Business mode management method based on multi-scene electric energy metering experiment scheduling

Abstract

The invention relates to a business mode management method based on multi-scene electric energy metering experiment scheduling, and the method comprises the steps: receiving and analyzing a system data set reported by an external system through an electric energy metering experiment scheduling management platform, and recognizing the electric energy metering business attribute of the system data set; according to the electric energy metering service attribute, a corresponding service mode is matched and activated, and the service mode comprises a conventional mode, a disaster recovery mode and a maintenance mode. Therefore, by automatically matching three business modes of routine, disaster recovery and maintenance, development and maintenance of central production business are ensured. Through mode configuration, the platform realizes automatic verification with a marketing system, a data center, a six-line one-library system and the like, and the verification efficiency is greatly improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electric energy metering management, and in particular to a business model management method, system and electronic equipment based on electric energy metering experiment scheduling in multiple scenarios. Background Art

[0002] The electric energy metering experiment dispatching management platform can be interconnected with the existing management system through the interface program, including the marketing platform, the "six lines and one warehouse" system, the security system, etc., and the basic business data generated on the decision-making analysis site is summarized to form analysis data, including planning, arrival, warehousing, verification, distribution and comprehensive management, etc. Digital analysis provides scientific data support for decision-making. Through analysis, a more reasonable plan can be formulated to achieve lean production. Each complements each other and interacts with each other to improve production efficiency and automation.

[0003] In the daily scheduling and management of electric energy metering experiments, it is necessary to deal with three business modes: routine, disaster recovery, and maintenance, to ensure the development of the center's production business and maintenance. Summary of the invention

[0004] In order to solve the above problems, the present application proposes a business model management method, system and electronic equipment based on electric energy metering experiment scheduling in multiple scenarios.

[0005] On the one hand, the present application proposes a business model management method based on electric energy metering experiment scheduling in multiple scenarios, which is implemented based on an electric energy metering experiment scheduling management platform. The electric energy metering experiment scheduling management platform includes:

[0006] 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;

[0007] The metrology technology supervision and management module is used to provide online verification management of assembly line standards;

[0008] APP mobile application module, used to provide distribution business, turnover box recycling and auxiliary function services;

[0009] 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;

[0010] Intelligent operation and maintenance management module, which 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;

[0011] System auxiliary module, used to provide statistical reports, indicator system management, announcement management, low-value vulnerable and consumables management, and business delivery services;

[0012] 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;

[0013] Large screen management module, used to provide metering center business monitoring, flow and storage three-dimensional monitoring, power metering key data monitoring, intelligent building monitoring, video monitoring integration services;

[0014] System support module, used to provide message management, custom query, organization and authority management, version management, system parameter management, system operation monitoring, report parameter customization, and log management services;

[0015] The method comprises the following steps:

[0016] Collect system data sets and report them to the electric energy metering experiment scheduling management platform;

[0017] The electric energy metering experiment scheduling management platform receives and parses the system data set, and identifies the electric energy metering service attributes of the system data set;

[0018] According to the electric energy metering service attributes, the corresponding service mode is matched and activated, wherein the service mode includes regular, disaster recovery, and maintenance modes.

[0019] Furthermore, the normal mode is a closed-loop business management process between the electric energy metering experiment scheduling management platform and the marketing system and the six-line-one-storage system, wherein the six-line-one-storage system includes a calibration line and a vertical warehouse system.

[0020] Furthermore, the conventional mode includes the following process:

[0021] The marketing system constructs the corresponding electric energy metering verification experiment management task, and sends the verification experiment management task to the electric energy metering experiment scheduling management platform;

[0022] The electric energy metering experiment scheduling management platform receives the verification experiment management task and sends it to the verification assembly line;

[0023] The verification pipeline receives the verification experiment management task and applies for a task table from the electric energy metering experiment scheduling management platform;

[0024] The electric energy metering experiment scheduling management platform receives and uploads the meter application, generates a verification and outbound task, and sends it to the library system;

