Digitized marketing management metering method and system for micro-grid

Abstract

The invention provides a digitized marketing management metering method and system for a micro-grid, and the method comprises the steps: constructing an equipment behavior state graph which comprises an equipment unique number, an initial state, a life cycle event set which is arranged according to a time sequence, and a state attribute set, the event set comprises goods arrival, verification, assembly and disassembly, allocation and return events; constructing an error sequence, calculating a health score of the equipment based on an error change trend, a time fluctuation penalty and a local mutation sensitive item, and generating an error trend label according to the health score; calculating an allocation adaptability score of the equipment based on the health score, the error trend tag and the current state of the equipment, and generating a judgment result of whether the equipment is allowed to be allocated or reused in combination with a fluctuation risk regular item and a state consistency entropy item; and screening available equipment according to the judgment result, and calculating a task adaptation score in combination with task context features.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electric power measurement, and particularly relates to a digital marketing management measurement method and system for a micro-grid. BACKGROUND

[0002] With the gradual evolution of the power system in China towards intelligence and distribution of the power grid side, the measurement management system of the micro-grid, as an important form supporting multi-energy complementation, flexible scheduling and new energy access, is facing more complex operating environments and business challenges. Especially in the ultra-high voltage power transmission scenario, the measurement equipment has a large span from procurement, arrival, calibration, installation, operation, allocation to reuse in the whole life cycle, events are frequent, and the change of equipment state and the switching of use scenarios present high complexity. The traditional system management mode mainly based on modularization has been difficult to meet the demand. Although the current mainstream measurement management system realizes informatization in device registration, calibration record, allocation management and operation plan, there are still several outstanding problems. First, the state information in the whole life cycle of the equipment is updated late, which leads to a broken state chain and makes it impossible to completely trace or dynamically evaluate the safety. Second, although error data is collected in the calibration link, it is only used for compliance judgment, and there is no mechanism to convert it into a quantitative index of the health state of the equipment, making it difficult to identify the potential degradation trend. Third, although mobile terminals are introduced at the operation level, the allocation and disassembly decisions still rely on static plans and manual judgment, and lack of support for the linkage analysis of the historical state and operation risk of the equipment, which easily leads to improper selection of the equipment. Finally, although the interfaces between systems are gradually improved, there is still a lack of a unified device state expression model, and the state information is inconsistent between different subsystems, which makes it difficult to link across links. In the micro-grid and ultra-high voltage power transmission scenarios, the equipment operation risk is high, the scheduling nodes are numerous, and the business cooperation requirement is strong. The above defects of the existing system have made it difficult to support the operation goals of fine management, intelligent decision and safe and controllable business execution. SUMMARY

[0003] The purpose of the present application is to design a digital marketing management measurement method and system for a micro-grid, which can realize fine management, intelligent decision and safe and controllable business execution of measurement equipment in the micro-grid and ultra-high voltage scenarios.

[0004] In order to achieve the above purpose, a digital marketing management measurement method for a micro-grid is provided in the first aspect of the present application, which comprises:

[0005] Constructing a device behavior state graph, based on raw data from asset management system, procurement and acceptance system, work order management system and field operation records, a unified graph node is generated for each metering device, which includes device unique number, initial state, time-sequenced life cycle event set and state attribute set, the event set includes arrival, calibration, installation and removal, allocation and return events, each event contains event type, event time, associated work order number and parameter content;

[0006] Extracting calibration event records in the graph node, constructing error sequence, calculating device health score based on error change trend, time fluctuation penalty and local mutation sensitive term, and generating error trend label according to the health score;

[0007] Based on the health score, error trend label and current state of the device, the allocation adaptability score of the device is calculated, combined with the fluctuation risk regularization term and the state consistency entropy term, the determination result of whether the device is allowed to be allocated or reused is generated;

[0008] According to the determination result, the available devices are screened, the task adaptation score is calculated combined with the task context features, the task context features include load level, urgency and purpose category, the binding plan of the device and the task is generated, and the future event node is recorded in the device graph.

[0009] Further, in the step of constructing the device behavior state graph, the event set of the graph node needs to contain time stamp and work order number, if missing, it is marked as to be supplemented, to ensure the integrity and traceability of the graph.

