Metering production scheduling platform management application method and system

Abstract

The invention discloses a metering production scheduling platform management application method and system, and relates to the technical field of power system automation, and the method comprises the steps: collecting power grid data, carrying out the encryption transmission of the collected data, transmitting the data to a platform data layer, building a database, and carrying out the data synchronization through a data exchange algorithm, data analysis is carried out on database data on the platform service layer, a metering prediction and scheduling decision is generated, data interaction is carried out on the interaction layer, an access control and identity verification mechanism is provided on the user layer, metering data is analyzed, risk early warning is carried out on the electric energy metering equipment, and purchasing and scheduling strategies of the electric energy metering equipment are optimized according to early warning results. According to the method, the power grid data is collected, encrypted and transmitted, the data characteristics are converted and then input into the machine learning model for electric energy demand prediction, high-precision power grid load prediction is realized, a reasonable scheduling scheme is formulated, the power grid operation efficiency is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation, and in particular to a metering production scheduling platform management application method and system. Background Art

[0002] With the continuous development and technological progress of the global power industry, the concept of smart grid and the corresponding metering management system have received more and more attention; the design of modern power grids aims to achieve higher energy efficiency and reliability. The introduction of smart sensors, advanced communication technologies and data analysis methods makes real-time monitoring, data collection and analysis possible; especially in the field of electric energy metering, with the increase in the proportion of renewable energy and the opening of the power market, traditional metering methods can no longer meet the needs of real-time data acquisition and analysis; currently, many power companies are transforming to smart meters and centralized control systems to achieve dynamic load management, demand-side response and economic dispatch decision-making capabilities.

[0003] Although many power grid management platforms have made certain progress in data collection and processing, existing technologies still have many shortcomings. For example, many systems lack effective encryption mechanisms during data transmission, which can easily lead to data leakage and tampering. At the same time, the mechanisms for data storage and exchange are often complex, resulting in delays and inconsistencies in data synchronization. In addition, the existing platforms are still insufficient in terms of intelligent analysis and decision support, making it difficult to respond quickly to complex power demands and equipment status, especially in high-risk environments. The risk assessment and early warning mechanisms for electricity metering equipment are still not perfect. These problems limit the security and sustainability of smart grids. Summary of the invention

[0004] In view of the above-mentioned existing problems, the present invention provides a method and system for managing a metering production scheduling platform, so as to solve the problems of data leakage and tampering, delay and inconsistency of data synchronization, and imperfect risk assessment and early warning mechanism in the prior art.

[0005] In order to solve the above technical problems, a method for managing and applying a metering production scheduling platform is proposed, including:

[0006] Collect power grid data, encrypt the collected data for transmission, transfer the data to the platform data layer to establish a database, and synchronize the data through the data exchange algorithm; analyze the database data at the platform service layer, generate metering predictions and scheduling decisions, interact with data at the interaction layer, and provide access control and identity authentication mechanisms at the user layer; analyze metering data, issue risk warnings for electric energy metering equipment, and optimize the procurement and scheduling strategies of electric energy metering equipment based on the warning results.

[0007] As a preferred solution of the metering production scheduling platform management application method described in the present invention, the collection of power grid data includes using sensors to collect electric energy metering data, equipment status information, load data, power generation data, transmission and distribution data, user data, economic and environmental data and safety maintenance data, encrypting and transmitting the collected data, and transmitting the data to the platform data layer to establish a database.

[0008] The electric energy metering data includes real-time current, voltage and power; the equipment status information includes the online status and maintenance status of transformers, switchgear and distribution cabinets; the power generation data includes the power generation and power generation of renewable energy.

[0009] As a preferred solution of the metering production scheduling platform management application method described in the present invention, the encrypted transmission includes collecting power grid data, encrypting and transmitting the collected data using an asymmetric encryption formula, transmitting the data to the platform data layer to establish a database, and synchronizing the data through a data exchange algorithm.

[0010] The establishment of the database includes deciding to use a relational database for data model design according to business requirements and data characteristics, building a data table structure and field definition, decrypting the received encrypted data, verifying the decrypted data, and writing the verified data into the database.

