resource usage status. The intelligent algorithm model library stores and manages various intelligent algorithm models required by the system. The core business operation layer includes multiple independent but collaborative business operation modules. Each module interacts with the intelligent collaboration engine layer and the adaptive process engine layer through standardized interfaces to achieve data interaction and command response; specifically, it includes a user operation module, a system monitoring module, a resource management module, and a fault operation and maintenance module. The adaptive process engine layer comprises a business operations module, a data analysis module, and a strategy optimization module. Based on evaluation results, it dynamically adjusts and iteratively optimizes operational strategies, ensuring their effectiveness and adaptability. The adaptive process engine layer includes a process modeling module, a process configuration module, a process execution module, a process monitoring module, and a process optimization module. The process modeling module provides a visual process modeling tool, allowing users to build operational process models through drag-and-drop and component-based configuration. The process configuration module is used to configure the built process model in detail, including the execution logic of process nodes, jump rules between nodes, process triggering conditions, process timeout handling mechanisms, and role permission configuration. The process execution module receives collaborative scheduling instructions from the intelligent collaboration engine layer, driving each business operations module to execute operational tasks according to the configured process model. The process monitoring module monitors the execution status of the process in real time. The process optimization module uses the monitoring data from the process monitoring module and the operational data from the data fusion layer to optimize the operational process.

[0029] Data Acquisition and Fusion: The data acquisition layer collects real-time user access data, order transaction data, and resource usage data from historical promotional events, as well as current user growth trend data and external environmental data (such as weather and holidays); the data fusion layer cleans, correlates, and standardizes the collected data to construct a subset of operational data specific to the promotional event. Resource demand prediction: The intelligent resource configuration module of the intelligent collaboration engine layer calls the LSTM-XGBoost fusion prediction model in the intelligent algorithm model library, inputs a subset of operational data, and predicts the peak user access, peak order transaction, and corresponding resource demands such as CPU, memory, network, and storage at different time periods during the "Double Eleven" promotion. Resource optimization and allocation: Based on the resource demand forecast results, combined with business priorities (such as order payment business having higher priority than product browsing business), SLA constraints, resource costs, and other conditions, the resource optimization and allocation unit uses the NSGA-Ⅲ multi-objective optimization algorithm to generate a resource allocation plan for the promotion period and clarify the resource allocation amount for each business module in different time periods. Process Configuration and Collaborative Scheduling: The adaptive process engine layer configures "exclusive operational processes for major promotional events" based on the operational needs of the event, including user access diversion processes, order rapid processing processes, and emergency fault response processes; the collaborative scheduling module of the intelligent collaborative engine layer triggers "collaborative rules for major promotional events," generates collaborative scheduling instructions, and sends them to the resource management module, system monitoring module, and business operation module. Dynamic resource scheduling and business execution: The resource management module expands the resources needed during the promotion period in advance according to the resource allocation plan and collaborative scheduling instructions; the system monitoring module monitors the system operation status and resource usage in real time; the business operation module executes business tasks such as user access diversion and order processing according to the configured promotion-specific process; when the user access volume exceeds the predicted peak, the dynamic resource adjustment unit automatically triggers the emergency expansion process to ensure system response speed; Multi-role collaboration and decision support: The multi-role collaboration layer provides dedicated work interfaces for operations personnel, technical maintenance personnel, and customer service personnel during major promotions, synchronizing information such as resource usage status, system operation status, and order processing progress in real time; user issues collected by customer service personnel can be quickly assigned to relevant roles for handling; the decision support layer generates real-time operational analysis reports for major promotion activities, displaying key indicators such as user traffic, order transaction volume, and resource utilization; the early warning and prediction module provides real-time warnings of system operation risks. Post-event optimization: After the promotional event ends, the data analysis module conducts in-depth analysis of the operational data during the event to evaluate the effectiveness of the resource allocation plan and the rationality of the process configuration; the strategy optimization module generates resource scheduling optimization suggestions and process optimization suggestions; the operations staff adjusts the resource allocation strategy and process templates according to the suggestions to provide a reference for subsequent promotional events.

[0030] Example 3: Based on the above examples, such as... Figure 1 - Figure 2 As shown, the system's control over user information is disclosed.

