588 results about "Smart meter" patented technology

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention provides a fault detection method and system based on an intelligent electric meter, and relates to the technical field of fault detection and diagnosis of the intelligent electric meter, and the key points of the technical scheme are that three-dimensional data are obtained, and the three-dimensional data comprise intelligent electric meter data, environment perception data, power grid topology data and line load data; predicting a normal data range of the next one minute in real time according to the three-dimensional data, and judging whether actual data exceed the normal data range or not; when the actual data exceed the normal data range, uploading all the data to a cloud end to analyze and detect faults; and collecting fault data in real time and generating a fault early warning list. The fault detection method and system based on the intelligent electric meter have the advantages that the fault false alarm rate is reduced, the fault detection response efficiency is enhanced, and fault risk early warning is achieved.

The invention discloses a non-intrusive household load identification method based on CUSUM event detection and a convolutional neural network, which is applied to total load data output by an intelligent electric meter installed at a user total incoming line, and comprises the following steps: data preprocessing; carrying out load event detection based on a sliding window bilateral CUSUM algorithm so as to determine an occurrence moment and a continuous interval of a load event; in a continuous interval corresponding to each load event, calculating an active power variation delta P, a reactive power variation delta Q, a current harmonic distortion variation delta Ihd and a current span Itc in steady-state data segments before and after the load event, and combining to form a multi-dimensional event feature vector; the method comprises the following steps: sequentially executing the steps on operation data of each electric device in a family in different operation modes to obtain a multi-dimensional event feature vector marked with a device type and an operation state label, and constructing a standardized device load feature database; and training a convolutional neural network classification model and realizing load identification.

The invention relates to the technical field of ammeter fault detection, and provides an intelligent ammeter series arc fault detection method, system and device, a medium and equipment, and the method comprises the steps: firstly obtaining an ammeter data set of an intelligent ammeter, which comprises a pre-ammeter voltage, a post-ammeter voltage and a total loop current in a continuous power frequency period, and then carrying out the division through a sliding time window, obtaining a plurality of ammeter data windows; for each window, calculating a voltage difference sequence and a dynamic loss impedance sequence; when the absolute value of the total loop current is smaller than a preset threshold value, determining the current zero-break interval of each power frequency period and the current zero-break time corresponding to the window; determining an arc voltage estimation value according to a voltage difference between non-zero-break intervals in the zero-break interval; and finally, integrating the current zero break time, the arc voltage estimated value and the dynamic loss impedance of each window to judge whether a series arc fault exists, and if so, triggering a protection action. According to the embodiment of the invention, high-reliability identification of the series arc fault is realized.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention discloses a metering performance evaluation method and system for an intelligent electric energy meter, a medium and a product, and the method comprises the steps: employing a standard meter for synchronous operation for a to-be-evaluated intelligent electric energy meter, so as to obtain a field test value of a basic error of the intelligent electric energy meter, inputting the environment detection data and the operation time of the intelligent electric energy meter to be evaluated into the trained basic error prediction model corresponding to the model of the intelligent electric energy meter to be evaluated so as to obtain a model prediction value of a basic error; the basic error prediction model comprises an input layer, a one-dimensional CNN layer, an LSTM layer fused with a time attention mechanism, a full connection layer and a basic error output layer, and the metering performance level of the intelligent electric energy meter is determined according to a field test value and a model prediction value of a basic error. The invention aims to realize metering performance evaluation of the intelligent electric energy meter in a typical environment so as to realize higher operation efficiency and solve the bottleneck problem of long-term dependence on modeling and local feature extraction.