[0011] The data exchange algorithm includes using a data synchronization mechanism to synchronize data during data transmission, and using hash verification to verify data during data synchronization. If a data conflict occurs, conflict resolution is required.

[0012] As a preferred solution of the method for managing a metrology production scheduling platform described in the present invention, the data analysis includes performing data analysis on database data at the platform service layer, generating metrology forecasts and scheduling decisions, performing data interaction at the interaction layer, and providing access control and identity authentication mechanisms at the user layer.

[0013] Before data analysis, data cleaning and data integration are carried out, and important data features are identified. The data features are converted and input into the machine learning model to predict electricity demand, and a dispatch plan is formulated based on the prediction results and the real-time status of the power grid. At the data interaction layer, a user-friendly interface is designed for users at different levels, and a dynamic dashboard of real-time data, prediction results and performance indicators is provided, allowing users to quickly obtain the required information, submit feedback and instructions through interactive devices, modify dispatch parameters and adjust the parameters of the prediction model. At the user layer, different access roles are assigned according to the user's functions and permissions, all user behaviors are recorded, user permissions are regularly reviewed and evaluated, and identity authentication and access control policies are updated in a timely manner based on security assessment results and emerging security threats.

[0014] As a preferred solution of the metering production scheduling platform management application method described in the present invention, the data feature conversion includes data cleaning and data integration of the collected data, and converting the data features and inputting them into the machine learning model to predict the power demand, and formulating a scheduling plan based on the prediction results and the real-time status of the power grid.

[0015] The machine learning model formula is:

[0016]

[0017] Among them, Y t is the predicted value, N is the number of decision trees, f i (X) is the output of the i-th tree, and i is the variable index.

[0018] The formula for formulating the scheduling plan is:

[0019] D t =arg min(E(Y t ,P t )+E penalty (Y t ))

[0020] Among them, D t is the scheduling decision, E(Y t , P t ) is the predicted power demand Y t and available power generation P t The cost of calculation, E penalty (Y t ) is the penalty cost for exceeding the load.

[0021] The generator set output adjustment formula is:

[0022] P output =P base +k·(Y t -Y actual )

[0023] Among them, P output is the output of the generator set after adjustment, P base is the baseline output, Y t To predict the power demand, Y actual is the real-time power demand, and k is the adjustment coefficient.

[0024] As a preferred solution of the metering production scheduling platform management application method described in the present invention, the risk warning includes collecting historical operation data from electric energy metering equipment, analyzing the normal operating range of the equipment using statistical methods, and issuing warnings for risks, dividing the warning results into risk levels, and evaluating the severity of equipment risks.

[0025] The risk level classification includes setting the normal operating range of the equipment as The first-level risk range is The secondary risk range is in, is the mean of historical data, and ω is the standard deviation of historical data.

[0026] As a preferred solution of the metering production scheduling platform management application method described in the present invention, wherein: the adjustment of the metering management strategy includes optimizing the procurement and scheduling strategy of the electric energy metering equipment according to the early warning results.

[0027] The optimization strategy for purchasing and scheduling electric energy metering equipment includes: when the real-time monitored equipment data is within the normal operating range, continuing the current monitoring and maintenance plan, and regularly inspecting and maintaining the equipment; when the real-time monitored equipment data is within the first-level risk range, implementing preventive maintenance, increasing the monitoring frequency of equipment status, tracking equipment data, collecting operating data, performing data analysis to identify equipment failures, and preparing emergency plans; when the real-time monitored equipment data is within the second-level risk range, immediately starting the troubleshooting and repair procedure, giving priority to second-level risk equipment, dynamically adjusting the procurement plan, and purchasing replacement equipment with the best quality and reliability for replacement.

[0028] Another object of the present invention is to provide a metering production scheduling platform management application system, which improves the management efficiency and data utilization level of the power grid, and enhances the security and reliability of the system; the system of the present invention significantly improves efficiency through real-time monitoring and automated scheduling, and uses machine learning to analyze historical and real-time data to provide a basis for demand forecasting and equipment maintenance; at the same time, high-standard data encryption and abnormal monitoring measures are adopted to ensure data security; the user interface is user-friendly and supports data-driven decision-making and resource optimization; the system promotes compliance management, ensures the implementation of metering standards, and provides technical supervision support; in addition, it strengthens the user interaction experience, enhances user satisfaction through mobile applications, supports renewable energy access and carbon emission monitoring, and contributes to sustainable development goals.

