Power supply service intelligent management system

Abstract

The invention discloses a power supply service intelligent management system, and relates to the technical field of intelligent power grids, and the system comprises a power grid holographic sensing module which obtains multi-dimensional unstructured data of a power system, extracts core features through cross-domain fusion analysis, and generates a power grid real-time operation state map; the equipment health twinning module is used for constructing an equipment health model in combination with a digital twinning technology and outputting an equipment fault early warning signal and a residual life prediction value; the customer energy efficiency service module is used for analyzing the association relationship and then generating a personalized energy efficiency optimization scheme and a power utilization suggestion; and the dynamic scheduling decision module is used for generating a power grid optimal scheduling strategy and an emergency response scheme. The power grid holographic sensing, digital twinning technology, dynamic scheduling, block chain and encryption technology are utilized, the power system is monitored in real time, equipment health is evaluated, faults are predicted, power utilization, energy conservation and emission reduction are optimized, data safety and stable operation of the system are ensured, and the cooperation efficiency of power enterprises and users is improved.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and more specifically to a smart management system for power supply services. Background Technology

[0002] With the rapid development of smart grid technology, the operation, maintenance and management of power systems are becoming increasingly complex. Traditional power supply management models usually rely on manual operation and single data processing methods, which are difficult to effectively cope with challenges such as rapid growth in electricity demand, frequent equipment failures and power safety. In addition, most existing grid dispatching, equipment maintenance and user service systems lack systematic data fusion and intelligent decision-making capabilities, resulting in long response times, low efficiency and even potentially significant operational risks.

[0003] With the maturation of emerging technologies such as big data, artificial intelligence, the Internet of Things, and digital twins, the power industry has begun to explore the use of these technologies to achieve comprehensive real-time perception of the power grid, equipment health management, customer energy efficiency optimization, and intelligent dispatching decisions, thereby improving the operational reliability, service quality, and management efficiency of the power system. Therefore, there is an urgent need for a comprehensive and intelligent power supply service smart management system to address the complexity of the power system and the future needs of sustainable development. Summary of the Invention

[0004] To address the aforementioned technical problems, a smart power supply service management system is provided, and this technical solution resolves the problems described above.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A smart power supply service management system includes: a power grid holographic perception module, an equipment health twin module, a customer energy efficiency service module, and a dynamic dispatch decision module;

[0007] The power grid holographic perception module acquires multi-dimensional unstructured data of the power system, extracts core features through cross-domain fusion analysis, and generates a real-time power grid operation status map.

[0008] The equipment health twin module connects to the power equipment full life cycle database to obtain equipment operating parameters and maintenance records, and uses digital twin technology to build an equipment health model, outputting equipment fault early warning signals and remaining life prediction values.

[0009] The customer energy efficiency service module is electrically connected to the power grid holographic perception module, receives user electricity consumption behavior data and regional electricity load characteristics output by it, and generates personalized energy efficiency optimization schemes and electricity consumption suggestions after analyzing the correlation.

[0010] The dynamic scheduling decision module is electrically connected to the power grid holographic perception module and the equipment health twin module. It receives the real-time operation status map of the power grid and the equipment health model, and calculates the load change trend by combining the load forecasting algorithm to generate the optimal power grid scheduling strategy and emergency response plan.

[0011] Preferably, the power grid holographic sensing module includes:

[0012] Data acquisition unit: Based on smart meters, sensor networks, and drone inspection equipment, it collects and outputs load data, equipment status data, and environmental monitoring data in categories.

[0013] Data preprocessing unit: Receives multi-dimensional power data, performs outlier removal and missing value completion, uses wavelet transform for noise reduction, and outputs clean power data;

[0014] Feature fusion unit: Receives clean power data, constructs a fusion function using a CNN-LSTM hybrid model, calculates data correlation, performs cross-modal fusion, and outputs a comprehensive power grid feature vector. The formula is:

[0015] F = α·CNN(X) t )+β·LSTM(X d )+γ·ML(X e )

[0016] In the formula, F is the comprehensive feature vector of the power grid, and CNN(X) is the comprehensive feature vector of the power grid. t ), LSTM(X) d ), ML(X e ) are the feature extraction functions for load, equipment status, and environmental data, respectively, and α, β, and γ are weighting coefficients;

[0017] State map generation unit: Receives the comprehensive feature vector of the power grid, combines it with GIS to construct the power grid topology model, updates parameters with real-time data, and outputs a dynamic and visualized real-time operating state map of the power grid;

[0018] Load forecasting unit: Receives historical load, meteorological, and holiday data, constructs a time-series forecasting model (with attention mechanism to optimize sensitivity), analyzes the influencing relationships, executes forecasts, and outputs load forecast curves;

[0019] Risk assessment unit: It statistically analyzes three types of risks: line overload, voltage anomaly, and equipment aging; receives real-time power grid operation status map and load forecast curve; calculates the probability and quantifies the impact of historical fault data; calculates the comprehensive risk value; and outputs a power grid risk assessment report.