The invention relates to the technical field of power distribution network bidirectional power flow coordination control, and discloses a power distribution network bidirectional power flow coordination control method based on V2G aggregation, and the method comprises the following steps: S1, constructing a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharge; s2, aggregating the logic energy storage units into virtual power plant nodes which can be dispatched by a power grid through an information communication technology (ICT); and S3, bidirectional power flow sensing and real-time state estimation: utilizing an intelligent electric meter, a sensor and a topology identification algorithm. According to the method, a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharging is constructed, the logic energy storage unit is aggregated into a virtual power plant node capable of being dispatched by a power grid through an information communication technology (ICT), and specific charging and discharging actions are decomposed and executed, so that dispersed V2G resources can be effectively integrated, and the V2G resources can be effectively integrated. And collaborative optimization with operation states such as voltage and power flow of the power distribution network is carried out.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention provides an intelligent electric meter HPLC communication module dynamic calibration method suitable for environmental disturbance, and relates to the technical field of dynamic calibration, and the method comprises the steps: collecting multi-source data, collecting time sequence communication data of a communication module in real time, and carrying out the analysis to obtain a communication index. Initial calibration parameters are formulated, dynamic adjustment parameters are calculated, and the initial calibration parameters are adjusted according to the dynamic adjustment parameters to obtain real-time operation parameters. And judging whether the current communication index reaches a preset communication threshold, if so, maintaining the real-time operation parameter, and otherwise, optimizing the dynamic adjustment parameter. And analyzing the environmental disturbance data and the power line channel characteristic data to obtain associated influence data, carrying out feature extraction on the current communication index to obtain a communication abnormal feature, and adjusting the dynamic adjustment parameter in combination with the associated influence data to obtain a calibration correction parameter. According to the invention, intelligence and automation of communication module calibration are realized, manual intervention and experience dependence are reduced, and calibration efficiency and accuracy are improved.

The invention provides a power distribution network multi-source cooperative scheduling method and system based on big data, relates to the field of power distribution network scheduling, and solves the technical problem that an optimization strategy is not flexible enough in the prior art. The method comprises the following steps: acquiring multi-source real-time data of a power distribution network through an Internet of Things terminal and an intelligent electric meter; based on the multi-source real-time data, predicting a behavior mode of the electric vehicle and an output value of renewable energy by adopting a machine learning algorithm, and estimating a charging state of the electric vehicle by using a probability SOC prediction model; the behavior pattern is used for representing a travel rule of the user; generating a multi-time-scale scheduling strategy based on the behavior pattern, the output value of the renewable energy source and the charging state by adopting a collaborative optimization algorithm; and testing the scheduling strategy based on a simulation environment constructed by a digital twinning technology, and issuing a control signal based on a test result.

The invention discloses a data anomaly detection method and device for a low-voltage power distribution network, a medium and equipment, and belongs to the field of anomaly detection. Inputting the data into a preset LSTMAE-GRU knowledge distillation model to carry out anomaly detection, and outputting an anomaly detection result of each time step; the model is obtained by taking normal measurement time sequence data covering load characteristics of multiple courts as a training sample, extracting high-dimensional time sequence characteristics based on an initial LSTMAE teacher model, aligning teacher model characteristic distribution through latent variable projection by an initial GRU student model, and combining distillation loss training formed by KL divergence and mean square error. The problem that the abnormal data of the low-voltage power distribution network cannot be accurately and efficiently detected in the prior art is effectively solved.

The embodiment of the invention discloses a malignant load identification method, device and equipment, a medium and a program product, and relates to the technical field of power load monitoring. The method comprises the following steps: performing modal decomposition on an original power utilization sequential sequence to obtain a plurality of intrinsic mode components, and reconstructing intrinsic mode components which do not belong to noise components to obtain a target power utilization sequential sequence; performing feature extraction on the target power consumption time sequence to obtain target power consumption features, and inputting the target power consumption features into a pre-trained malignant load identification model for identification to obtain an identification result; the malignant load identification model is obtained by updating model parameters of a weak learner based on a natural gradient descent method and performing training optimization. The lightweight malignant load learning model obtained through training in the scheme can be deployed and operated on the intelligent electric meter, high-quality input features are obtained through multi-mode decomposition and reconstruction, the accuracy of malignant load recognition is improved, and accurate recognition of the malignant load based on the lightweight model is achieved.