[0029] As a preferred solution of the metrology production scheduling platform management application system described in the present invention, it is characterized by including a data acquisition and transmission module, a data analysis and decision-making module, a data interaction module, a risk warning and monitoring module and a procurement plan adjustment module.

[0030] The data acquisition and transmission module is used to collect power grid data, encrypt the collected data, and verify and resolve conflicts in the data to ensure the integrity and consistency of the data.

[0031] The data analysis and decision-making module is used to perform in-depth analysis on the collected database data at the platform service layer, generate predictions and scheduling decisions on power demand using machine learning algorithms, and formulate corresponding scheduling plans based on the prediction results and real-time power grid status.

[0032] The data interaction module is used to provide a user-friendly interactive interface, monitor data, predict results and performance indicators in real time, and submit feedback and instructions through interactive devices to obtain required information.

[0033] The risk warning and monitoring module is used to use the historical operation data collected from the electric energy metering equipment, adopt statistical methods to analyze the normal operation range of the equipment, and issue risk warnings, classify the risk level according to the operating status of the equipment, evaluate the risk severity of the equipment, and monitor the abnormal conditions of the equipment in a timely manner.

[0034] The procurement plan adjustment module is used to dynamically adjust the procurement and scheduling strategies of electric energy metering equipment according to risk warning and real-time monitoring equipment data.

[0035] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in a metrology production scheduling platform management application are implemented.

[0036] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method described in a metrology production scheduling platform management application are implemented.

[0037] The beneficial effects of the present invention are as follows: the present invention ensures the real-time and consistency of power grid data through encrypted transmission and synchronization algorithms, and all-round monitoring improves the accuracy of metering predictions and scheduling decisions; data cleaning and integration improve analysis accuracy, and the user-friendly interface enhances experience and security; the high-precision prediction of the machine learning model helps reasonable scheduling and reduces operating costs; the risk warning mechanism monitors equipment in real time, detects risks in advance, optimizes procurement and scheduling strategies, and improves the reliability and economy of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0039] Figure 1 An overall flow chart of a metrology production scheduling platform management application method provided for one embodiment of the present invention.

[0040] Figure 2 A system solution flow chart of a metering production scheduling platform management application system provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0044] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0045] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0046] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for managing and applying a metering production scheduling platform, including:

[0048] S1: Collect power grid data, encrypt and transmit the collected data, transfer the data to the platform data layer to establish a database, and synchronize the data through the data exchange algorithm.

[0049] The collection of power grid data includes using sensors to collect power metering data, equipment status information, load data, power generation data, transmission and distribution data, user data, economic and environmental data, and safety maintenance data, encrypting and transmitting the collected data, and transmitting the data to the platform data layer to establish a database.

[0050] The electric energy metering data includes real-time current, voltage and power; the equipment status information includes the online status and maintenance status of transformers, switchgear and distribution cabinets; the power generation data includes the power generation and power generation of renewable energy.

[0051] It should be noted that the encrypted transmission includes collecting power grid data, encrypting and transmitting the collected data using an asymmetric encryption formula, transmitting the data to the platform data layer to establish a database, and synchronizing the data through a data exchange algorithm.

[0052] The establishment of the database includes deciding to use a relational database for data model design according to business requirements and data characteristics, building a data table structure and field definition, decrypting the received encrypted data, verifying the decrypted data, and writing the verified data into the database.

[0053] The data exchange algorithm includes using a data synchronization mechanism to synchronize data during data transmission, and using hash verification to verify data during data synchronization. If a data conflict occurs, conflict resolution is required.

[0054] The conflict resolution includes when T A >T B When D A Data, when T A <T B When D B Data, when T A =T B When D A data.

[0055] Among them, D A and D B For the data from the two conflicting data sources, T A and T B The timestamps of the two conflicting data points.