[0025] The vertical warehouse system receives and executes the verification and outbound task, and feeds back the verification and outbound details to the electric energy metering experiment scheduling and management platform, and the electric energy metering experiment scheduling and management platform forwards the verification and outbound details to the verification pipeline;

[0026] The verification assembly line executes the verification outbound details and determines whether to apply for an empty container:

[0027] If so, apply for an empty box from the electric energy metering experiment scheduling management platform, and the electric energy metering experiment scheduling management platform dispatches the corresponding empty box from the vertical warehouse system to the calibration assembly line;

[0028] If not, upload the verification conclusion data to the electric energy metering experiment scheduling management platform;

[0029] Repeat the above steps until all tasks are completed;

[0030] The electric energy metering experiment scheduling management platform feeds back all the verification conclusion data to the marketing system.

[0031] Furthermore, the disaster recovery mode is an abnormal data business management process between the electric energy metering experiment scheduling management platform and the marketing system.

[0032] Furthermore, the disaster recovery mode includes the following processes:

[0033] The electric energy metering experiment scheduling management platform determines whether the marketing system has communication anomalies:

[0034] If there is an exception, cache the current real-time interaction information with the marketing system and store it in the exception cache queue; synchronize the cached data to the marketing system;

[0035] Waiting for the marketing system communication to be restored, if the communication is restored, retrieving the corresponding real-time interaction information from the abnormal cache queue in sequence, and resuming the interaction with the marketing system;

[0036] After the marketing system communication is restored, the real-time interactive information sent by the electric energy metering experiment scheduling management platform is verified using the cache data synchronized in the previous period. If the data is consistent, interactive communication with the electric energy metering experiment scheduling management platform is established; otherwise, it is rejected;

[0037] If normal, give up.

[0038] Furthermore, the maintenance mode is a line-depot maintenance business management process between the six-line-one-depot system and the electric energy metering experiment dispatching management platform without generating marketing process business data.

[0039] Furthermore, the maintenance mode includes the following process:

[0040] The electric energy metering experiment dispatching management platform generates maintenance outbound tasks and issues the inspection assembly line / vertical warehouse system;

[0041] The inspection assembly line / vertical warehouse system receives and executes the inspection and outbound delivery task;

[0042] The electric energy metering experiment scheduling management platform finds that the verification assembly line / vertical warehouse system has been executed. When the execution is completed, the corresponding maintenance warehousing task details are generated and sent to the verification assembly line / vertical warehouse system;

[0043] The inspection assembly line / vertical warehouse system receives and executes the inspection and warehousing task details, and feeds back the inspection and warehousing data;

[0044] After the calibration is completed, the electric energy metering experiment scheduling management platform saves the maintenance warehousing data and pushes the box-meter relationship data.

[0045] In another aspect, the present application further provides an electronic device, comprising:

[0046] processor;

[0047] a memory for storing processor-executable instructions;

[0048] Among them, the processor is configured to implement the business model management method based on electric energy metering experiment scheduling in multiple scenarios when executing the executable instructions.

[0049] Technical effects of the present invention:

[0050] This application receives and analyzes the system data set reported by the external system through the electric energy metering experiment scheduling management platform, identifies the electric energy metering business attributes of the system data set; matches and activates the corresponding business mode according to the electric energy metering business attributes, wherein the business mode includes conventional, disaster recovery, and maintenance modes. In this way, the three business modes of conventional, disaster recovery, and maintenance are automatically matched to ensure the development and maintenance of the center's production business. Through mode configuration, the platform can realize automated calibration with the marketing system, data center, six-line and one-database system, etc., greatly improving the calibration efficiency.

[0051] 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

[0052] 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.

[0053] Figure 1 A schematic diagram of the system framework of the electric energy metering experiment scheduling management platform of the present invention is shown;

[0054] Figure 2 Shown is a schematic diagram of a conventional mode application system of the present invention;

[0055] Figure 3 A schematic diagram of an application system of the disaster recovery mode of the present invention is shown;

[0056] Figure 4 A schematic diagram of an application system of the maintenance mode of the present invention is shown;

[0057] Figure 5 It is a schematic diagram showing the application of the electronic device of the present invention. DETAILED DESCRIPTION

[0058] 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.

