Power grid marketing data penetration analysis and operation management method and system

Abstract

The invention discloses a power grid marketing data penetration analysis and operation management method and system, and relates to the technical field of power system operation management, and the method comprises the steps: constructing a data label and index library, and carrying out the layered collection and unified arrangement of electricity charge data; developing a penetration analysis model, and realizing business whole-process visualization based on micro-service; a multi-service system is integrated, and a dynamic security policy is deployed to guarantee data and application operation. The method improves the accuracy and efficiency of data processing, enhances the transparency of a business process, optimizes the use of data storage and computing resources, improves the safety and stability of the system, achieves the efficient utilization of power grid marketing data and the optimization of operation management, improves the operation management level of a power enterprise, and also improves the safety of the power enterprise. And reliable data support is provided for enterprise decision making, so that sustainable development of enterprises and improvement of market competitiveness are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation management, and specifically to a method and system for power grid marketing data penetration analysis and operation management. Background Art

[0002] In the current information age, the large amount of data processing involved in power grid marketing has attracted widespread attention in the industry. With the maturity of smart meters and sensor technology, power companies can collect more fine-grained electricity bills and user usage data, which provides a basis for the richness of data and makes electricity bill data an important source of analysis for user consumption habits and electricity demand. In recent years, data warehouses and big data analysis technologies have been vigorously developed, and their application in the power industry has gradually matured. By integrating and deeply mining electricity bill data, power companies can optimize power loads and formulate more accurate pricing strategies. With the advancement of cyber-physical systems (CPS) and industrial Internet of Things (IIoT), the digitalization and intelligentization of power grid data management and scheduling have become more and more obvious. The overall trend is to combine data processing capabilities with real-time power grid management needs, which constitutes the key development direction of smart grid marketing in the future.

[0003] Although the power grid industry has made certain progress in data analysis technology, it still faces many shortcomings. Traditional data collection and organization methods often rely on static models, lack flexibility, and are difficult to meet the requirements of real-time and multi-dimensional analysis. Most models in existing technologies are basic statistics, which fail to fully explore deep customer behavior and demand patterns. In actual implementation, they lack precise business insights, which limits the differentiated service capabilities of power companies. There are problems of isolated system architecture in power data analysis, which makes it difficult to achieve data interoperability and collaborative operations between multiple business systems, which not only causes low operational efficiency, but also implies problems of data redundancy and inconsistency. In terms of data security management, the current security policy design cannot dynamically respond to changing network environments and data leakage risks, which makes the power grid system particularly vulnerable to diverse network attacks. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing power grid marketing data analysis technology has the problems of low data processing efficiency, insufficient visualization of the entire business process, low system security, and how to achieve efficient penetrating analysis and operational management of power grid marketing data.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for penetration analysis and operation management of power grid marketing data, including building a data label and indicator library, collecting and organizing electricity bill data in a layered manner; developing a penetration analysis model, and realizing full-process visualization of the business based on microservices; integrating multiple business systems, and deploying dynamic security strategies to ensure data and application operation.

[0007] As a preferred solution of the power grid marketing data penetration analysis and operation management method described in the present invention, the construction of data tags and indicator libraries includes generating user characteristic tags based on user categories, regional differences and time characteristics using a clustering algorithm. The user characteristic tags include classification tags for industrial users, commercial users and residential users. In the unified indicator library, a version control module is designed to support the backtracking and dynamic updating of historical indicators, confirm the traceability of indicator definitions in different time periods, connect the marketing system with the metering system, collect and integrate electricity meter reading, electricity price and recovery rate data in real time, and optimize data storage and computing efficiency through dynamic resource allocation.

[0008] As a preferred solution of the power grid marketing data penetration analysis and operation management method described in the present invention, the hierarchical collection and unified organization include setting up a rule engine, automatically generating business scenario labels, and supporting label extension functions. Based on the anomaly detection algorithm, the integrity and consistency of the data in the indicator library are verified in real time, and alarms and repair programs are triggered when the data is abnormal. The distributed processing capabilities of the big data platform are utilized to perform high-concurrency computing and dynamic scheduling during label generation and indicator update.

[0009] As a preferred solution of the power grid marketing data penetration analysis and operation management method described in the present invention, the development of the penetration analysis model includes hierarchical penetration of the business processes of electricity meter reading, electricity bill calculation and recovery based on a distributed penetration analysis engine, supporting the tracing of business anomalies from the head office to the outlets, and designing a visual dashboard. Users can select data indicators and display forms through custom configuration, including bar charts, line charts and heat maps, and integrate multi-dimensional anomaly analysis charts in early warning management to dynamically display the occurrence location of abnormal data, change trends and the scope of its impact on the overall business.