[0056] S2: Perform data analysis on database data at the platform service layer to generate metering forecasts and scheduling decisions, perform data interaction at the interaction layer, and provide access control and identity authentication mechanisms at the user layer.

[0057] Furthermore, the data analysis includes performing data analysis on database data at the platform service layer, generating metering forecasts and scheduling decisions, performing data interaction at the interaction layer, and providing access control and identity authentication mechanisms at the user layer.

[0058] Before data analysis, data cleaning and data integration are carried out, and important data features are identified. The data features are converted and input into the machine learning model to predict electricity demand, and a dispatch plan is formulated based on the prediction results and the real-time status of the power grid. At the data interaction layer, a user-friendly interface is designed for users at different levels, and a dynamic dashboard of real-time data, prediction results and performance indicators is provided, allowing users to quickly obtain the required information, submit feedback and instructions through interactive devices, modify dispatch parameters and adjust the parameters of the prediction model. At the user layer, different access roles are assigned according to the user's functions and permissions, all user behaviors are recorded, user permissions are regularly reviewed and evaluated, and identity authentication and access control policies are updated in a timely manner based on security assessment results and emerging security threats.

[0059] Furthermore, the conversion of data features includes data cleaning and data integration of the collected data, and converting the data features and inputting them into a machine learning model to predict electricity demand, and formulating a scheduling plan based on the prediction results and the real-time status of the power grid.

[0060] The conversion formula is expressed as:

[0061]

[0062] Among them, Load norm is the converted load data, Load is the original load data, μ is the mean of the load data, and σ is the standard deviation of the load data.

[0063] The machine learning model formula is:

[0064]

[0065] Among them, Y t is the predicted value, N is the number of decision trees, f i (X) is the output of the i-th tree, and i is the variable index.

[0066] The formula for formulating the scheduling plan is:

[0067] D t =arg min(E(Y t ,P t )+E penalty (Y t ))

[0068] Among them, D t is the scheduling decision, E(Y t , P t ) is the predicted power demand Y t and available power generation P t The cost of calculation, E penalty (Y t ) is the penalty cost for exceeding the load.

[0069] The generator set output adjustment formula is:

[0070] P output =P base +k·(Y t -Y actual )

[0071] Among them, P output is the output of the generator set after adjustment, P base is the baseline output, Y t To predict the power demand, Y actual is the real-time power demand, and k is the adjustment coefficient.

[0072] S3: Analyze metering data, issue risk warnings for electric energy metering equipment, and optimize the procurement and scheduling strategies of electric energy metering equipment based on the warning results.

[0073] Furthermore, the risk warning includes collecting historical operation data from electric energy metering equipment, analyzing the normal operation range of the equipment using statistical methods, and issuing risk warnings, classifying the warning results into risk levels, and evaluating the severity of equipment risks.

[0074] The use of statistical methods to analyze the normal operating range of the equipment includes calculating the mean, variance and standard deviation of historical data.

[0075] The calculation formula of the mean is:

[0076]

[0077] in, is the mean of historical data, m is the total number of historical samples, x i is the historical sample value, and i is the variable index.

[0078] The variance calculation formula is:

[0079]

[0080] Among them, ω 2 is the variance of historical data, is the mean of historical data, m is the total number of historical samples, x i is the historical sample value, and i is the variable index.

[0081] The standard deviation calculation formula is:

[0082]

[0083] Among them, ω 2 is the variance of historical data, and ω is the standard deviation of historical data.

[0084] The risk level classification includes setting the normal operating range of the equipment as The first-level risk range is The secondary risk range is

[0085] Furthermore, the adjusting the metering management strategy includes optimizing the purchasing and dispatching strategy of the electric energy metering equipment according to the early warning result.

