Intelligent clothes supply command management system based on data fusion

Abstract

The invention relates to an intelligent service supply command management system based on data fusion, which belongs to the technical field of power supply services and comprises an application layer, a service layer, a data layer, a display layer and a support layer. Wherein the application layer is used for providing function applications and comprises a service architecture and an application architecture; the service layer is used for uniformly managing all public services facing the application layer and is convenient to call and use; the data layer is used for providing data aggregation processing; the display layer comprises a PC end and a visual large screen, and the display and use requirements of the PC end and the visual large screen are met; the supporting layer is used for providing power grid basic capabilities of storage, network, communication and safety. Based on intelligent application of an artificial intelligence technology in power supply service business data, business scenes and business processes, monitoring processes, analysis results and business flow conditions of various work orders are mastered in real time, crux handling problems are found in time, collaboration of each specialty of supply services is promoted, and the problem handling efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply services, and in particular relates to an intelligent power supply command and management system based on data fusion. Background Art

[0002] At present, State Grid Corporation's power supply service command has realized the operation of online work orders and business systems such as marketing, distribution and dispatching, digital urban management, and 12345, and has saved various types of supply and service work order data such as non-repair work orders, repair work orders, urban management collaborative work orders, 12345 work orders, etc. Some data have been connected and data sharing has been realized.

[0003] For example, Chinese patent number CN112150027A discloses a power supply service command platform system, including a business system, an application system, a data system, and business processes and data interactions with other systems. However, the current patent technology lacks multi-professional and business system integration, intelligent analysis applications, and automatic business process processing based on the power supply service command business scenario. Summary of the invention

[0004] To solve the above problems, the technical solution adopted by the present invention is: An intelligent service command and management system based on data fusion, including application layer, service layer, data layer, display layer and support layer; Wherein: the application layer is used to provide functional applications, including business architecture and application architecture; The service layer is used to uniformly manage all public services for the application layer to facilitate calling and use; The data layer is used to provide data aggregation processing; The display layer includes PC and large visualization screen, which can meet the display and use needs of PC and large visualization screen. The support layer is used to provide storage, networking, communication, and secure grid infrastructure capabilities.

[0005] According to the above technical solution, the business architecture of the application layer includes intelligent monitoring work orders, distribution network operation monitoring and analysis, distribution network scheduling analysis, video analysis, intelligent AI services and early warning management and control; The distribution network operation monitoring and analysis includes power outage information push, line power outage, heavy overload, open capacity, and distribution network tripping. It is used to monitor and analyze the three-level power outage data of customers, substations, and lines, and early warning and supervision functions, realize repeated power outage statistics and online feedback, and realize statistical analysis of power outage causes, types, true rates, feedback rates, and governance effectiveness through feedback. Heavy overload data can realize automatic statistical analysis of open capacity based on the rated current value, substation photovoltaic, distribution transformers, and line data reported; Intelligent monitoring of work orders, distribution network operation monitoring and analysis, distribution network dispatching analysis, and video analysis of business hall behavior norms. According to business rules, intelligent AI services can perform voice scene query, broadcast, WeChat notification, and instant telephone outbound call services according to business scenario needs; Early warning management and control includes work order early warning business, work order supervision business, work order escalation business and work order link early warning business. Early warning management and control conducts early warning supervision analysis and inquiry in accordance with early warning rules, and dispatches early warning notifications by WeChat notifications and instant outbound calls. Early warning management and control is connected with the visualization section to display or issue early warning notifications through graphics.

