Electricity consumption monitoring method and system based on digital management and electronic equipment

Abstract

The invention provides a power consumption monitoring method and system based on digital management and electronic equipment, and the method comprises the steps: obtaining basic data of a power system, the basic data comprising a standard data element, a standard work flow and a knowledge graph of the power system; constructing a rule engine and a workflow engine; acquiring a task of a manager, and executing the task by utilizing the rule engine and the workflow engine according to the standard workflow to generate a real-time task result; and training a monitoring model by using a historical monitoring result, and judging whether the real-time task result meets a monitoring requirement or not by using the monitoring model. The digital management technology is utilized to solve the problems of low efficiency, low automation degree and difficult management during power consumption monitoring.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to electricity monitoring methods, systems and electronic devices based on digital management. Background Technology

[0002] In the field of electricity monitoring (such as power line inspection, electronic equipment status monitoring, and electricity inspection), process management currently relies mainly on paper work orders, manual records, and post-event computer entry. This approach has many technical problems.

[0003] For example, if the process of task assignment, execution, and reporting is entirely driven by manual processes, information flow will be slow, collaboration will be difficult, and efficiency will be low. Managers will not be able to grasp the accurate progress and quality of on-site operations in real time, and will not be able to effectively supervise whether operators are strictly following safety regulations and technical standards. They can only check after the fact, which is risky. The quality of operations is highly dependent on personal experience and sense of responsibility, and it is difficult to ensure the uniformity of standards performed by different personnel and shifts. Tacit knowledge such as excellent operational experience and tricks for handling specific faults is difficult to accumulate and replicate, and cannot be formed into organizational capabilities. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a method, system and electronic equipment for electricity monitoring based on digital management, which uses digital management technology to solve the problems of low efficiency, low degree of automation and difficulty in management during electricity monitoring.

[0005] To achieve one of the aforementioned objectives, this application provides a method for monitoring electricity consumption based on digital management, the method comprising: Acquire basic data of the power system, including standard data elements, standard workflows, and knowledge graphs of the power system; Build a rules engine and a workflow engine; The task is obtained from the administrator, and the task is executed according to the standard workflow using the rule engine and the workflow engine to generate real-time task results. A monitoring model is trained using historical monitoring results, and the monitoring model is used to determine whether the real-time task results meet the monitoring requirements.

[0006] As a further improvement to one embodiment of this application, the step of obtaining the manager's task, causing the rule engine and the workflow engine to execute the task according to the standard workflow, and generating real-time task results, includes: The basic data is encapsulated using a rules engine; The workflow engine is used to drive the task through the standard workflow. The task is calculated and judged according to the business rules in the rule engine, and the real-time task result is generated.

[0007] As a further improvement to one embodiment of this application, after determining whether the real-time task result meets the monitoring requirements using the monitoring model, the process includes: If the judgment result is that the monitoring requirements are not met, then the real-time monitoring result corresponding to the real-time task result is generated as abnormal.

[0008] Based on the same inventive concept, this application also provides a power consumption monitoring system based on digital management, comprising: The acquisition module is used to acquire basic data of the power system, including standard data elements, standard workflows, and knowledge graphs of the power system. Build modules are used to build the rules engine and workflow engine; The first generation module is used to obtain the tasks of the management personnel, execute the tasks according to the standard workflow using the rule engine and the workflow engine, and generate real-time task results. The judgment module is used to train a monitoring model using historical monitoring results, and to use the monitoring model to judge whether the real-time task results meet the monitoring requirements.

[0009] As a further improvement to one embodiment of this application, a digital management-based electricity monitoring system includes an electricity monitoring model, the electricity monitoring model comprising: The base layer stores basic data, which includes the standard data sources, standard workflows, and knowledge graphs of the power system. The application layer is used to obtain the tasks of the administrator and display monitoring reports; The core layer is used to execute the task and generate real-time task results and real-time detection results; A feedback optimization layer is used to train the monitoring model.