[0059] 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.

[0060] 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.

[0061] Example 1

[0062] like Figure 1 As shown in the figure, it is a platform architecture for executing this method. The electric energy metering experiment scheduling management platform includes:

[0063] 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;

[0064] The metrology technology supervision and management module is used to provide online verification management of assembly line standards;

[0065] APP mobile application module, used to provide distribution business, turnover box recycling and auxiliary function services;

[0066] 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;

[0067] Intelligent operation and maintenance management module, which 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;

[0068] System auxiliary module, used to provide statistical reports, indicator system management, announcement management, low-value vulnerable and consumables management, and business delivery services;

[0069] 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;

[0070] Large screen management module, used to provide metering center business monitoring, assembly line and storage three-dimensional monitoring, power metering key data monitoring, intelligent building monitoring, video monitoring integration services;

[0071] 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.

[0072] In this embodiment, 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;

[0073] 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;

[0074] The six-line-one-reservoir system is used to perform specific electric energy metering experiment scheduling and management tasks;

[0075] The marketing end and the six-line-one-storage system are respectively communicatively connected with the electric energy metering experiment scheduling management platform.

[0076] The marketing end is the marketing terminal at the front end of the electric energy metering, which can send task plans to the backend;

[0077] 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.

[0078] The electric energy metering experiment dispatching management platform is connected to the marketing platform (marketing end, using the marketing 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 based on the user's production system.

[0079] 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, experiment dispatching service, metering experiment calibration business application, statistical analysis and decision support, and realizes effective dispatch 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.

[0080] 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.

[0081] The functional business of the platform is based on a data platform with functions such as metering 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 metering assets. Combined with comprehensive statistics, summary and analysis of data to assist decision-making, as well as visual display dashboards, a professional metering management system with the characteristics of Inner Mongolia Western Power Grid has been formed, covering all metering businesses.

[0082] 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.

[0083] 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.

[0084] On the one hand, the present application proposes a business model management method based on electric energy metering experiment scheduling in multiple scenarios, which is implemented based on an electric energy metering experiment scheduling management platform. The method comprises the following steps:

[0085] Collect system data sets and report them to the electric energy metering experiment scheduling management platform;

[0086] The electric energy metering experiment scheduling management platform receives and parses the system data set, and identifies the electric energy metering service attributes of the system data set;

[0087] According to the electric energy metering service attributes, the corresponding service mode is matched and activated, wherein the service mode includes regular, disaster recovery, and maintenance modes.

[0088] This solution receives and analyzes the system data set reported by the external system through the electric energy metering experiment scheduling management platform, identifies the electric energy metering business attributes of the system data set; matches and activates the corresponding business mode according to the electric energy metering business attributes, wherein the business mode includes conventional, disaster recovery, and maintenance modes. In this way, the three business modes of conventional, disaster recovery, and maintenance are automatically matched to ensure the development of the center's production business and maintenance. Through mode configuration, the platform can realize automated verification with the marketing system, data center, six-line and one-database system, etc., greatly improving the verification efficiency.

[0089] Different business model management processes are pre-configured and stored on the electric energy metering experiment scheduling management platform.

[0090] After various systems, such as the marketing system, data middle platform, six-line and one-database system, etc., report the business data set to the platform, the electric energy metering experiment scheduling management platform identifies the electric energy metering business attributes of the business data set, and then matches and activates the corresponding business mode as regular, disaster recovery or maintenance mode according to the electric energy metering business attributes, thereby realizing an automated calibration process.

[0091] The business data set contains the business types and corresponding business systems involved in this calibration task. Therefore, the platform can automatically match the corresponding business model according to the attributes of the electric energy metering business and the corresponding business system. For example, a calibration experiment management task can be formed between the marketing end (marketing system, which can be a terminal APP or other terminal system) and the calibration pipeline and the vertical warehouse system. The platform automatically recognizes that the current task belongs to the normal mode. The platform implements the normal mode process between the marketing end and the calibration pipeline and the vertical warehouse system to achieve automated calibration.