[0010] As a preferred solution of the power grid marketing data penetration analysis and operation management method described in the present invention, the full business process visualization includes designing a task decomposition mechanism in the analysis model, allocating big data processing tasks to multiple nodes, processing large-scale business data, introducing a timeline function in the visualization of abnormal data, allowing users to view the time distribution and frequency of abnormal events, assisting subsequent decision analysis, configuring data screening logic for the penetration analysis model, and screening key data according to indicator priority for real-time display.

[0011] As a preferred solution of the power grid marketing data penetration analysis and operation management method described in the present invention, the integrated multi-business system includes seamless data interaction with the energy trading platform and the metering system based on standardized interface technology, adopts a zero-trust security architecture, performs dynamic permission authentication and behavior analysis on all access requests to prevent unauthorized access, and sets a simulation test module during the system integration process to verify the correctness of the integration logic and data flow in advance.

[0012] As a preferred solution of the power grid marketing data penetration analysis and operation management method described in the present invention, the deployment of dynamic security strategies includes designing a hierarchical encryption storage mechanism for sensitive data, using asymmetric encryption for highly sensitive data, and using symmetric encryption for low-sensitivity data, configuring a security event monitoring module in the system operation log, analyzing access behavior and network traffic in real time, generating potential threat alerts, configuring backup strategies in fault scenarios, supporting business switching to redundant systems, and quickly recovering and verifying the integrity and consistency of data.

[0013] Another object of the present invention is to provide a power grid marketing data penetration analysis and operation management system, which can realize full business process visualization based on microservices by developing a penetration analysis model, thereby solving the problem that current power grid marketing data analysis technology contains insufficient business process visualization.

[0014] As a preferred solution of the power grid marketing data penetration analysis and operation management system described in the present invention, it includes: a labeling processing module, a visualization analysis module, and a security integration module.

[0015] The label processing module is used to build a data label and indicator library, and to collect and organize electricity bill data in a layered manner; the visualization analysis module is used to develop a penetration analysis model and realize full-process visualization of the business based on microservices; the security integration module is used to integrate multiple business systems and deploy dynamic security policies to ensure data and application operations.

[0016] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for penetrating analysis and operation management of power grid marketing data.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for penetration analysis and operation management of power grid marketing data.

[0018] Beneficial effects of the present invention: The power grid marketing data penetration analysis and operation management method provided by the present invention constructs a data tag and indicator library, collects and uniformly organizes electricity fee data in layers, improves data collection efficiency, optimizes data storage and computing efficiency, and provides an accurate and reliable basis for subsequent data analysis. A penetration analysis model is developed, and the visualization of the entire business process is realized based on microservices. Business anomalies can be traced step by step from the head office to the outlets, and problems can be discovered and solved in a timely manner, thereby improving the transparency and controllability of the business process. A visual dashboard and a multi-dimensional anomaly analysis chart are designed so that users can intuitively understand the occurrence location, change trend and impact range of abnormal data on the overall business, thereby improving decision-making efficiency and quality. Multiple business systems are integrated, and dynamic security policies are deployed to ensure data and application operation, thereby improving the system's integration and data processing capabilities, effectively preventing unauthorized access and data leakage, and ensuring the security of sensitive data. A security event monitoring module and a backup policy are configured, so that the consistency of business and data can be quickly restored in a fault scenario, thereby improving the security and stability of the system and reducing operational risks. The present invention achieves better results in terms of refined data management, transparent business processes and improved system security. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 An overall flow chart of a power grid marketing data penetration analysis and operation management method provided for the first embodiment of the present invention.

[0021] Figure 2 An overall flow chart of a power grid marketing data penetration analysis and operation management system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0023] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a method for penetrating analysis and operation management of power grid marketing data, including:

[0024] S1: Build a data label and indicator library to collect and organize electricity bill data in a layered manner.

[0025] Furthermore, building a data label and indicator library includes generating user characteristic labels based on user categories, regional differences and time characteristics using clustering algorithms. User characteristic labels include classification labels for industrial users, commercial users and residential users. In the unified indicator library, a version control module is designed to support the backtracking and dynamic updating of historical indicators, confirm the traceability of indicator definitions in different time periods, connect the marketing system with the metering system, collect and integrate electricity meter reading, electricity price and recovery rate data in real time, and optimize data storage and computing efficiency through dynamic resource allocation.