[0006] Preferably, the application architecture of the application layer includes intelligent monitoring work order applications, distribution network scheduling analysis applications, intelligent report generation tool applications, visual management modules, intelligent AI service applications, intelligent voice phone outbound call applications, RPA process automation applications, WeChat SMS applications, and system support management applications; The distribution network dispatch analysis application includes risk warning feedback, maintenance application process, maintenance dispatch log, distribution network line archive and important user power protection, which is used to apply work order monitoring and analysis information, distribution network operation monitoring and early warning information, large screen visualization information, etc. through voice interactive query, voice broadcast, WeChat and telephone. Intelligent applications of monitoring work orders include upgrading and supervising work orders, focusing on work orders, work order satisfaction analysis, and repair rate statistics for 10,000 households. It is used to provide data analysis and early warning functions for 95598 and 12398 according to the rules, as well as monitoring and analysis of work order data, reminders for each link of the work order, time limit for arriving at the site, and duration of emergency repairs; The intelligent report generation tool application automatically aggregates and integrates data through robots, automatically generates daily, weekly, and monthly reports, and realizes one-click reporting; The video analysis application is used to build a business hall behavior normative analysis model, conduct patrol analysis of business hall videos, discover irregular service behaviors in real time, and use RPA automation technology to distribute behavior records to marketing to urge rectification, and support intelligent outbound call reminders; System support management applications include organizational structure, authority management, and configuration management, which are used to provide support management for application architecture; The visual management section includes work order information visualization and early warning supervision information visualization, and uses PC and large-screen visualization tools for planning and display.

[0007] Preferably, intelligent AI service applications include intelligent voice interaction, RPA automated processes and instant messaging. The intelligent voice interaction of intelligent AI services is divided into voice interaction and voice broadcasting. The RPA automated process provides comprehensive artificial intelligence services for dispatching and early warning according to business scenarios and monitoring needs; instant messaging is divided into WeChat, SMS and outbound calls.

[0008] Preferably, the service layer includes data fusion analysis services, scenario business services, visualization services, automation services and AI artificial intelligence services, wherein the data fusion analysis service adopts the segmentation principle to segment big data into multiple small data sets, and performs rapid processing and analysis on each of the small data sets according to their respective model algorithms, and then merges the analysis results of each small data set; the scenario business service is split into multiple modules according to different business scenarios, and realizes intelligent scheduling and collaborative work between modules through preset rules and algorithms; the automation service is used to automatically identify business scenarios, intelligently match service modules according to different business scenarios, and realize automation and intelligence of services.

[0009] Preferably, the data layer architecture includes a data source module, an application data module, and a data integration module; the data layer obtains marketing business, procurement business, emergency repair business, and OMS business data through the data source module or the data middle platform, and interacts with the data integration module; the application data module includes distribution network operation monitoring and analysis, intelligent AI service application, and intelligent monitoring work order; the data integration module includes archival data, collected data, distribution network data, and a data platform. The data source is stored on the data platform and data conversion, data fusion, data analysis, and data modeling are carried out to form emergency repair work order analysis data, outbound call data, distribution network scheduling analysis, intelligent report generation tools, video analysis, and omni-channel work order data.

[0010] Preferably, the support layer complies with the State Grid security architecture system, which includes data security, host security, network security, physical security, boundary security, application security and security management, and is used to achieve comprehensive security protection of information systems.

[0011] Preferably, the physical security includes computer room security measures, computer room access security management and intrusion detection. The computer room access security management includes biometric management and material field monitoring. Biometric management is divided into face recognition and fingerprint recognition. Material field monitoring uses Internet of Things communication to conduct real-time monitoring and early warning of the material field environment. Intrusion detection uses machine learning algorithms to improve the accuracy and real-time performance of intrusion detection. The data security includes data storage security, data analysis security, data transmission security, and data backup and recovery. Quantum encryption algorithms are used for data transmission to improve data transmission security. Differential privacy algorithms are used for data analysis security to dynamically adjust the privacy protection level according to user needs and scenarios, so that data can still be safely and effectively analyzed in an encrypted state.