[0092] Different business models require administrators to activate the corresponding business model based on the attributes of the electricity metering business. The platform needs to connect to business data sets reported by marketing systems, data middle platforms, six-line and one-database systems, etc. The data sets are huge, and different systems contain several business models, with a total of no less than dozens or hundreds of business models (for example, the data middle platform reports the middle platform data, and the platform needs to give the corresponding business response model for the middle platform data). Therefore, if the scheduling of these massive business models is completed by the scheduling administrator, it is undoubtedly a tedious scheduling task, which is prone to scheduling errors. Because administrators need to learn the attributes of the business data sets corresponding to these business models, there are certain technical thresholds and application difficulties.

[0093] In this embodiment, intelligent identification and activation of different business models can be achieved based on AI.

[0094] The following is an AI solution that uses AI to identify the energy metering business attributes of a system data set, and matches and automatically activates the corresponding business mode based on these attributes:

[0095] 1. Dataset construction and preprocessing

[0096] Dataset collection:

[0097] Collect data sets related to the energy metering business, including but not limited to user information, meter readings, electricity usage time periods, electricity usage, electricity bill settlement records, and business data (attributes) under different business modes, etc. Please collect corresponding data sets in conjunction with the business data involved in different business modes of the subsequent invention.

[0098] Ensure the comprehensiveness, accuracy, and timeliness of data sets to provide a reliable training foundation for AI models.

[0099] Data preprocessing:

[0100] The collected data were cleaned to remove missing values, outliers and duplicate values.

[0101] Standardize data to ensure that data from different sources and formats can be unified under the same standards.

[0102] Extract key features, such as user type, electricity usage habits, electricity bill payment status, etc., to provide effective input for the AI ​​model. NLP technology can be used here to perform keyword mining of attribute keywords and business model keywords, and keyword mining of attributes and patterns can be performed through keyword mining algorithms. The keyword mining algorithm is an important technology in the field of natural language processing (NLP) and information retrieval, which aims to automatically extract the most representative and important keywords or phrases from text data. These keywords can summarize the main content of the text and help users quickly understand the core information of the text. They are also often used in applications such as text classification, clustering, information retrieval and recommendation systems. For example:

[0103] 1)TF-IDF (Term Frequency-Inverse Document Frequency)

[0104] TF (Term Frequency) indicates how often a word appears in a document.

[0105] IDF (Inverse Document Frequency) represents the inverse of the number of documents that contain the word in the entire corpus, and is used to measure the general importance of a word. Words with high TF-IDF values ​​are usually considered to be keywords in the document.

[0106] 2) TextRank:

[0107] A variation of the PageRank algorithm for graph-structured text data.

[0108] The words or sentences in the text are regarded as nodes, and a graph is constructed based on the similarities between the nodes.

[0109] By iteratively calculating the weights of the nodes, keywords or key sentences are finally obtained.

[0110] The model keywords of the corresponding business model and the business keywords of the corresponding business data set form a set of feature data. Collect the feature data of massive business models and form a data set for model training.

[0111] 2. AI model construction and training

[0112] Model selection:

[0113] According to the characteristics and needs of the electricity metering business, select a suitable AI model for construction. You can consider using deep learning models (such as CNN, RNN, etc.) or machine learning models (such as decision trees, random forests, etc.).

[0114] When selecting a model, factors such as model accuracy, generalization ability, and computational efficiency should be considered comprehensively.

[0115] Feature Engineering:

[0116] Based on the data set, feature selection and feature extraction are performed. Through statistical analysis, correlation analysis and other methods, key features related to the attributes of the electric energy metering business are found.

[0117] The features are encoded, normalized, and processed to provide effective input for the AI ​​model.

[0118] Model training:

[0119] Use the preprocessed data set (divided into training set and validation set according to the proportion, and the training set is used for model training here) to train the AI ​​model. Improve the accuracy and generalization ability of the model by adjusting model parameters and optimizing algorithms.

[0120] During the training process, we should pay attention to preventing overfitting and underfitting to ensure the stability and reliability of the model.