[0010] Further, in the calculation of the health score, the error change trend is measured by the Euclidean distance of the error vector of two consecutive calibrations, the time fluctuation penalty is based on the deviation of the calibration time interval from the average interval, and the local mutation penalty is based on whether the error change exceeds a preset threshold.

[0011] Further, the error trend label includes three categories of stable, drift and mutation, which correspond to different health score intervals respectively.

[0012] Further, in the calculation of the allocation adaptability score, the fluctuation risk regularization term is obtained by normalizing the variance of the error score sequence, and the state consistency entropy term is calculated based on the Shannon entropy of the state sequence of the device in the past six months.

[0013] Further, the allocation determination result includes three categories of allow, delay and prohibit, which correspond to different allocation adaptability score intervals respectively.

[0014] Furthermore, in the calculation of the task adaptation score, a device historical state complexity regularization term is introduced. This regularization term is calculated based on the switching frequency of the device's state labels throughout its entire lifecycle. Specifically, it involves counting the number of state switches that occur in the device's historical state sequence and normalizing it with the total number of events in the lifecycle. The resulting value is used to quantify the instability of the device's state. The higher the value, the more chaotic the device's state management. This regularization term will correspondingly reduce the task adaptation score to prevent devices with frequent state switches from being assigned to critical tasks.

[0015] Furthermore, in the calculation of the task adaptation score, a deployment scenario adaptability penalty term is introduced to evaluate whether the device status matches the task's load level, urgency, and usage category.

[0016] Furthermore, after generating the binding plan between the device and the task, the method also includes: adding a future event node to the device in the device behavior state graph, which records the bound task information, the planned execution time, and the expected changes in the device's state.

[0017] In a second aspect, the present invention provides a digital marketing management metering system for microgrids, the system comprising:

[0018] The graph construction module is used to construct a device behavior status graph. Based on the raw data from the asset management system, the procurement and acceptance system, the work order management system, and the on-site operation records, it generates a unified graph node for each metering device. The graph node includes a unique device number, initial state, a set of life cycle events arranged in chronological order, and a set of status attributes. The event set includes arrival, inspection, installation and disassembly, transfer, and return events. Each event includes event type, event time, associated work order number, and parameter content.

[0019] The health assessment module is used to extract the verification event records from the map nodes, construct the error sequence, calculate the health score of the device based on the error change trend, time fluctuation penalty and local mutation sensitivity, and generate error trend labels based on the health score;

[0020] The allocation determination module is used to calculate the allocation adaptability score of the equipment based on the health score, error trend label and the current status of the equipment, and combine the fluctuation risk regularization term and the state consistency entropy term to generate a determination result on whether the equipment is allowed to be allocated or reused.

[0021] The task scheduling module is used to filter available devices based on the judgment results, calculate the task adaptation score by combining task context features, including load level, urgency level and usage category, generate a binding plan between devices and tasks, and record future event nodes in the device map.

[0022] The beneficial technical effects of the present invention are at least as follows:

[0023] To address the aforementioned issues, this invention provides a digital marketing management metering method and system for microgrids. By constructing a unified state graph model around the entire equipment lifecycle, it structurally integrates event data scattered across different business stages, forming a traceable and evolvable expression of equipment behavior, thereby solving the problems of state fragmentation and delayed updates. This invention further designs a health measurement mechanism based on calibration data. While performing trend analysis on historical error sequences, it introduces time fluctuation and abrupt change sensitive terms to form health scores and trend labels that reflect long-term equipment degradation and short-term anomalies, thus compensating for the shortcomings of traditional systems in utilizing errors insufficiently. In the equipment allocation and reuse stage, this invention proposes a multi-factor judgment method that combines health scores, trend labels, and equipment lifecycle status. By adding fluctuation risk and state consistency constraints, it achieves quantitative assessment and control of equipment transfer risks, ensuring that high-risk equipment is not mistakenly transferred to critical nodes. At the job scheduling level, this invention introduces a task adaptability model, matching equipment state attributes with task context features (such as load level, usage category, and task urgency). Regularization is used to prevent equipment with frequent state transitions or those in boundary states from entering high-risk tasks, ultimately generating an optimal equipment-task binding plan to ensure high adaptability and reliability in the scheduling process. Through this design, this invention achieves end-to-end linkage from state modeling, performance quantification, flow control to task execution, enabling refined management, intelligent decision-making, and safe and controllable business execution of metering equipment in microgrid and ultra-high voltage scenarios. Attached Figure Description

[0024] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0025] Figure 1 This is a flowchart of a digital marketing management metering method for microgrids according to the present invention.