[0012] Preferably, the host security includes the service operating system security and the database system security, the network security includes the Internet channel security and the network equipment security; the boundary security includes the unified Internet boundary and the external network security interaction platform mobile terminal access authentication combination, the information internal and external vertical boundaries, and the information intranet user domain boundary; the application security includes the server-side application security combined with the interactive security authentication, the client security and the application system structure security; the security architecture system and the data middle station communication adopt firewall and intrusion monitoring protection measures; the information intranet server installs vulnerability scanning and traffic security monitoring software to ensure system security.

[0013] The present invention also provides an intelligent service supply command method based on data fusion, which is implemented by the above-mentioned intelligent service supply command management system based on data fusion, and specifically includes the following steps: Step S1: Data information collection: obtaining marketing business, procurement business, OMS business and emergency repair business data through data sources; Step S2, data information processing, including data conversion, data fusion, data cleaning, data standardization, data modeling and data analysis, for obtaining comprehensive application data; Step S3: feature extraction, using a deep learning algorithm to extract features from the comprehensive application data after data processing; Step S4: feature fusion, using a recurrent neural network (RNN) to fuse the extracted feature data to form a comprehensive data set; Step S5: training the model, using the comprehensive data set to train the deep learning model; Step S6: Generate a command decision, apply the trained deep learning model to the command decision, and obtain a service command decision generated by comprehensive application data; Step S7: Implement the command decision, implement the supply and service command decision, and realize real-time monitoring, statistical analysis and overall command of the power supply service network; The steps of data information processing in step S2 include: Step S21: data conversion, converting the acquired data; Step S22: data fusion, fusing the converted data; Step S23: data cleaning: cleaning the data after data fusion; Step S24: data standardization: standardizing the cleaned data; Step S25: data modeling, establishing a data model based on the acquired data; Step S26: data analysis; In step S3, a convolutional neural network (CNN) is used to extract features from the data after data information processing.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides an intelligent power supply and service command and management system based on data fusion. The present invention is based on the intelligent application of artificial intelligence technology in power supply service business data, business scenarios and business processes, and can grasp the monitoring process, analysis results and business flow of various work orders in real time, timely discover the key problems in handling, promote the collaboration of various professional power supply and service, and improve the efficiency of problem handling.

[0015] 2. The present invention provides an intelligent power supply and service command and management system based on data fusion. The present invention is based on the deep integration of new technologies such as artificial intelligence, big data analysis and cloud computing with power supply service command business, promotes power supply service command to move towards intelligent command, and improves the quality and efficiency of power supply service command. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the system architecture structure of the present invention; Figure 2 This is a diagram of the business architecture of the system of the present invention; Figure 3 This is a structural diagram of the application architecture of the system of the present invention; Figure 4 This is a structural diagram of the data architecture of the system of the present invention; Figure 5 This is a structural diagram of the system security architecture of the present invention.

[0017] In the figure: 1. Application layer, 2. Service layer, 3. Data layer, 4. Presentation layer, 5. Support layer. DETAILED DESCRIPTION

[0018] In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "first", "second", "middle end" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present disclosure, and do not specifically refer to any part or element in the present disclosure and should not be understood as limitations on the present disclosure.

[0019] The present invention is further described in detail below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work are within the protection scope of the present invention.

[0020] like Figure 1 to Figure 5As shown, the present invention provides an embodiment of an intelligent supply and service command and management system based on data fusion, including an application layer 1, a service layer 2, a data layer 3, a display layer 4 and a support layer 5; wherein: the application layer 1 is used to provide functional applications, including a business architecture and an application architecture; the service layer 2 is used to uniformly manage all public services facing the application layer 1, which is convenient for calling and using; the data layer 3 is used to provide data aggregation processing; the display layer 4 includes a PC terminal and a large visualization screen to meet the display and use needs of the PC terminal and the large visualization screen; the support layer 5 is used to provide storage, network, communication, and secure basic power grid capabilities.