[0121] Use the validation set to verify the model performance, such as accuracy, fitting curve, etc.

[0122] 3. Business attribute identification and business model matching

[0123] Business attribute identification:

[0124] Use the trained AI model to identify the business attributes of new electricity metering data. By analyzing the key features in the data, determine business attributes such as user type and electricity usage habits.

[0125] During the recognition process, attention should be paid to the real-time and accuracy of the data to ensure the reliability of the recognition results.

[0126] Business model matching:

[0127] According to the identified business attributes, the corresponding business model is matched. Multiple business models can be pre-defined, such as residential power consumption model, commercial power consumption model, industrial power consumption model, etc.

[0128] During the matching process, the user's actual needs and electricity usage characteristics must be considered to ensure the rationality and applicability of the business model.

[0129] 4. Automatic activation of business mode

[0130] Automatic activation mechanism:

[0131] Design an automatic activation mechanism to automatically activate the corresponding business mode when specific business attributes are identified.

[0132] During the activation process, attention should be paid to integration and coordination with existing systems to ensure smooth switching and execution of business models.

[0133] Monitoring and Optimization:

[0134] Real-time monitoring and evaluation of automatically activated business models. By collecting user feedback and analyzing business data, we can understand the execution and effect of business models.

[0135] According to the monitoring results and user needs, the business model and automatic activation mechanism are optimized and adjusted to improve the intelligence level and service quality of the electricity metering business.

[0136] In summary, by building an energy metering business attribute recognition model based on the AI ​​recognition system data set, and matching and automatically activating the corresponding business model according to the recognition results, the intelligent management of energy metering business can be realized. This helps to improve business efficiency, reduce human errors, improve user satisfaction, and provide strong support for the digital transformation of power companies.

[0137] like Figure 2 As shown, further, the normal mode is a closed-loop business management process between the electric energy metering experiment scheduling management platform and the marketing system and the six-line one-storage system respectively, wherein the six-line one-storage system includes a calibration line and a vertical warehouse system.

[0138] The normal mode means that under normal circumstances, the platform works together with the line warehouse system and the marketing system. The marketing system formulates corresponding planned tasks such as arrival, calibration, and distribution and sends them to the metrology experiment scheduling management platform. The platform coordinates the line warehouse system to complete related processes such as new purchases, production calibration, and distribution according to the received tasks, and returns to the marketing system to complete the business closed-loop working mode.

[0139] For specific processes and task management modes, please refer to the following process.

[0140] Furthermore, the conventional mode includes the following process:

[0141] The marketing system constructs the corresponding electric energy metering verification experiment management task, and sends the verification experiment management task to the electric energy metering experiment scheduling management platform;

[0142] The electric energy metering experiment scheduling management platform receives the verification experiment management task and sends it to the verification assembly line;

[0143] The verification pipeline receives the verification experiment management task and applies for a task table from the electric energy metering experiment scheduling management platform;

[0144] The electric energy metering experiment scheduling management platform receives and uploads the meter application, generates a verification and outbound task, and sends it to the library system;

[0145] The vertical warehouse system receives and executes the verification and outbound task, and feeds back the verification and outbound details to the electric energy metering experiment scheduling and management platform, and the electric energy metering experiment scheduling and management platform forwards the verification and outbound details to the verification pipeline;

[0146] The verification assembly line executes the verification outbound details and determines whether to apply for an empty container:

[0147] If so, apply for an empty box from the electric energy metering experiment scheduling management platform, and the electric energy metering experiment scheduling management platform dispatches the corresponding empty box from the vertical warehouse system to the calibration assembly line;

[0148] If not, upload the verification conclusion data to the electric energy metering experiment scheduling management platform;

[0149] Repeat the above steps until all tasks are completed;

[0150] The electric energy metering experiment scheduling management platform feeds back all the verification conclusion data to the marketing system.

[0151] like Figure 3 As shown, further, the disaster recovery mode is an abnormal data business management process between the electric energy metering experiment scheduling management platform and the marketing system.