The invention relates to the technical field of electric meters, in particular to an internet of things intelligent electric meter supporting edge computing, which comprises an intelligent electric meter shell and a circuit board arranged in the intelligent electric meter shell, and further comprises an elastic mounting system which comprises a plurality of clamping seats fixed on the inner wall of the intelligent electric meter shell and a plurality of clamping heads fixed on the edge of the circuit board, a bidirectional buffer mechanism is arranged between the clamping head and the clamping seat, the bidirectional buffer mechanism allows the circuit board to perform multi-degree-of-freedom micro motion in a vibration environment so as to buffer impact, the heat exchange and dehumidification system comprises a heat conduction copper rod penetrating through the clamping seat, one end of the heat conduction copper rod is thermally connected with the bidirectional buffer mechanism, and the other end of the heat conduction copper rod is provided with heat dissipation fins. The heat conduction copper rod is sleeved with a moisture absorption filter element and a breathing heat dissipation system, the breathing heat dissipation system comprises a breathing air shell fixed in the intelligent electric meter shell, a suction groove is formed in the breathing air shell, an intermittent air exchange mechanism is arranged in the breathing air shell, and the air exchange mechanism can periodically suck external cooling air into the shell and then discharge the air out of the shell to blow the circuit board.

The invention is suitable for the technical field of smart power grids, and relates to a low-voltage transformer area undisturbed topology identification method and system, a medium and a terminal, and the method comprises the steps: S10, cleaning and screening original communication and measurement data; s20, the RSSI between the devices is converted into the distance between the nodes, and an undirected weighted graph is formed; s30, determining an affiliation relationship between the meter box and the electric meter by using a clustering algorithm; s40, performing constraint matching on the transformer, the line and the meter box node according to the power utilization information, and optimizing the topological structure by using the law of conservation of energy and the Kirchhoff's current law; and S50, evaluating the accuracy of the identification result by using the adjusted Lam index and the normalized mutual information. The method is simple in process and convenient to operate, a complete topological structure is constructed step by step on the premise of not adding extra hardware, uninterruptible power and not injecting characteristic signals by relying on electric power data of an existing intelligent electric meter, a communication signal and an electricity utilization information acquisition system, and high-precision and automatic identification of the topological structure of the transformer area is achieved.

The invention discloses an automatic voice response fault diagnosis method and system based on multi-source information fusion. The method comprises the following steps: receiving user voice input, synchronously obtaining user side intelligent electric meter data, meteorological environment information and historical service records, and extracting multi-modal features; semantic association is realized through an electric power service knowledge graph, and voice features, equipment data and environmental parameters are fused through space-time alignment; a machine learning dynamic decision tree engine is combined with a power consumption behavior analysis model to form a fault reasoning model, and a diagnosis result is effectively verified; and outputting the grading disposal scheme and triggering intelligent chemical order distribution. According to the invention, various information sources of the calling platform are fully utilized, a fault reasoning model with high accuracy is formed, the technical problems of inaccurate fault positioning and insufficient multi-source data collaboration of traditional voice response in power customer service are effectively solved, the diagnosis accuracy of customer power consumption problems is effectively improved, and the average processing time is shortened.

The invention discloses a multi-task NILM low-voltage transformer area energy management system, and aims to solve the problems that a traditional low-voltage transformer area energy management system (EMS) is low in load identification precision and does not give consideration to operation cost and user satisfaction. According to the system, firstly, through an NILM module based on a multi-task recurrent neural network (taking a GRU as a core), power utilization states and power consumption of various electric appliances are decomposed from total load data of an intelligent electric meter, and a user power utilization behavior portrait is constructed; and taking the portrait as an input, and carrying out cooperative scheduling on photovoltaic, energy storage and controllable loads in a transformer area through a multi-objective optimization model considering system operation cost and user satisfaction. Experimental verification shows that compared with a traditional EMS, the operation cost of the system is reduced by 32.59%, the user satisfaction degree is improved by 65.89%, the load identification precision of an NILM module is remarkably superior to that of CNN, LSTM and a single-task GRU model (MAE is as low as 1.574 W, and the F1 score is as high as 0.973), and intelligent and economical operation of a low-voltage transformer area can be effectively promoted.

