Multi-modular ammeter power management system for adaptive load management

Abstract

The invention discloses an adaptive load management multi-modular ammeter power management system, which comprises an ammeter module, a load monitoring unit, a data storage unit and a double-layer optimization control unit, and is characterized in that the double-layer optimization control unit comprises a particle swarm optimization layer and a fuzzy logic control layer; the particle swarm optimization layer is used for searching an optimal load distribution scheme in a multi-dimensional decision space; the fuzzy logic control layer is used for taking a load state and an environmental factor as input variables, and generating a load migration decision through fuzzy rule reasoning; and the double-layer optimization control unit combines the search capability of the particle swarm optimization layer with the expert knowledge reasoning capability of the fuzzy logic control layer, and dynamically adjusts the weight ratio of the control output of the two layers through an adaptive weight function W (t). The invention provides a self-adaptive load management system combining a particle swarm optimization system and fuzzy logic control, intelligent distribution and balance of power loads are realized, and the overall efficiency and the resource utilization rate of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a multi-modular electric meter power management system for adaptive load management. Background Art

[0002] The power management system has been gradually developed since the 1980s, and has undergone significant technological innovation from the initial simple measurement equipment to today's intelligent multi-module system. As an important part of smart grid technology, multi-module meters have gradually become a key node in the modernization of power systems. Early power management mainly adopted manual meter reading and fixed rate billing. By the early 21st century, with the advancement of microprocessor and communication technology, single-function electronic meters were replaced by smart meters with remote data collection and simple load management functions. In recent years, the introduction of modular design concepts has enabled smart meter systems to achieve functional partitioning and on-demand expansion, creating a hardware foundation for adaptive load management.

[0003] The load management of the current multi-modular meter power management system mainly relies on three technical solutions. The static load distribution system is the most traditional method, which distributes the power load to different modules through a preset rule matrix. Make a decision, where P i Represents module priority, L j This method is easy to implement, consumes less resources, and is suitable for scenarios with small load changes, but lacks the ability to cope with dynamic power consumption patterns. The threshold-based dynamic load balancing system sets upper and lower thresholds (T high ,T low ) monitors the module load level and uses Calculate the load migration amount. This method can respond appropriately when the load fluctuates, but its migration decision mechanism is too simplified and fails to fully consider the load characteristics and system efficiency. The predictive load management system uses time series models such as ARMA to predict future load trends, which is mathematically expressed as ,By planning the allocation scheme in advance, it can reduce sudden adjustments.,This system has prediction capabilities, but it relies heavily on historical data,,and has high computational complexity, making it difficult to deal with abnormal power consumption,events.

[0004] The existing system generally regards each meter module as an independent unit, lacks an effective multi-module coordination mechanism, and leads to low overall system efficiency. For example, the static allocation system cannot adjust resource allocation in time when the load fluctuates sharply, resulting in some modules being overloaded and others being idle; although the dynamic balance system can respond to load changes, the simple migration decision mechanism is difficult to adapt to the complex power consumption environment, the threshold setting and balance factor selection are too dependent on experience, and the adjustment frequency is difficult to balance the system stability and response speed. Secondly, the existing system is not adaptable enough to user behavior patterns and environmental factors, and cannot dynamically adjust the management strategy according to external factors such as temperature changes and seasonal demand. In addition, the system's optimization goal is single, and most of them only focus on load balancing, ignoring the coordinated optimization of multi-dimensional goals such as power quality and equipment service life. In addition, although the predictive system has certain practicality, it has high computational complexity and insufficient real-time performance. The prediction accuracy is affected by many factors and cannot effectively cope with sudden power consumption peaks. Summary of the invention

[0005] The purpose of the present invention is to overcome the technical problems existing in the existing multi-modular electric meter power management system, and to provide an adaptive load management system that combines a particle swarm optimization system with fuzzy logic control to achieve intelligent distribution and balance of power loads and improve the overall system efficiency and resource utilization. The main design idea of ​​the present invention is to organically combine the search capability of the particle swarm optimization system with the expert knowledge reasoning capability of fuzzy logic control, and to achieve dynamic balanced distribution of power loads through a double-layer optimization control structure.

[0006] To achieve the above objectives, the present invention provides a multi-modular electric meter power management system with adaptive load management, the system comprising: an electric meter module, a load monitoring unit, a data storage unit, and a two-layer optimization control unit, wherein the two-layer optimization control unit comprises a particle swarm optimization layer and a fuzzy logic control layer.

[0007] The load monitoring unit is mainly responsible for collecting the load level L of each meter module in real time. currenti , where i represents the number of the meter module. This unit monitors the current, voltage and power parameters of each module in real time through the sensor network, calculates the current load level, and transmits the collected data to the system central processing unit for subsequent analysis and decision-making.

[0008] The data storage unit is used to store historical load data, user power consumption patterns and environmental parameters. This unit establishes a structured database to record various data during system operation, including: historical load change curves of each module, user power consumption behavior characteristics, ambient temperature change records, etc., to provide data support for load prediction and decision optimization.

[0009] The particle swarm optimization layer is an important component of the two-layer optimization control unit, which is used to search for the optimal load distribution solution in the multidimensional decision space. This layer uses an improved particle swarm optimization system to find the optimal solution by iteratively updating the particle position vector X and velocity vector V, where X represents the target load distribution ratio of each module and V represents the load distribution adjustment rate. The particle swarm optimization system can find a near-optimal solution in a complex nonlinear decision space by simulating group intelligent behavior.

[0010] The fuzzy logic control layer is another core component of the two-layer optimization control unit, which is used to take load status and environmental factors as input variables and generate load migration decisions through fuzzy rule reasoning. This layer includes the migration timing judgment function F timing And migration calculation function F amount The two parts transform expert experience into specific control decisions through three steps: fuzzification, rule reasoning and defuzzification. Fuzzy logic control can handle uncertainty and ambiguity in the system and is suitable for decision-making in complex power load environments.

[0011] The dual-layer optimization control unit combines the search capability of the particle swarm optimization layer with the expert knowledge reasoning capability of the fuzzy logic control layer, and dynamically adjusts the weight ratio of the two-layer control output through the adaptive weight function W(t). This combination overcomes the disadvantages of the particle swarm system being prone to fall into local optimality and the disadvantages of the fuzzy logic control lacking optimization capability, and realizes the dynamic balance distribution of the load.

