Electric energy metering experiment scheduling management system

Abstract

The invention relates to an electric energy metering experiment scheduling management system, which realizes interconnection and intercommunication with an existing management system through an interface program, comprises a marketing platform, a six-line one-library system, a safety system and the like, carries out decision analysis on basic business data generated on site, and summarizes the basic business data to form analysis data, including planning, arrival of goods, warehousing, verification, distribution, comprehensive management and the like. And the digital analysis provides scientific data support for decision making. A more reasonable plan is made through analysis, and lean production is achieved. All the components complement each other and interact with each other, so that the production efficiency and the automation degree are improved, and effective scheduling and comprehensive integration of a verification line and a three-dimensional library in the metering center are realized by taking operation result feedback as an optimization basis; and on the other hand, a second, third and fourth-level warehouse management function is realized, so that a good foundation is laid for fine management of metering equipment assets in a full life cycle.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electric energy metering management, and in particular to an electric energy metering experiment scheduling management system, an electric energy metering experiment scheduling management method and an electronic device. Background Art

[0002] The current status of electric energy metering equipment management is that power companies and league and city power supply units are managed at different levels, and the league and city power supply units are relatively closed. In addition, there are different management systems and operating mechanisms for rural power grids and infrastructure production, which makes it impossible to form a full life cycle management model for assets with standard specifications, flexible allocation, unified procurement, centralized calibration, and unified distribution. As a result, the effective utilization rate of electric energy metering equipment is low, the technical functions are not unified, and the level of intelligent application is uneven. It is mainly reflected in three aspects: First, the sources of funds for electric energy metering equipment are diverse, the procurement methods are different, and the management models are different, which makes it impossible to standardize and unify inventory asset management. To a certain extent, there is a low utilization rate of electric energy metering equipment, a relative backlog of inventory, and a waste of funds; second, under the decentralized and multi-system management model, the technical standards and management models of electric energy metering equipment are different, resulting in imperfect application of many functions of electric energy metering equipment in marketing intelligent systems, such as customer data collection, metering fee control, and line loss management; third, for a long time, customers have purchased new electric energy metering equipment at their own expense. The main management issues are as follows:

[0003] (1) Insufficient production efficiency of metrology

[0004] The current situation of electricity metering equipment management is that power companies and league and city power supply units are managed at different levels. The league and city power supply units operate relatively closed to each other. In addition, there are different production management systems and operating mechanisms for rural power grids and infrastructure, making it impossible to form an asset life cycle management model with standard specifications, flexible allocation, unified procurement, centralized calibration, and unified distribution. As a result, the effective utilization rate of electricity metering equipment is low, the technical functions are not unified, and the level of intelligent application is uneven.

[0005] (2) Insufficient information technology in production

[0006] With the gradual advancement of the construction of intelligent power grid, a large number of smart energy meters and mutual inductors need to be rotated, and a large number of concentrators and collectors need to be newly installed. The workload of energy meter calibration is increasing, which puts higher requirements on the calibration production capacity of the measurement center. In order to solve the contradiction between the large workload and the shortage of human resources, the company is organizing and promoting the construction of automated calibration, and has unified the appearance, structure, size, etc. of energy meter, creating certain conditions for the realization of automated calibration. In addition, the measurement center is also preparing to build a more advanced automated logistics system, but there are still a large number of manual operation modes in the unpacking, transportation, calibration, programming, sealing, packaging and other links, and it is difficult to meet the increasing demand for calibration, warehousing and distribution of energy meter. Therefore, it is required to adopt a higher degree of automation and information management methods to improve work efficiency and avoid problems caused by the shortage of human resources.

[0007] (3) Intelligent equipment does not have ideal intelligent dispatching management level

[0008] With the development of science and technology and the improvement of management level, the metrology center will surely realize the large-scale promotion of automated information operation, the application of stereoscopic warehouses, automatic calibration lines, and AGV systems, and realize intelligence in some work processes, but the operation efficiency in the process is also restricted by the bottleneck of manual efficiency. On the other hand, the existing manual operation mode is also difficult to match with the high automation and informationization of the work site, and it is difficult to achieve comprehensive and efficient intelligent production, especially the lack of a unified dispatching and command balance to maximize the overall operation efficiency. Therefore, a management platform is needed to optimize the overall dispatching capability and improve the application efficiency of intelligent equipment.

[0009] (4) There is an "information island" phenomenon among existing systems, which urgently needs to be integrated and connected

[0010] At present, the metrology center has built some functional systems and formed an important auxiliary means of production, but the systems are relatively isolated from each other, and there is a phenomenon of mutual isolation between the systems. There is a lack of comprehensive data transmission and process control integration, and there is also a lack of a large experimental scheduling platform that can carry out company-level centralized operations. At the same time, the interfaces between systems need to ensure the use of unified standards. At present, the standards of self-built systems in various places are not unified, and data interfaces are not easy to be consistent. It requires additional investment to integrate and communicate, which easily leads to waste of manpower, financial resources and material resources. Therefore, the system construction needs to realize unified data management, unified data model, unified data standard through the metrology data center planned and constructed by this project, and use metrology data analysis to deeply explore the advanced model of data value to realize innovative applications of metrology business management.

[0011] (5) Lack of integrated advanced experimental scheduling information system

[0012] Due to different management needs, the Metrology Center has used multiple sets of software and hardware systems. Whether the existing systems can be well integrated is the key to improving work efficiency. If integration cannot be achieved, isolated production islands will be formed, and the connection in the middle must be handled manually, which will greatly reduce the efficiency and management capabilities of production. At the same time, there are overlapping parts in the existing management system, which leads to duplication of work and loss of financial and material resources. Therefore, it is necessary to sort out the relationship between the various systems, build an integrated experimental scheduling management platform, comprehensively manage the production data of the Metrology Center, comprehensively integrate the existing systems, and reconstruct and transform some existing software when necessary to achieve the construction of an advanced management system with integrated and collaborative interaction between systems.

[0013] (6) Lack of conditions for asset full life cycle management

[0014] According to the company's development and construction requirements, the metering center needs to manage the electric energy metering assets throughout the entire process and life cycle. The development of our center requires that the calibration, distribution and asset management business of metering instruments undertaken by the power supply bureau be centralized at the municipal level, and centralized calibration and distribution of metering instruments and unified asset management be implemented. The metering center does not have a system to support timely monitoring of the inventory of metering assets purchased by each power supply unit; at the same time, for assets purchased by the power supply unit itself, the metering center does not have any ledger records and cannot perform management and control. Therefore, it is necessary to refine the management data of assets and equipment at each stage, throughout the entire life cycle of the assets, and implement full-level and full life cycle management of assets. Summary of the invention

[0015] In order to solve the above problems, the present application proposes an electric energy metering experiment scheduling management platform, an electric energy metering experiment scheduling management method and an electronic device.