[0026] Figure 2 This is a framework diagram of a digital marketing management metering system for microgrids according to the present invention. Detailed Implementation

[0027] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0028] In one or more embodiments, such as Figure 1 As shown, a digital marketing management metering method for microgrids is disclosed, the method comprising the following:

[0029] S1: Construct a device behavior status map. Based on the raw data from the asset management system, procurement and acceptance system, work order management system and on-site operation records, generate a unified map node for each metering device. The map node includes a unique device number, initial state, a set of life cycle events arranged in chronological order and a set of status attributes. The event set includes arrival, inspection, installation and disassembly, transfer and return events. Each event includes event type, event time, associated work order number and parameter content.

[0030] Specifically, in this step, a device behavior state map G is constructed. d This method is used to comprehensively represent the entire lifecycle status and behavior trajectory of metering equipment in a microgrid scenario, from arrival, verification, installation / disassembly, allocation to return. Due to the long lifecycle, numerous business processes, and dispersed records across systems of ultra-high voltage metering equipment, traditional systems struggle to form a unified representation, resulting in fragmented status information and difficulties in traceability. This step extracts and reassembles scattered data to form a device-centric graph node structure, enabling subsequent health modeling and task scheduling to operate based on this unified lifecycle representation.

[0031] The input for this step comes from the company's existing business systems:

[0032] Retrieve device ID from asset management system d Static attributes such as equipment type, model, and manufacturer;

[0033] Obtain the initial business status S from the procurement and goods acceptance system. d For example, "qualified in stock";

[0034] Obtain the event set E from the work order management and inspection log. d This includes the event type, time of occurrence, work order number, and responsible personnel;

[0035] Information on operational events such as assembly / disassembly, return / transportation, and installation location can be obtained from on-site work records.

[0036] These inputs are all data generated in existing business processes, and no new data collection devices are needed.

[0037] The system first uses the device ID. d As a unique index, a node G is generated for each device. d This node contains four parts: the current device state S. d Historical event collection E d State attribute set Pd (Initialized to empty), and the device's basic attributes (type, manufacturer, affiliated unit, etc.). Among these, the event set E... d It is a record of business activities arranged in chronological order, with each event e j Each event consists of four parts: event type, event time, associated work order number, and parameter content.

[0038] Event types are selected from a finite set, such as arrival, inspection, assembly / disassembly, transfer, and return; event time records the exact time the operation occurred; work order number corresponds to the business work order system to ensure traceability; parameter content stores event-specific data, such as arrival events recording the person and location of the inspector, inspection events recording error data and the inspector, and assembly / disassembly events recording the operator and installation location.

[0039] Specifically, a device ID d =SG-2025-001122, completed delivery acceptance on November 15, 2024, completed verification on January 10, 2025, and installed at a certain factory site on January 15, 2025. Its event set E d It will contain three records: "Arrival," "Inspection," and "Assembly / Disassembly," along with parameters such as time and operator. These events are organized chronologically in the graph nodes to form the equipment's lifecycle trajectory.

[0040] The entire equipment map node format is as follows:

[0041]

[0042] Among them: ID d Each device has a unique identification number, provided by the asset management system. This represents the initial state of the equipment, typically "qualified and in stock" upon arrival, provided by the delivery acceptance system; E d All lifecycle events that have occurred on the equipment, sorted by time, are provided by the work order management system and field records; P d This is the device's status attribute set, currently empty, and will be filled with derived attributes such as health score in subsequent steps.

[0043] When generating the map, the system checks whether the event logs are complete, such as each e j The event must include a timestamp and work order number. If these are missing, the system will mark the event as needing to be added, ensuring the completeness and traceability of the final map.