[0152] Disaster recovery mode refers to a working mode in which the platform can temporarily cache the real-time interactive information with the marketing system when the marketing system is abnormal or the interface communication is abnormal, to ensure the normal operation of the center's production work. The abnormal data of the platform and the marketing system interface call is stored in the abnormal interface cache queue. After the marketing system interface returns to normal, the cached data is synchronously pushed to the marketing system according to the rules to ensure that the business data generated by the platform in the disaster recovery mode is consistent with the marketing system data.

[0153] The specific process is as follows.

[0154] Furthermore, the disaster recovery mode includes the following processes:

[0155] The electric energy metering experiment scheduling management platform determines whether the marketing system has communication anomalies:

[0156] If there is an exception, cache the current real-time interaction information with the marketing system and store it in the exception cache queue; synchronize the cached data to the marketing system;

[0157] Waiting for the marketing system communication to be restored, if the communication is restored, retrieving the corresponding real-time interaction information from the abnormal cache queue in sequence, and resuming the interaction with the marketing system;

[0158] After the marketing system communication is restored, the real-time interactive information sent by the electric energy metering experiment scheduling management platform is verified using the cache data synchronized in the previous period. If the data is consistent, interactive communication with the electric energy metering experiment scheduling management platform is established; otherwise, it is rejected;

[0159] If normal, give up.

[0160] like Figure 4 As shown, further, the maintenance mode is a line warehouse maintenance business management process between the six-line and one-warehouse system on the basis that the electric energy metering experiment scheduling management platform does not generate marketing process business data.

[0161] Maintenance mode refers to the working mode in which the platform can realize the coordinated operation with the line warehouse system without generating marketing process business data, and support the maintenance of the six-line one-warehouse one-platform system. After the maintenance is completed, the platform pushes the updated data of the box table binding relationship of the in-and-out equipment to the marketing system.

[0162] The specific maintenance mode is as follows.

[0163] Furthermore, the maintenance mode includes the following process:

[0164] The electric energy metering experiment dispatching management platform generates maintenance outbound tasks and issues the inspection assembly line / vertical warehouse system;

[0165] The inspection assembly line / vertical warehouse system receives and executes the inspection and outbound delivery task;

[0166] The electric energy metering experiment scheduling management platform finds that the verification assembly line / vertical warehouse system has been executed. When the execution is completed, the corresponding maintenance warehousing task details are generated and sent to the verification assembly line / vertical warehouse system;

[0167] The inspection assembly line / vertical warehouse system receives and executes the inspection and warehousing task details, and feeds back the inspection and warehousing data;

[0168] After the calibration is completed, the electric energy metering experiment scheduling management platform saves the maintenance warehousing data and pushes the box-meter relationship data.

[0169] 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.

[0170] 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.

[0171] Example 3

[0172] like Figure 5 As shown, further, in another aspect, the present application also proposes an electronic device, including:

[0173] processor;

[0174] a memory for storing processor-executable instructions;

[0175] Among them, the processor is configured to implement the business model management method based on electric energy metering experiment scheduling in multiple scenarios when executing the executable instructions.

[0176] The electronic device of the embodiment of the present disclosure includes a processor and a memory for storing processor executable instructions. The processor is configured to implement any of the above-mentioned business mode management methods based on multi-scenario electric energy metering experiment scheduling when executing the executable instructions.

[0177] 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.

[0178] 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 business model management method based on multi-scenario electric energy metering experiment scheduling 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.

[0179] 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.

[0180] 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. A business model management method based on electric energy metering experiment scheduling in multiple scenarios, implemented based on an electric energy metering experiment scheduling management platform, characterized in that: The electric energy metering experiment scheduling management platform 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, which 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; 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, assembly line and storage three-dimensional monitoring, power metering key data monitoring, intelligent building monitoring, video monitoring integration services; System support module, used to provide message management, custom query, organization and authority management, version management, system parameter management, system operation monitoring, report parameter customization, and log management services; The method comprises the following steps: Collect system data sets and report them to the electric energy metering experiment scheduling management platform; The electric energy metering experiment scheduling management platform receives and parses the system data set, and identifies the electric energy metering service attributes of the system data set; According to the electric energy metering service attributes, the corresponding service mode is matched and activated, wherein the service mode includes regular, disaster recovery, and maintenance modes.