[0016] On the one hand, the present application proposes an electric energy metering experiment scheduling management system, comprising:

[0017] The marketing end is used to formulate corresponding electric energy metering experiment scheduling management tasks and send them to the electric energy metering experiment scheduling management platform;

[0018] The electric energy metering experiment scheduling management platform is used to execute the electric energy metering experiment scheduling management tasks, coordinate the six-line and one-storage system to complete the corresponding task process, and return the task execution results to the marketing end;

[0019] The six-line-one-reservoir system is used to perform specific electric energy metering experiment scheduling and management tasks;

[0020] The marketing end and the six-line-one-storage system are respectively connected to the electric energy metering experiment scheduling management platform for communication;

[0021] The electric energy metering experiment scheduling management platform includes:

[0022] The basic layer is used to provide basic network services;

[0023] The data layer is used to provide data storage services and store structured and unstructured data generated in the scheduling management of electric energy metering experiments;

[0024] The support layer is used to provide data support services in the scheduling and management of electric energy metering experiments;

[0025] The application layer is used to provide on-site experiment scheduling services for electric energy metering experiment scheduling management;

[0026] The presentation layer is used to display the scheduling and management process of the electric energy metering experiment;

[0027] The application layer includes:

[0028] The experimental operation management module is used to provide the laboratory with procurement management, arrival and acceptance management, auxiliary management, verification experiment execution, warehousing management and distribution management services;

[0029] The metrology technology supervision and management module is used to provide online verification management of assembly line standards;

[0030] APP mobile application module, used to provide distribution business, turnover box recycling and auxiliary function services;

[0031] The experimental scheduling and monitoring module is used to provide operation control, operation monitoring, laboratory key monitoring, delivery tracking, experimental business subject analysis and assembly line detection system overall control and scheduling services;

[0032] Intelligent operation and maintenance management module, used to provide ledger management, inspection management, fault alarm management, production facility maintenance plan monitoring, key equipment health status evaluation, maintenance management, repair management, production facility status change management, automated inspection, fault linkage, fault diagnosis and early warning services;

[0033] System auxiliary module, used to provide statistical reports, indicator system management, announcement management, low-value vulnerable and consumables management, and business delivery services;

[0034] The full life cycle monitoring module is used to provide overall display of the full life cycle, full life cycle status monitoring, and full life cycle quality analysis services;

[0035] Large screen management module, used to provide metering center business monitoring, flow and storage three-dimensional monitoring, electric energy metering key data monitoring, intelligent building monitoring, and video monitoring integration services;

[0036] The system support module is used to provide message management, custom query, organization and permission management, version management, system parameter management, system operation monitoring, report parameter customization, and log management services.

[0037] As an optional embodiment of the present application, optionally, the base layer includes:

[0038] Network equipment, servers, storage equipment, backup equipment and security equipment.

[0039] As an optional implementation scheme of the present application, optionally, the data layer includes:

[0040] Real-time data, structured data, and unstructured data.

[0041] As an optional embodiment of the present application, optionally, the support layer comprises:

[0042] Data access module, used for users to access the platform;

[0043] The permission management module is used for the platform to manage the permissions of users at all levels;

[0044] Identity authentication module, used for platform user identity authentication;

[0045] Task scheduling module, used to perform various scheduling tasks;

[0046] Parameter configuration module, used for parameter configuration;

[0047] Report service module, used to provide electric energy metering report service;

[0048] The workflow engine module is used to build the workflow of the power metering task and execute the workflow;

[0049] Interface management module, used to manage various data interfaces of the access platform;

[0050] Operation monitoring module, used to monitor the operation status of the platform;

[0051] The log management module is used to record and manage platform operation logs.

[0052] As an optional implementation scheme of the present application, optionally, the presentation layer includes:

[0053] BS browser / large screen, used to display the scheduling management process of electric energy metering experiment.

[0054] On the other hand, the present application proposes a method for scheduling and managing an electric energy metering experiment, which is implemented based on the electric energy metering experiment scheduling and management system, and includes the following steps:

[0055] The marketing end formulates the corresponding electric energy metering experiment scheduling management tasks and sends them to the electric energy metering experiment scheduling management platform;

[0056] The electric energy metering experiment scheduling management platform executes the electric energy metering experiment scheduling management tasks, coordinates the six-line and one-reservoir system to complete the corresponding task process, and returns the task execution results to the marketing end.

[0057] In another aspect, the present application further provides an electronic device, comprising:

[0058] processor;

[0059] a memory for storing processor-executable instructions;

[0060] Wherein, the processor is configured to implement the electric energy metering experiment scheduling management method when executing the executable instructions.

[0061] Technical effects of the present invention:

[0062] This application proposes an electric energy metering experiment scheduling management system, which mainly includes four levels from the management level. The first is the interface layer, which realizes interconnection with the existing management system through the interface program, including the interface with the marketing platform, the "six-line and one-warehouse" system interface, the security system interface, etc. At the same time, the standardized interface has strong scalability and can quickly realize the interface with other systems; the second is the operation layer, which manages the operation scheduling, intelligent warehousing, internal logistics and automatic calibration of the production site, replaces the manual operation mode, and realizes automated production and intelligent scheduling; the third is the management level, including the supervision and maintenance of equipment, monitoring of the operation process, electronic dashboards, etc., to realize standardized management; the fourth is the decision-making analysis layer, which summarizes the basic business data generated on site to form analysis data, including planning, arrival, warehousing, calibration, distribution and comprehensive management, etc. Digital analysis provides scientific data support for decision-making.

[0063] First, we make a production plan, schedule operations, prepare equipment and materials, and put all products and materials in designated locations through the internal logistics system. Then, we use the intelligent calibration station to perform quality management. Qualified products are put into storage, and unqualified products are sent back to the factory. All data can be used as the basis for decision-making analysis. Through analysis, we can make a more reasonable plan and realize lean production. Each part complements each other and interacts with each other to improve production efficiency and automation. Based on the feedback of operation results as the basis for optimization, the entire business process forms a closed loop, which can continuously optimize production and self-improve.