[0020] Warning threshold setting unit: Receives power grid risk assessment reports and sets dynamic warning thresholds for three types of risks in conjunction with industry safety standards. When parameters exceed the limits, a warning is triggered and pushed to the management terminal.

[0021] Preferably, the device health twin module includes:

[0022] Twin model construction unit: Receives equipment design drawings, material parameters, and installation environment data, constructs an initial digital twin using 3D modeling, receives real-time equipment operation data and calibration parameters, and outputs a calibrated digital twin of the equipment;

[0023] Health Status Assessment Unit: Receives the digital twin of the equipment, extracts key health indicators, constructs an assessment system using the analytic hierarchy process (AHP), calculates the equipment health index and classifies it into levels (Excellent 85-100, Good 70-84, Average 55-69, Warning 40-54, Fault <40), and outputs the health index and level. The formula is as follows:

[0024]

[0025] In the formula, HI represents the health index, m represents the number of indicators, and w k I represents the weights, and the sum is 1. k Standardized values ​​for the indicators;

[0026] Fault Diagnosis Unit: Constructs a fault knowledge base and trains a deep learning diagnostic model, receives the equipment health index (when it is below 40), extracts real-time fault features, obtains similarity with standard features, matches fault types, locates fault locations, analyzes causes, and outputs a fault diagnosis report.

[0027] Lifetime prediction unit: Receives the trend of equipment health index changes, aging patterns and environmental data, analyzes the decay rate and the environmental acceleration effect, constructs the remaining lifetime prediction model, and outputs the remaining lifetime prediction range after substituting the data.

[0028] Maintenance plan generation unit: Receives equipment health level, fault diagnosis report, remaining lifespan estimate range and maintenance resource data, and generates a personalized plan including maintenance time, content and resource list;

[0029] Maintenance effectiveness evaluation unit: After maintenance, it receives equipment operation data, compares health indices and parameters before and after maintenance, calculates the improvement rate to evaluate effectiveness, and updates the fault knowledge base and maintenance plan templates.

[0030] Preferably, the customer energy efficiency service module includes:

[0031] User profile building unit: Receives user electricity consumption data, household population and residential area data, uses the minimum sample-center distance sum of squares to build a profile containing electricity consumption time / load and population / area characteristics, classifies high-energy-consuming households, energy-saving households, and commercial / industrial users, and outputs the profile and type;

[0032] Energy Efficiency Analysis Unit: Receives user type, calculates energy consumption per unit area and equipment energy efficiency ratio, compares with the average value of users of the same type, analyzes differences and deviations, identifies energy-saving potential, and outputs an energy efficiency analysis report containing potential values ​​and directions.

[0033] Personalized suggestion generation unit: Receives energy efficiency analysis reports, user electricity consumption habits, and tiered electricity pricing data, formulates a plan including electricity consumption periods, equipment replacement models, and energy-saving renovation measures, and pushes it to the user's APP;

[0034] Demand Response Unit: During peak grid load periods, it receives user profiles to filter adjustable load users, pushes invitations containing electricity price incentives and points, collects baseline / actual load after receiving response confirmation, calculates the total load reduction and feeds it back to the dynamic scheduling decision module.

[0035] Electricity anomaly monitoring unit: Receives users’ historical electricity consumption data to establish a normal mode, compares the current load with the historical average load for the same period, and determines an anomaly when the deviation exceeds 3 times the standard deviation, triggers an alert, and provides repair guidance and meter inspection appointment after receiving feedback on the cause.

[0036] Service Satisfaction Assessment Unit: Receives APP survey data and work order feedback, constructs an evaluation system that includes repair speed, customer service quality, and energy efficiency suggestions, calculates scores using fuzzy comprehensive evaluation method, analyzes shortcomings, and proposes optimization measures.

[0037] Preferably, the dynamic scheduling decision module includes:

[0038] Dispatch target setting unit: Receives targets for power supply reliability, economy, and environmental protection, as well as real-time power grid operation status graphs and load forecast curves; sets three types of optimization indicators: line loss rate, wind and solar curtailment rate, and power supply reliability rate; and outputs multi-objective optimization indicators.