[0021] The business architecture of the application layer 1 includes intelligent monitoring work orders, distribution network operation monitoring and analysis, distribution network scheduling analysis, video analysis, intelligent AI services and early warning management and control; The distribution network operation monitoring and analysis includes power outage information push, line power outage, heavy overload, open capacity, and distribution network tripping. It is used to monitor and analyze the three-level power outage data of customers, substations, and lines, and early warning and supervision functions, realize repeated power outage statistics and online feedback, and realize statistical analysis of power outage causes, types, true rates, feedback rates, and governance effectiveness through feedback. Heavy overload data can realize automatic statistical analysis of open capacity based on the rated current value, substation photovoltaic, distribution transformers, and line data reported; Intelligent monitoring of work orders, distribution network operation monitoring and analysis, distribution network dispatching analysis, and video analysis of business hall behavior norms. According to business rules, intelligent AI services can perform voice scene query, broadcast, WeChat notification, and instant telephone outbound call services according to business scenario needs; Early warning management and control includes work order early warning business, work order supervision business, work order escalation business and work order link early warning business. Early warning management and control conducts early warning supervision analysis and inquiry in accordance with early warning rules, and dispatches early warning notifications by WeChat notifications and instant outbound calls. Early warning management and control is connected with the visualization section to display or issue early warning notifications through graphics.

[0022] The application architecture of the application layer 1 includes intelligent monitoring work order applications, distribution network scheduling analysis applications, intelligent report generation tool applications, visual management modules, intelligent AI service applications, intelligent voice phone outbound call applications, RPA process automation applications, WeChat SMS applications, and system support management applications; The distribution network dispatch analysis application includes risk warning feedback, maintenance application process, maintenance dispatch log, distribution network line archive and important user power protection, which is used to apply work order monitoring and analysis information, distribution network operation monitoring and early warning information, large screen visualization information, etc. through voice interactive query, voice broadcast, WeChat and telephone. Intelligent applications of monitoring work orders include upgrading and supervising work orders, focusing on work orders, work order satisfaction analysis, and repair rate statistics for 10,000 households. It is used to provide data analysis and early warning functions for 95598 and 12398 according to the rules, as well as monitoring and analysis of work order data, reminders for each link of the work order, time limit for arriving at the site, and duration of emergency repairs; The intelligent report generation tool application automatically aggregates and integrates data through robots, automatically generates daily, weekly, and monthly reports, and realizes one-click reporting; The video analysis application is used to build a business hall behavior normative analysis model, conduct patrol analysis of business hall videos, discover irregular service behaviors in real time, and use RPA automation technology to distribute behavior records to marketing to urge rectification, and support intelligent outbound call reminders; System support management applications include organizational structure, authority management, and configuration management, which are used to provide support management for application architecture; The visual management section includes work order information visualization and early warning supervision information visualization, and uses PC and large-screen visualization tools for planning and display.

[0023] Intelligent AI service applications include intelligent voice interaction, RPA automated processes and instant messaging. The intelligent voice interaction of intelligent AI services is divided into voice interaction and voice broadcasting. The RPA automated process provides comprehensive artificial intelligence services for scheduling and early warning based on business scenarios and monitoring needs; instant messaging is divided into WeChat, SMS and outbound calls.

[0024] The service layer 2 includes data fusion analysis services, scenario business services, visualization services, automation services and AI artificial intelligence services. The data fusion analysis service adopts the segmentation principle to divide the big data into multiple small data sets, and quickly processes and analyzes them according to their respective model algorithms, and then merges the analysis results of each small data set; the scenario business service is divided into multiple modules according to different business scenarios, and realizes intelligent scheduling and collaborative work between modules through preset rules and algorithms; the automation service is used to automatically identify business scenarios, and intelligently match service modules according to different business scenarios to realize automation and intelligence of services.