The invention relates to the technical field of power grid data privacy protection, in particular to a smart power grid data privacy protection system and method based on a homomorphic encryption technology. The system protects data privacy of an intelligent power grid based on a homomorphic encryption technology, and comprises eight modules: a data acquisition module is connected with an intelligent electric meter in a multi-protocol manner in a 15-minute period to adopt electric data; the data filtering and preprocessing module optimizes data quality; the homomorphic encryption module generates a ciphertext capable of being added and multiplied by using an FHE algorithm; the multi-level key management module manages multiple types of keys in the whole life cycle; the secure transmission module transmits the ciphertext by means of a hybrid protocol; the data analysis module decrypts and analyzes the ciphertext in the data center; the decryption module decrypts after authorization; and the auditing and monitoring module records operation and monitors abnormity. According to the invention, it is ensured that the power use data is kept in an encrypted state in the whole life cycle of collection, transmission and analysis, the data is available and invisible, and the data privacy protection level and quantum attack resistance of the smart grid are improved.

The invention discloses a power grid time frequency change trend prediction method and system based on intelligent electric meter data fusion, and the method comprises the steps: measuring the network transmission delay and time deviation between a master clock and a slave clock of a power grid, obtaining an initial time frequency deviation sequence, carrying out the error calibration, carrying out the data fusion after time domain alignment, and carrying out the prediction of the time frequency change trend. Generating a fusion time sequence; performing recursive estimation on the fusion time sequence, outputting an optimal time frequency deviation sequence, and performing synchronous estimation to obtain a clock phase error, a frequency error and frequency drift as state characteristic quantities; and training a preset machine learning prediction model by taking the optimal time frequency deviation sequence as a supervision signal and taking the state characteristic quantity of the corresponding moment as input, and obtaining a prediction result of the power grid time frequency change trend in the future period based on the trained time frequency change trend prediction model. According to the method, the power grid time synchronization precision and prediction capability are remarkably improved, and reliable technical guarantee is provided for stable operation of a power system.

The invention discloses an electric arc dynamic suppression method for hot plugging of an electric meter connector, relates to the technical field of electric energy metering equipment, and aims to solve the problems of electric arc generation, overproof metering error and the like caused by passive protection and lack of dynamic cooperative control in the prior art. The core of the method is that gap distance and power grid voltage phase signals are acquired in real time through a microcontroller, and the plugging speed, the signal injection and the position fine adjustment are dynamically regulated and controlled in three stages of pre-contact, low-current transition and full connection; and a machine learning model is combined to optimize a parameter threshold value, a redundancy safety mechanism and a multi-phase electric meter cooperative control strategy, so that double management and control of arc peak energy and metering errors are realized. The method can actively inhibit arc generation, avoid connector ablation and insulation deterioration, guarantee metering continuity and accuracy, adapt to complex power grid and environment working conditions, and meet the precision requirement of the intelligent electric meter.

The invention discloses a distributed photovoltaic decomposition method and system based on intelligent electric meter data, and relates to the technical field of non-intrusive power load monitoring, and the method comprises the steps: constructing a data set, and carrying out the normalization processing of the data set, and obtaining the input data of model training, verification and testing; constructing a teacher neural network structure and a student neural network structure, and performing supervised training and verification; setting hyper-parameters of a knowledge distillation algorithm, calculating difference loss, and retraining to obtain a student photovoltaic power decomposition model; and inputting the total power signal data of the target load equipment into the student photovoltaic power decomposition model to obtain photovoltaic power generation equipment power data, and performing evaluation. According to the invention, high-precision analysis of the photovoltaic power generation power decomposed from the total power signal is realized. The teacher model can learn a complex nonlinear mapping relationship, and the student model learns the decomposition capability of the teacher model through a knowledge distillation process, so that the model complexity is greatly reduced while the higher precision is maintained.

The invention discloses a behavior baseline-based ammeter state monitoring method, system, equipment and medium, and relates to the technical field of equipment behavior anomaly detection, and the method comprises the steps: collecting operation parameter time sequence data of an intelligent ammeter in a preset monitoring region during operation, carrying out the combined processing of the operation parameter time sequence data, extracting the multi-dimensional features of the region, and carrying out the detection of the behavior baseline; based on regional multi-dimensional features in a historical normal state, a regional behavior baseline is established through unsupervised learning, regional multi-dimensional features at a current monitoring moment are acquired, a regional consistency index is generated through matching degree calculation with the regional behavior baseline, and an abnormal ammeter is identified according to an individual deviation degree. And based on the region consistency index and a preset period, triggering update maintenance of the region behavior baseline. According to the method, visual visualization and a model self-evolution mechanism are combined, and the state of the industrial electric meter is changed from passive warning to active, accurate and large-scale predictive intelligent operation and maintenance.