[0012] The particle swarm optimization layer of the present invention adopts an improved particle swarm optimization system, including an adaptive inertia weight factor, a particle position and velocity update formula, and a multi-objective fitness function. The adaptive inertia weight factor w(t) is calculated by the function Calculate, where t represents the current iteration number, T max represents the maximum number of iterations, w max and w min are the upper and lower limits of inertia weight respectively, and α is the nonlinear adjustment factor. The particle position update formula is: , the particle velocity update formula is: ,in represents the best historical position of particle i, G best represents the best position found by the group, c1 and c2 are acceleration constants, and r1 and r2 are random numbers in the interval [0,1]. The fitness function f(X) takes into account load balance, power quality, and system efficiency, and is expressed as , where β i is the weight coefficient of each sub-goal.

[0013] The fuzzy logic control layer of the present invention includes an input fuzzification unit, a fuzzy rule base, a fuzzy reasoning mechanism and an output defuzzification unit. The input fuzzification unit converts the current load level of the meter module into , Load change rate , System load imbalance D imbalance and ambient temperature T are converted into fuzzy sets. The fuzzy rule base contains a set of IF-THEN rules formed by expert knowledge to deal with the complexity and uncertainty of load migration decisions. The fuzzy reasoning mechanism uses the Mamdani reasoning method to execute fuzzy rules. The output defuzzification unit converts the fuzzy reasoning results into a clear load migration timing judgment value F timing and the calculated migration value F amount .

[0014] The adaptive weight function W(t) in the present invention dynamically adjusts the output weights of the particle swarm optimization layer and the fuzzy logic control layer according to the system operation state. When the system is in a stable state, W fuzzy (t)>W PSO (t), the output of the fuzzy logic control layer is used first to reduce the computational overhead; when the system load fluctuates greatly or the imbalance exceeds the preset threshold, W PSO (t)>W fuzzy (t), the output of the particle swarm optimization layer is preferentially used to obtain the optimal solution; at the same time, W fuzzy (t)+W PSO (t)=1, ensuring weight normalization.

[0015] The present invention also includes a load prediction unit, which predicts the load trend of future time periods based on historical load data and calculates the future load using a recursive neural network model. , where k is the prediction step size, and the prediction results are used as the input of the particle swarm optimization layer and the fuzzy logic control layer to improve the prediction of decision making.

[0016] The load balancing evaluation subfunction f in the fitness function of the present invention balance (X) is calculated as the ratio of the standard deviation of each module's load level to the average load: , where σ(L current ) represents the standard deviation of the current load of each module, L average Indicates the average load level. The smaller the value of this sub-function is, the more balanced the load distribution is.

[0017] The fuzzy rule base of the present invention contains professional rules for different load scenarios, specifically including: a rule set for peak load scenarios, which is used for rapid response and load dispersion; a rule set for valley load scenarios, which is used for equipment efficiency and energy conservation; a rule set for rapidly fluctuating load scenarios, which is used for system stability and buffering capacity; a rule set for seasonal changes, which dynamically adjusts the load distribution strategy according to the ambient temperature T and historical data for the same period.

[0018] The present invention sets up a multi-level load migration priority mechanism, which comprehensively considers the current load level of the meter module. 、Historical load fluctuation range Var(L i ) and equipment health index H i Determine the source module and target module for load migration, and prioritize migrating load from high-load and healthy modules to low-load modules. The migration priority calculation formula is: , where γ1, γ2, and γ3 are weight coefficients.

[0019] The present invention realizes a collaborative learning mechanism of a particle swarm optimization system and fuzzy logic control, which is specifically manifested in that the search results of the particle swarm optimization system are used to dynamically adjust the membership function parameters of the fuzzy rules; the expert knowledge of the fuzzy control is used to guide the initialization and mutation operations of the particle swarm; through collaborative learning, the system can simultaneously overcome the unfavorable factors of the slow convergence speed of the particle swarm system and the difficulty in designing the fuzzy logic rule base.

[0020] The present invention also includes an emergency response module. When an abnormality in the power grid or a sudden load change is detected, the conventional decision-making process is bypassed and a preset emergency load distribution strategy is directly adopted to ensure the robustness and safety of the system under extreme conditions. The emergency response trigger conditions are: , where ε is the load imbalance threshold and η is the load change rate threshold.

[0021] In summary, the present invention combines the particle swarm optimization system with the fuzzy logic control technology to construct a two-layer optimization control structure, thereby realizing the adaptive performance optimization of the multi-modular electric meter power management system, and has the following beneficial effects: 1. Improved accuracy and balance of load distribution: Traditional static load distribution systems and threshold-based dynamic load balancing systems are not accurate enough when facing complex power load environments. The present invention uses the search capability of the particle swarm optimization system to find a near-optimal load distribution solution in the multidimensional decision space, thereby improving load balance. Compared with traditional methods, the load balancing index f of this system is balance (X) is reduced, effectively alleviating the risk of module overload.

[0022] 2. Enhanced system adaptability to dynamic changes: The existing load management system is not able to respond to changes in the power environment. This invention combines fuzzy logic control technology to convert expert experience into adaptive decision rules, and can dynamically adjust the load distribution strategy according to real-time load status, power consumption mode and environmental factors. The system's response time to sudden load changes is shortened, and its adaptability to seasonal load changes is improved.

[0023] 3. Achieved multi-objective collaborative optimization: Traditional systems often focus on a single optimization target. The present invention builds a multi-dimensional evaluation system, taking into account load balance, power quality and system efficiency at the same time, and achieves a collaborative balance of optimization targets through a multi-objective fitness function. While ensuring load balance, system efficiency is improved and power quality indicators are improved.

[0024] 4. Improved the balance between computational efficiency and optimization accuracy: Although the particle swarm optimization system has powerful search capabilities, it has high computational complexity; although the fuzzy logic control has high computational efficiency, it has limited optimization accuracy. The present invention dynamically adjusts the output weights of the two systems through an adaptive weight function W(t), gives priority to using fuzzy logic control with high computational efficiency when the system is stable, and switches to the particle swarm system with high optimization accuracy when the load fluctuates violently, thus achieving a dynamic balance between computational efficiency and optimization accuracy. The average computational load of the system is reduced while maintaining a high optimization accuracy.

[0025] 5. Enhanced system reliability: By setting up an emergency response module and a multi-level load migration priority mechanism, the system can effectively respond to grid anomalies and sudden load changes, ensuring the stable operation of the system under extreme conditions. The system has a lower failure rate when facing sudden load peaks, and the system instability caused by grid fluctuations is reduced.