[0064] The electric energy metering experiment dispatching management platform is based on data interaction with six lines and one warehouse. On the one hand, it realizes the metering data integration system, experimental scheduling services, metering experiment calibration business applications, statistical analysis and auxiliary decision-making, and realizes effective scheduling and comprehensive integration of the internal calibration lines and three-dimensional warehouses of the metering center; on the other hand, it realizes the second, third and fourth level warehouse management functions, thus laying a good foundation for the refined management of the entire life cycle of metering equipment assets.

[0065] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0067] Figure 1 It is a schematic diagram of the technical architecture of the platform of the present invention;

[0068] Figure 2 It is a functional architecture diagram of the system of the present invention;

[0069] Figure 3 A schematic diagram of the business system architecture connected to the platform of the present invention is shown;

[0070] Figure 4 Shown is a schematic diagram of the structure of the system data of the present invention;

[0071] Figure 5 A functional architecture diagram of an exemplary experiment operation management module of the present invention is shown;

[0072] Figure 6 It is a schematic diagram showing the application of the electronic device of the present invention. DETAILED DESCRIPTION

[0073] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0074] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0075] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure.

[0076] Example 1

[0077] like Figure 1 The following is the technical architecture of this platform.

[0078] On the one hand, the present application proposes an electric energy metering experiment scheduling management system, comprising:

[0079] The marketing end is used to formulate corresponding electric energy metering experiment scheduling management tasks and send them to the electric energy metering experiment scheduling management platform;

[0080] The electric energy metering experiment scheduling management platform is used to execute the electric energy metering experiment scheduling management tasks, coordinate the six-line and one-storage system to complete the corresponding task process, and return the task execution results to the marketing end;

[0081] The six-line-one-reservoir system is used to perform specific electric energy metering experiment scheduling and management tasks;

[0082] The marketing end and the six-line-one-storage system are respectively communicatively connected with the electric energy metering experiment scheduling management platform.

[0083] The marketing end is the marketing terminal at the front end of the electric energy metering, which can send task plans to the backend;

[0084] The six-line-one-warehouse system is the six production lines and warehouse system of the power center, which can be connected to the platform to complete the corresponding power experiment scheduling work.

[0085] It can be specifically combined with the task configuration management work of the power center.

[0086] Among them, the electric energy metering experiment dispatching management platform includes:

[0087] The basic layer is used to provide basic network services;

[0088] The data layer is used to provide data storage services and store structured and unstructured data generated in the scheduling management of electric energy metering experiments;

[0089] The support layer is used to provide data support services in the scheduling and management of electric energy metering experiments;

[0090] The application layer is used to provide on-site experiment scheduling services for electric energy metering experiment scheduling management;

[0091] The presentation layer is used to display the scheduling management process of the electric energy metering experiment.

[0092] like Figure 2 The following is the management function architecture of this platform, which mainly involves the logical flow and connection of data.

[0093] The first is the data platform layer (integrated service layer), which is interconnected with the existing management system through interface programs, including data integration with the marketing platform, the "six lines and one library" system, the security environment system, the video system, etc.;

[0094] The second is the experimental scheduling layer (business logic layer), which manages the operation scheduling, intelligent warehousing, internal logistics and automatic calibration of the production site, replaces the manual operation mode, and realizes automated production and intelligent scheduling;

[0095] The third is the business application layer (data resource layer), which includes the compilation and tracking of production plans, the tracking of production operation processes, the circulation of asset experimental business, and the integrated management and control of second, third and fourth level warehouses, to achieve standardized and lean management;

[0096] The fourth is the auxiliary decision-making layer (infrastructure layer), which aggregates the basic business data generated on site into analytical data to provide scientific data support for decision-making;

[0097] The fifth is the large-screen display layer (presentation layer), which forms a visual and graphical comprehensive display through the analysis results of the measurement data.

[0098] like Figure 3 As shown, the electric energy metering experiment dispatching management platform of this application is connected to the marketing platform (marketing end, using the marketing 2.0 system), the "six lines and one warehouse" system (six production lines and one warehouse), the safety environment system, the video system, etc. The ports required for each docking data are determined according to the user's production system.

[0099] The electric energy metering experiment dispatching management platform is based on data interaction with six lines and one warehouse. On the one hand, it realizes the metering data integration system, experimental scheduling services, metering experiment calibration business applications, statistical analysis and auxiliary decision-making, and realizes effective scheduling and comprehensive integration of the internal calibration lines and three-dimensional warehouses of the metering center; on the other hand, it realizes the second, third and fourth level warehouse management functions, thus laying a good foundation for the refined management of the entire life cycle of metering equipment assets.

[0100] The functional modules of each architecture layer will be deployed on the corresponding servers for operation when implemented, such as:

[0101] The metering data platform and the electric energy metering experiment dispatching management platform are both centrally deployed in the metering center computer room. The electric energy metering experiment dispatching management platform is installed with database servers, application servers, interface servers, monitoring servers, switches, load balancing and other equipment, and the metering data platform is installed with data servers, aggregation switches and other equipment. The two platforms are responsible for data storage, business function applications, etc. of the metering center. System security must follow the company's overall information security strategy, and the security protection strength must meet the company's intranet security protection standards to avoid the spread of security risks.

[0102] like Figure 1 As shown,

[0103] As an optional embodiment of the present application, optionally, the base layer includes:

[0104] Network equipment, servers, storage equipment, backup equipment and security equipment.

[0105] As an optional implementation scheme of the present application, optionally, the data layer includes:

[0106] Real-time data, structured data, and unstructured data.

[0107] like Figure 4 As shown in the figure, the system data of the electric energy metering experiment dispatching management platform is divided into three categories: structured data, unstructured data and real-time data. Among them, structured data is divided into data domains such as experiment operation management, experiment dispatching monitoring, full life cycle monitoring, measurement technology supervision management, intelligent operation and maintenance management, large-screen management, APP mobile application, system assistance, and system support; unstructured data mainly includes various types of document data such as contract texts, calibration certificates, and appearance pictures; real-time data includes real-time monitoring data such as calibration line conditions, three-dimensional warehouse conditions, environment, video, and location.

[0108] As an optional embodiment of the present application, optionally, the support layer comprises:

[0109] Data access module, used for users to access the platform;

[0110] The permission management module is used for the platform to manage the permissions of users at all levels;

[0111] Identity authentication module, used for platform user identity authentication;

[0112] Task scheduling module, used to perform various scheduling tasks;

[0113] Parameter configuration module, used for parameter configuration;

[0114] Report service module, used to provide electric energy metering report service;

[0115] The workflow engine module is used to build the workflow of the power metering task and execute the workflow;

[0116] Interface management module, used to manage various data interfaces of the access platform;

[0117] Operation monitoring module, used to monitor the operation status of the platform;

[0118] The log management module is used to record and manage platform operation logs.