[0031] The multi-role collaboration layer includes a role management module, a permission configuration module, a collaborative work platform, and a message notification module. The role management module manages all roles involved in software operation in a unified manner. The permission configuration module adopts an RBAC (Role-Based Access Control) model-based permission management mechanism to achieve fine-grained configuration of permissions for each role. The collaborative work platform provides a unified work entry point and collaborative work interface for each role. The message notification module enables real-time information transmission between roles and supports multiple notification methods. The decision support layer includes a report generation module, a visualization module, a decision suggestion module, and an early warning and prediction module. The report generation module uses the analysis results from the operational data center and data analysis module of the data fusion layer. The visualization module uses big data visualization technology and provides various visualization components. The early warning and prediction module... The dynamic state perception module uses state assessment results and predictive models from the intelligent algorithm model library to achieve early warning and trend prediction of operational risks. The decision suggestion module provides targeted intelligent suggestions for operational decisions based on the analysis results of the intelligent algorithm model library and the industry knowledge base. The system support layer includes a security protection module, a log management module, an interface management module, an authorization and authentication module, and a system monitoring module. The security protection module is used to ensure the safe operation of the system, including data security protection, network security protection, and application security protection. The log management module is used to log all operation behaviors and operating statuses of the system. The interface management module is used to uniformly manage the interfaces between various layers and modules within the system, as well as the interfaces between the system and external systems. The system monitoring module is used to monitor the operating status of the system itself in real time.

[0032] User data collection and fusion: The data collection layer collects user login data, operation behavior data, order transaction data, user feedback data, etc. in real time; the data fusion layer cleans and correlates this data to build a complete user profile, including dimensions such as user activity, spending power, function usage preferences, and satisfaction with feedback handling. Churn Risk Identification and Prediction: The dynamic state perception module of the intelligent collaboration engine layer calls the user churn prediction model (a fusion model based on logistic regression and random forest) in the intelligent algorithm model library, inputs user profile data, quantifies the churn risk of each user, and generates the user churn risk level; Collaborative Scheduling and Strategy Generation: The collaborative scheduling module triggers the "User Churn Warning Collaborative Rule," coordinating the user operations module, data analysis module, and strategy optimization module to work together; the data analysis module conducts in-depth analysis of the behavioral data of users at high churn risk to identify key factors leading to user churn (such as poor user experience, slow customer service response, and few promotional activities); based on the analysis results, the strategy optimization module generates targeted user retention strategies, such as pushing exclusive coupons to users at high churn risk, optimizing the handling process for user feedback issues, and assigning customer service personnel for one-on-one follow-up. Retention strategy execution and process-driven: The adaptive process engine layer configures "user retention exclusive processes" based on the generated retention strategies, including coupon push processes, problem follow-up processes, and satisfaction survey processes; the user operation module of the core business operation layer executes the retention strategies according to the processes, and customer service personnel receive follow-up tasks for high-churn-risk users through the collaborative work platform of the multi-role collaboration layer. Effectiveness Evaluation and Optimization: The decision support layer tracks the execution effect of retention strategies in real time, generates user retention rate analysis reports, and evaluates the effectiveness of different retention strategies; for users who are successfully retained, it analyzes their behavioral changes and summarizes effective retention experiences; for users who are not retained, it conducts in-depth analysis of the reasons for churn and optimizes subsequent retention strategies; and through the intelligent collaboration engine layer, it drives the user operation module to iteratively optimize retention strategies and improve the overall user retention rate.

[0033] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A software operation and management system based on intelligent collaboration, characterized in that, It includes a data acquisition layer, a data fusion layer, an intelligent collaboration engine layer, a core business operation layer, an adaptive process engine layer, a multi-role collaboration layer, a decision support layer, and a system support layer; The data acquisition layer is used to collect multi-dimensional data throughout the entire software operation lifecycle, including system operation data, user behavior data, business transaction data, resource usage data, fault alarm data, and external environment data. The data fusion layer connects to the data acquisition layer and performs multi-dimensional fusion processing on the standardized raw data, including data cleaning, data association, data desensitization, data standardization, and data storage, forming a unified operational data center. The intelligent collaboration engine layer, as the core collaboration unit of the system, connects to the data fusion layer and is used to operate the data in the data center. The core business operation layer connects to the intelligent collaboration engine layer and includes multiple business operation modules. The adaptive process engine layer connects the intelligent collaboration engine layer and the core business operation layer. The multi-role collaboration layer connects the core business operation layer and the decision support layer. The decision support layer connects the data fusion layer and the intelligent collaboration engine layer. The system support layer provides basic support services for the above layers.

2. The software operation and management system based on intelligent collaboration according to claim 1, characterized in that: The data acquisition layer includes a multi-source data acquisition module and a data preprocessing module; the multi-source data acquisition module adopts a distributed acquisition architecture, including a system operation data acquisition unit, a user behavior data acquisition unit, a business transaction data acquisition unit, a resource usage data acquisition unit, a fault alarm data acquisition unit, and an external environment data acquisition unit; The data preprocessing module includes a data filtering unit, a data format conversion unit, a data deduplication unit, and a data completion unit; the data filtering unit is used to filter out noisy and abnormal data in the collected data.