[0026] 6. Realizes self-learning and evolution of the system: The collaborative learning mechanism of the present invention enables the particle swarm optimization system and fuzzy logic control to promote each other and evolve together. The search results of the particle swarm optimization system are used to dynamically adjust the membership function parameters of the fuzzy rules, which improves the accuracy of fuzzy control; the expert knowledge of fuzzy control is used to guide the initialization and mutation operations of the particle swarm, which accelerates the convergence speed of the particle swarm system. This improves the optimization performance of the system and enhances its adaptability to new load modes.

[0027] 7. Improved overall system efficiency and resource utilization: Through the adaptive load management solution of the present invention, the resource utilization of the meter module is improved, the peak-to-valley difference of the power grid load is reduced, the system operation energy consumption is reduced, the service life of the equipment is extended, and the comprehensive economic benefits are significantly improved.

[0028] In summary, this technical solution combines the particle swarm optimization system with fuzzy logic control technology to construct a load management system with adaptive, collaborative learning and multi-objective optimization capabilities, effectively overcoming the limitations of traditional load management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0031] Reference Figure 1 As shown, the multi-modular electric meter power management system for adaptive load management of the present invention mainly includes: an electric meter module, a load monitoring unit, a data storage unit, a two-layer optimization control unit, a load prediction unit and an emergency response module. The system connects each functional unit through a bus and is coordinated by a central processing unit.

[0032] The load monitoring unit adopts a distributed sensor network structure, and collects the electrical parameters of each meter module in real time through high-precision current transformers and voltage sensors, including current (I, unit: ampere), voltage (U, unit: volt), power factor (cosφ), etc., to calculate the current load level. (Unit: kilowatt). The collected data is filtered and calibrated before being transmitted to the central processing unit in a standard format. The sampling frequency of the load monitoring unit can be dynamically adjusted according to system requirements, usually set to once per minute in a stable state and increased to multiple times per second during load fluctuations.

[0033] The data storage unit adopts a layered architecture, including real-time data cache and historical database. The real-time data cache uses high-speed memory to store the load data of the last 24 hours; the historical database uses a relational database to store long-term operation data, including the historical load curves of each module. (unit: kilowatt, t is time), user power consumption pattern feature vector P user (including power consumption period, power consumption, power consumption stability and other parameters), environmental parameters E (including temperature T, humidity H, air pressure P, etc.). The data storage unit is also responsible for data compression, backup and recovery functions to ensure system data security.

[0034] The double-layer optimization control unit is the core technical solution of the present invention, including a particle swarm optimization layer, a fuzzy logic control layer and an adaptive weight adjustment mechanism.

[0035] The particle swarm optimization layer uses an improved particle swarm optimization system to search for the optimal solution in the load distribution decision space. The specific implementation steps of this layer are as follows:

[0036] 1. Initialize the particle swarm: Initialize m particles in the N-dimensional decision space, where N is equal to the number of meter modules and m is usually set to 10×N. Each particle represents a possible load distribution scheme, and its position vector X i Indicates the target load distribution ratio of each module, the speed vector V i Represents the load distribution adjustment rate. The initial position and speed are randomly generated using uniform distribution, and the position vector satisfies the normalization constraint: .

[0037] 2. Evaluate particle fitness: Calculate the fitness value f(X i ), the fitness function includes three aspects: load balance, power quality and system efficiency: Load balancing function , where σ(L current ) represents the standard deviation of the current load of each module (unit: kilowatt), L average Indicates the average load level (unit: kilowatt). The smaller the value, the more balanced the load distribution.

[0038] Power quality sub-function f quality (X) Considering voltage stability, harmonic content and power factor, the calculation formula is: , where VFI is the voltage fluctuation index (dimensionless), THD is the total harmonic distortion (%), PF is the power factor, and w1, w2, and w3 are weight coefficients.

[0039] System efficiency subfunction f efficiency (X) Considering energy utilization and operating costs, the calculation formula is: , where E loss is the energy loss (unit: kWh), E total is the total power consumption (unit: kWh), C operation is the operating cost (unit: yuan), C max It is the preset maximum cost (unit: yuan).

[0040] The overall fitness function: , where β1, β2, and β3 are weight coefficients, satisfying β1+β2+β3=1. The initial values ​​are set to β1=0.5, β2=0.3, and β3=0.2, which can be dynamically adjusted according to actual application requirements.

[0041] 3. Update individual optimal solution and optimal solution: compare the current fitness of each particle with its historical best fitness, and update the individual optimal position P best ; Compare the fitness of all particles and update the optimal position G of the group best .

[0042] 4. Update particle speed and position: Use improved speed and position update formula, introduce adaptive inertia weight and convergence factor: Adaptive Inertia Weight , where t is the current iteration number, T max is the maximum number of iterations, usually set to 100-200, w max and w min They are the upper and lower limits of the inertia weight, usually set to 0.9 and 0.4, and α is the nonlinear adjustment factor, usually set to 1.2.

[0043] Particle velocity update formula: , where c1 and c2 are acceleration constants, usually set to 2.0, and r1 and r2 are random numbers in the interval [0,1].

[0044] Particle position update formula: .

[0045] Position constraint processing: The updated position vector may not satisfy the normalization constraint and needs to be normalized: .

[0046] 5. Convergence judgment: Check whether the maximum number of iterations T is reached max , or whether the improvement of the optimal solution after multiple consecutive iterations is lower than the preset threshold ε convergence (Usually set to 0.001). If any condition is met, the iteration stops; otherwise, return to step 2 to continue the iteration.

[0047] The disadvantages of the particle swarm optimization layer mainly include: high computational complexity, especially in systems with a large number of modules, where the iterative computational overhead is significant; it is easy to fall into the local optimal solution and it is difficult to find the true optimal solution; it is sensitive to parameter settings, and different parameter combinations may lead to significant differences in system performance; the convergence speed is slow, and more iterations are required to achieve satisfactory results.

[0048] The fuzzy logic control layer converts expert experience into specific load migration decisions through fuzzy rule reasoning. The specific implementation steps of this layer are as follows:

[0049] 1. Fuzzification of input variables: The current load level of the meter module , Load change rate , System load imbalance D imbalance and ambient temperature T are converted into fuzzy sets. Each input variable defines 5-7 fuzzy sets, as follows:

[0050] Load level L currenti : Very low (VL), Low (L), Medium (M), High (H), Very high (VH).

[0051] Load change rate :Fast descent (FD), slow descent (SD), stable (S), slow rise (SI), fast rise (FI).

[0052] Load imbalance D imbalance : Balanced (B), slightly unbalanced (SU), moderately unbalanced (MU), severely unbalanced (HU), extremely unbalanced (VU).