[0086] The optimization strategy for purchasing and dispatching electric energy metering equipment includes: when the real-time monitored equipment data is within the normal operating range, continuing the current monitoring and maintenance plan, and regularly inspecting and maintaining the equipment; when the real-time monitored equipment data is within the first-level risk range, implementing preventive maintenance, increasing the monitoring frequency of equipment status, strengthening data tracking of related equipment, collecting operating data, performing data analysis to identify equipment failures, and preparing emergency plans; when the real-time monitored equipment data is within the second-level risk range, immediately starting the troubleshooting and repair procedure, giving priority to second-level risk equipment, dynamically adjusting the procurement plan, and purchasing replacement equipment with the best quality and reliability for replacement.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0088] Example 2, reference Figure 2 , which is the second embodiment of the present invention, provides a metrology production scheduling platform management application system, including a data acquisition and transmission module M101, a data analysis and decision-making module M201, a data interaction module M301, a risk warning and monitoring module M401 and a procurement plan adjustment module M501.

[0089] The data acquisition and transmission module M101 is used to collect power grid data, encrypt the collected data, and verify and resolve conflicts in the data to ensure the integrity and consistency of the data.

[0090] The data analysis and decision module M201 is used to perform in-depth analysis on the collected database data at the platform service layer, generate predictions and scheduling decisions on power demand using machine learning algorithms, and formulate corresponding scheduling plans based on the prediction results and real-time power grid status.

[0091] The data interaction module M301 is used to provide a user-friendly interactive interface, monitor data, predict results and performance indicators in real time, and submit feedback and instructions through interactive devices to obtain required information.

[0092] The risk warning and monitoring module M401 is used to use the historical operation data collected from the electric energy metering equipment, adopt statistical methods to analyze the normal operation range of the equipment, and issue risk warnings, classify the risk level according to the operating status of the equipment, evaluate the risk severity of the equipment, and monitor the abnormal conditions of the equipment in a timely manner.

[0093] The procurement plan adjustment module M501 is used to dynamically adjust the procurement and scheduling strategy of electric energy metering equipment according to risk warning and real-time monitoring equipment data.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0095] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0096] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0098] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0099] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for managing and applying a metering production scheduling platform, characterized in that: include, Collect power grid data, encrypt and transmit the collected data, transfer the data to the platform data layer to establish a database, and synchronize the data through the data exchange algorithm; Perform data analysis on database data at the platform service layer to generate metering forecasts and scheduling decisions, perform data interaction at the interaction layer, and provide access control and identity authentication mechanisms at the user layer; Analyze metering data, issue risk warnings for electric energy metering equipment, and optimize the procurement and scheduling strategies of electric energy metering equipment based on the warning results.

2. A method for managing and applying a metering production scheduling platform according to claim 1, characterized in that: The collecting of power grid data includes using sensors to collect power metering data, equipment status information, load data, power generation data, transmission and distribution data, user data, economic and environmental data, and safety maintenance data, encrypting and transmitting the collected data, and transmitting the data to the platform data layer to establish a database; The electric energy metering data includes real-time current, voltage and power; the equipment status information includes the online status and maintenance status of transformers, switchgear and distribution cabinets; the power generation data includes the power generation and power generation of renewable energy.

3. A method for managing and applying a metering production scheduling platform as claimed in claim 2, characterized in that: The encrypted transmission includes collecting power grid data, encrypting and transmitting the collected data using an asymmetric encryption formula, transmitting the data to the platform data layer to establish a database, and synchronizing the data through a data exchange algorithm; The establishment of the database includes determining the use of a relational database for data model design according to business requirements and data characteristics, constructing a data table structure and field definition, decrypting the received encrypted data, verifying the decrypted data, and writing the verified data into the database; The data exchange algorithm includes using a data synchronization mechanism to synchronize data during data transmission, and using hash verification to verify data during data synchronization. If a data conflict occurs, conflict resolution is required.

4. A method for managing and applying a metering production scheduling platform as claimed in claim 3, characterized in that: The data analysis includes data analysis of database data at the platform service layer, generating metering forecasts and scheduling decisions, data interaction at the interaction layer, and access control and identity authentication mechanisms at the user layer; Before data analysis, data cleaning and data integration are carried out, and important data features are identified. The data features are converted and input into the machine learning model to predict electricity demand, and a dispatch plan is formulated based on the prediction results and the real-time status of the power grid. At the data interaction layer, a user-friendly interface is designed for users at different levels, and a dynamic dashboard of real-time data, prediction results and performance indicators is provided, allowing users to quickly obtain the required information, submit feedback and instructions through interactive devices, modify dispatch parameters and adjust the parameters of the prediction model. At the user layer, different access roles are assigned according to the user's functions and permissions, all user behaviors are recorded, user permissions are regularly reviewed and evaluated, and identity authentication and access control policies are updated in a timely manner based on security assessment results and emerging security threats.