2. According to claim 1, a business model management method based on multi-scenario electric energy metering experiment scheduling is characterized in that: The conventional mode is a closed-loop business management process between the electric energy metering experiment scheduling management platform and the marketing system and the six-line-one-storage system, wherein the six-line-one-storage system includes a calibration line and a vertical storage system.

3. According to claim 2, a business model management method based on multi-scenario electric energy metering experiment scheduling is characterized in that: The conventional mode includes the following process: The marketing system constructs the corresponding electric energy metering verification experiment management task, and sends the verification experiment management task to the electric energy metering experiment scheduling management platform; The electric energy metering experiment scheduling management platform receives the verification experiment management task and sends it to the verification assembly line; The verification pipeline receives the verification experiment management task and applies for a task table from the electric energy metering experiment scheduling management platform; The electric energy metering experiment scheduling management platform receives and uploads the meter application, generates a verification and outbound task, and sends it to the library system; The vertical warehouse system receives and executes the verification and outbound task, and feeds back the verification and outbound details to the electric energy metering experiment scheduling and management platform, and the electric energy metering experiment scheduling and management platform forwards the verification and outbound details to the verification pipeline; The verification assembly line executes the verification outbound details and determines whether to apply for an empty container: If so, apply for an empty box from the electric energy metering experiment scheduling management platform, and the electric energy metering experiment scheduling management platform dispatches the corresponding empty box from the vertical warehouse system to the calibration assembly line; If not, upload the verification conclusion data to the electric energy metering experiment scheduling management platform; Repeat the above steps until all tasks are completed; The electric energy metering experiment scheduling management platform feeds back all the verification conclusion data to the marketing system.

4. According to claim 1, a business model management method based on multi-scenario electric energy metering experiment scheduling is characterized in that: The disaster recovery mode is an abnormal data business management process between the electric energy metering experiment scheduling management platform and the marketing system.

5. According to claim 4, a business model management method based on multi-scenario electric energy metering experiment scheduling is characterized in that: The disaster recovery mode includes the following processes: The electric energy metering experiment scheduling management platform determines whether the marketing system has communication anomalies: If there is an exception, the current real-time interaction information with the marketing system is cached and stored in an exception cache queue; Synchronize the cached data to the marketing system; Waiting for the communication of the marketing system to be restored, if the communication is restored, retrieving the corresponding real-time interaction information from the abnormal cache queue in sequence, and resuming the interaction with the marketing system; After the marketing system communication is restored, the real-time interactive information sent by the electric energy metering experiment scheduling management platform is verified using the cache data synchronized in the previous period. If the data are consistent, interactive communication with the electric energy metering experiment scheduling management platform is established; On the contrary, refuse; If normal, give up.

6. The business model management method based on multi-scenario electric energy metering experiment scheduling according to claim 1 is characterized in that: The maintenance mode is a line and warehouse maintenance business management process between the six-line and one-warehouse system on the basis that the electric energy metering experiment scheduling management platform does not generate marketing process business data.

7. A business model management method based on multi-scenario electric energy metering experiment scheduling according to claim 6, characterized in that: The maintenance mode includes the following process: The electric energy metering experiment dispatching management platform generates maintenance outbound tasks and issues the inspection assembly line / vertical warehouse system; The inspection assembly line / vertical warehouse system receives and executes the inspection and outbound delivery task; The electric energy metering experiment scheduling management platform finds that the verification assembly line / vertical warehouse system has been executed. When the execution is completed, the corresponding maintenance warehousing task details are generated and sent to the verification assembly line / vertical warehouse system; The inspection assembly line / vertical warehouse system receives and executes the inspection and warehousing task details, and feeds back the inspection and warehousing data; After the calibration is completed, the electric energy metering experiment scheduling management platform saves the maintenance warehousing data and pushes the box-meter relationship data.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement a business mode management method based on electric energy metering experiment scheduling in multiple scenarios as described in any one of claims 1-7 when executing the executable instructions.

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