[0053] Ambient temperature T: Very low (VC), Low (C), Moderate (M), High (H), Very high (VH).

[0054] Each fuzzy set is described by a triangular or trapezoidal membership function μ(x). The triangular membership function is defined as:

[0055] Where a, b, and c are membership function parameters, satisfying a≤b≤c.

[0056] 2. Construct a fuzzy rule base: Based on the expert knowledge of the power system, a fuzzy rule base containing multiple rule sets is constructed. The rules adopt an IF-THEN structure.

[0057] Rules for peak load scenarios:

[0058] Rules for valley load scenarios:

[0059] Rules for fast-fluctuating load scenarios:

[0060] Rules for seasonal changes:

[0061] Rule base rule settings can cover various possible load scenarios and environmental conditions.

[0062] 3. Fuzzy reasoning: The Mamdani reasoning method is used to execute fuzzy rules. For each rule, the membership of the antecedent part is calculated first, and then the trigger strength of the consequent part is determined. The specific steps are:

[0063] Calculate the membership of the antecedent part of the rule: for conditions connected by AND, take the minimum value; for conditions connected by OR, take the maximum value.

[0064] Determine the trigger strength of the consequent part of the rule: use the truncation method and take the membership degree of the antecedent part as the maximum height of the fuzzy set of the consequent part.

[0065] Aggregate the outputs of all rules: Use the maximum method to merge the output fuzzy sets of all rules.

[0066] 4. Output defuzzification: Convert the fuzzy reasoning result into a clear load migration timing judgment value F timing and the calculated migration value F amount . Defuzzification uses the centroid method: ; Where μ(x) is the membership function of the aggregated output fuzzy set, x is the value of the output variable, and F is the output value after defuzzification.

[0067] F timing The output is a value in the range [0,1], indicating the urgency of load migration. 0 means no migration is required, and 1 means immediate migration.

[0068] F amount The output is a value in the interval [0,1], which indicates the relative amount of load migration. The actual migration amount ΔL=F amount ×L max _ transfer , where L max _ transfer The maximum migration load allowed by the system (unit: kilowatt).

[0069] The disadvantages of the fuzzy logic control layer mainly include: the design of the rule base relies on expert experience and it is difficult to cover all possible scenarios; the setting of membership function parameters lacks theoretical guidance and mostly relies on trial and error to determine; it lacks optimization capabilities and it is difficult to ensure the optimality of decisions; the number of rules is proportional to the complexity of the system, and the rule base of a complex system may be too large and difficult to maintain.

[0070] The dynamic adjustment mechanism of the adaptive weight function W(t) dynamically adjusts the output weights of the particle swarm optimization layer and the fuzzy logic control layer according to the system operation status to achieve the collaborative work of the two systems. The specific implementation is as follows:

[0071] 1. System status evaluation: Calculate the system status index S(t) based on the load monitoring data, including: Load fluctuation index , which represents the ratio of the maximum load change to the average load.

[0072] Load imbalance index , which represents the ratio of the load standard deviation to the average load.

[0073] System Status Index , where λ1 and λ2 are weight coefficients, satisfying λ1+λ2=1.

[0074] 2. Weight function calculation: Calculate the weight function value based on the system state index S(t): When S(t)≤S threshold_low When , the system is in a stable state. Setting W fuzzy (t) = W fuzzy_max , W PSO (t)=1-W fuzzy (t).

[0075] When S(t)≥S threshold_high When , the system is in a fluctuating state. Setting W PSO (t) = W PSO_max , W fuzzy (t)=1-W PSO (t).

[0076] When S threshold_low <S(t)<S threshold_high When , linear interpolation is used to calculate the weights: W fuzzy (t) = W fuzzy_max -(W fuzzy_max -W fuzzy_min )×(S(t)-S threshold_low ) / (S threshold_high -S threshold_low ); W PSO (t)=1-W fuzzy (t).

[0077] Where S threshold_low and S threshold_high are the lower and upper limits of the system status threshold, respectively, and W fuzzy_max and W fuzzy_min are the upper and lower limits of the fuzzy logic control layer weights, W PSO_max and W PSOmin are the upper and lower limits of the particle swarm optimization layer weights. The typical parameter settings are: threshold_low =0.1, S threshold_high =0.3, W fuzzy_max =0.8,W fuzzy_min =0.2, W PSO_max =0.8,W PSOmin =0.2.

[0078] 3. Control output fusion: The outputs of the particle swarm optimization layer and the fuzzy logic control layer are fused into the final control decision according to the weight function: Load migration timing: F timing_final =W fuzzy (t)×F timingfuzzy +W PSO (t)×F timingPSO ; Load migration: Famount _final=W fuzzy (t)×F amountfuzzy +W PSO (t)×F amountPSO .

[0079] The present invention realizes the collaborative learning mechanism of particle swarm optimization system and fuzzy logic control, which is specifically manifested as follows: 1. Particle swarm optimization results guide fuzzy rule adjustment: Record the optimal solution of the particle swarm optimization system under different load scenarios opt and their corresponding system status parameters.

[0080] Analyze the difference between the optimal solution and the current fuzzy rule output ΔF=F PSO -F fuzzy - The membership function parameters of the fuzzy rules are adjusted according to the differences: a'=a+η·ΔF, b'=b+η·ΔF, c'=c+η·ΔF, where η is the learning rate, which is usually set to 0.05-0.1.

[0081] Regularly evaluate the performance of adjusted rules, keep those that improve performance, and discard those that degrade performance.

[0082] 2. Fuzzy control guided particle swarm system optimization: The expert knowledge of fuzzy control is used to guide the initialization of the particle swarm: according to the current system state and the fuzzy rule reasoning results, the initial particles close to the potential optimal solution are generated to accelerate the convergence of the system.

[0083] Fuzzy rules are used to evaluate the diversity of the particle swarm. When the diversity is insufficient, the mutation operation is triggered: the positions of some particles are randomly disturbed to avoid falling into the local optimum.

[0084] Construct a speed constraint mechanism based on fuzzy rules: adjust the speed limit V according to the system state max , in critical states, lowering the upper speed limit improves search accuracy, and in general states, increasing the upper speed limit accelerates convergence.

[0085] 3. Collaborative learning performance evaluation and adjustment: Regularly evaluate the collaborative learning effect and calculate the system performance indicators P(t), including load balancing, response time, energy efficiency, etc.

[0086] Dynamically adjust learning parameters according to performance change trend ΔP=P(t)-P(tT), including learning rate η, mutation probability p mutation , speed constraint coefficient, etc.