[0026] It should be noted that layered collection and unified organization include setting up a rule engine, automatically generating business scenario labels, and supporting label extension functions. Based on anomaly detection algorithms, the integrity and consistency of data in the indicator library are verified in real time, and alarms and repair programs are triggered when data is abnormal. The distributed processing capabilities of the big data platform are used to perform high-concurrency computing and dynamic scheduling during label generation and indicator update processes.

[0027] It should also be noted that the integrity and consistency of the data in the indicator library are verified in real time through the anomaly detection algorithm, and alarms and repair programs are triggered when the data is abnormal. The specific steps are as follows: a series of data quality rules are set through the algorithm, including data range, data type, and data format, and the data flowing into the indicator library is verified. When it is detected that the data value exceeds the preset range, the data type does not match, or the data format is wrong, the algorithm immediately marks the anomaly. For the marked abnormal data, the system will automatically trigger the alarm mechanism and promptly notify the data management personnel through SMS, email or system notification. At the same time, the system starts the repair program. The program will automatically perform data cleaning, data filling or data correction operations according to the preset repair rules. For missing values, the program can use historical data for the same period or industry averages to fill in; for erroneous data, the program will try to correct it according to the logical relationship between the data. The system will also record abnormal events and processing logs for subsequent data audits and quality analysis to ensure that the integrity and consistency of the data in the indicator library are continuously maintained.

[0028] It should also be noted that the distributed processing capabilities of the big data platform are used to achieve high-concurrency computing and dynamic scheduling in the process of label generation and indicator update. The specific steps are as follows: the data label generation and indicator update tasks are decomposed into multiple subtasks and assigned to multiple computing nodes in the cluster. Each node executes tasks in parallel according to its processing capacity. Through distributed computing frameworks such as MapReduce, the parallelization and high efficiency of data processing are ensured. In order to achieve dynamic scheduling, the platform adopts a load balancing algorithm to monitor the computing load and resource usage of each node in real time, and dynamically adjust the task allocation according to the needs of the current task and the performance of the node. When the processing capacity of a node is idle, the system will automatically migrate part of the tasks of the remaining nodes to this node to optimize the utilization of the overall computing resources. The platform also adopts a data localization processing strategy to schedule computing tasks to nodes that store data as much as possible, reduce data transmission in the network, thereby reducing latency and increasing computing speed. During the entire processing process, the platform also has a failover mechanism. Once a node fails, the system will automatically reallocate tasks to other healthy nodes to ensure the continuity and stability of the computing process.

[0029] It should also be noted that by constructing a data label and indicator library, the electricity bill data is collected and organized in a layered manner, and the multi-dimensional user characteristics, including industrial users, commercial users, and residential users, are accurately classified. The user characteristic labels are generated by clustering algorithms, so that power marketing can formulate personalized marketing strategies based on the needs of different users. This not only improves the efficiency of data integration, but also enables accurate analysis of the consumption behavior of each user group, thereby strengthening the grasp of market dynamics. By introducing a rule engine, real-time verification and alarm of abnormal data are achieved, ensuring the integrity and consistency of the data, improving the accuracy of data in operations, and reducing decision-making errors caused by data errors. The design of the version control module ensures the traceability of quantitative indicators, supports the review and dynamic update of historical data, and improves the flexibility of power companies in data management and decision optimization.

[0030] S2: Develop a penetration analysis model to visualize the entire business process based on microservices.

[0031] Furthermore, the development of a penetration analysis model includes hierarchical penetration of the business processes of electricity meter reading, electricity bill calculation and recovery based on a distributed penetration analysis engine, supporting the tracing of business anomalies from the head office to the outlets, and designing a visual dashboard. Users can select data indicators and display formats through custom configurations, including bar charts, line charts and heat maps, and integrate multi-dimensional anomaly analysis charts in early warning management to dynamically display the location of abnormal data, changing trends and the scope of its impact on the overall business.

[0032] It should be noted that the visualization of the entire business process includes designing a task decomposition mechanism in the analysis model, allocating big data processing tasks to multiple nodes, processing large-scale business data, introducing a timeline function in the visualization of abnormal data, allowing users to view the time distribution and frequency of abnormal events, assisting in subsequent decision-making analysis, configuring data screening logic for the penetration analysis model, and screening key data according to indicator priority for real-time display.