[0039] Scheduling strategy generation unit: Receives optimization indicators, constructs a scheduling optimization model, solves for the optimal solution using the PSO algorithm, and generates a scheduling strategy that includes unit output, line power flow, and energy storage charging / discharging. The PSO algorithm formula is as follows:

[0040]

[0041] In the formula, v i,d Where is the particle velocity, ω is the inertial weight (0.4–0.9), c1 and c2 are learning factors, r1 and r2 are random numbers, and p... i,d The particle itself is optimal, g d For global optimality, x i,d v represents the particle's position. i,d (t+1) represents the velocity of particle i in the dimension d direction, v i,d (t) represents the current velocity of particle i in the d-dimensional direction, x i,d(t+1) represents the updated position of particle i in the d-dimensional direction, x i,d (t) represents the current position of particle i in the d-dimensional direction;

[0042] Emergency dispatch unit: When a power grid failure occurs, it receives the power grid topology model and equipment health model, identifies the fault range, calculates the load transfer path, generates an emergency plan including backup power supply, load switching and cross-regional support, and tracks the execution progress.

[0043] Dispatch effect evaluation unit: After dispatching, it receives grid operation data, compares the line loss rate, wind and solar curtailment rate and power supply reliability before and after dispatching, obtains the cost difference to evaluate the economy and effectiveness, and feeds the results back to the dispatching strategy generation unit to optimize model parameters.

[0044] Data sharing unit: Based on blockchain, a sharing platform is built to receive power grid operation, equipment health and user electricity consumption data, and realize data sharing among power supply companies, users and regulatory authorities through decentralization and encryption;

[0045] Strategy Iteration Unit: Receives historical scheduling, power grid operation, and service satisfaction data, trains the scheduling decision model using reinforcement learning, optimizes parameters based on performance and satisfaction, and performs autonomous strategy iteration.

[0046] Visualization unit: Receives real-time power grid status graphs, equipment health status, user energy efficiency data, and dispatch execution status, and dynamically displays them on the command center's large screen, supporting managers in monitoring the situation and issuing dispatch instructions.

[0047] Preferably, the data acquisition unit includes an edge computing subunit: deployed near smart meters and sensor terminals, it receives raw power data, performs format conversion and preliminary anomaly screening, and outputs preprocessed data in a unified format, reducing transmission volume and cloud pressure, and providing low-latency support for state map generation.

[0048] Preferably, the twin model construction unit includes a parameter adaptive calibration subunit: receiving device operation data, obtaining drift trends and parameter fluctuation amplitudes; when the amplitude exceeds a preset threshold, increasing the calibration frequency and expanding the range to output a high-precision twin; when the amplitude is less than or equal to the threshold, decreasing the frequency to output a normal-precision twin, thus balancing accuracy and resource efficiency.

[0049] Preferably, the personalized suggestion generation unit includes a scheme dynamic adjustment subunit: receiving user execution feedback and electricity behavior change data, updating the scheme monthly, providing adjustment suggestions and supplementary details when the energy-saving effect does not meet expectations, and regenerating profiles and adaptation schemes when users add high-energy-consuming equipment or when there are population changes.

[0050] Preferably, the emergency dispatch unit includes a multi-scenario contingency plan library subunit: receiving historical fault cases and extreme weather response experience, constructing a multi-scenario emergency contingency plan library, and in the event of a power grid emergency, matching the scenario to retrieve the contingency plan and adjusting it in conjunction with real-time data.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] This invention monitors the power system operation in real time through a power grid holographic perception module, uses digital twin technology to assess equipment health and predict faults, reducing equipment failure rates, provides personalized optimization suggestions based on electricity consumption data to promote energy conservation and emission reduction, intelligently dispatches grid loads through a dynamic scheduling decision module to ensure stable system operation, ensures data security and sharing through blockchain technology to enhance the collaborative efficiency of power companies, users, and regulatory authorities, and employs encryption technology and intrusion detection to ensure data and system security through a system security protection module. Attached Figure Description

[0053] Figure 1 This is a system framework diagram of the present invention. Detailed Implementation

[0054] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0055] Reference Figure 1 As shown, a smart power supply service management system includes: a power grid holographic perception module, an equipment health twin module, a customer energy efficiency service module, and a dynamic scheduling decision module;

[0056] The power grid holographic perception module acquires multi-dimensional unstructured data of the power system, extracts core features through cross-domain fusion analysis, and generates a real-time power grid operation status map.

[0057] The equipment health twin module connects to the power equipment full life cycle database to obtain equipment operating parameters and maintenance records, and uses digital twin technology to build an equipment health model, outputting equipment fault early warning signals and remaining life prediction values.

[0058] The customer energy efficiency service module is electrically connected to the power grid holographic perception module, receives user electricity consumption behavior data and regional electricity load characteristics output by it, and generates personalized energy efficiency optimization schemes and electricity consumption suggestions after analyzing the correlation.