[0119] As an optional implementation scheme of the present application, optionally, the application layer includes:

[0120] The experimental operation management module is used to provide the laboratory with procurement management, arrival and acceptance management, auxiliary management, verification experiment execution, warehousing management and distribution management services;

[0121] The metrology technology supervision and management module is used to provide online verification management of assembly line standards;

[0122] APP mobile application module, used to provide distribution business, turnover box recycling and auxiliary function services;

[0123] The experimental scheduling and monitoring module is used to provide operation control, operation monitoring, laboratory key monitoring, delivery tracking, experimental business subject analysis and assembly line detection system overall control and scheduling services;

[0124] The intelligent operation and maintenance management module is used to provide ledger management, inspection management, fault alarm management, production facility maintenance plan monitoring, key equipment health status evaluation, maintenance management, repair management, production facility status change management, automated inspection, fault linkage, fault diagnosis and early warning services; specific AI operation management content includes:

[0125] 1. Ledger management

[0126] Data integration: Use API interfaces or data synchronization tools to integrate basic information such as production facilities, key equipment, and inspection records into the intelligent operation and maintenance management platform.

[0127] Automated data entry: Develop an intelligent recognition system to automatically enter device information by scanning QR codes, RFID tags, etc., to reduce human errors.

[0128] Electronic management: Establish an electronic ledger system to support multi-dimensional query, export and report generation to ensure the accuracy and traceability of data.

[0129] 2. Inspection management

[0130] Automated inspection: Deploy drones, robots, or smart sensors to conduct regular inspections and collect data on equipment operating status.

[0131] Intelligent Scheduling: Optimize inspection routes and frequencies based on AI algorithms to ensure that key equipment receives priority attention.

[0132] Inspection report: Automatically generate inspection reports, including inspection time, location, problems found and recommended measures, and support online approval and tracking.

[0133] Traditional inspection routes are mainly constructed by administrators based on the information reported by on-site inspection equipment. It is impossible to optimize the inspection routes in combination with surrounding inspection images, which often leads to inspection blind spots and blocked routes.

[0134] The present invention optimizes the inspection route and frequency based on the AI ​​algorithm, with particular emphasis on the AI ​​data optimization processing algorithm for the inspection route:

[0135] The technical architecture is as follows:

[0136] 1) Data acquisition system:

[0137] Deploy sensors, cameras and other equipment in the inspection area to collect status data of roads, equipment or facilities in real time.

[0138] The data includes but is not limited to road cracks, potholes, traffic flow, equipment operating status, etc.

[0139] 2) AI data processing and analysis platform:

[0140] Image and pattern recognition: Use deep learning algorithms to analyze images and videos captured by cameras to identify problems such as road damage and damaged traffic signs.

[0141] Data cleaning and preprocessing: Clean the collected raw data to remove noise and outliers and improve data quality.

[0142] Feature extraction: Extract key features from the cleaned data for subsequent analysis and prediction.

[0143] Inspection route optimization algorithm:

[0144] Path planning algorithm: Based on historical inspection data and real-time road conditions, Dijkstra, A* and other algorithms are used to calculate the optimal inspection path.

[0145] AI dynamic adjustment mechanism: Combined with the results of the AI ​​prediction model, the inspection route and frequency are dynamically adjusted. For example, the inspection frequency is increased for areas where problems are predicted, and the inspection frequency is reduced for normal areas.

[0146] Predictive Maintenance Model:

[0147] Data modeling and training: Use machine learning or deep learning algorithms (such as RNN training generation models) to analyze historical inspection data and establish equipment failure prediction models.

[0148] Predictive analysis: The model is used to predict possible equipment failures or road problems in the future, providing a scientific basis for inspection plans.

[0149] Intelligent inspection execution system:

[0150] Intelligent inspection vehicles / robots: equipped with high-definition cameras, sensors and other equipment to achieve autonomous inspections.

[0151] Real-time feedback and decision-making: During the inspection process, the AI ​​system (dynamically calls the equipment fault prediction model to execute the corresponding inspection prediction) analyzes the inspection data in real time, immediately provides feedback when problems are found, and automatically generates processing suggestions.

[0152] 3) AI data optimization processing algorithm

[0153] Image recognition algorithms:

[0154] Algorithms such as convolutional neural networks (CNN) are used to recognize inspection images to quickly and accurately detect problems such as road damage and traffic sign damage.

[0155] Use technologies such as transfer learning to improve the adaptability and accuracy of the algorithm in different inspection scenarios.

[0156] Time Series Analysis Algorithms:

[0157] Process time series data, such as traffic flow data, and use algorithms such as ARIMA and LSTM to predict future traffic conditions, providing a basis for optimizing inspection routes.

[0158] Clustering and association rule mining:

[0159] Cluster analysis is performed on inspection data to identify common characteristics of equipment failures or road problems.

[0160] By using association rule mining algorithms, potential associations between equipment failures and inspection data are discovered, providing clues for predictive maintenance.

[0161] 4) Reinforcement Learning Algorithm:

[0162] In a dynamic inspection environment, the reinforcement learning algorithm is introduced to enable the inspection system to automatically adjust the inspection strategy based on real-time feedback and optimize the inspection route and frequency.

[0163] Implementation steps:

[0164] Data collection and preprocessing;

[0165] AI model training and optimization;

[0166] Development of inspection route and frequency optimization algorithm;

[0167] Intelligent inspection execution system integration and testing;

[0168] System deployment and operation maintenance.

[0169] On the one hand, AI algorithms are used to optimize inspection routes and frequencies, improve inspection efficiency, reduce resource waste, and ensure the comprehensiveness and timeliness of inspections. The solution combines sensor data collection, image recognition, data analysis and prediction technologies to achieve intelligent management of inspection work.

[0170] On the other hand, optimize inspection reports: automatically generate inspection reports, including inspection time, location, problems found and recommended measures, and support online approval and tracking.

[0171] Based on the description of the above intelligent operation and maintenance management system, the embodiment of the present invention further proposes the following key mathematical models and their character meanings, and explains the relationship and working mechanism between the key mathematical models.

[0172] 1. Data collection and preprocessing model

[0173] Data Integration Model:

[0174] D = {d1, d2, ..., dn}: represents the set of all collected equipment and inspection data.

[0175] API: Application Programming Interface for data integration.

[0176] Data cleaning model:

[0177] C(D): represents the cleaned data set, removing noise and outliers.