[0010] As a further improvement to one embodiment of this application, the core layer includes: The core layer includes a rules engine, which makes execution decisions for the task based on the basic data. The core layer also includes a workflow engine, which is used to execute the standard workflow and drive the tasks to flow according to the standard workflow; The core layer also includes an algorithm model library for storing data analysis algorithms and artificial intelligence algorithms.

[0011] As a further improvement to one embodiment of this application, the feedback optimization layer includes: The sensing module is used to acquire the operating data of the power system; The analysis and feedback module is used to determine whether the results of the real-time task meet the monitoring requirements; The optimization module is used to discover basic data that cannot form command targets and generate optimization suggestions.

[0012] As a further improvement of one embodiment of this application, the rule engine is used to receive natural language commands from managers and generate decision-making organizations using the tasks in the commands.

[0013] As a further improvement to one embodiment of this application, the basic layer utilizes an application data platform and master data management technology to manage the basic data.

[0014] Based on the same inventive concept, this application also provides an electronic device, including: a processor and a memory; The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the electricity monitoring method based on digital management.

[0015] Compared with existing technologies, the technical advantages of this invention are as follows: it constructs a standardized digital workflow for the electricity monitoring process, automates the data collection, verification, analysis, and result generation processes, improves work efficiency, ensures the standardization of data processing and the consistency of analysis results, provides a reliable data foundation for accurate decision-making, builds intelligent early warning for real-time monitoring, supports rapid response by managers, reduces reliance on personal experience, and improves team management capabilities. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the implementation methods or related technologies will be briefly introduced below. Obviously, the drawings described below are only the implementation methods of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a digital management-based electricity monitoring method provided in one embodiment of this application; Figure 2 A schematic diagram of a power consumption monitoring system based on digital management provided in one embodiment of this application; Figure 3 A schematic diagram of an electricity monitoring model provided for one embodiment of this application; Figure 4 A schematic diagram of an electronic device provided for another embodiment of this application. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present invention, and any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the scope of protection of the present invention.

[0019] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by those skilled in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0020] In the field of electricity monitoring (such as power line inspection, electronic equipment status monitoring, and electricity inspection), process management currently relies mainly on paper work orders, manual records, and post-event computer entry. This approach has the following core technical problems.

[0021] Low management efficiency: Task assignment, execution, and reporting processes rely entirely on manual processes, resulting in slow information flow and difficulties in collaboration. Traditional methods involve manually exporting data reports from multiple independent systems (such as electricity data collection systems, marketing systems, and enterprise ERP systems), followed by manual data cleaning, format conversion, and statistical summarization. This approach is time-consuming and labor-intensive, leading to long data analysis cycles and failing to meet the needs of real-time monitoring and rapid decision-making.

[0022] Process out of control: Managers cannot monitor the accurate progress and quality of on-site operations in real time, and cannot effectively supervise whether workers are strictly following safety regulations and technical standards; they can only conduct post-event inspections, which carries high risks. Manual processing is highly susceptible to data entry errors, calculation errors, and logical errors. Differences in understanding data definitions and statistical rules among different analysts lead to a lack of consistency and comparability in analytical results. Data quality issues are often only discovered in the later stages of analysis or even after decision-making, resulting in high costs for error correction.

[0023] The implementation of standards is disconnected from reality: the management standards and operating procedures written in documents are not actually implemented. The quality of operations depends heavily on individual experience and sense of responsibility, making it difficult to ensure that different personnel and shifts adhere to the standards consistently.

[0024] Insufficient monitoring and early warning capabilities: Due to the lag in data processing and analysis, traditional methods struggle to detect abnormal electricity consumption in a timely manner. Abnormal fluctuations in regional or industry-specific electricity consumption are often only detected after the problem has manifested and caused impact, lacking the ability for pre-emptive warnings and in-process intervention, thus limiting the effectiveness of decision support.