[0044] S2: Extract the verification event records from the graph nodes, construct the error sequence, calculate the health score of the device based on the error change trend, time fluctuation penalty and local mutation sensitivity, and generate error trend labels based on the health score;

[0045] Specifically, this step aims to leverage the device behavior state map G d With the set of historical events E contained therein d Extract error records from previous equipment calibrations and structure these records into an error sequence W. d This allows for a quantitative assessment of the performance stability of the equipment in microgrid application scenarios, resulting in a health score H. d Error trend label L d The key to this step lies in constructing a health model with trend perception, frequency penalty, and abrupt change sensitivity, taking into account the characteristics of microgrid metering equipment, such as multiple nodes, frequent switching, and intermittent power fluctuations. This model not only scores the absolute value of the error but also considers the trend and frequency of error changes over the calibration cycle. Compared to traditional systems that judge equipment usability solely based on whether a single error value is acceptable, this solution proposes a scoring mechanism based on sequential evolution, enabling the early identification of equipment that "appears acceptable but is actually degraded." This is particularly suitable for microgrid sites with extremely high requirements for accuracy and stability.

[0046] This step begins in E d Filter out all events of type "test" and extract the error vector w in chronological order. j This constitutes the error sequence W. d ={w1,w2,...,w n}, where w j This represents the error data from the j-th test, arranged in a fixed order to ensure that the dimension of each vector is consistent, facilitating subsequent processing.

[0047] Unlike traditional methods that only analyze the mean error, this scheme analyzes the health score H. d The following three features were considered in the modeling:

[0048] Consider the rate (trend) of change of the error vector between multiple tests;

[0049] Add a penalty term for high-frequency error fluctuations to reflect the volatility of equipment status;

[0050] A local mutation sensitivity term is introduced to detect whether the device experiences drastic degradation in a short period of time.

[0051] Based on the above objectives, the health scoring model takes the following form:

[0052]

[0053] Where: w j Let w be the error vector of the j-th verification, collected on the verification table; j -w j-1 ||2 represents the magnitude of the error change between two consecutive verification tests, indicating the trend; Δtj The time interval (in days) between the j-th test and the (j-1)-th test is calculated from the event time field; λ1 is the average value of all verification intervals; λ2 is the time fluctuation penalty coefficient, which controls the weight of the impact on irregular verification; λ3 is the weight of the local mutation penalty term; δ is the acceptable maximum error mutation threshold, a system setting value, such as 0.05.

[0054] The three weighting terms in this formula have the following meanings:

[0055] The first item reflects the overall trend of error. If the equipment error gradually increases, this item will be amplified and lower the score.

[0056] The second item indicates whether the verification time is uneven. The higher the verification frequency, the more stable the equipment. Abnormal fluctuations in the verification frequency reflect that there may be uncertain faults in the equipment, and the score will be reduced.

[0057] The third feature identifies sudden changes in behavior. For example, if a certain error increases dramatically compared to the previous one, exceeding the threshold δ, it will be amplified, reflecting a high sensitivity to short-term degradation risks.

[0058] Finally, the system is based on H d Generate error trend label L d :

[0059]

[0060] This label serves as the rule basis for subsequent steps in device status filtering. The label value is built into the system and does not depend on external training.

[0061] This scoring method is easy to implement in the actual calibration business of China Southern Power Grid: the data all come from the existing calibration ledger system, and error data can be obtained through the interface; the time field is taken from the calibration records of the business system, and the penalty items and weights can be set by the operation and maintenance experts according to the equipment type. The scoring model can be automatically calculated after each equipment calibration is completed without manual intervention.

[0062] S3: Based on the health score, error trend label and current status of the equipment, calculate the equipment's transfer adaptability score, and combine the fluctuation risk regularization term and the state consistency entropy term to generate a judgment result on whether the equipment is allowed to be transferred or reused.

[0063] Specifically, this step aims to apply the device health score H calculated in the previous step... d Error trend label L d Compared with the device map structure G constructed in step one d The status field S in d Together, they form an executable determination of whether the equipment is suitable for allocation or reuse. dTraditional allocation approval systems rely on manual experience and often use "qualification" as the sole criterion, failing to identify equipment that, while qualified, exhibits performance degradation or increased risk. In microgrid scenarios, metering equipment is often situated in complex structures with multiple access points, rapid switching, and diverse nodes. The influx of any unstable equipment into critical nodes can degrade system measurement accuracy and even affect electricity pricing accuracy and operational safety. This step, tailored to the microgrid application scenario, introduces an allocation judgment index V based on historical trend behavior. d In addition to traditional state logic, performance risk quantification is added to build a device flow control mechanism oriented towards high reliability.

[0064] The system reads the map node G d Then, first check the device's current status field S. d A legality screening process is conducted to ensure that the judgment operation is performed only on equipment currently in the "qualified in stock" or "retirement pending" status, and equipment in other statuses is not involved in this step.