[0178] 2. Image recognition and pattern recognition models

[0179] Convolutional Neural Network (CNN) Model:

[0180] I={i1,i2,...,i m}: represents the input image dataset.

[0181] f(I)→O: represents the output after CNN processing, O is the identified feature or problem (such as road damage). Feature extraction model:

[0182] F(I)={f1,f2,...,f k}: Represents the set of features extracted from the image.

[0183] 3. Inspection route optimization model

[0184] Path planning algorithm:

[0185] P = {p1, p2, ..., p j}: represents a collection of inspection paths.

[0186] Cost(p i ): indicates path p i cost (time, distance, etc.).

[0187] Optimization goal: Minimize total cost:

[0188] Dynamic adjustment mechanism:

[0189] A(t): represents the inspection adjustment strategy at time t.

[0190] 4. Predictive maintenance model

[0191] Failure prediction model:

[0192] M(X)→Y: indicates that the input feature X (such as historical data) predicts the output Y (equipment failure probability).

[0193] Time Series Analysis Model:

[0194] T={t1,t2,...,t k}: Represents time series data (such as traffic flow).

[0195] 5. Reinforcement Learning Algorithm Model

[0196] Reward function:

[0197] R(s, a): represents the reward obtained by taking action a in state s.

[0198] Value function:

[0199] V(s): represents the expected return in state s.

[0200] The working mechanism of the above mathematical models is as follows:

[0201] The data acquisition and preprocessing model is the basis of the entire system and ensures the quality of input data.

[0202] Image recognition and pattern recognition models rely on the results of data collection, analyzing images through deep learning models to identify potential problems.

[0203] The inspection route optimization model uses the output of image recognition and real-time data, applies a path planning algorithm to calculate the optimal inspection route, and optimizes the inspection frequency in real time through a dynamic adjustment mechanism.

[0204] The predictive maintenance model predicts equipment failures based on historical data and real-time data, providing a scientific basis for inspection plans and thus improving the targeted nature of inspections.

[0205] The reinforcement learning algorithm model continuously learns and adjusts the inspection strategy during the inspection process to optimize resource allocation and inspection efficiency.

[0206] The embodiment of the present invention provides the following technical effects by using the above key mathematical model:

[0207] Improve inspection efficiency: Optimize inspection routes and frequencies through AI algorithms, reduce unnecessary inspections, ensure that key equipment receives priority attention, and reduce resource waste.

[0208] Enhance data accuracy: Electronic management and automated data entry reduce manual intervention, lower error rates, and improve data accuracy and traceability.

[0209] Real-time feedback and decision-making: The intelligent inspection execution system can analyze inspection data in real time, quickly identify problems and generate treatment suggestions, thereby improving response speed.

[0210] Predictive maintenance: By establishing a fault prediction model, potential problems can be warned in advance, reducing the risk of equipment failure and extending the service life of equipment.

[0211] Comprehensiveness and timeliness: Combining sensor data and image recognition technology to achieve comprehensive inspection coverage, ensuring that problems are discovered and addressed in a timely manner.

[0212] In summary, through the synergistic effect of the above-mentioned mathematical model and working mechanism, the intelligent operation and maintenance management system in the embodiment of the present invention can significantly improve the intelligence level of inspection, optimize resource allocation, and enhance the scientificity and effectiveness of equipment management.

[0213] 3. Fault alarm management

[0214] Real-time monitoring: Use IoT technology to monitor the operating status of equipment in real time, and trigger an alarm immediately if an abnormality is found.

[0215] Intelligent classification: Intelligent classification is performed according to the severity of the fault, and high-priority faults are handled first.

[0216] Fault linkage: Linked with the emergency response system, it automatically notifies relevant personnel and initiates the emergency plan.

[0217] 4. Monitoring of production facility maintenance plans

[0218] Preventive maintenance: Based on historical data and AI prediction models, formulate preventive maintenance plans to reduce unplanned downtime.

[0219] Progress monitoring: Real-time monitoring of the execution of the maintenance plan, including maintenance progress, resource consumption, etc.

[0220] Effect evaluation: After the maintenance is completed, the maintenance effect is automatically evaluated and feedback is collected to optimize subsequent plans.

[0221] 5. Health status evaluation of key equipment

[0222] Multi-source data collection: Integrate data from different sensors and systems to comprehensively assess the health status of equipment.

[0223] Health index model: Establish a health index model to comprehensively evaluate factors such as equipment performance, lifespan, and maintenance records.

[0224] Visual display: The health status of the equipment is intuitively displayed through dashboards, heat maps, etc., making it easy to quickly locate problems.

[0225] 6. Maintenance management

[0226] Work order system: Establish an electronic work order system to support automatic dispatching, progress tracking and completion confirmation.

[0227] Knowledge base: Establish a maintenance knowledge base to record common faults and solutions to improve maintenance efficiency.

[0228] Skill training: Combine virtual reality (VR) and augmented reality (AR) technologies to provide skills training for maintenance personnel.

[0229] 7. Maintenance management

[0230] Standardized process: Develop standardized maintenance process to ensure that maintenance work is standardized and efficient.

[0231] Material management: Establish a material management system to ensure that the materials required for maintenance are in place in a timely manner.

[0232] Quality Control: Implement strict quality control measures to ensure that the maintenance quality meets the standards.

[0233] 8.Management of production facility status changes

[0234] Status monitoring: Real-time monitoring of changes in the status of production facilities, including start and stop, load changes, etc.

[0235] Change approval: Approval management of changes in production facility status to ensure that the changes are reasonable and compliant.

[0236] Historical records: Record the status change history of production facilities to facilitate subsequent analysis and traceability.

[0237] 9. Fault linkage

[0238] Cross-system linkage: Link with ERP, MES and other systems to achieve rapid transmission and processing of fault information.

[0239] External resource coordination: Coordinate external experts or service providers to participate in troubleshooting when necessary.

[0240] 10. Fault diagnosis and early warning

[0241] Intelligent diagnosis: Use machine learning algorithms to intelligently analyze fault data and quickly locate the cause of the fault; Early warning: Provide early warning of potential faults based on prediction models and take measures in advance to avoid faults; Continuous optimization: Continuously collect new fault data and optimize the diagnosis model to improve diagnostic accuracy and warning capabilities; The specific intelligent diagnosis methods are as follows:

[0242] The fault location, cause, and treatment measures taken, these fault data can be recorded through fault logs. In the early stage, these data can be collected and preprocessed, and the fault features in each data can be extracted through feature engineering, and feature learning is performed based on machine learning algorithms. The corresponding prediction model is trained and used for fault monitoring, prediction, and output of corresponding fault treatment measures. Specifically:

[0243] Using machine learning algorithms to identify the features of fault data and their corresponding treatment measures, and then generate a fault prediction model, involves a complex process of data preprocessing, feature engineering, model training and evaluation, and final deployment and application:

[0244] 1) Data collection and preprocessing

[0245] Data collection: Collect fault data and its corresponding treatment measures from various sources (such as sensors, log files, maintenance records, etc.).