[0025] Knowledge loss: Tacit knowledge, such as excellent operational experience and tricks for handling specific faults, is difficult to retain and replicate, and cannot be transformed into organizational capabilities. Current analytical work heavily relies on the individual experience and professional skills of analysts. Excellent analytical methods and experience are difficult to effectively retain and standardize, leading to inconsistent analytical quality. Different personnel use different analytical models and judgment criteria, resulting in a lack of continuity and authority in the analytical results.

[0026] Traditional power consumption monitoring management is facing increasing complexity and a faster pace, highlighting its inherent limitations: its reliance on documents, meetings, and manual communication leads to delayed and distorted information transmission, departmental silos, and low efficiency in cross-domain and cross-organizational collaboration, severely hindering project delivery speed; fragmented project status information makes it difficult for managers to accurately and in real-time grasp project health; the discovery of delays, cost overruns, and quality defects is often delayed, risk response is passive, and corrective measures are costly; best practices and lessons learned in the project management process often exist as tacit knowledge or scattered documents, lost with project completion and staff turnover, making effective retention, reuse, and inheritance difficult, leading to repeated learning experiences within the organization.

[0027] At the same time, the wave of digital technologies, represented by big data, artificial intelligence, cloud computing, and the Internet of Things, has provided an unprecedented historical opportunity to overcome the aforementioned challenges. Digital transformation has become an inevitable path for enterprises to enhance their core competitiveness. Against this backdrop, in-depth research on how to digitally reconstruct and empower existing standard systems for operational process management, and exploring their practical application in typical business scenarios, is of great urgency and importance.

[0028] To address the aforementioned problems, this application provides a method for monitoring electricity consumption based on digital management, such as... Figure 1 As shown, it includes the following steps: Step S100: Obtain basic data of the power system, including standard data elements, standard workflows, and knowledge graphs of the power system.

[0029] Specifically, the first step is to define the most basic and indivisible standard data elements in work management, along with their attributes and specifications, such as "project code" and "cost item code," to ensure a unified data language across the entire project. Using standard modeling languages ​​such as BPMN, the standard workflows for work management (such as change management processes and acceptance processes) are defined visually and formally, serving as a blueprint for executable processes. A knowledge graph organizes the relationships between entities such as projects, tasks, risks, resources, and documents in a graph structure, forming the "knowledge brain" of work management and supporting intelligent search and recommendation.

[0030] Step S200: Build the rules engine and workflow engine.

[0031] Specifically, the rules engine and workflow engine are responsible for driving the automated operation of the business and activating the basic data. The rules engine is used to encapsulate and manage the basic data, while the workflow engine is the motor in the workflow, automatically driving the task flow.

[0032] Step S300: Obtain the tasks from the management personnel, execute the tasks according to the standard workflow using the rules engine and workflow engine, and generate real-time task results.

[0033] Specifically, after receiving tasks from managers, the rules engine calculates and judges based on preset rules and outputs decision results (such as whether approval is granted, or what kind of warning is triggered). The workflow engine automatically drives task flow according to the definition, assigns work items, reminds relevant personnel, and ensures that the process is executed according to standards.

[0034] In one possible implementation of this application, step S300 includes: Step S310: Encapsulate the basic data using the rule engine.

[0035] Step S320: Use the workflow engine to drive the task through the standard workflow.

[0036] Step S330: Calculate and judge the task based on the business rules in the rule engine, and generate real-time task results.

[0037] Specifically, rule engine technology is the core of achieving "digitalization of rule elements," acting as the "brain" of the standard digital system, responsible for processing complex business logic and making intelligent judgments. The core idea of ​​a rule engine is to separate the volatile business decision-making logic (i.e., "business rules") from the stable application code. In traditional development, a rule such as "costs exceeding the budget by 10% require approval from the relevant supervisor" necessitates code modification, retesting, and re-deployment upon change. However, rule engine administrators define, manage, and maintain these rules through a user-friendly interface using near-natural language syntax. This decoupling significantly improves the system's agility in responding to business changes.