[0087] Build a knowledge base to record successful learning cases as a reference for self-learning.

[0088] Through the collaborative learning mechanism, the system can simultaneously overcome the unfavorable factors of slow convergence of particle swarm system and difficult design of fuzzy logic rule base, and achieve continuous improvement of system performance.

[0089] The emergency response module is a key component to ensure the safe and stable operation of the system under extreme conditions. The specific implementation is as follows: 1. Abnormal state detection: Continuously monitor key system parameters, including: Load imbalance:

[0090] Load change rate:

[0091] Voltage deviation:

[0092] Abnormal temperature:

[0093] 2. Emergency trigger conditions: When any parameter exceeds the preset threshold, an emergency response is triggered: >ε, where ε is the load imbalance threshold, which is usually set to 0.3-0.5. >η, where η is the load change rate threshold, usually set to 0.2-0.3kW / min.

[0094] >δ, where δ is the voltage deviation threshold, usually set to 0.1.

[0095] >τ, where τ is the temperature anomaly threshold, usually set to 15°C.

[0096] 3. Emergency response strategy: After the emergency response is triggered, bypass the regular decision-making process and directly execute the preset strategy: Load balancing strategy: quickly redistribute the load so that the load of each module tends to the average value L average .

[0097] Load limit strategy: Implement load limit on overloaded modules to ensure that the load does not exceed the safety threshold L safe_i .

[0098] Backup module startup strategy: Start the backup meter module in extreme cases to share the system load.

[0099] Fault isolation strategy: Identify and isolate faulty modules, and reorganize the remaining modules to form a new operating network.

[0100] 4. Recovery mechanism: After the abnormal situation is resolved, the system smoothly transitions back to the normal control mode: Calculate the transition period length T transition, usually 5-10 minutes.

[0101] During the transition period, the emergency response weight is linearly reduced and the conventional control weight is increased.

[0102] After the transition period, normal control mode will be fully restored.

[0103] The load prediction unit uses a recursive neural network model to predict future loads and improve the prediction of system decisions. The specific implementation is as follows: 1. Data preprocessing: Time window selection: Select an appropriate time window length based on the prediction target. Usually, a 10-30 minute window is selected within an hour, a 4-6 hour window is selected within a day, and a 3-7 day window is selected within a week.

[0104] Feature extraction: In addition to load history data, time features, temperature data, user behavior features, etc. are also extracted as input. -Data standardization: All input features are standardized and mapped to the [0,1] or [-1,1] interval.

[0105] 2. Model structure: The long short-term memory network (LSTM) is used as the core prediction model, which includes an input layer, an LSTM hidden layer (1-2 layers) and an output layer.

[0106] The number of nodes in the input layer is equal to the number of input features, usually 10-20 nodes. - Each LSTM hidden layer contains 30-50 neurons and uses the tanh activation function. - The number of nodes in the output layer is equal to the prediction step size k, usually 6-12 nodes (predicting the next 1-2 hours, one prediction point every 10 minutes).

[0107] 3. Model training: The sliding window method is used to generate training samples with a window step size of 10 minutes.

[0108] The batch size is set to 32-64 and the number of training rounds is 100-200.

[0109] The Adam optimizer is used, the initial value of the learning rate is set to 0.001, and the learning rate decay strategy is adopted.

[0110] The loss function uses the mean square error (MSE), while the mean absolute percentage error (MAPE) is monitored to evaluate the prediction accuracy.

[0111] 4. Prediction result processing: Compute prediction confidence intervals: ±1.96σ prediction , where σ prediction is the predicted standard deviation.

[0112] Abnormal prediction detection: If the predicted value deviates too much from the historical data for the same period, an early warning will be triggered and the prediction result will be adjusted.

[0113] Smoothing of prediction results: Apply sliding average filtering to the prediction curve to reduce abnormal fluctuations.

[0114] 5. Application of prediction results: Short-term prediction results are directly used as inputs to the particle swarm optimization layer and the fuzzy logic control layer. Medium- and long-term prediction results, more than 1 hour, are used for system resource planning and peak and valley load pre-adjustment. Prediction error feedback: The deviation between the actual load and the predicted load is used for continuous model optimization.

[0115] The multi-level load migration priority mechanism determines the source module and target module of load migration by comprehensively considering multiple factors. The specific implementation is as follows: 1. Migration priority calculation: Current load level L currenti : The real-time load level of the module in kilowatts.

[0116] Historical load fluctuation range Var(L i ): The variance of the load within 24 hours, reflecting the load stability.

[0117] Equipment health index H i : Health status index calculated based on equipment running time, fault records, temperature, etc. The value range is [0,1], where 1 indicates complete health.

[0118] Migration priority calculation formula: , where γ1, γ2, and γ3 are weight coefficients, satisfying γ1+γ2+γ3=1, and the typical settings are γ1=0.6, γ2=0.3, and γ3=0.1.

[0119] 2. Migration source module selection: For all modules, press Sort in descending order, select the top N source Modules (usually 20%-30% of the total number of modules) are selected as candidate source modules.

[0120] Check whether the load level of the candidate source module exceeds the migration threshold L threshold_source , usually set to L average ×(1+δ source ), where δ source is the source module threshold coefficient, which is 0.2.

[0121] Finally, the modules that meet the conditions are determined to be the actual source module set S source .

[0122] 3. Migration target module selection: For all modules, press Sort in ascending order, select the top N target (usually 20%-30% of the total number of modules) as candidate target modules. - Check whether the load level of the candidate target module is lower than the migration threshold L threshold_target , usually set to L average ×(1-δ target ), where δ target is the target module threshold coefficient, usually 0.2.

[0123] Check whether the remaining capacity of the candidate target module is sufficient to receive the migration load. The remaining capacity is calculated as C remaini =L maxi -L currenti , where L maxi is the maximum load capacity of module i. -Finally, the modules that meet the conditions are determined as the actual target module set S target .

[0124] 4. Migration amount calculation and allocation: Calculate the total migration amount of the source module set: L migration_total =∑(L current_i -L balance_i ), where i∈S source , L balance_i The target load balance for module i is usually set to L average .

[0125] Calculate the total receiving capacity of the target module set: C receive_total =∑(L balancei -L currenti ), where i∈S target - If L migration_total >C receive_total , distribute the migration amount in proportion; if L migration_total ≤C receive_total , fully allocate the migration amount.

[0126] For each source module i and target module j, calculate the migration amount L from i to j migration_i ,j, optimize the migration path by considering factors such as network topology, distance cost, etc.