[0059] The dynamic scheduling decision module is electrically connected to the power grid holographic perception module and the equipment health twin module. It receives the real-time operation status map of the power grid and the equipment health model, and calculates the load change trend by combining the load forecasting algorithm to generate the optimal power grid scheduling strategy and emergency response plan.

[0060] The power grid holographic sensing module includes:

[0061] Data acquisition unit: Based on smart meters, sensor networks, and drone inspection equipment, it collects and outputs load data, equipment status data, and environmental monitoring data in categories.

[0062] Data preprocessing unit: Receives multi-dimensional power data, performs outlier removal and missing value completion, uses wavelet transform for noise reduction, and outputs clean power data;

[0063] Feature fusion unit: Receives clean power data, constructs a fusion function using a CNN-LSTM hybrid model, calculates data correlation, performs cross-modal fusion, and outputs a comprehensive power grid feature vector. The formula is:

[0064] F = α·CNN(X) t )+β·LSTM(X d )+γ·ML(X e )

[0065] In the formula, F is the comprehensive feature vector of the power grid, and CNN(X) is the comprehensive feature vector of the power grid. t ), LSTM(X) d ), ML(X e ) are the feature extraction functions for load, equipment status, and environmental data, respectively, and α, β, and γ are weighting coefficients;

[0066] State map generation unit: Receives the comprehensive feature vector of the power grid, combines it with GIS to construct the power grid topology model, updates parameters with real-time data, and outputs a dynamic and visualized real-time operating state map of the power grid;

[0067] Load forecasting unit: Receives historical load, meteorological, and holiday data, constructs a time-series forecasting model (with attention mechanism to optimize sensitivity), analyzes the influencing relationships, executes forecasts, and outputs load forecast curves;

[0068] Risk assessment unit: It statistically analyzes three types of risks: line overload, voltage anomaly, and equipment aging; receives real-time power grid operation status map and load forecast curve; calculates the probability and quantifies the impact of historical fault data; calculates the comprehensive risk value; and outputs a power grid risk assessment report.

[0069] Warning threshold setting unit: Receives power grid risk assessment report, combines industry safety standards to set dynamic warning thresholds for three types of risks, and triggers warnings and pushes them to the management terminal when parameters exceed limits;

[0070] A CNN-LSTM hybrid model is adopted to achieve deep fusion of multimodal data of load, equipment and environment, which solves the problem that traditional single algorithms are difficult to extract spatial and temporal features at the same time. An attention mechanism is introduced to optimize the load forecasting model, enhance the sensitivity to key influencing factors such as weather and holidays, improve forecast accuracy, and build a dynamic early warning threshold mechanism to break the limitation that fixed thresholds cannot adapt to changes in power grid operating conditions, making the early warning more in line with the actual operation scenario.

[0071] The device health twin module includes:

[0072] Twin model construction unit: Receives equipment design drawings, material parameters, and installation environment data; uses 3D modeling to construct an initial digital twin; receives real-time equipment operation data and calibration parameters; and outputs a calibrated digital twin of the equipment.

[0073] Health Status Assessment Unit: Receives the digital twin of the equipment, extracts key health indicators, constructs an assessment system using the analytic hierarchy process (AHP), calculates the equipment health index and classifies it into levels (Excellent 85-100, Good 70-84, Average 55-69, Warning 40-54, Fault <40), and outputs the health index and level. The formula is as follows:

[0074]

[0075] In the formula, HI represents the health index, m represents the number of indicators, and w k I represents the weights, and the sum is 1. k Standardized values ​​for the indicators;

[0076] Fault Diagnosis Unit: Constructs a fault knowledge base and trains a deep learning diagnostic model, receives the equipment health index (when it is below 40), extracts real-time fault features, obtains similarity with standard features, matches fault types, locates fault locations, analyzes causes, and outputs a fault diagnosis report.

[0077] Lifetime prediction unit: Receives the trend of equipment health index changes, aging patterns and environmental data, analyzes the decay rate and the environmental acceleration effect, constructs the remaining lifetime prediction model, and outputs the remaining lifetime prediction range after substituting the data.

[0078] Maintenance plan generation unit: Receives equipment health level, fault diagnosis report, remaining lifespan estimate range and maintenance resource data, and generates a personalized plan including maintenance time, content and resource list;

[0079] Maintenance effectiveness evaluation unit: After maintenance, it receives equipment operation data, compares health indices and parameters before and after maintenance, calculates the improvement rate to evaluate effectiveness, and updates the fault knowledge base and maintenance plan templates;

[0080] By integrating data from the entire lifecycle of equipment design, operation, and maintenance into a digital twin, precise synchronization between the twin model and the physical equipment is achieved. A dual-dimensional assessment system of health index and remaining lifespan is established, breaking through the traditional passive maintenance mode that relies solely on fault alarms and shifting towards predictive maintenance. Resource scheduling data is integrated when maintenance plans are generated, and the knowledge base is continuously optimized through closed-loop feedback of maintenance effects, thereby improving the iterative capabilities of diagnosis and maintenance.