[0038] Many management rules in the operational process are complex and interconnected. For example, classifying a risk may require a comprehensive assessment of multiple dimensions, such as its probability of occurrence, degree of impact, and scope of influence. A rule engine can efficiently handle this multi-condition, multi-level reasoning logic. It organizes a series of rules into a "decision table" or "decision tree," and quickly derives inference conclusions through pattern matching algorithms (such as the Rete algorithm), thereby enabling advanced functions such as automatic risk classification, automatic review of contract compliance, and automatic selection of resource allocation strategies. The rule engine ensures that management standards are applied consistently in practice, avoiding interference from human factors. At the same time, it provides standards with great flexibility. When the market environment or company strategy changes, managers can quickly adjust the business rules in the system without waiting for a lengthy development cycle, enabling operational management standards to keep pace with the times and dynamically adapt to external challenges.

[0039] Step S400: Train the monitoring model using historical monitoring results, and use the monitoring model to determine whether the real-time task results meet the monitoring requirements.

[0040] Specifically, the monitoring model is key to achieving adaptive capabilities in electricity consumption monitoring, forming a complete closed loop of "perception-decision-execution-optimization." First, historical monitoring results are continuously collected through the aforementioned methods. Big data technology is used to compare and analyze the differences between "standard presets" (such as planned baselines and quality benchmarks) and "actual execution" data to assess the effectiveness of the standards themselves. When the analysis reveals that certain standard rules or process models do not conform to the actual situation or cannot achieve management objectives, the system can provide optimization suggestions to managers. Furthermore, when conditions are ripe in the future, techniques such as reinforcement learning can be used to automatically fine-tune the model parameters, thereby completing the continuous evolution of the standards.

[0041] After obtaining the monitoring results, the monitoring model generates a monitoring report and displays it to the management personnel to facilitate their next decision-making.

[0042] In one possible implementation of this application, step S400 is followed by: If the judgment result is that the monitoring requirements are not met, the real-time monitoring result corresponding to the real-time task result will be generated as abnormal.

[0043] Specifically, monitoring results that meet the requirements are recorded as progress, and monitoring results that do not meet the requirements are recorded as abnormalities, all of which are recorded in the monitoring report.

[0044] The monitoring model, leveraging artificial intelligence and big data analytics, serves as a "smart engine" driving digital management from "automation" to "intelligence," ultimately achieving "adaptive" capabilities. Artificial intelligence enables predictive analysis and intelligent early warning. By analyzing massive amounts of historical work data using machine learning algorithms (such as regression analysis and time series forecasting), more accurate project schedule prediction and cost estimation models can be constructed. The system not only monitors current deviations from standards but also predicts future deviations, thus achieving truly proactive management. This not only reduces the burden of manual data entry but also activates tacit knowledge embedded in documents, automatically enriching it into a knowledge graph for system learning and shared by all employees.

[0045] The rational use of artificial intelligence technology can achieve intelligent optimization and decision support. Operations research algorithms and reinforcement learning can find optimal resource allocation and task prioritization schemes for tasks under complex constraints. When faced with multiple projects competing for key resources, the system can simulate multiple solutions and recommend the optimal solution based on preset strategic objectives (such as maximizing overall profits or prioritizing strategic tasks), providing strong data support and scenario simulation for managers' decision-making.

[0046] In summary, the rules engine defines the standard execution logic; artificial intelligence and big data analytics are integrated throughout, drawing wisdom from data to feed back into and optimize business processes, business rules, and even the entire standards system itself. It is this synergy of the technological ecosystem that has transformed the standardization of operational process management from a grand blueprint into a tangible reality.

[0047] In one possible implementation of this application, such as Figure 2 As shown, a power consumption monitoring method system based on digital management is also proposed, including: The acquisition module is used to acquire basic data of the power system, including standard data elements, standard workflows, and knowledge graphs of the power system. Build modules are used to build the rules engine and workflow engine; The first generation module is used to obtain the tasks from the management personnel, execute the tasks according to the standard workflow using the rule engine and workflow engine, and generate real-time task results. The judgment module is used to train a monitoring model using historical monitoring results and to use the monitoring model to determine whether the real-time task results meet the monitoring requirements.