[0025] The data layer 3 architecture includes a data source module, an application data module, and a data integration module; the data layer 3 obtains marketing business, procurement business, emergency repair business, and OMS business data through the data source module or the data middle platform, and interacts with the data integration module; the application data module includes distribution network operation monitoring and analysis, intelligent AI service application, and intelligent monitoring work order; the data integration module includes archival data, collected data, distribution network data, and a data platform. The data source is stored on the data platform and data conversion, data fusion, data analysis, and data modeling are carried out to form emergency repair work order analysis data, outbound call data, distribution network scheduling analysis, intelligent report generation tools, video analysis, and omni-channel work order data.

[0026] The support layer 5 complies with the State Grid security architecture system, which includes data security, host security, network security, physical security, boundary security, application security and security management, and is used to achieve comprehensive security protection of information systems.

[0027] The physical security includes computer room security measures, computer room access security management and intrusion detection. The computer room access security management includes biometric management and material field monitoring. Biometric management is divided into face recognition and fingerprint recognition. Material field monitoring uses Internet of Things communication to conduct real-time monitoring and early warning of the material field environment. Intrusion detection uses machine learning algorithms to improve the accuracy and real-time performance of intrusion detection. The data security includes data storage security, data analysis security, data transmission security and data backup and recovery. Quantum encryption algorithms are used for data transmission to improve data transmission security. Differential Privacy algorithms are used for data analysis security to dynamically adjust the privacy protection level according to user needs and scenarios, so that data can still be safely and effectively analyzed in an encrypted state.

[0028] The host security includes the service operating system security and the database system security, the network security includes the Internet channel security and the network equipment security; the boundary security includes the unified Internet boundary and the external network security interaction platform mobile terminal access authentication combination, the internal and external vertical boundaries of information, and the information intranet user domain boundary; the application security includes the server-side application security combined with interactive security authentication, client security and application system structure security; the security architecture system and the data middle station communication adopt firewall and intrusion monitoring protection measures; the information intranet server installs vulnerability scanning and traffic security monitoring software to ensure system security.

[0029] The present invention also provides an intelligent service supply command method based on data fusion, which is implemented by an intelligent service supply command management system based on data fusion, and specifically includes the following steps: Step S1: Data information collection: obtaining marketing business, procurement business, OMS business and emergency repair business data through data sources; Step S2, data information processing, including data conversion, data fusion, data cleaning, data standardization, data modeling and data analysis, for obtaining comprehensive application data; Step S3: feature extraction, using a deep learning algorithm to extract features from the comprehensive application data after data processing; Step S4: feature fusion, using a recurrent neural network (RNN) to fuse the extracted feature data to form a comprehensive data set; Step S5: training the model, using the comprehensive data set to train the deep learning model; Step S6: Generate a command decision, apply the trained deep learning model to the command decision, and obtain a service command decision generated by comprehensive application data; Step S7: Implement the command decision, implement the supply and service command decision, and realize real-time monitoring, statistical analysis and overall command of the power supply service network; The steps of data information processing in step S2 include: Step S21: data conversion, converting the acquired data; Step S22: data fusion, fusing the converted data; Step S23: data cleaning: cleaning the data after data fusion; Step S24: data standardization: standardizing the cleaned data; Step S25: data modeling, establishing a data model based on the acquired data; Step S26: data analysis; In step S3, a convolutional neural network (CNN) is used to extract features from the data after data information processing.

[0030] Of course, the above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. An intelligent supply and service command and management system based on data fusion, characterized by: It includes application layer (1), service layer (2), data layer (3), presentation layer (4) and support layer (5); Wherein: the application layer (1) is used to provide functional applications, including business architecture and application architecture; The service layer (2) is used to centrally manage all public services for the application layer (1) to facilitate calling and use; The data layer (3) is used to provide data aggregation processing; The display layer (4) includes a PC terminal and a large visualization screen, meeting the display and usage needs of the PC terminal and the large visualization screen; The support layer (5) is used to provide storage, network, communication, and secure grid infrastructure capabilities.