3. The software operation and management system based on intelligent collaboration according to claim 2, characterized in that: The data fusion layer includes a data cleaning module, a data association module, a data desensitization module, a data standardization module, and a distributed data storage module. The data cleaning module is used to perform deep cleaning on standardized raw data, including processing missing values, outliers, and inconsistent data. The data association module is used to establish association relationships between different types of data based on preset association rules.

4. The software operation and management system based on intelligent collaboration according to claim 3, characterized in that: The data anonymization module is used to anonymize data involving user privacy and trade secrets. The anonymization methods include data encryption, data replacement, data masking, and custom data anonymization rules. The data standardization module is used to perform unified standardization processing on the cleaned and associated data, including data dimension standardization, data unit standardization, and data encoding standardization. The distributed data storage module adopts a hybrid storage architecture of "relational database + non-relational database + time-series database".

5. A software operation and management system based on intelligent collaboration according to claim 4, characterized in that: The intelligent collaboration engine layer is the core innovative unit of this invention, comprising a collaborative scheduling module, an intelligent resource configuration module, a dynamic state perception module, and an intelligent algorithm model library. The collaborative scheduling module, as the core hub for collaborative work among various modules, adopts an "event-driven + rule engine" collaborative scheduling mechanism to achieve collaborative work between various business operation modules. The intelligent resource configuration module is used to manage data in the data center, achieving precise resource configuration and dynamic adjustment through intelligent algorithm models. The dynamic state perception module is used to perceive the overall state of software operation in real time, including system running status, business execution status, user experience status, and resource usage status. The intelligent algorithm model library is used to store and manage various intelligent algorithm models required by the system.

6. A software operation and management system based on intelligent collaboration according to claim 5, characterized in that: The core business operation layer includes multiple independent yet collaborative business operation modules. Each module interacts with the intelligent collaboration engine layer and the adaptive process engine layer through standardized interfaces to achieve data exchange and command response. Specifically, it includes a user operation module, a system monitoring module, a resource management module, a fault operation and maintenance module, a business operation module, a data analysis module, and a strategy optimization module. Based on the evaluation results, it can dynamically adjust and iteratively optimize the operation strategy to ensure the effectiveness and adaptability of the operation strategy.

7. A software operation and management system based on intelligent collaboration according to claim 6, characterized in that: The adaptive process engine layer includes a process modeling module, a process configuration module, a process execution module, a process monitoring module, and a process optimization module. The process modeling module provides a visual process modeling tool, allowing users to build operational process models through drag-and-drop and component-based configuration. The process configuration module is used to configure the built process model in detail, including the execution logic of process nodes, jump rules between nodes, process triggering conditions, process timeout handling mechanisms, and role permission configuration. The process execution module is used to receive collaborative scheduling instructions from the intelligent collaboration engine layer and drive each business operation module to execute operational tasks according to the configured process model. The process monitoring module is used to monitor the execution status of the process in real time. The process optimization module uses the monitoring data from the process monitoring module and the operational data from the data fusion layer to optimize the operational process.

8. A software operation and management system based on intelligent collaboration according to claim 7, characterized in that: The multi-role collaboration layer includes a role management module, a permission configuration module, a collaborative work platform, and a message notification module. The role management module is used to uniformly manage various roles involved in software operation. The permission configuration module adopts a permission management mechanism based on the RBAC (Role-Based Access Control) model to achieve fine-grained configuration of permissions for each role. The collaborative work platform provides a unified work entry and collaborative work interface for each role. The message notification module is used to realize real-time information transmission between roles and supports multiple notification methods.

9. A software operation and management system based on intelligent collaboration according to claim 8, characterized in that: The decision support layer includes a report generation module, a visualization module, a decision suggestion module, and an early warning and prediction module. The report generation module is used to analyze the results of the operation data center and data analysis module in the data fusion layer. The visualization module uses big data visualization technology to provide a variety of visualization components. The early warning and prediction module is used to analyze the status assessment results of the dynamic status perception module and the prediction model of the intelligent algorithm model library to achieve early warning and trend prediction of operational risks. The decision suggestion module provides targeted intelligent suggestions for operational decisions based on the analysis results of the intelligent algorithm model library and the industry knowledge base.

10. A software operation and management system based on intelligent collaboration according to claim 9, characterized in that: The system support layer includes a security protection module, a log management module, an interface management module, an authorization module, and a system monitoring module. The security protection module is used to ensure the secure operation of the system, including data security protection, network security protection, and application security protection. The log management module is used to log all operations and operating statuses of the system. The interface management module is used to uniformly manage the interfaces between various layers and modules within the system, as well as the interfaces between the system and external systems. The system monitoring module is used to monitor the operating status of the system itself in real time.

Images