[0081] The customer energy efficiency service module includes:

[0082] User profile building unit: Receives user electricity consumption data, household population and residential area data, uses the minimum sample-center distance sum of squares to build a profile containing electricity consumption time / load and population / area characteristics, classifies high-energy-consuming households, energy-saving households, and commercial / industrial users, and outputs the profile and type;

[0083] Energy Efficiency Analysis Unit: Receives user type, calculates energy consumption per unit area and equipment energy efficiency ratio, compares with the average value of users of the same type, analyzes differences and deviations, identifies energy-saving potential, and outputs an energy efficiency analysis report containing potential values ​​and directions.

[0084] Personalized suggestion generation unit: Receives energy efficiency analysis reports, user electricity consumption habits, and tiered electricity pricing data, formulates a plan including electricity consumption periods, equipment replacement models, and energy-saving renovation measures, and pushes it to the user's APP;

[0085] Demand Response Unit: During peak grid load periods, it receives user profiles to filter adjustable load users, pushes invitations containing electricity price incentives and points, collects baseline / actual load after receiving response confirmation, calculates the total load reduction and feeds it back to the dynamic scheduling decision module.

[0086] Electricity anomaly monitoring unit: Receives users’ historical electricity consumption data to establish a normal mode, compares the current load with the historical average load for the same period, and determines an anomaly when the deviation exceeds 3 times the standard deviation, triggers an alert, and provides repair guidance and meter inspection appointment after receiving feedback on the cause.

[0087] Service Satisfaction Evaluation Unit: Receives APP survey data and work order feedback, constructs an evaluation system that includes emergency repair speed, customer service quality, and energy efficiency suggestions, calculates scores using fuzzy comprehensive evaluation method, analyzes shortcomings, and proposes optimization measures;

[0088] The user profile construction incorporates the dimensions of household population and residential area, breaking through the one-sidedness of traditional classification based solely on electricity consumption data. This makes the profile more closely aligned with users' actual electricity consumption scenarios. Energy efficiency recommendations combine numerical potential with concrete measures, avoiding the problem of vague recommendations in traditional approaches. User demand response is linked with grid dispatch, and user participation is enhanced through precise screening and incentive feedback. At the same time, it provides flexible support for grid load regulation. The determination of electricity consumption anomalies adopts a quantitative standard of 3 times the standard deviation, ensuring the objectivity and accuracy of anomaly identification.

[0089] The dynamic scheduling decision module includes:

[0090] Dispatch target setting unit: Receives targets for power supply reliability, economy, and environmental protection, as well as real-time power grid operation status graphs and load forecast curves; sets three types of optimization indicators: line loss rate, wind and solar curtailment rate, and power supply reliability rate; and outputs multi-objective optimization indicators.

[0091] Scheduling strategy generation unit: Receives optimization indicators, constructs a scheduling optimization model, solves for the optimal solution using the PSO algorithm, and generates a scheduling strategy that includes unit output, line power flow, and energy storage charging / discharging. The PSO algorithm formula is as follows:

[0092]

[0093] In the formula, v i,d Where is the particle velocity, ω is the inertial weight (0.4–0.9), c1 and c2 are learning factors, r1 and r2 are random numbers, and p... i,d The particle itself is optimal, g d For global optimality, x i,d v represents the particle's position. i,d (t+1) represents the velocity of particle i in the dimension d direction, v i,d (t) represents the current velocity of particle i in the d-dimensional direction, x i,d (t+1) represents the updated position of particle i in the d-dimensional direction, x i,d (t) represents the current position of particle i in the d-dimensional direction;

[0094] Emergency dispatch unit: When a power grid failure occurs, it receives the power grid topology model and equipment health model, identifies the fault range, calculates the load transfer path, generates an emergency plan including backup power supply, load switching and cross-regional support, and tracks the execution progress.

[0095] Dispatch effect evaluation unit: After dispatching, it receives grid operation data, compares the line loss rate, wind and solar curtailment rate and power supply reliability before and after dispatching, obtains the cost difference to evaluate the economy and effectiveness, and feeds the results back to the dispatching strategy generation unit to optimize model parameters.

[0096] Data sharing unit: Based on blockchain, a sharing platform is built to receive power grid operation, equipment health and user electricity consumption data, and realize data sharing among power supply companies, users and regulatory authorities through decentralization and encryption;

[0097] Strategy Iteration Unit: Receives historical scheduling, power grid operation, and service satisfaction data, trains the scheduling decision model using reinforcement learning, optimizes parameters based on performance and satisfaction, and performs autonomous strategy iteration.