2. According to claim 1, the intelligent supply and service command and management system based on data fusion is characterized by: The business architecture of the application layer (1) includes intelligent monitoring work orders, distribution network operation monitoring and analysis, distribution network scheduling analysis, video analysis, intelligent AI services and early warning management and control; The distribution network operation monitoring and analysis includes power outage information push, line power outage, heavy overload, open capacity, and distribution network tripping. It is used to monitor and analyze the three-level power outage data of customers, substations, and lines, and early warning and supervision functions, realize repeated power outage statistics and online feedback, and realize statistical analysis of power outage causes, types, true rates, feedback rates, and governance effectiveness through feedback. Heavy overload data can realize automatic statistical analysis of open capacity based on the rated current value, substation photovoltaic, distribution transformers, and line data reported; Intelligent monitoring of work orders, distribution network operation monitoring and analysis, distribution network dispatching analysis, and video analysis of business hall behavior norms. According to business rules, intelligent AI services can perform voice scene query, broadcast, WeChat notification, and instant telephone outbound call services according to business scenario needs; Early warning management and control includes work order early warning business, work order supervision business, work order escalation business and work order link early warning business. Early warning management and control conducts early warning supervision analysis and inquiry in accordance with early warning rules, and dispatches early warning notifications by WeChat notifications and instant outbound calls. Early warning management and control is connected with the visualization section to display or issue early warning notifications through graphics.

3. According to claim 1, the intelligent service command and management system based on data fusion is characterized by: The application architecture of the application layer (1) includes intelligent monitoring work order applications, distribution network scheduling analysis applications, intelligent report generation tool applications, visual management modules, intelligent AI service applications, intelligent voice phone outbound call applications, RPA process automation applications, WeChat SMS applications, and system support management applications; The distribution network dispatch analysis application includes risk warning feedback, maintenance application process, maintenance dispatch log, distribution network line archive and important user power protection, which is used to apply work order monitoring and analysis information, distribution network operation monitoring and early warning information, large screen visualization information, etc. through voice interactive query, voice broadcast, WeChat and telephone. Intelligent applications of monitoring work orders include upgrading and supervising work orders, focusing on work orders, work order satisfaction analysis, and repair rate statistics for 10,000 households. It is used to provide data analysis and early warning functions for 95598 and 12398 according to the rules, as well as monitoring and analysis of work order data, reminders for each link of the work order, time limit for arriving at the site, and duration of emergency repairs; The intelligent report generation tool application automatically aggregates and integrates data through robots, automatically generates daily, weekly, and monthly reports, and realizes one-click reporting; The video analysis application is used to build a business hall behavior normative analysis model, conduct patrol analysis of business hall videos, discover irregular service behaviors in real time, and use RPA automation technology to distribute behavior records to marketing to urge rectification, and support intelligent outbound call reminders; System support management applications include organizational structure, authority management, and configuration management, which are used to provide support management for application architecture; The visual management section includes work order information visualization and early warning supervision information visualization, and uses PC and large-screen visualization tools for planning and display.

4. According to claim 3, the intelligent supply and service command and management system based on data fusion is characterized in that: Intelligent AI service applications include intelligent voice interaction, RPA automated processes and instant messaging. The intelligent voice interaction of intelligent AI services is divided into voice interaction and voice broadcasting. The RPA automated process provides comprehensive artificial intelligence services for scheduling and early warning based on business scenarios and monitoring needs; instant messaging is divided into WeChat, SMS and outbound calls.

5. According to claim 1, the intelligent service command and management system based on data fusion is characterized by: The service layer (2) includes data fusion analysis services, scenario business services, visualization services, automation services and AI artificial intelligence services. The data fusion analysis service adopts the segmentation principle to segment big data into multiple small data sets, and performs rapid processing and analysis on each of them according to their respective model algorithms, and then merges the analysis results of each small data set. The scenario business service is divided into multiple modules according to different business scenarios, and realizes intelligent scheduling and collaborative work between modules through preset rules and algorithms. Automation services are used to automatically identify business scenarios and intelligently match service modules according to different business scenarios to achieve automation and intelligence of services.