[0127] The multi-modular electric meter power management system with adaptive load management proposed in the present invention constructs a two-layer optimization control structure by combining the particle swarm optimization system with the fuzzy logic control technology, thus realizing the intelligent distribution and balance of power loads. The system uses an adaptive weight function to dynamically adjust the output weights of the two systems, which reduces the computational overhead while ensuring the optimization accuracy. Through the collaborative learning mechanism, the system can continuously improve the fuzzy rules and optimize the system performance to achieve continuous evolution. The multi-level load migration priority mechanism and the emergency response module further enhance the flexibility and reliability of the system.

[0128] Compared with the traditional static load distribution system, threshold-based dynamic load balancing system and predictive load management system, the solution provided by the present invention has significant advantages: more balanced load distribution, stronger adaptability to dynamic changes, ability to achieve multi-objective collaborative optimization, higher system efficiency and resource utilization. The present invention is suitable for a variety of application scenarios such as smart buildings, industrial parks, smart communities, new energy microgrids, electric vehicle charging stations, etc., and has broad application prospects.

[0129] In order to better understand the present invention, the present application provides an example calculation process of a multi-modular meter power management system for adaptive load management: 1. Scene Setting This case simulates a multi-module electricity meter load management system in a large commercial building. The building has a total of 6 electricity meter modules, each responsible for different functional areas:

[0130] Module 1: Office Area Module 2: Commercial Retail Area Module 3: Dining Area Module 4: Public facilities, elevators, lighting, etc. Module 5: Air Conditioning Systems Module 6: Backup Power Module Assumption: At 14:00 in the afternoon on a summer weekday, the outdoor temperature reaches 35°C, the air conditioning load increases suddenly, and at the same time, the commercial retail area has an increased demand for electricity due to promotional activities. The system needs to manage the load to avoid overloading individual modules.

[0131] 2. Data Input 2.1 Current load data of each module Module Number <![CDATA[Current load L current， (kW)]]> Load change rate (kW / min) <![CDATA[Maximum load capacity L max (kW)]]> <![CDATA[Historical load fluctuation range Var(L i )]]> <![CDATA[Device health status index H i > 1 120 +0.5 200 15 0.95 2 180 +2.5 220 25 0.90 3 90 -0.2 150 20 0.85 4 110 +0.3 180 10 0.98 5 210 +3.0 230 35 0.92 6 40 +0.1 200 5 1.00 2.2 System Parameters Total system load: L total =750kW Average load: L average =125kW System load standard deviation: σ(L current )=58.14kW Load imbalance: D imbalance =σ(L current ) / L average =0.465 Ambient temperature: T = 35 °C Predicted 1-hour load trend: Continuously increasing, L predicted_t+60 =820kW 2.3 System parameter settings 1. Threshold-based dynamic load balancing system parameters: High load threshold: T high =L average ×1.2=150kW Low load threshold: T low =L average ×0.8=100kW Balance factor: α=0.3 2. Particle swarm optimization system parameters: Number of particles: m=30 Maximum number of iterations: T max =100 Inertia weight range: w max =0.9,w min =0.4 Nonlinear adjustment factor: α=1.2 Acceleration constant: c1=c2=2.0 Fitness function weights: β1=0.6 (load balance), β2=0.3 (power quality), β3=0.1 (system efficiency) 3. Fuzzy logic control parameters: Input variable membership function: Load level L currenti :Very low (VL), low (L), medium (M), high (H), very high (VH) Load change rate ΔL i / Δt: fast descent (FD), slow descent (SD), stable (S), slow rise (SI), fast rise (FI) Load imbalance D imbalance : Balanced (B), slightly unbalanced (SU), moderately unbalanced (MU), severely unbalanced (HU), extremely unbalanced (VU) Ambient temperature T: Very low (VC), Low (C), Moderate (M), High (H), Very high (VH).

[0132] 4. Adaptive weight function parameters: System status threshold: S threshold_low =0.1,S threshold_high =0.3 Weight range: W fuzzy_max =0.8,W fuzzy_min =0.2,W PSO_max =0.8,W PSOmin =0.2 State evaluation weights: λ1=0.4 (load fluctuation index), λ2=0.6 (load imbalance index) 5. Multi-level load migration priority parameters: Weight coefficient: γ1=0.6,γ2=0.3,γ3=0.1 System state assessment and adaptive weight function calculation 1. Calculate the load fluctuation index: LFI(t)=max|ΔL i / Δt| / L average =3.0 / 125=0.024 2. Calculate the load imbalance index: LUI(t)=σ(L current ) / L average =58.14 / 125=0.465 3. Calculate the system status index: S(t)=λ1·LFI(t)+λ2·LUI(t)=0.4×0.024+0.6×0.465=0.0096+0.279=0.289 4. Due to S threshold_low <S(t)<S threshold_high (0.1<0.289<0.3), Calculate adaptive weights: W fuzzy (t) = W fuzzy_max -(W fuzzy_max -W fuzzy_min )×(S(t)-S threshold_low ) / (S threshold_high -S threshold_high -S threshold_low )=0.8-(0.8-0.2)×(0.289-0.1) / (0.3-0.1)=0.8-0.6×0.945=0.8-0.567=0.233; W PSO (t)=1-W fuzzy (t)=1-0.233=0.767.

[0133] 5. Weight analysis: Due to the high system load imbalance, close to S threshold_high , the system preferentially uses the output of the particle swarm optimization layer (W PSO (t)=0.767) to obtain a solution closer to the optimal one.

[0134] Fuzzy logic control layer calculation 1. Fuzzification of input variables: Take module 5 as an example, its current load L current =210kW, membership calculation: μVL(210)=0, μL(210)=0, μM(210)=0, μH(210)=0.2, μVH(210)=0.8 Load change rate ΔL5 / Δt=+3.0kW / min, Membership calculation: μFD(3.0)=0,μSD(3.0)=0,μS(3.0)=0,μSI(3.0)=0.1,μFI(3.0)=0.9 - system load imbalance D imbalance =0.465, Membership calculation: μB(0.465)=0,μSU(0.465)=0,μMU(0.465)=0.3,μHU(0.465)=0.7,μVU(0.465)=0-ambient temperature T=35℃.