[0098] Visualization unit: Receives real-time power grid status map, equipment health status, user energy efficiency data and dispatch execution status, and dynamically visualizes the data on the command center's large screen, supporting managers to monitor the situation and issue dispatch instructions;

[0099] A scheduling strategy generation mechanism based on multi-objective optimization and PSO algorithm is constructed to balance power supply reliability, economy and environmental protection, avoiding the limitations of traditional single-objective scheduling. Emergency scheduling integrates grid topology and equipment health data to ensure that load transfer path selection takes into account both grid structural safety and equipment carrying capacity. Blockchain technology is introduced to realize secure data sharing among multiple entities, resolving the contradiction between power data privacy protection and regulatory transparency. Reinforcement learning enables autonomous iteration of scheduling strategies. Combined with service satisfaction feedback, scheduling not only focuses on grid operation but also considers user service experience.

[0100] The data acquisition unit includes an edge computing subunit: deployed near smart meters and sensor terminals, it receives raw power data, performs format conversion and preliminary anomaly screening, and outputs pre-processed data in a unified format to reduce transmission volume and cloud pressure, providing low-latency support for state map generation.

[0101] By introducing edge computing at the data acquisition end, the traditional model of uploading all data to the cloud for processing is broken. Local preprocessing reduces the amount of data transmission and cloud pressure, and solves the low latency requirement for real-time power grid monitoring. The edge computing sub-unit simultaneously performs format conversion and anomaly screening, providing clean initial data for subsequent data preprocessing units and shortening the overall data processing chain.

[0102] The twin model construction unit includes a parameter adaptive calibration subunit: it receives equipment operation data, obtains drift trends and parameter fluctuation amplitudes, and when the amplitude exceeds a preset threshold, it increases the calibration frequency and expands the range to output a high-precision twin; when the amplitude is less than or equal to the threshold, it reduces the frequency and outputs a normal-precision twin, balancing accuracy and resource efficiency.

[0103] The personalized suggestion generation unit includes a dynamic solution adjustment subunit: it receives user feedback and data on changes in electricity consumption behavior, updates the solution monthly, and provides adjustment suggestions and supplementary details when the energy-saving effect does not meet expectations. When users add high-energy-consuming equipment or there are population changes, it regenerates profiles and adaptation solutions.

[0104] The emergency dispatch unit includes a multi-scenario contingency plan library subunit: it receives historical fault cases and extreme weather response experience to build a multi-scenario emergency contingency plan library. When a power grid emergency occurs, it matches the scenario to retrieve the contingency plan and adjusts it in combination with real-time data.

[0105] In summary, the advantages of this invention are:

[0106] Through cross-domain fusion analysis of the power grid holographic perception module, the system can acquire and analyze multi-dimensional unstructured data of the power system in real time, thereby generating a real-time operation status map of the power grid, providing accurate data support for dynamic monitoring and optimization decision-making of power grid operation;

[0107] The equipment health twin module uses digital twin technology, combined with equipment lifecycle database and real-time data, to comprehensively assess the operating status of power equipment and predict equipment failures and remaining lifespan in advance, effectively reducing equipment failure rate and improving the reliability and efficiency of equipment operation.

[0108] The customer energy efficiency service module can generate personalized energy efficiency optimization solutions and electricity usage suggestions based on user electricity consumption behavior, load characteristics and other data, effectively helping users reduce energy consumption and improve electricity efficiency. At the same time, it can also optimize the grid load through the demand response mechanism during peak hours and reduce grid pressure.

[0109] The dynamic scheduling decision module combines real-time grid operation status and load forecasting to intelligently generate optimal grid scheduling strategies and emergency response plans. In the event of grid failure or load changes, it can quickly adjust the power supply strategy to ensure the stability and reliability of the power system.

[0110] Through blockchain technology, the system realizes decentralized and encrypted sharing of power grid operation, equipment health and user electricity consumption data, ensuring data security and privacy protection, and promoting efficient collaboration among power companies, users and regulatory authorities;

[0111] This system also incorporates a system security protection module, employing national cryptographic algorithms for data encryption, role-based access control, and intrusion detection to comprehensively enhance system security, prevent data leakage and tampering, and ensure the reliability and security of system operation.