5. A method for managing and applying a metering production scheduling platform as claimed in claim 4, characterized in that: The converting of data features includes cleaning and integrating the collected data, converting the data features and inputting them into a machine learning model to predict power demand, and formulating a dispatching plan based on the prediction results and the real-time status of the power grid; The machine learning model formula is: Among them, Y t is the predicted value, N is the number of decision trees, f i (X) is the output of the i-th tree, i is the variable index; The formula for formulating the scheduling plan is: D t =arg min(E(Y t ,P t )+E penalty (THE t )) Among them, D t is the scheduling decision, E(Y t , P t ) is the predicted power demand Y t and available power generation P t The cost of calculation, E penalty (Y t ) is the penalty cost for exceeding the load; The generator set output adjustment formula is: P output =P base +k·(Y t -Y actual ) Among them, P output is the output of the generator set after adjustment, P base is the baseline output, Y t To predict the power demand, Y actual is the real-time power demand, and k is the adjustment coefficient.

6. A method for managing and applying a metering production scheduling platform as claimed in claim 5, characterized in that: The risk warning includes collecting historical operation data from the electric energy metering equipment, analyzing the normal operation range of the equipment using statistical methods, and issuing early warnings for risks, classifying the early warning results into risk levels, and evaluating the severity of equipment risks; The risk level classification includes setting the normal operating range of the equipment as The first-level risk range is The secondary risk range is in, is the mean of historical data, and ω is the standard deviation of historical data.

7. A method for managing and applying a metering production scheduling platform as claimed in claim 6, characterized in that: The adjusting the metering management strategy includes optimizing the purchasing and dispatching strategy of the electric energy metering equipment according to the early warning results; The strategy for optimizing the procurement and dispatch of electric energy metering equipment includes, when the real-time monitored equipment data is within the normal operating range, continuing the current monitoring and maintenance plan, and regularly inspecting and maintaining the equipment; When the real-time monitored equipment data is within the first-level risk range, preventive maintenance is implemented, the monitoring frequency of equipment status is increased, data tracking of equipment is performed, operation data is collected, data analysis is performed to identify equipment failures, and emergency plans are prepared; When the real-time monitored equipment data is within the secondary risk range, the troubleshooting and repair procedures are immediately initiated, secondary risk equipment is given priority, the procurement plan is dynamically adjusted, and replacement equipment with the best quality and reliability is purchased for replacement.

8. A system using a metrological production scheduling platform management application method as claimed in any one of claims 1 to 7, characterized in that: It includes data collection and transmission module, data analysis and decision-making module, data interaction module, risk warning and monitoring module and procurement plan adjustment module; The data acquisition and transmission module is used to collect power grid data, encrypt the collected data, and verify and resolve conflicts to ensure the integrity and consistency of the data; The data analysis and decision-making module is used to conduct in-depth analysis of the collected database data at the platform service layer, generate predictions and scheduling decisions on power demand using machine learning algorithms, and formulate corresponding scheduling plans based on the prediction results and real-time power grid status; The data interaction module is used to provide a user-friendly interactive interface, monitor data, predict results and performance indicators in real time, and submit feedback and instructions through interactive devices to obtain required information; The risk warning and monitoring module is used to use the historical operation data collected from the electric energy metering equipment, adopt statistical methods to analyze the normal operation range of the equipment, and issue risk warnings, classify the risk level according to the operation status of the equipment, evaluate the risk severity of the equipment, and monitor the abnormal situation of the equipment in a timely manner; The procurement plan adjustment module is used to dynamically adjust the procurement and scheduling strategies of electric energy metering equipment according to risk warning and real-time monitoring equipment data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a metrology production scheduling platform management application method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a metrology production scheduling platform management application method described in any one of claims 1 to 7 are implemented.

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