[0135] Membership calculation: μVC(35)=0,μC(35)=0,μM(35)=0,μH(35)=0.3,μVH(35)=0.7 2. Fuzzy rule activation: Rule 1 IF(L currenti isVH)AND(ΔL i / ΔtisFI)AND(D imbalance isHU)AND(TisVH)THEN(F timing isImmediate)AND(F amount isLarge); Rule strength = min (0.8, 0.9, 0.7, 0.7) = 0.7 Rule 2: IF(L currenti isH)AND(ΔL i / ΔtisSI)AND(D imbalance isMU)THEN(F timing isSoon)AND(F amount isMedium); Rule strength = min(0.2, 0.1, 0.3) = 0.1; Rule 3: IF(TisVH)AND(Lcurrenti isVH)THEN(F timing isImmediate)AND(F amount isMedium); Rule strength = min (0.7, 0.8) = 0.7 3. Defuzzification calculation - simplified to weighted average: F timingfuzzy =(0.7×1.0+0.1×0.8+0.7×1.0) / (0.7+0.1+0.7)=1.47 / 1.5=0.98 F amountfuzzy =(0.7×0.8+0.1×0.5+0.7×0.5) / (0.7+0.1+0.7)=0.91 / 1.5=0.61 4. Fuzzy control output results: Module 5 load migration urgency: F timingfuzzy =0.98 (very urgent) Relative amount of load migration of module 5: F amountfuzzy =0.61 Actual migration amount: ΔL fuzzy =F amountfuzzy ×(L current5 -L average )=0.61×(210-125)=51.85kW Particle swarm optimization layer calculation 1. Particle initialization: Generate 30 random particles, each particle represents a load distribution scheme. Take a representative particle as an example: X i =[0.16,0.17,0.15,0.16,0.18,0.18] (normalized load distribution ratio)

[0136] 2. Particle evaluation (taking the initial representative particle as an example): The load distribution corresponding to this particle is: L PSO =X i ×L total =[120,127.5,112.5,120,135,135] Load balancing degree: f balance (X i )=σ(L PSO ) / L average =8.37 / 125=0.067 Assume that the power quality evaluation is: f quality (X i )=0.15-Assuming system efficiency evaluation: fefficiency (X i )=0.08 Comprehensive fitness: f(X i )=0.6×0.067+0.3×0.15+0.1×0.08=0.0402+0.045+0.008=0.0932 3. Iterative optimization: The best particle at the 100th iteration: X best =[0.156,0.173,0.165,0.158,0.184,0.164] The corresponding load distribution is: L PSO =X best ×L total =[117,129.75,123.75,118.5,138,123] Final fitness: f(X best )=0.047 (significantly lower than the initial value of 0.0932) 4. Particle swarm optimization output results: Module 1: 117kW (reduced by 3kW) Module 2: 129.75kW (reduced by 50.25kW) Module 3: 123.75kW (increase of 33.75kW) Module 4: 118.5kW (increase of 8.5kW) Module 5: 138kW (72kW reduction) Module 6: 123kW (increase of 83kW) Load migration of module 5: ΔL PSO =210-138=72kW Two-layer optimization control output fusion 1. Fusion of two-layer control results (taking module 5 as an example): F timing_final =W fuzzy (t)×F timingfuzzy +W PSO (t)×F timingPSO =0.233×0.98+0.767×1.0=0.228+0.767=0.995 Actual migration amount of module 5: ΔL_final=W fuzzy (t)×ΔL fuzzy +W PSO (t)×ΔL PSO=0.233×51.85+0.767×72=12.08+55.22=67.3kW 2. Migration priority calculation: P migration_i =γ1L currenti +γ2Var(L i )+γ3(1-H i )-P migration_1 =0.6×120+0.3×15+0.1×(1-0.95)=72+4.5+0.005=76.505 P migration_2 =0.6×180+0.3×25+0.1×(1-0.90)=108+7.5+0.01=115.51 P migration_3 =0.6×90+0.3×20+0.1×(1-0.85)=54+6+0.015=60.015 P migration_4 =0.6×110+0.3×10+0.1×(1-0.98)=66+3+0.002=69.002 P migration_5 =0.6×210+0.3×35+0.1×(1-0.92)=126+10.5+0.008=136.508 P migration_6 =0.6×40+0.3×5+0.1×(1-1.00)=24+1.5+0=25.5 3. Based on the migration priority, determine the source module and target module: Source modules: Module 5 > Module 2 > Module 1 > Module 4 > Module 3 > Module 6 Target module: Module 6 < Module 3 < Module 4 < Module 1 < Module 2 < Module 5 4. Determine the migration path and load: 67.3kW migrated from module 5, distributed to module 6 (40kW) and module 3 (27.3kW) Migrate 50.25kW from module 2 and distribute to modules 3 (23kW) and 4 (27.25kW) 5. Final result of double-layer optimization control: Module 1: 120kW (unchanged) Module 2: 180-50.25=129.75kW Module 3: 90+27.3+23=140.3kW Module 4: 110+27.25=137.25kW Module 5: 210-67.3=142.7kW - Module 6: 40+40=80kW 6. System parameters after double-layer optimization control adjustment: Adjusted standard deviation: σ(L current _new)=22.06kW Adjusted load imbalance: D imbalance _new=22.06 / 125=0.176 Summary and analysis: 1. Load balancing: The two-layer optimization control system performs best in load balancing, with the load imbalance reduced from the initial 0.465 to 0.176, which is better than the dynamic balance system (0.311) and the predictive system (0.342). The static system is completely unbalanced, resulting in overload of a single module.

[0137] 2. Reasonable load distribution: The two-layer optimization control system takes into account the module characteristics and the overall system efficiency. The load of each module is relatively even and within the safe range. The maximum load rate is only 62.0%, which is much lower than other systems, providing the system with sufficient load margin to deal with emergencies.

[0138] 3. Intelligent decision-making ability: The two-layer optimization control system combines fuzzy rules and particle swarm optimization to more intelligently consider multiple factors such as load characteristics, ambient temperature and equipment health status, making decisions more comprehensive and reasonable. For example, considering the particularity of air-conditioning load under high temperature (35℃) in summer, the load distribution of module 5 is reasonably adjusted.

[0139] 4. Ability to adapt to dynamic changes: The two-layer optimization control system can dynamically adjust the control strategy according to the load change rate and system status. The adaptive weight function enables the system to flexibly switch the control strategy when the system status changes, and its adaptability far exceeds that of traditional systems.

[0140] 5. Computational resource balance: Although the computational complexity of the two-layer optimization control system is higher than that of the basic system, the system can select the appropriate computing intensity in different situations through the adaptive weight mechanism, balancing computing resources and optimization effects. In this case, due to the high system load imbalance (0.289 close to the threshold of 0.3), the particle swarm optimization result (weight 0.767) was used first, and a solution closer to the optimal solution was obtained.