[0033] It should also be noted that the development of a penetration analysis model, based on a distributed penetration analysis engine, supports hierarchical traceability of the entire business process from electricity meter reading to accounting and recovery, realizes transparency of business processes, and helps managers quickly identify and locate business anomalies. Through the visual dashboard, users can customize data indicators and display formats according to their own needs, which improves the intuitiveness and user-friendliness of data interaction. The introduction of multi-dimensional anomaly analysis charts allows the occurrence location, change trend and business impact range of abnormal data to be intuitively displayed, thereby providing a more accurate and timely basis for operational decisions; the design of the task decomposition mechanism ensures that big data processing can be effectively distributed to multiple nodes, which improves the system's processing capabilities and efficiency when facing large-scale business data, not only optimizing resource utilization, but also laying the foundation for real-time identification and response to business anomalies.

[0034] S3: Integrate multiple business systems and deploy dynamic security policies to ensure data and application operations.

[0035] Furthermore, the integration of multiple business systems includes seamless data interaction with energy trading platforms and metering systems based on standardized interface technology, the use of a zero-trust security architecture, dynamic permission authentication and behavior analysis for all access requests to prevent unauthorized access, and the setting of simulation test modules during the system integration process to verify the correctness of the integration logic and data flow in advance.

[0036] It should be noted that the deployment of dynamic security strategies includes designing a hierarchical encryption storage mechanism for sensitive data, using asymmetric encryption for highly sensitive data and symmetric encryption for less sensitive data, configuring a security event monitoring module in the system operation log, analyzing access behavior and network traffic in real time, generating potential threat alerts, configuring backup strategies in failure scenarios, supporting business switching to redundant systems, and performing rapid recovery verification on data integrity and consistency.

[0037] It should also be noted that a simulation test module is set up during the system integration process to verify the correctness of the integration logic and data flow in advance. The specific steps are as follows: Build a series of predefined test scenarios that cover all key paths and potential abnormal situations of system integration. The simulation test module simulates the data interaction process in the actual operating environment by generating simulation data or simulating the response of the external system interface. During the test execution phase, the module will send simulation requests to the integrated system according to the predetermined test cases, and record the response data and behavior of the system. By comparing the actual response with the expected result, the correctness of the integration logic is automatically detected, including whether the data mapping, conversion and execution of business rules meet the design requirements. The simulation test module will also detect the consistency and integrity of the data flow to ensure that there is no data loss or error during the integration process. For any deviations or errors detected, the module will provide a detailed error report, including the error type, location and possible causes, so that developers can quickly locate and fix the problem. Through this simulation test method, potential integration problems can be discovered and solved in a timely manner before the system integration is deployed, thereby ensuring the stability of the system operation after deployment.

[0038] It should also be noted that by integrating multiple business systems and seamlessly connecting with energy trading platforms and metering systems based on standardized interface technology, efficient interaction and integration of data flows are achieved, which improves the data collaboration capabilities between systems and enables various business units to work together on the same platform to respond to market changes. The adoption of a zero-trust security architecture, dynamic permission authentication and behavior analysis mechanisms can effectively prevent unauthorized access, improve data security and operational security, and verify the correctness of the integrated logic and data flow in advance by setting up a simulation test module, ensuring the stable operation of the system and accurate data transmission. The deployment of dynamic security policies, through hierarchical encryption storage mechanisms and real-time monitoring measures, ensures the security of sensitive data, generates potential threat alerts in a timely manner, ensures rapid recovery verification in fault scenarios, and reduces the impact of security risks on enterprise operations. Therefore, on the premise of ensuring data security, enterprises can focus more on optimizing and innovating business processes and improving overall operational efficiency.

[0039] Example 2 is an embodiment of the present invention, which provides a method for penetration analysis and operation management of power grid marketing data. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0040] First, before the experiment began, a detailed experimental plan was developed. According to the characteristics of different users, a data label and indicator library was constructed, and a clustering algorithm was used to classify electricity users to generate user characteristic labels and establish a unified indicator library. In the process, key data indicators such as electricity meter reading, recovery rate, and electricity consumption were set, and a version control module was developed to trace and update indicators in different time periods; the development of a penetration analysis model was implemented, and a distributed penetration analysis engine was designed based on the microservice architecture. This engine supports hierarchical analysis of electricity meter reading, accounting, and recovery business processes; with the visual dashboard, users can customize the way information is displayed according to their own needs, including bar charts and line charts, providing managers with powerful data support. The system integrates the power company's energy trading platform and the metering system using standardized interface technology to achieve efficient data interaction. To ensure security, a zero-trust security architecture is adopted, and dynamic permission management and behavior analysis prevent unauthorized access. During the implementation process, a corresponding encryption storage solution is designed for highly sensitive data, and a security event monitoring module is set up to analyze system access behavior in real time. The experimental results show that the present invention is innovative and practical in the field of power marketing data management, which not only enhances the power company's superiority in competition, but also provides a scientific basis for future operational decisions.