The invention relates to the technical field of electric power measurement, in particular to a novel intelligent electric meter box electricity larceny prevention system and an application method. The electricity larceny prevention system comprises a microcontroller, a data transmission module, a data storage module, a camera, a box door detection switch, various sensors, an electricity larceny detection and remote control alarm module, an identity recognition module, a circuit breaker module and a strong magnetic and strong electric shock detection module. The application method comprises the following steps: starting and self-checking the system; judging and processing a self-checking result; real-time monitoring and event monitoring are carried out; an abnormal event response process; a remote instruction response process; recovering monitoring; the electric energy metering box is economical, practical, safe and reliable, real-time monitoring, electricity stealing behavior detection and alarm can be carried out on the electric energy metering box, remote control over the electric energy metering box can be further achieved, the identity of a box opening person can be effectively recognized, and protection over the metering box is achieved.

The invention discloses a visual inspection sorting method and system for surface defects of intelligent electric meter shell injection molded parts, and relates to the technical field of visual inspection, and the method comprises the following steps: selecting an intelligent electric meter shell as a reference part, dividing the surface of the shell into a plurality of functional partitions, collecting structured light form and brightness information, and obtaining a reference appearance map; and positioning a to-be-detected electric meter shell to a position consistent with the reference part, obtaining the online three-dimensional form and brightness response of each functional zone, mapping the online three-dimensional form and brightness response to the reference appearance map, marking a stable mirror surface with high brightness, and interpolating along an observation track to generate an online appearance map. According to the invention, through functional partition sampling layout of a window sealing area, a wiring terminal area, a stud and reinforcing rib area, a nameplate and two-dimensional code area and a general decoration side wall area, and in cooperation with double-light-source collection of diffused light and oblique incident light combined with structured light, a reference appearance map containing a highlight tolerance mask is constructed; stable characterization of mirror reflection and a transparent window is realized, and false detection caused by high brightness of the mirror surface is remarkably reduced.

The invention relates to the technical field of power retail, in particular to a power retail package recommendation method and system, and the method comprises the steps: obtaining user historical power consumption data through an intelligent electric meter or a power data collection terminal, and constructing user power consumption sample data after denoising and abnormal value processing; and in combination with the user attribute data and the power retail package attribute data, adding a user power consumption feature tag for the sample data. And training a package recommendation model by using the power consumption sample data and the feature tags. And acquiring real-time power consumption data and user attribute data of the current user as model input, calculating a recommendation score of each power retail package through a model, and pushing the package with the highest score to a user terminal as a recommendation result. According to the method, personalized recommendation is realized through data driving, the power retail service precision is improved, and the user experience is optimized.

Techniques for smart electricity meter operation and management of open-switch events are described. In an example, a voltage value of a transformer-side of a service site is measured. Power consumption at the service site is measured. Based at least in part on the voltage value being non-zero and the power consumption being zero, it is determined that an open-switch event occurred. That is, because the utility company is supplying typical voltage to the electricity meter, and because the power consumption is zero, a breaker switch may have tripped. Accordingly, a customer of the service site is notified of the open-switch event.

The invention relates to the technical field of power system energy storage equipment testing, in particular to a small and medium-sized industrial and commercial energy storage cabinet twin-trawling testing system which comprises a main loop, a secondary control loop and an anti-countercurrent detection unit, the main loop comprises double testing loops and four sets of alternating current contactors, and multi-machine twin-trawling testing frameworks such as one-to-one, one-trawling-two and one-trawling-three can be flexibly combined; independent switching between grid connection and grid disconnection is supported, the secondary control loop comprises a button with a lamp and a grouping reset button, and the countercurrent prevention detection unit detects the energy flow direction in real time through an intelligent electric meter and a current sensor and has a mains supply charging function. According to the test system, through a double-loop parallel test architecture and a multi-machine flexible combination design, the system can be synchronously butted with multiple energy storage cabinets to carry out parallel test, the test efficiency can be improved, and a safety system with anti-countercurrent detection, rapid mode switching and precise control protection guarantees the test safety; the arrangement of the standardized terminal and the button with the lamp simplifies the operation difficulty, and reduces the test threshold.