6. According to claim 1, the intelligent service command and management system based on data fusion is characterized by: The data layer (3) architecture includes a data source module, an application data module, and a data integration module; the data layer (3) obtains marketing business, procurement business, emergency repair business, and OMS business data through the data source module or the data middle platform, and interacts with the data integration module; the application data module includes distribution network operation monitoring and analysis, intelligent AI service application, and intelligent monitoring work order; the data integration module includes archive data, collection data, distribution network data, and a data platform. The data source is stored in the data platform and data conversion, data fusion, data analysis, and data modeling are carried out to form emergency repair work order analysis data, outbound call data, distribution network scheduling analysis, intelligent report generation tools, video analysis, and omni-channel work order data.

7. According to claim 1, the intelligent service command and management system based on data fusion is characterized by: The support layer (5) complies with the State Grid security architecture system, which includes data security, host security, network security, physical security, boundary security, application security and security management, and is used to achieve comprehensive security protection of the information system.

8. The intelligent service command and management system based on data fusion according to claim 7 is characterized by: The physical security includes computer room security measures, computer room access security management and intrusion detection. The computer room access security management includes biometric management and material field monitoring. Biometric management is divided into face recognition and fingerprint recognition. Material field monitoring uses Internet of Things communication to conduct real-time monitoring and early warning of the material field environment. Intrusion detection uses machine learning algorithms to improve the accuracy and real-time performance of intrusion detection. The data security includes data storage security, data analysis security, data transmission security and data backup and recovery. Quantum encryption algorithms are used for data transmission to improve data transmission security. Differential Privacy algorithms are used for data analysis security to dynamically adjust the privacy protection level according to user needs and scenarios, so that data can still be safely and effectively analyzed in an encrypted state.

9. The intelligent supply and service command and management system based on data fusion according to claim 7 is characterized by: The host security includes the service operating system security and the database system security, the network security includes the Internet channel security and the network equipment security; the boundary security includes the unified Internet boundary and the external network security interaction platform mobile terminal access authentication combination, the internal and external vertical boundaries of information, and the information intranet user domain boundary; the application security includes the server-side application security combined with interactive security authentication, client security and application system structure security; the security architecture system and the data middle station communication adopt firewall and intrusion monitoring protection measures; the information intranet server installs vulnerability scanning and traffic security monitoring software to ensure system security.

10. An intelligent service command method based on data fusion, characterized in that: The command method is implemented by an intelligent service command and management system based on data fusion as described in any one of claims 1 to 9, and specifically comprises the following steps: Step S1: Data information collection: obtaining marketing business, procurement business, OMS business and emergency repair business data through data sources; Step S2, data information processing, including data conversion, data fusion, data cleaning, data standardization, data modeling and data analysis, for obtaining comprehensive application data; Step S3: feature extraction, using a deep learning algorithm to extract features from the comprehensive application data after data processing; Step S4: feature fusion, using a recurrent neural network (RNN) to fuse the extracted feature data to form a comprehensive data set; Step S5: training the model, using the comprehensive data set to train the deep learning model; Step S6: Generate a command decision, apply the trained deep learning model to the command decision, and obtain a service command decision generated by comprehensive application data; Step S7: Implement the command decision, implement the supply and service command decision, and realize real-time monitoring, statistical analysis and overall command of the power supply service network; The steps of data information processing in step S2 include: Step S21: data conversion, converting the acquired data; Step S22: data fusion, fusing the converted data; Step S23: data cleaning: cleaning the data after data fusion; Step S24: data standardization: standardizing the cleaned data; Step S25: data modeling, establishing a data model based on the acquired data; Step S26: data analysis; In step S3, a convolutional neural network (CNN) is used to extract features from the data after data information processing.

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