[0141] 6. Reasonableness of migration strategy: The dual-layer optimization control system takes into account the current load, load fluctuation and equipment health status through a multi-level load migration priority mechanism, making the migration decision more targeted. Although the total migration volume (117.55kW) is greater than other systems, the migration effect is better and the system is more balanced.

[0142] Summary: The two-layer optimization control system realizes the intelligent management of multi-modular electric meter loads by combining the particle swarm optimization system and fuzzy logic control technology. It has advantages over traditional basic systems, especially in load balancing, intelligent decision-making and system adaptability. The system can dynamically adjust the control strategy according to the actual situation, which not only ensures the optimization effect but also takes into account the computing efficiency, and provides an advanced management solution for the complex and changeable power load environment.

Claims

1. A multi-modular electric meter power management system for adaptive load management, characterized in that: The system includes: an electric meter module, a load monitoring unit, a data storage unit, and a double-layer optimization control unit, wherein the double-layer optimization control unit includes a particle swarm optimization layer and a fuzzy logic control layer, wherein: The load monitoring unit is used to collect the load level of each meter module in real time. , where i represents the number of the meter module; The data storage unit is used to store historical load data, user power usage patterns and environmental parameters; The particle swarm optimization layer is used to search for the optimal load distribution solution in the multidimensional decision space by iteratively updating the particle position vector X and the velocity vector V, where X represents the target load distribution ratio of each module and V represents the load distribution adjustment rate; The fuzzy logic control layer is used to take the load state and environmental factors as input variables and generate load migration decisions through fuzzy rule reasoning, including the migration timing judgment function F timing And migration calculation function F amount ; The two-layer optimization control unit combines the search capability of the particle swarm optimization layer with the expert knowledge reasoning capability of the fuzzy logic control layer, and dynamically adjusts the weight ratio of the two-layer control output through the adaptive weight function W(t), thereby overcoming the disadvantages of the particle swarm system being prone to falling into local optimality and the disadvantages of the fuzzy logic control lacking optimization capability, and realizing dynamic balanced distribution of the load.

2. The multi-modular electric meter power management system for adaptive load management according to claim 1, characterized in that: The particle swarm optimization layer adopts an improved particle swarm optimization system, including: Adaptive inertia weight factor w(t), through the function Calculate, where t represents the current iteration number, T max represents the maximum number of iterations, w max and w min are the upper and lower limits of inertia weight respectively, α is the nonlinear adjustment factor; the particle position update formula is: ; Particle velocity update formula: ,in represents the best historical position of particle i, G best represents the best position found by the group, c1 and c2 are acceleration constants, r1 and r2 are random numbers in the interval [0,1]; The fitness function f(X) takes into account load balance, power quality and system efficiency, and is expressed as , where β i is the weight coefficient of each sub-goal.

3. The multi-modular electric meter power management system for adaptive load management according to claim 1, characterized in that: The fuzzy logic control layer includes: input fuzzification unit, the current load level of the meter module , Load change rate , System load imbalance D imbalance and ambient temperature T are converted into fuzzy sets; Fuzzy rule base, which contains a set of IF-THEN rules formed by expert knowledge, used to deal with the complexity and uncertainty of load migration decisions; Fuzzy reasoning mechanism, using Mamdani reasoning method to execute fuzzy rules; Output defuzzification unit, converting the fuzzy reasoning result into a clear load migration timing judgment value F timing and the calculated migration value F amount .

4. The multi-modular electric meter power management system for adaptive load management according to claim 1, characterized in that: The adaptive weight function W(t) dynamically adjusts the output weights of the particle swarm optimization layer and the fuzzy logic control layer according to the system operation status, specifically: When the system is in a stable state, W fuzzy (t)>W PSO (t), the output of the fuzzy logic control layer is preferentially adopted to reduce computational overhead; When the system load fluctuates greatly or the imbalance exceeds the preset threshold, W PSO (t)>W fuzzy (t), the output of the particle swarm optimization layer is preferentially used to obtain the optimal solution; W fuzzy (t)+W PSO (t)=1, ensuring weight normalization.

5. The multi-modular electric meter power management system for adaptive load management according to claim 1, characterized in that: The system also includes a load prediction unit, which predicts the load trend of future periods based on historical load data and calculates the future load using a recursive neural network model. , where k is the prediction step size, and the prediction results are used as the input of the particle swarm optimization layer and the fuzzy logic control layer to improve the prediction of decision making.

6. The multi-modular electric meter power management system for adaptive load management according to claim 2, characterized in that: The load balancing evaluation subfunction f in the fitness function balance (X) is calculated as the ratio of the standard deviation of each module's load level to the average load: , where σ(L current ) represents the standard deviation of the current load of each module, L average Indicates the average load level. The smaller the value of this sub-function is, the more balanced the load distribution is.

7. The multi-modular electric meter power management system for adaptive load management according to claim 3, characterized in that: The fuzzy rule base contains professional rules for different load scenarios, including: A ruleset for peak load scenarios for fast response and load spreading; A set of rules for valley load scenarios for equipment efficiency and energy conservation; A set of rules for fast-fluctuating load scenarios for system stability and buffering capabilities; For the rule set with seasonal changes, the load distribution strategy is dynamically adjusted according to the ambient temperature T and historical data for the same period.

8. The multi-modular electric meter power management system for adaptive load management according to claim 1, characterized in that: The system sets up a multi-level load migration priority mechanism, which comprehensively considers the current load level of the meter module. , Historical load fluctuation range and equipment health index H i Determine the source module and target module for load migration, and prioritize migrating load from high-load and healthy modules to low-load modules. The migration priority calculation formula is: , where γ1, γ2, and γ3 are weight coefficients.

9. The multi-modular electric meter power management system for adaptive load management according to claim 1, characterized in that: The system sets up a collaborative learning mechanism of particle swarm optimization system and fuzzy logic control, which is specifically manifested as follows: The search results of the particle swarm optimization system are used to dynamically adjust the membership function parameters of the fuzzy rules; The expert knowledge of fuzzy control is used to guide the initialization and mutation operations of the particle swarm; Through collaborative learning, the system can simultaneously overcome the disadvantages of slow convergence of particle swarm systems and difficult design of fuzzy logic rule bases.

10. The multi-modular electric meter power management system for adaptive load management according to claim 1, characterized in that: The system also includes an emergency response module. When an abnormality in the power grid or a sudden load change is detected, the system bypasses the conventional decision-making process and directly adopts a preset emergency load distribution strategy to ensure the robustness and safety of the system in extreme situations. The emergency response trigger conditions are: , where ε is the load imbalance threshold and η is the load change rate threshold.

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