[0041] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a power grid marketing data penetration analysis and operation management system, including a labeling processing module, a visualization analysis module, and a security integration module.

[0042] The label processing module is used to build a data label and indicator library, and to collect and organize electricity bill data in a layered manner; the visualization analysis module is used to develop a penetration analysis model and realize full-process visualization of the business based on microservices; the security integration module is used to integrate multiple business systems and deploy dynamic security policies to ensure data and application operations.

[0043] If the function 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 technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0044] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0045] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0046] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for penetrating analysis and operation management of power grid marketing data, characterized in that: include: Build a data label and indicator library to collect and organize electricity bill data in layers; Develop a penetration analysis model to visualize the entire business process based on microservices; Integrate multiple business systems and deploy dynamic security policies to ensure data and application operations.

2. The method for penetrating analysis and operation management of power grid marketing data according to claim 1, characterized in that: The construction of the data label and indicator library includes generating user characteristic labels based on user categories, regional differences and time characteristics using a clustering algorithm. The user characteristic labels include classification labels for industrial users, commercial users and residential users. In the unified indicator library, a version control module is designed to support the backtracking and dynamic update of historical indicators, confirm the traceability of indicator definitions in different time periods, connect the marketing system with the metering system, collect and integrate electricity meter reading, electricity price and recovery rate data in real time, and optimize data storage and computing efficiency through dynamic resource allocation.

3. The method for penetrating analysis and operation management of power grid marketing data according to claim 2, characterized in that: The layered collection and unified organization include setting up a rule engine, automatically generating business scenario labels, and supporting label extension functions. Based on the anomaly detection algorithm, the integrity and consistency of the data in the indicator library are verified in real time, and alarms and repair programs are triggered when the data is abnormal. The distributed processing capabilities of the big data platform are used to perform high-concurrency computing and dynamic scheduling during the label generation and indicator update process.

4. The method for penetrating analysis and operation management of power grid marketing data according to claim 3, characterized in that: The developed penetration analysis model includes hierarchical penetration of the business processes of electricity meter reading, electricity fee calculation and recovery based on a distributed penetration analysis engine, supporting the tracing of business anomalies from the head office to the outlets, and designing a visual dashboard. Users can select data indicators and display formats through custom configuration, including bar charts, line charts and heat maps, and integrate multi-dimensional anomaly analysis charts in early warning management to dynamically display the occurrence location of abnormal data, change trends and the scope of its impact on the overall business.

5. The method for penetrating analysis and operation management of power grid marketing data according to claim 4, characterized in that: The full-process visualization of the business includes designing a task decomposition mechanism in the analysis model, allocating big data processing tasks to multiple nodes, processing large-scale business data, introducing a timeline function in the visualization of abnormal data, allowing users to view the time distribution and frequency of abnormal events, assisting subsequent decision analysis, configuring data screening logic for the penetration analysis model, and screening key data according to indicator priority for real-time display.

6. The method for penetrating analysis and operation management of power grid marketing data according to claim 5, characterized in that: The integrated multi-business system includes seamless data interaction with the energy trading platform and metering system based on standardized interface technology, adopts a zero-trust security architecture, performs dynamic permission authentication and behavior analysis on all access requests to prevent unauthorized access, and sets up a simulation test module during the system integration process to verify the correctness of the integration logic and data flow in advance.

7. The method for penetrating analysis and operation management of power grid marketing data according to claim 6, characterized in that: The deployment of dynamic security strategies includes designing a hierarchical encryption storage mechanism for sensitive data, using asymmetric encryption for highly sensitive data and symmetric encryption for less sensitive data, configuring a security event monitoring module in the system operation log, analyzing access behavior and network traffic in real time, generating potential threat alerts, configuring backup strategies in failure scenarios, supporting business switching to redundant systems, and quickly recovering and verifying the integrity and consistency of data.

8. A system using the power grid marketing data penetration analysis and operation management method according to any one of claims 1 to 7, characterized in that: Including label processing module, visual analysis module, and security integration module; The label processing module is used to construct a data label and index library, and to collect and organize electricity fee data in layers; The visualization analysis module is used to develop a penetration analysis model and realize the visualization of the entire business process based on microservices; The security integration module is used to integrate multiple business systems and deploy dynamic security policies to ensure data and application operations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power grid marketing data penetration analysis and operation management method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid marketing data penetration analysis and operation management method described in any one of claims 1 to 7 are implemented.

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