[0048] In one possible implementation of this application, the first generation module includes: The encapsulation module is used to encapsulate basic data using the rules engine; The push module is used to drive tasks through the standard workflow using the workflow engine; The second generation module calculates and judges the task based on the business rules in the rule engine, and generates real-time task results.

[0049] One possible implementation of this application also includes: The third generation module, in response to the judgment result being that it does not meet the monitoring requirements, generates a real-time monitoring result corresponding to the real-time task result as abnormal.

[0050] In one possible implementation of this application, such as Figure 3 As shown, the electricity monitoring system based on digital management also includes an electricity monitoring model, which comprises a basic layer, an application layer, a core layer, and a feedback optimization layer. The basic layer stores basic data, including standard data sources, standard workflows, and knowledge graphs of the power system. The application layer is used to obtain tasks from management personnel and display monitoring reports. The core layer is used to execute tasks and generate real-time task results and real-time detection results. The feedback optimization layer is used to train the monitoring model.

[0051] In one possible implementation of this application, the foundational layer serves as the cornerstone of digitalization, aiming to "connect data and establish a mirror image." Its primary task is to construct a unified, standardized data foundation and business model. The foundational layer includes a standard data meta-model, a business process model, and a knowledge graph model. The standard data meta-model defines the most basic, indivisible data units in work management, along with their attributes and specifications, such as "project codes" and "cost item codes," ensuring a unified data language across the entire project. The business process model uses standard modeling languages ​​such as BPMN to visually and formally define standard work management processes (such as change management processes and acceptance processes), serving as a blueprint for executable processes. The knowledge graph model organizes the relationships between entities such as projects, tasks, risks, resources, and documents in a graph structure, forming a "knowledge brain" for work management, supporting intelligent search and recommendation.

[0052] In one possible implementation of this application, the base layer utilizes an application data platform and master data management technology to manage basic data.

[0053] Specifically, the data platform and Master Data Management (MDM) together constitute the "blood system" of standardized digitization. The data platform is a concept and architecture that aggregates, integrates, and processes data scattered across various siloed systems within an enterprise (such as cost data in ERP, schedule data in MES, and customer data in CRM) to form standardized data services. For operational process management, the data platform can build a unified project view, breaking down system silos and creating a 360-degree panoramic view for each project, integrating information from all dimensions such as schedule, cost, quality, and risk. Simultaneously, the data platform provides reusable data capabilities, offering cleaned and processed data (such as operational health index and cost performance index SPI / CPI) via API interfaces to front-end BPM systems, rule engines, and various application scenarios, avoiding repetitive data processing work. Furthermore, the data platform supports in-depth analysis and intelligent applications, providing high-quality, integrated data raw materials for big data analysis and artificial intelligence algorithms, forming the foundation for the operation of the "feedback optimization layer."

[0054] MDM is responsible for the unified, centralized, and authoritative management of the most critical and shareable data (i.e., master data) during the project process, such as "job codes," "organizational personnel," "customer information," "material codes," and "cost accounts." It establishes a "single data source" for the enterprise, ensuring that the code and basic information of the same entity (such as a job) are unique and accurate in any system. This is a prerequisite for achieving cross-system process integration and data analysis, completely solving the "information silo" problem caused by data inconsistency.

[0055] In one possible implementation of this application, the core layer includes a rules engine, a workflow engine, and an algorithm model library. The rules engine is used to make execution decisions for tasks based on basic data; the workflow engine is used to execute standard workflows and drive tasks to flow according to standard workflows; the algorithm model library is used to store data analysis algorithms and artificial intelligence algorithms.

[0056] In one possible implementation of this application, the rule engine is used to receive natural language commands from managers and generate decision-making organizations using the tasks in the commands.