[0246] Data cleaning: Process missing values, outliers, duplicate values, etc. to ensure data quality.

[0247] Data standardization / normalization: Standardize or normalize numerical features to eliminate dimensional differences.

[0248] 2) Feature Engineering

[0249] Feature selection: Select features useful for fault prediction from raw data.

[0250] Feature extraction: extract new features from raw data through statistics, transformation and other methods.

[0251] Feature dimensionality reduction: If the feature dimension is too high, PCA, LDA and other methods can be used to reduce the dimension.

[0252] 3) Model selection and training

[0253] Algorithm selection: According to the characteristics of fault data and prediction requirements, select appropriate machine learning algorithms, such as decision trees, random forests, support vector machines, neural networks, etc.

[0254] Model training: Use the training dataset to train the model and adjust the model parameters to optimize performance.

[0255] Cross-validation: Use methods such as cross-validation to evaluate the generalization ability of the model and prevent overfitting.

[0256] 4) Model evaluation and optimization

[0257] Evaluation Metrics: Choose appropriate evaluation metrics (such as accuracy, recall, F1 score, etc.) to evaluate model performance.

[0258] Model optimization: Tune the model based on the evaluation results, such as adjusting hyperparameters and improving feature engineering.

[0259] 5) Fault prediction and explanation

[0260] Fault identification and prediction: Use the trained model to predict new fault data and identify potential faults.

[0261] Fault cause output: Output the corresponding fault cause based on the model's prediction results.

[0262] Avoidance suggestions: Based on the prediction results and fault causes, corresponding avoidance suggestions or treatment measures are proposed.

[0263] 6) Model deployment and application

[0264] Model deployment: Deploy the trained model to the actual production environment for real-time monitoring and fault prediction.

[0265] Continuous monitoring and updating: Continuously monitor the deployed models to promptly detect and address issues with model performance degradation; at the same time, regularly update the models based on new fault data.

[0266] Precautions

[0267] Data quality: Ensure the quality of the collected fault data and its handling measures, which is the basis for building accurate prediction models.

[0268] Feature Importance: A deep understanding of the importance of features for fault prediction helps optimize feature engineering and model training processes.

[0269] Model interpretability: While pursuing model performance, we should also consider model interpretability to better understand the model's prediction results and causes of failure.

[0270] Through the above steps, machine learning algorithms can be used to generate fault prediction models, achieve accurate identification and prediction of faults, and provide targeted avoidance suggestions for fault handling.

[0271] System auxiliary module, used to provide statistical reports, indicator system management, announcement management, low-value vulnerable and consumables management, and business delivery services;

[0272] The full life cycle monitoring module is used to provide overall display of the full life cycle, full life cycle status monitoring, and full life cycle quality analysis services;

[0273] Large screen management module, used to provide metering center business monitoring, flow and storage three-dimensional monitoring, electric energy metering key data monitoring, intelligent building monitoring, and video monitoring integration services;

[0274] The system support module is used to provide message management, custom query, organization and permission management, version management, system parameter management, system operation monitoring, report parameter customization, and log management services.

[0275] like Figure 5 The figure shows the functional architecture diagram of the experiment operation management module.

[0276] For the functional architecture of other modules in the application layer, please refer to Figure 4 The functional architecture design shown is sufficient.

[0277] For the functions provided by each module, the administrator can perform specific program configuration.

[0278] As an optional implementation scheme of the present application, optionally, the presentation layer includes:

[0279] BS browser / large screen, used to display the scheduling management process of electric energy metering experiment.

[0280] On the other hand, the present application proposes a method for scheduling and managing an electric energy metering experiment, which is implemented based on the electric energy metering experiment scheduling and management system, and includes the following steps:

[0281] The marketing end formulates the corresponding electric energy metering experiment scheduling management tasks and sends them to the electric energy metering experiment scheduling management platform;

[0282] The electric energy metering experiment scheduling management platform executes the electric energy metering experiment scheduling management tasks, coordinates the six-line and one-reservoir system to complete the corresponding task process, and returns the task execution results to the marketing end.

[0283] The deployment architecture should meet the following requirements:

[0284] The physical structure of the metrology data platform consists of data servers, aggregation switch equipment and related network equipment. The metrology data platform of this project accesses the operating data of the "six lines and one warehouse" production equipment, monitors the equipment of the "six lines and one warehouse", provides data services for the dispatching platform to monitor the equipment, and ensures the normal operation of the equipment. In the long-term plan, the metrology data platform accesses plan management data and other data, and achieves seamless compatibility with mainstream big data underlying technologies, providing big data management, development and computing capabilities, and supporting data analysis of core businesses in the metrology experiment scheduling of the metrology center. In order to support big data management, development, and computing capabilities, this phase of the metrology data platform is equipped with 10 sets of data servers and 1 aggregation switch.

[0285] It is mainly composed of production database servers, application servers, interface servers, monitoring servers, disaster recovery servers, storage array devices, switch devices and related network devices. The electric energy metering experiment scheduling management platform accesses the production data of "six lines and one warehouse" to schedule and monitor the production situation of "six lines and one warehouse".

[0286] The electric energy metering experiment dispatching management platform, six automated calibration systems, intelligent warehousing system, and visual large-screen display system must be networked separately and securely isolated from other application systems and public network channels using firewalls to ensure the information security of the system.

[0287] The traditional way of dispatching and monitoring the production situation of "six lines and one warehouse" by the traditional electric energy metering experimental dispatching management platform is mainly through dispatching managers to complete the dispatching work. In order to complete the dispatching of various aspects, several additional managers need to be dispatched, which will increase the management cost; in addition, manual dispatching is prone to fatigue and mistakes, and it is impossible to conduct real-time online dispatching and monitoring.

[0288] The present invention proposes an AI model that allows the electric energy metering experiment scheduling management platform to build based on big data, to supervise the collected working condition data of six lines and one warehouse (calibration data of six automated calibration systems and storage data of intelligent storage systems), and to realize intelligent scheduling, specifically:

[0289] Based on the AI ​​model, the inspection data and warehouse data are identified to output the matching inspection management tasks and warehouse management tasks. The following steps can be followed:

[0290] 1. Data integration and preprocessing

[0291] Data Collection:

[0292] Integrate calibration data and warehouse data, including equipment calibration records, test results, warehouse inventory information, in-and-out records, etc.