[0112] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A smart management system for power supply services, characterized in that, It includes a power grid holographic sensing module, an equipment health twin module, a customer energy efficiency service module, and a dynamic dispatch decision module; The power grid holographic perception module acquires multi-dimensional unstructured data of the power system, extracts core features through cross-domain fusion analysis, and generates a real-time power grid operation status map. The equipment health twin module connects to the power equipment full life cycle database to obtain equipment operating parameters and maintenance records, and uses digital twin technology to build an equipment health model, outputting equipment fault early warning signals and remaining life prediction values. The customer energy efficiency service module is electrically connected to the power grid holographic perception module, receives user electricity consumption behavior data and regional electricity load characteristics output by it, and generates personalized energy efficiency optimization schemes and electricity consumption suggestions after analyzing the correlation. The dynamic scheduling decision module is electrically connected to the power grid holographic perception module and the equipment health twin module. It receives the real-time operation status map of the power grid and the equipment health model, and calculates the load change trend by combining the load forecasting algorithm to generate the optimal power grid scheduling strategy and emergency response plan.

2. The intelligent power supply service management system according to claim 1, characterized in that, The power grid holographic sensing module includes: Data acquisition unit: Based on smart meters, sensor networks, and drone inspection equipment, it collects and outputs load data, equipment status data, and environmental monitoring data in categories. Data preprocessing unit: Receives multi-dimensional power data, performs outlier removal and missing value completion, uses wavelet transform for noise reduction, and outputs clean power data; Feature fusion unit: Receives clean power data, constructs a fusion function using a CNN-LSTM hybrid model, calculates data correlation, performs cross-modal fusion, and outputs a comprehensive power grid feature vector. The formula is: F=α·CNN(X t )+β·LSTM(X d )+γ·ML(X e ) In the formula, F is the comprehensive feature vector of the power grid, and CNN(X) is the comprehensive feature vector of the power grid. t ), LSTM(X) d ), ML(X e ) are the feature extraction functions for load, equipment status, and environmental data, respectively, and α, β, and γ are weighting coefficients; State map generation unit: Receives the comprehensive feature vector of the power grid, combines it with GIS to construct the power grid topology model, updates parameters with real-time data, and outputs a dynamic and visualized real-time operating state map of the power grid; Load forecasting unit: Receives historical load, meteorological, and holiday data, constructs a time-series forecasting model, analyzes the influencing relationships, executes forecasts, and outputs load forecasting curves; Risk assessment unit: It statistically analyzes three types of risks: line overload, voltage anomaly, and equipment aging. It receives real-time power grid operation status graphs and load forecast curves, statistically analyzes the probability and quantifies the impact of historical fault data, obtains a comprehensive risk value, and outputs a power grid risk assessment report. Warning threshold setting unit: Receives power grid risk assessment reports and sets dynamic warning thresholds for three types of risks in conjunction with industry safety standards. When parameters exceed the limits, a warning is triggered and pushed to the management terminal.

3. The intelligent power supply service management system according to claim 2, characterized in that, The device health twin module includes: Twin model construction unit: Receives equipment design drawings, material parameters and installation environment data, constructs an initial digital twin using 3D modeling, receives real-time equipment operation data calibration parameters, and outputs a calibrated digital twin of the equipment; Health Status Assessment Unit: Receives the digital twin of the equipment, extracts key health indicators, constructs an assessment system using the analytic hierarchy process (AHP), calculates the equipment health index and classifies it into levels, and outputs the health index and level. The formula is: In the formula, HI represents the health index, m represents the number of indicators, and w k As the weight, I k Standardized values ​​for the indicators; Fault Diagnosis Unit: Constructs a fault knowledge base and trains a deep learning diagnostic model, receives equipment health index, extracts real-time fault features, obtains similarity with standard features, matches fault type, locates fault location, analyzes causes, and outputs fault diagnosis report; Lifetime prediction unit: Receives the trend of equipment health index changes, aging patterns and environmental data, analyzes the decay rate and the environmental acceleration effect, constructs the remaining lifetime prediction model, and outputs the remaining lifetime prediction range after substituting the data. Maintenance plan generation unit: Receives equipment health level, fault diagnosis report, remaining lifespan estimate range and maintenance resource data, and generates a personalized plan including maintenance time, content and resource list; Maintenance effectiveness evaluation unit: After maintenance, it receives equipment operation data, compares health indices and parameters before and after maintenance, calculates the improvement rate to evaluate effectiveness, and updates the fault knowledge base and maintenance plan templates.