[0057] Specifically, the core layer is the digital "central nervous system," responsible for driving the automated operation of business. It "activates" the model of the foundation layer. The rule engine is the core component that encapsulates and manages various business rules in the foundation layer. It receives data from the application layer, performs calculations and judgments according to preset rules, and outputs decision results (such as whether approval is granted, or what kind of warning is triggered). The workflow engine is the "motor" of the business process model. According to the model definition, it automatically drives task flow, assigns work items, and reminds relevant personnel to ensure that the process is executed according to standards. The algorithm model library integrates various data analysis and artificial intelligence algorithms, such as regression models for project schedule prediction, machine learning models for risk classification, and operations research algorithms for resource optimization, providing intelligent support for management decisions.

[0058] In one possible implementation of this application, the application layer directly faces the end user, encapsulating the capabilities of the core layer into specific, configurable business applications. This embodies the scenario-based value of standardized digitization. This layer provides standardized application interfaces, such as progress monitoring dashboards, risk warning centers, and reporting systems, based on different user roles (project managers, team members, senior leaders) and different business scenarios (progress management, cost management, quality management). Users interact with the digitization system through these interfaces, performing standardized operations and obtaining standardized information.

[0059] In one possible implementation of this application, the feedback optimization layer is key to achieving the model's "adaptive" capability, forming a complete "perception-decision-execution-optimization" closed loop. The perception stage involves continuously collecting real operational data through the aforementioned methods. The analysis and feedback stage utilizes big data technology to compare and analyze the differences between "standard presets" (such as planned baselines and quality benchmarks) and "actual execution" data, evaluating the effectiveness of the standards themselves. The optimization stage involves providing optimization suggestions to managers when analysis reveals that certain standard rules or process models do not conform to the actual situation or cannot achieve management goals. Furthermore, when conditions are ripe in the future, the system can automatically fine-tune model parameters through techniques such as reinforcement learning, thereby completing the continuous evolution of the standards.

[0060] The electricity consumption monitoring system based on digital management proposed in this application has the following beneficial effects: Automating workflows: By building standardized digital workflows, the processes of data collection, cleaning, verification, analysis, and report generation are automated, significantly improving work efficiency, shortening analysis cycles, and enabling analysts to focus on high-value insights.

[0061] Ensure data quality and standardization: Establish a digital data quality control system, automatically verify data quality through preset rules, ensure the standardization of data processing and the consistency of analysis results, and provide a reliable data foundation for accurate decision-making.

[0062] Improve the timeliness of monitoring and early warning: Build a real-time monitoring and intelligent early warning mechanism to automatically identify and promptly warn of abnormal power consumption, support managers to respond quickly, and realize the transformation from passive response to proactive management.

[0063] Promote the effective accumulation and reuse of knowledge: solidify excellent analytical experience and methods into reusable digital rules and models, establish an organizational knowledge asset library, reduce reliance on individual experience, and improve the team's overall analytical capabilities.

[0064] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected within the electronic device via the bus 1050.

[0065] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0066] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage electronic device, dynamic storage electronic device, etc. The memory 1020 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0067] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in electronic devices (not shown in the figure) or externally connected to electronic devices to provide corresponding functions. Input electronic devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output electronic devices may include displays, speakers, vibrators, indicator lights, etc.

[0068] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this electronic device and other electronic devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, radio (shortwave / ultra-shortwave) communication, satellite communication, data link communication, etc.).

[0069] Bus 1050 includes pathways for transmitting information between various components of an electronic device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0070] It should be noted that although the above-described electronic device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the electronic device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described electronic device may only include the components necessary for implementing the embodiments described in this specification, and does not necessarily include all the components shown in the figures.

[0071] The electronic devices described above are used to implement the data anomaly detection method in the corresponding border gateway protocol of any of the foregoing embodiments, and have the beneficial effects of the corresponding method implementation, which will not be elaborated here.

[0072] Based on the same inventive concept, corresponding to any of the above-described embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the data anomaly detection method in the border gateway protocol as described in any of the above embodiments.

[0073] The computer-readable medium in this embodiment includes permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage electronic devices, or any other non-transfer medium that can be used to store information accessible by a computing electronic device.