[0065] For devices that pass the screening, the system further utilizes the health score H. d and trending tags L d Construct a comprehensive allocation adaptability score V d This is used to quantitatively describe the operational safety of equipment. To align with the risk management needs of equipment deployment in microgrid scenarios, this step introduces a specially designed fluctuation risk regularization term to penalize high-frequency oscillation trends in the health score, and a state consistency entropy term to assess the frequency of historical state transitions of equipment, thereby capturing potential instability factors. These special terms are targeted enhancements to the multi-state, weakly constrained operational characteristics of equipment in microgrid application environments.

[0066] The final scoring formula is as follows:

[0067]

[0068] Wherein: H d Equipment health score, given by the error trend model, ranges from [0,1]. The indicator function takes the value 1 when the error trend is "abrupt" and 0 otherwise; R d : Volatility risk regularization term, representing the volatility intensity in the equipment error score sequence. It is calculated as the normalized coefficient of error growth variance with respect to changes in health score, reflecting the performance instability of the equipment over a short period of time; ε d State consistency entropy term, measures the historical S of the device dThe switching frequency in the status label sequence, such as the repeated occurrence of status switching such as "running-retirement-qualified" in a short period of time, indicates that the equipment life cycle process is discontinuous and the risk is high; α, β, μ: respectively, mutation penalty coefficient, fluctuation risk coefficient and consistency entropy penalty coefficient, the magnitude of the coefficient can be set by the power dispatch management strategy, generally α=0.2, β=0.1, μ=0.05.

[0069] In particular, R d The calculation method is as follows:

[0070] For all error vectors, calculate the score difference for each segment by time difference;

[0071] Calculate the variance of the difference sequence, normalize it, and use it as R. d enter.

[0072] ε d The calculations come from the device in the graph G d State sequence of the past 6 months Shannon entropy values ​​are constructed using probability distributions. High values ​​indicate unstable equipment lifecycles, while low values ​​indicate high state persistence.

[0073] Whether the final equipment is allowed to circulate is determined by V. d With system set threshold V thresh Comparison determines the transfer result U d The generation rules are as follows:

[0074]

[0075] Compared with the traditional "qualified equipment is allocated" logic, this judgment rule is based on a multi-dimensional combination of historical behavior, trend indicators and state stability, which significantly enhances the system's ability to identify potential equipment risks and avoids equipment that is already in the early stages of degradation being incorrectly assigned to high-load scenarios.

[0076] S4: Based on the judgment result, select available devices, calculate the task adaptation score by combining the task context features, including load level, urgency level and usage category, generate a device-task binding plan, and record future event nodes in the device map.

[0077] Specifically, this step builds upon the equipment status assessment and workflow determination results from the previous stage, based on the equipment status map G. d The structured state fields (including S) in d H d L d ) and the transferable tag U output in step three d With the task object T to be scheduled in the current system i The system performs adaptation and matching to ultimately generate a complete job plan task package T.i (D d In practical microgrid metering systems, tasks are diverse, such as installation / removal, reuse / replacement, and temporary verification. Each type of task has different requirements for equipment performance, status, and historical behavior. For example, some tasks may be for newly built important sites, requiring equipment with high stability and reliability, while other tasks may be for low-priority, temporary backup scenarios, allowing for a certain degree of equipment aging. Traditional systems often only screen "qualified inventory equipment," ignoring the actual compatibility requirements between equipment and tasks. This step introduces a joint modeling mechanism of task context features and equipment evolution status, constructing a task compatibility score. A task-equipment integrated binding decision model is established, and a regularization term for the historical state complexity of equipment and a penalty term for the critical difference of microgrid nodes are introduced into the scoring function. This makes equipment matching not only consider health values, but also take into account the long-term state evolution stability and the risk attributes of deployment nodes, thereby achieving a highly adaptable scheduling control that is truly oriented towards microgrid metering and allocation scenarios.

[0078] The system first filters out nodes that satisfy U from all the graph nodes. d =The set of allowed devices constitutes the candidate device set D cand Subsequently, for each task object T... i Calculate the required number of devices, k. i And in turn, task adaptation scores are built for each candidate device d. This step proposes an adaptability scoring model for complex deployment scenarios of microgrids, which includes the following four types of features: (1) health score (stability index); (2) trend sensitivity (reflecting whether there is a risk in the current performance evolution); (3) historical state complexity regularization (representing the frequency of equipment state changes and reflecting management uncertainty); (4) deployment scenario adaptability penalty (whether the equipment state matches the requirements of the task deployment node, such as load level, purpose level, urgency level, etc.).