The invention discloses a virtual electric energy meter implementation method supporting multiple protocols, and belongs to the technical field of power equipment testing. The method comprises the following steps: (1) a protocol plug-in engine packages protocols such as DL / T645, DL / T698, ModBus and the like into source code level plug-ins capable of being hot-plugged; 2) the virtual table manager dynamically creates / destroys a virtual table instance according to a collection command, and supports automatic table number generation and resource recovery; the intelligent configuration unit (3) is specially used for configuring abnormal response modes including negative acknowledgement, null data and communication timeout to realize dynamic fault simulation; according to the scheme, the problems that real faults are difficult to simulate, protocol switching efficiency is low, virtual table configuration is rigid and the like in a traditional test are solved, the protocol compatibility test coverage rate and automatic test efficiency of the electric power acquisition terminal can be remarkably improved, and the method is particularly suitable for full-scene verification of equipment such as an intelligent electric meter and a concentrator.

The invention discloses an intelligent electric energy meter degradation prediction method and system based on pre-training. The method comprises the steps of obtaining operation state data X of an intelligent electric energy meter in a target typical environment; the operation state data X comprises a part or all of an environment stress parameter, data acquisition time, a load power level of the intelligent electric energy meter, a load power factor and a performance reduction index; dividing the running state data X into a plurality of slices and constructing a slice sequence Xp; and obtaining a degradation prediction result of the intelligent electric energy meter by using a pre-trained degradation trend prediction model DeGra-PTM on the slice sequence Xp, wherein the degradation trend prediction model DeGra-PTM is pre-trained to establish a mapping relation between the input slice sequence Xp and the output basic error. The method aims to improve the generalization ability and training efficiency of the prediction model, and achieves the accurate prediction of the degradation trend of the intelligent electric energy meter in the high dry heat environment at a lower training cost.

The invention relates to a port area illumination energy saving and visual performance comprehensive evaluation method based on multi-objective optimization, and the method specifically comprises the following steps: deploying an illumination sensor and an intelligent electric meter at a key position of a port area, and collecting illumination and energy consumption parameters to form a training data set containing a performance label; defining a sensitivity coefficient and a normalization index for each data feature and completing normalization calculation; constructing a comprehensive evaluation model, inputting a normalized value, and then realizing classification prediction through a non-linear interaction kernel illumination-energy consumption feature fusion enhancement module and a double-branch attention feature extraction deep neural network backbone module; fusing multiple constraints to construct a composite multi-objective loss function to calculate model loss; iteratively optimizing the model by adopting a gradient descent algorithm in combination with the training data set until a stop condition is met; and normalizing new data, inputting the normalized new data into the trained model, and selecting the highest probability category as an evaluation result. According to the invention, the energy-saving and visual performance of harbor lighting can be accurately balanced, and effective guidance is provided for optimization of a harbor lighting system.

The invention discloses a line loss positioning method based on phase feature clustering, belongs to the technical field of power system line loss treatment, and constructs a three-dimensional feature space through high-frequency collection of HPLC carrier signal phase offset, current ripple frequency spectrum features and intelligent electric meter load rate to comprehensively characterize the equipment operation state. An improved DBSCAN clustering algorithm is utilized, the neighborhood radius is dynamically calculated according to the feature space density, the minimum sample number MinPts is adaptively set according to the transformer area equipment scale, and clustering parameter adaptation is achieved; meanwhile, a noise filtering mechanism is introduced, secondary verification is carried out on initial clustering noise points, effective abnormal points are screened out by calculating local density and overall average density, and the misjudgment rate is reduced. The method can quickly and accurately position the position of the line loss abnormal equipment, greatly improves the line loss abnormal processing efficiency, can be widely applied to power distribution network line loss management, effectively improves the line loss analysis accuracy and treatment efficiency, and provides powerful technical support for reducing the power grid loss and improving the power supply reliability.