[0074] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the data anomaly detection method in the border gateway protocol as described in any of the above embodiments, and have the beneficial effects of the corresponding method implementation, which will not be repeated here.

[0075] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; this manner of description is merely for clarity, and those skilled in the art should consider the specification as a whole. Within the framework of this application, the above embodiments or the technical features of different embodiments can also be appropriately combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0076] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). While specific details (e.g., circuits) are set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0077] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; this manner of description is merely for clarity, and those skilled in the art should consider the specification as a whole. Within the framework of this application, the above embodiments or the technical features of different embodiments can also be appropriately combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0078] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). While specific details (e.g., circuits) are set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0079] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0080] The embodiments described herein are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and principles of the embodiments described herein should be included within the protection scope of this application.

Claims

1. A method for monitoring electricity consumption based on digital management, characterized in that, The method includes: Acquire basic data of the power system, including standard data elements, standard workflows, and knowledge graphs of the power system; Build a rules engine and a workflow engine; The task is obtained from the administrator, and the task is executed according to the standard workflow using the rule engine and the workflow engine to generate real-time task results. A monitoring model is trained using historical monitoring results, and the monitoring model is used to determine whether the real-time task results meet the monitoring requirements.

2. The electricity consumption monitoring method based on digital management according to claim 1, characterized in that, The process of obtaining management personnel's tasks enables the rule engine and the workflow engine to execute the tasks according to the standard workflow, generating real-time task results, including: The basic data is encapsulated using a rules engine; The workflow engine is used to drive the task through the standard workflow. The task is calculated and judged according to the business rules in the rule engine, and the real-time task result is generated.

3. The electricity consumption monitoring method based on digital management according to claim 1, characterized in that, After determining whether the real-time task result meets the monitoring requirements using the monitoring model, the process includes: If the judgment result is that the monitoring requirements are not met, then the real-time monitoring result corresponding to the real-time task result is generated as abnormal.

4. A power consumption monitoring system based on digital management, characterized in that, include: The acquisition module is used to acquire basic data of the power system, including standard data elements, standard workflows, and knowledge graphs of the power system. Build modules are used to build the rules engine and workflow engine; The first generation module is used to obtain the tasks of the management personnel, execute the tasks according to the standard workflow using the rule engine and the workflow engine, and generate real-time task results. The judgment module is used to train a monitoring model using historical monitoring results, and to use the monitoring model to judge whether the real-time task results meet the monitoring requirements.

5. The electricity monitoring system based on digital management according to claim 4, characterized in that, Includes an electricity consumption monitoring model, which includes: The base layer stores basic data, which includes the standard data sources, standard workflows, and knowledge graphs of the power system. The application layer is used to obtain the tasks of the administrator and display monitoring reports; The core layer is used to execute the task and generate real-time task results and real-time detection results; A feedback optimization layer is used to train the monitoring model.

6. The electricity monitoring system based on digital management according to claim 5, characterized in that, The core layer includes: The rules engine is used to make execution decisions for the task based on the basic data. A workflow engine, which is used to execute the standard workflow and drive the task to flow according to the standard workflow; An algorithm model library for storing data analysis algorithms and artificial intelligence algorithms.

7. The electricity monitoring system based on digital management according to claim 5, characterized in that, The feedback optimization layer includes: The sensing module is used to acquire the operating data of the power system; The analysis and feedback module is used to determine whether the results of the real-time task meet the monitoring requirements; The optimization module is used to discover basic data that cannot form command targets and generate optimization suggestions.

8. The electricity monitoring system based on digital management according to claim 6, characterized in that, The rule engine is used to receive natural language commands from managers and generate decision-making organizations using the tasks in the commands.

9. The electricity monitoring system based on digital management according to claim 5, characterized in that, The foundational layer utilizes an application data platform and master data management technology to manage the foundational data.

10. An electronic device, characterized in that, include: Processor and memory; The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the electricity monitoring method based on digital management as described in any one of claims 1 to 3.

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