[0293] Ensure the integrity and accuracy of the data and handle missing values ​​and outliers.

[0294] Data Standardization:

[0295] Convert data from different sources and formats into unified formats and standards to facilitate subsequent processing and analysis.

[0296] 2. AI model construction and training

[0297] Model selection:

[0298] Select appropriate AI models based on data characteristics and task requirements, such as deep learning models (CNN, RNN, LSTM, etc.), machine learning models (decision tree, random forest, SVM, etc.), or hybrid models.

[0299] Feature Engineering:

[0300] Extract key features from calibration data and warehouse data, such as equipment performance parameters, inventory levels, in-and-out frequencies, etc.

[0301] Perform dimension reduction and encoding on features to improve model training efficiency and effectiveness.

[0302] Model training:

[0303] Use historical data to train AI models and adjust model parameters to optimize performance.

[0304] Cross-validation, overfitting detection and other methods are used to ensure the generalization ability and stability of the model.

[0305] The performance verification methods include: performance index comparison, cross-validation, confusion matrix, precision, recall, F1 score, ROC curve and AUC value. The administrator shall complete the corresponding verification.

[0306] 3. Matching of inspection management and warehouse management tasks

[0307] Task Identification:

[0308] Use trained AI models to identify and analyze real-time or newly collected inspection data and warehouse data.

[0309] Identify the verification management tasks and warehouse management tasks that need to be performed, such as equipment re-inspection, inventory adjustment, cargo sorting, etc.

[0310] Task matching:

[0311] According to the recognition results and preset business rules, the inspection management tasks and warehousing management tasks are matched.

[0312] Ensure the logical relationship and priority between tasks to avoid resource conflicts and delays.

[0313] 4. Task execution and monitoring

[0314] Task Assignment:

[0315] Assign the matched tasks to the corresponding executors or systems, such as inspectors, warehouse managers, automated equipment, etc.

[0316] Provide clear task instructions and execution guidance to ensure tasks are completed accurately and efficiently.

[0317] Real-time monitoring:

[0318] Monitor the task execution process in real time, collect execution data and feed it back to the AI ​​model for continuous optimization.

[0319] Provide early warning and handle abnormal situations to ensure that tasks can be carried out smoothly and achieve the expected results.

[0320] 5. Result feedback and optimization

[0321] Result evaluation:

[0322] Evaluate and analyze task execution results, including indicators such as task completion rate, accuracy, and efficiency.

[0323] Identify existing problems and deficiencies and provide a basis for subsequent improvements.

[0324] Model optimization:

[0325] Continuously optimize and iterate the AI ​​model based on evaluation results and new data feedback.

[0326] Introduce new algorithms and technical means to improve the recognition accuracy and generalization ability of the model.

[0327] 6. System integration and visualization

[0328] System Integration:

[0329] Integrate the smart management solution into the existing calibration management system and warehouse management system to achieve data sharing and process collaboration.

[0330] Ensure smooth interfaces, data consistency and business continuity between systems.

[0331] Visual display:

[0332] Provides an intuitive visual interface to display the recognition results of verification data and storage data, task matching status, execution progress and other information.

[0333] It is convenient for managers to conduct real-time monitoring and decision support.

[0334] Through the implementation of the above steps and framework, we can build an intelligent management solution for inspection and warehousing management based on AI models to improve the efficiency, accuracy and intelligence level of inspection and warehousing operations. At the same time, with the continuous development of technology and the continuous accumulation of data, the solution can also be continuously optimized and upgraded to adapt to new business needs and challenges.

[0335] By deploying multiple application servers, the company can provide unified metering management application services. The application server distributes WEB requests through the load balancer. All client business requests are dynamically distributed through the load balancer and evenly distributed to each application server to ensure the reliability and availability of the application.

[0336] The specific deployment and communication links of each server are not limited in this embodiment and are deployed by the administrator according to the configuration of the computer room.

[0337] The functional business of this platform is based on a data platform with functions such as metrology data collection, data integration, data cleaning, and data quality. It takes experimental scheduling, logistics distribution, asset management, and cost management as the core, and planning, arrival, experiment, warehousing, calibration, distribution, issuance, and dismantling and disposal as the main business applications. The integrated warehousing of the second, third, and fourth-level warehouses of the whole company is the extended management and control of metrology assets. In addition, comprehensive statistics, summary and analysis of data to assist decision-making, as well as visual display dashboards, form a professional metrology management system covering all metrology businesses.

[0338] The electric energy metering experiment dispatching management platform is based on mainstream big data underlying technology, realizes an open data management architecture, provides multi-source heterogeneous data acquisition modules, real-time data management framework, easy-to-use data application environment and multi-source data interface big data platform, provides big data management, development and computing capabilities, and supports the data analysis value of the core business in the metering experiment dispatching of the metering center.

[0339] The platform system software adopts the J2EE multi-layer technology architecture that combines technological advancement and maturity to improve the system's flexibility, scalability, security and concurrent processing capabilities, while realizing data interaction with other business applications such as marketing business applications through a unified data interface.

[0340] The platform business functions use component technology to separate interface control, business logic and data processing, and realize loose coupling within the system, so as to flexibly and quickly respond to the needs of business changes on the system. Technically, it is divided into the IoT perception layer, data collection layer, data storage layer, data service layer, technical support layer, business application layer, interface display layer, etc.

[0341] The technical design of the electric energy metering experiment scheduling management platform is based on the component development concept and consists of a series of independently deployed components. It should try to meet the requirement that each component can run multiple component instances at the same time to ensure the high reliability and scalability of the system. At the same time, it should comprehensively consider factors such as system performance and scalability, while taking into account the system's requirements in deployment, maintenance and management.

[0342] Therefore, this application has the following functional features:

[0343] (1) Business interface component interface definition is stable

[0344] The platform and marketing business system, six-line and one-warehouse automatic control system, etc. fully consider the stability of the data interface and use XML or similar structures to ensure the scalability of interface transmission parameters and content.

[0345] (2) High cohesion and loose coupling principle

[0346] System design must follow the principles of high cohesion and loose coupling. The system modules are loosely coupled, the specific implementation of the system is transparent to the user, and the functions and structures implemented within the system are highly logically related while ensuring the independence of the functional modules.