[0079] The comprehensive scoring formula is as follows:

[0080]

[0081] Wherein: H d The health score calculated in step two; L d Error trend labels generated in step two; An indicator function that identifies when the device is in a state of sudden performance change; The complexity measure of a device's historical state sequence is the normalized value of the number of state transitions over the total lifecycle, reflecting whether the device's state is stable; ρ(F i E i Ci ,S d ): Task scenario adaptation penalty item, which determines the device status and the load level F of the deployment site required for the task. i Urgency level E i Application Category C i Whether it matches or not, a weighted penalty is applied; λ1, λ2, λ3: penalty coefficients for each item. The system can set λ1 = 0.15, λ2 = 0.1, and λ3 = 0.2, which can be adjusted according to business strategies.

[0082] This step involves introducing... With ρ(F) i E i C i ,S d Two regularization factors. The former prevents equipment with frequent historical state changes from being assigned to critical tasks. For example, if a piece of equipment has undergone three "retirement-qualification-scrap" state changes in the past 6 months, its σ value is close to 1, indicating that the equipment's state management is chaotic and it is not suitable for reassignment. The latter ensures that the current state of the equipment matches the deployment environment required by the task, avoiding the allocation of boundary state equipment in high-load, metering master node tasks.

[0083] The system according to Sort the candidate devices and select the top k. i The devices form a set D d and the task object T i Bind and generate task schedule package T i (D d Each task plan package will include a matching score, current health status, and historical status score information for the device, facilitating subsequent tracking, analysis, and execution feedback.

[0084] After the task plan package is generated, the system will use the device map G. d Add future event nodes e n+1 The task binding information is written into the record as a future planned action record of the device, ensuring that the entire data chain is traceable and can be reviewed.

[0085] In one or more embodiments, such as Figure 2 As shown, a digital marketing management metering system for microgrids is disclosed, the system comprising:

[0086] The graph construction module is used to construct a device behavior status graph. Based on the raw data from the asset management system, the procurement and acceptance system, the work order management system, and the on-site operation records, it generates a unified graph node for each metering device. The graph node includes a unique device number, initial state, a set of life cycle events arranged in chronological order, and a set of status attributes. The event set includes arrival, inspection, installation and disassembly, transfer, and return events. Each event includes event type, event time, associated work order number, and parameter content.

[0087] The health assessment module is used to extract the verification event records from the map nodes, construct the error sequence, calculate the health score of the device based on the error change trend, time fluctuation penalty and local mutation sensitivity, and generate error trend labels based on the health score;

[0088] The allocation determination module is used to calculate the allocation adaptability score of the equipment based on the health score, error trend label and the current status of the equipment, and combine the fluctuation risk regularization term and the state consistency entropy term to generate a determination result on whether the equipment is allowed to be allocated or reused.

[0089] The task scheduling module is used to filter available devices based on the judgment results, calculate the task adaptation score by combining task context features, including load level, urgency level and usage category, generate a binding plan between devices and tasks, and record future event nodes in the device map.

[0090] It is worth noting that the specific workflow of the digital marketing management metering system for microgrids provided in this embodiment of the invention is the same as that of the digital marketing management metering method for microgrids described in the above embodiment, and will not be repeated here.

[0091] This invention also provides a digital marketing management metering device for microgrids, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiment of a digital marketing management metering method for microgrids, for example... Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0092] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the digital marketing management metering device of the microgrid.

[0093] The aforementioned digital marketing management metering device for a microgrid can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the device may also include input / output devices, network access devices, and buses.

[0094] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASACs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the digital marketing management metering equipment for the microgrid, connecting various parts of the equipment via various interfaces and lines.