4. The intelligent power supply service management system according to claim 3, characterized in that, The customer energy efficiency service module includes: User profile building unit: Receives user electricity consumption data, household population and residential area data, uses the minimum sample-center distance sum of squares to build a profile including electricity consumption time and population characteristics, classifies high-energy-consuming households, energy-saving households, and commercial / industrial users, and outputs the profile and type; Energy Efficiency Analysis Unit: Receives user type, calculates energy consumption per unit area and equipment energy efficiency ratio, compares with the average value of users of the same type, analyzes differences and deviations, identifies energy-saving potential, and outputs an energy efficiency analysis report containing potential values ​​and directions. Personalized suggestion generation unit: Receives energy efficiency analysis reports, user electricity consumption habits, and tiered electricity pricing data, formulates a plan including electricity consumption periods, equipment replacement models, and energy-saving renovation measures, and pushes it to the user's APP; Demand Response Unit: During peak grid load periods, it receives user profiles to filter adjustable load users, pushes invitations containing electricity price incentives and points, collects baseline / actual load after receiving response confirmation, calculates the total load reduction and feeds it back to the dynamic scheduling decision module. Electricity anomaly monitoring unit: Receives users’ historical electricity consumption data to establish a normal mode, compares the current load with the historical average load for the same period, and determines an anomaly when the deviation exceeds 3 times the standard deviation, triggers an alert, and provides repair guidance and meter inspection appointment after receiving feedback on the cause. Service Satisfaction Assessment Unit: Receives APP survey data and work order feedback, constructs an evaluation system that includes repair speed, customer service quality, and energy efficiency suggestions, calculates scores using fuzzy comprehensive evaluation method, analyzes shortcomings, and proposes optimization measures.

5. The intelligent power supply service management system according to claim 4, characterized in that, The dynamic scheduling decision module includes: Dispatch target setting unit: Receives targets for power supply reliability, economy, and environmental protection, as well as real-time power grid operation status graphs and load forecast curves; sets three types of optimization indicators: line loss rate, wind and solar curtailment rate, and power supply reliability rate; and outputs multi-objective optimization indicators. Scheduling strategy generation unit: Receives optimization indicators, constructs a scheduling optimization model, solves for the optimal solution using the PSO algorithm, and generates a scheduling strategy that includes unit output, line power flow, and energy storage charging / discharging. The PSO algorithm formula is as follows: In the formula, v i,d Where is the particle velocity, ω is the inertial weight (0.4–0.9), c1 and c2 are learning factors, r1 and r2 are random numbers, and p... i,d The particle itself is optimal, g d For global optimality, x i,d v represents the particle's position. i,d (t+1) represents the velocity of particle i in the dimension d direction, v i,d (t) represents the current velocity of particle i in the d-dimensional direction, x i,d (t+1) represents the updated position of particle i in the d-dimensional direction, x i,d (t) represents the current position of particle i in the d-dimensional direction; Emergency dispatch unit: When a power grid failure occurs, it receives the power grid topology model and equipment health model, identifies the fault range, calculates the load transfer path, generates an emergency plan including backup power supply, load switching and cross-regional support, and tracks the execution progress. Dispatch effect evaluation unit: After dispatching, it receives grid operation data, compares the line loss rate, wind and solar curtailment rate and power supply reliability before and after dispatching, obtains the cost difference to evaluate the economy and effectiveness, and feeds the results back to the dispatching strategy generation unit to optimize model parameters. Data sharing unit: Based on blockchain, a sharing platform is built to receive power grid operation, equipment health and user electricity consumption data, and realize data sharing among power supply companies, users and regulatory authorities through decentralization and encryption; Strategy Iteration Unit: Receives historical scheduling, power grid operation, and service satisfaction data, trains the scheduling decision model using reinforcement learning, optimizes parameters based on performance and satisfaction, and performs autonomous strategy iteration. Visualization unit: Receives real-time power grid status graphs, equipment health status, user energy efficiency data, and dispatch execution status.

6. The intelligent power supply service management system according to claim 5, characterized in that, The data acquisition unit includes an edge computing subunit: deployed near smart meters and sensor terminals, it receives raw power data, performs format conversion and preliminary anomaly screening, and outputs preprocessed data in a unified format.

7. The intelligent power supply service management system according to claim 6, characterized in that, The twin model construction unit includes a parameter adaptive calibration subunit: it receives equipment operation data, obtains drift trends and parameter fluctuation amplitudes, and when the amplitude exceeds a preset threshold, it increases the calibration frequency and expands the range to output a high-precision twin; when the amplitude is less than or equal to the threshold, it decreases the frequency and outputs a normal-precision twin.

8. The intelligent power supply service management system according to claim 7, characterized in that, The personalized suggestion generation unit includes a dynamic solution adjustment subunit: it receives user feedback and data on changes in electricity consumption behavior, updates the solution monthly, and provides adjustment suggestions and supplementary details when the energy-saving effect does not meet expectations. When users add high-energy-consuming equipment or there are population changes, it regenerates profiles and adaptation solutions.

9. A smart power supply service management system according to claim 8, characterized in that, The emergency dispatch unit includes a multi-scenario contingency plan library subunit: it receives historical fault cases and extreme weather response experience to build a multi-scenario emergency contingency plan library. When a power grid emergency occurs, it matches the scenario to retrieve the contingency plan and adjusts it in combination with real-time data.

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