[0347] (3) Shared information services

[0348] The system must provide shared information services that are independent of the business. The shared information services follow the enterprise's data model specifications and achieve shared information access and application integration of the production scheduling platform through the enterprise-level information integration platform.

[0349] (4) Separation of business processes and component implementation

[0350] The separation principle of business process and component implementation should be considered comprehensively. It is recommended to use process management, policy management and interface integration technology to dynamically define the behavior of the system to realize system functions. While applying this technology to obtain flexibility and scalability, the impact on system performance should also be fully anticipated.

[0351] (5) Distributed, application-oriented access

[0352] Implement a distributed transparent access mechanism for business applications. The application instances that make up the system can be deployed on one or more hosts. The service access provided by the application is transparent to the distribution location and position. Service users can obtain services through the logical name of the application regardless of the physical location of the host where the application is located.

[0353] Obviously, those skilled in the art should understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0354] Example 2

[0355] Based on the implementation principle of Example 1, the present application further provides an electronic device, including:

[0356] processor;

[0357] a memory for storing processor-executable instructions;

[0358] Wherein, the processor is configured to implement the electric energy metering experiment scheduling management method when executing the executable instructions.

[0359] The specific method process needs to be combined with the specific business model for task management and execution, and the administrator can perform the corresponding task configuration.

[0360] The modules or steps of the present invention described above can be implemented by a general-purpose computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0361] Example 3

[0362] like Figure 6 As shown, further, in another aspect, the present application also proposes an electronic device, including:

[0363] processor;

[0364] a memory for storing processor-executable instructions;

[0365] Wherein, the processor is configured to implement the electric energy metering experiment scheduling management method when executing the executable instructions.

[0366] The electronic device according to the embodiment of the present disclosure includes a processor and a memory for storing processor executable instructions, wherein the processor is configured to implement any of the above-mentioned methods for scheduling and managing electric energy metering experiments when executing the executable instructions.

[0367] Here, it should be noted that the number of processors can be one or more. At the same time, the electronic device of the embodiment of the present disclosure may also include an input system and an output system. Among them, the processor, memory, input system and output system may be connected through a bus or in other ways, which are not specifically limited here.

[0368] As a computer-readable storage medium, the memory can be used to store software programs, computer executable programs and various modules, such as the program or module corresponding to the method for scheduling and managing an electric energy metering experiment in the embodiment of the present disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0369] The input system can be used to receive input numbers or signals. The signal can be a key signal related to user settings and function control of the device / terminal / server. The output system can include display devices such as display screens.

[0370] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An electric energy metering experiment scheduling and management system, characterized in that: include: The marketing end is used to formulate corresponding electric energy metering experiment scheduling management tasks and send them to the electric energy metering experiment scheduling management platform; The electric energy metering experiment scheduling management platform is used to execute the electric energy metering experiment scheduling management tasks, coordinate the six-line and one-storage system to complete the corresponding task process, and return the task execution results to the marketing end; The six-line-one-reservoir system is used to perform specific electric energy metering experiment scheduling and management tasks; The marketing end and the six-line-one-storage system are respectively connected to the electric energy metering experiment scheduling management platform for communication; The electric energy metering experiment scheduling management platform includes: The basic layer is used to provide basic network services; The data layer is used to provide data storage services and store structured and unstructured data generated in the scheduling management of electric energy metering experiments; The support layer is used to provide data support services in the scheduling and management of electric energy metering experiments; The application layer is used to provide on-site experiment scheduling services for electric energy metering experiment scheduling management; The presentation layer is used to display the scheduling and management process of the electric energy metering experiment; The application layer includes: The experimental operation management module is used to provide the laboratory with procurement management, arrival and acceptance management, auxiliary management, verification experiment execution, warehousing management and distribution management services; The metrology technology supervision and management module is used to provide online verification management of assembly line standards; APP mobile application module, used to provide distribution business, turnover box recycling and auxiliary function services; The experimental scheduling and monitoring module is used to provide operation control, operation monitoring, laboratory key monitoring, delivery tracking, experimental business subject analysis and assembly line detection system overall control and scheduling services; Intelligent operation and maintenance management module, used to provide ledger management, inspection management, fault alarm management, production facility maintenance plan monitoring, key equipment health status evaluation, maintenance management, repair management, production facility status change management, automated inspection, fault linkage, fault diagnosis and early warning services; System auxiliary module, used to provide statistical reports, indicator system management, announcement management, low-value vulnerable and consumables management, and business delivery services; The full life cycle monitoring module is used to provide overall display of the full life cycle, full life cycle status monitoring, and full life cycle quality analysis services; Large screen management module, used to provide metering center business monitoring, flow and storage three-dimensional monitoring, electric energy metering key data monitoring, intelligent building monitoring, and video monitoring integration services; The system support module is used to provide message management, custom query, organization and permission management, version management, system parameter management, system operation monitoring, report parameter customization, and log management services.

2. The electric energy metering experiment scheduling and management system according to claim 1 is characterized in that: The base layer comprises: Network equipment, servers, storage equipment, backup equipment and security equipment.

3. The electric energy metering experiment scheduling and management system according to claim 1 is characterized in that: The data layer includes: Real-time data, structured data, and unstructured data.

4. The electric energy metering experiment scheduling and management system according to claim 1 is characterized in that: The supporting layer comprises: Data access module, used for users to access the platform; The permission management module is used for the platform to manage the permissions of users at all levels; Identity authentication module, used for platform user identity authentication; Task scheduling module, used to perform various scheduling tasks; Parameter configuration module, used for parameter configuration; Report service module, used to provide electric energy metering report service; The workflow engine module is used to build the workflow of the power metering task and execute the workflow; Interface management module, used to manage various data interfaces of the access platform; Operation monitoring module, used to monitor the operation status of the platform; The log management module is used to record and manage platform operation logs.

5. The electric energy metering experiment scheduling and management system according to claim 1 is characterized in that: The presentation layer includes: BS browser / large screen, used to display the scheduling management process of electric energy metering experiment.

6. A method for scheduling and managing an electric energy metering experiment, implemented based on the electric energy metering experiment scheduling and management system according to any one of claims 1 to 5, characterized in that: The steps include: The marketing end formulates the corresponding electric energy metering experiment scheduling management tasks and sends them to the electric energy metering experiment scheduling management platform; The electric energy metering experiment scheduling management platform executes the electric energy metering experiment scheduling management tasks, coordinates the six-line and one-reservoir system to complete the corresponding task process, and returns the task execution results to the marketing end.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement an electric energy metering experiment scheduling management method as described in claim 6 when executing the executable instructions.

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