[0095] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the digital marketing management metering device for a microgrid by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the operation of the air conditioner controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0096] If the module of the digital marketing management metering equipment integrated into the microgrid is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0097] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0098] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A digital marketing management metering method for microgrids, characterized in that, The method includes: A device behavior status graph is constructed. Based on the raw data from the asset management system, the procurement and acceptance system, the work order management system, and the on-site operation records, a unified graph node is generated for each metering device. The graph node includes a unique device number, initial status, a set of life cycle events arranged in chronological order, and a set of status attributes. The event set includes arrival, inspection, installation and disassembly, transfer, and return events. Each event includes event type, event time, associated work order number, and parameter content. Extract the verification event records from the graph nodes, construct the error sequence, calculate the health score of the device based on the error change trend, time fluctuation penalty and local mutation sensitivity, and generate error trend labels based on the health score; Based on the health score, error trend label and current equipment status, the equipment's transfer adaptability score is calculated. Combined with the fluctuation risk regularization term and the state consistency entropy term, a judgment result is generated on whether the equipment is allowed to be transferred or reused. Based on the judgment results, available devices are selected, and a task adaptation score is calculated by combining task context features, including load level, urgency level and usage category. A binding plan between devices and tasks is generated, and future event nodes are recorded in the device map.

2. The digital marketing management and metering method for microgrids according to claim 1, characterized in that, In the step of constructing the device behavior status map, the event set of the map node must include timestamps and work order numbers. If they are missing, they are marked as to be supplemented to ensure the integrity and traceability of the map.

3. The digital marketing management and metering method for microgrids according to claim 1, characterized in that, In the calculation of the health score, the error change trend is measured by the Euclidean distance between the error vectors of two consecutive tests, the time fluctuation penalty is based on the deviation between the test time interval and the average interval, and the local mutation penalty is based on whether the error change exceeds a preset threshold.

4. The digital marketing management and metering method for microgrids according to claim 1, characterized in that, The error trend labels include three categories: stable, drifting, and mutation, each corresponding to different health score ranges.

5. The digital marketing management and metering method for microgrids according to claim 1, characterized in that, In the calculation of the allocation adaptability score, the fluctuation risk regularization term is obtained based on the variance normalization of the error score sequence, and the state consistency entropy term is calculated based on the Shannon entropy of the equipment's state sequence over the past six months.

6. The digital marketing management and metering method for microgrids according to claim 1, characterized in that, The allocation determination results include three categories: permitted, postponed, and prohibited, each corresponding to a different allocation adaptability score range.

7. The digital marketing management and metering method for microgrids according to claim 1, characterized in that, In the calculation of the task adaptation score, a device historical state complexity regularization term is introduced. This regularization term is calculated based on the switching frequency of the device's state labels throughout its entire life cycle. Specifically, it counts the number of state switches that occur in the device's historical state sequence and normalizes it with the total number of events in the life cycle. The resulting value is used to quantify the instability of the device's state. The higher the value, the more chaotic the device's state management. This regularization term will correspondingly reduce the task adaptation score to prevent devices with frequent state switches from being assigned to critical tasks.

8. The digital marketing management and metering method for microgrids according to claim 1, characterized in that, In the calculation of the task adaptation score, a deployment scenario adaptability penalty is introduced to evaluate whether the device status matches the task's load level, urgency, and usage category.

9. The digital marketing management metering method for microgrids according to claim 1, characterized in that, After generating the binding plan between the device and the task, the method also includes: adding a future event node to the device in the device behavior state graph. This node records the bound task information, the planned execution time, and the expected changes in the device's state.

10. A digital marketing management metering system for microgrids, characterized in that, The system includes: The graph construction module is used to construct a device behavior status graph. Based on the raw data from the asset management system, the procurement and acceptance system, the work order management system, and the on-site operation records, it generates a unified graph node for each metering device. The graph node includes a unique device number, initial state, a set of life cycle events arranged in chronological order, and a set of status attributes. The event set includes arrival, inspection, installation and disassembly, transfer, and return events. Each event includes event type, event time, associated work order number, and parameter content. The health assessment module is used to extract the verification event records from the map nodes, construct the error sequence, calculate the health score of the device based on the error change trend, time fluctuation penalty and local mutation sensitivity, and generate error trend labels based on the health score; The allocation determination module is used to calculate the allocation adaptability score of the equipment based on the health score, error trend label and the current status of the equipment, and combine the fluctuation risk regularization term and the state consistency entropy term to generate a determination result on whether the equipment is allowed to be allocated or reused. The task scheduling module is used to filter available devices based on the judgment results, calculate the task adaptation score by combining task context features, including load level, urgency level and usage category, generate a binding plan between devices and tasks, and record future event nodes in the device map.

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