237 results about "Power user" patented technology

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention belongs to the technical field of power system load prediction, and discloses a cross-dimension multi-scale fusion load prediction method based on multi-user load time-space correlation, which comprises the following steps of: firstly, preprocessing user load statistical data, extracting time sequence dependence and periodic characteristics in a time sequence, and calculating the time sequence dependence and periodic characteristics of the user load statistical data; introducing a channel attention mechanism to adaptively mine key variable information; then, a multi-scale space-time fusion module is combined with frequency domain analysis and a graph convolutional network to realize depth feature interaction under different time scales and space levels; and finally, outputting a load prediction result under a plurality of time granularities in the future through a linear projection structure. Compared with an existing method, the method has the remarkable advantages in the aspects of capturing a complex load mode, improving model prediction precision and enhancing generalization ability, and is suitable for various application scenes such as power consumer energy consumption management and power grid load dispatching.

The invention relates to an important user-oriented power failure event digital management method, which comprises the following steps of S1, acquiring basic user data, and constructing a power user knowledge graph; s2, training a user importance scoring model, performing user automatic grading by applying a clustering algorithm, and constructing a user digital twinborn model; s3, constructing an equipment data acquisition network; s4, according to the equipment data acquired by the equipment data acquisition network, identifying a potential fault based on the time sequence prediction model; and S5, constructing a power failure propagation model based on a graph neural network, predicting a fault propagation path according to a potential fault identified by a time sequence prediction model, and combining a user digital twinning model to realize digital twinning of the real-time state of the power grid, and visually displaying the health state of the power grid. And S6, generating risk early warning in a customized manner for different levels of important users according to the predicted fault spreading path. The intelligent level of power failure management is improved, and the power supply reliability of important users is remarkably improved.

The invention discloses an electric power big data privacy protection method and system based on federated learning, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a historical session record set of an electric power user, carrying out the standardization preprocessing of the historical session record set, and obtaining a standardized session data set, extracting a time sequence feature set from the standardized session data set, calling a pre-configured federal learning framework to carry out privacy protection processing on the time sequence feature set, generating a privacy enhancement feature set, generating a power data privacy protection result based on the privacy enhancement feature set, and sending the power data privacy protection result to the server. And feeding back the power data privacy protection result to the power service system. According to the method, the privacy security in the data use process is guaranteed, the utilization value of the power big data in the unstructured session scene is improved, and the problem of adaptability of a traditional power privacy protection technology in unstructured session data processing is effectively solved.

The invention discloses a dynamically updated multi-source heterogeneous power consumer portrait method and system, and relates to the technical field of power consumer portrait generation, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing of the collected data, carrying out the feature splicing and weight adjustment of the preprocessed multi-source data, and carrying out the feature splicing and weight adjustment of the preprocessed multi-source data; according to the method, features of different data sources are mapped to the same embedding space, dimensionality reduction and noise filtering are carried out on fused high-dimensional features, user features are extracted, time sequence modeling is carried out, a dynamic user portrait is generated based on core features after dimensionality reduction and a time sequence modeling result, and the user portrait is updated in real time. And user portrait feature parameters are optimized according to the user behavior feedback, and the change and the dynamic state of the user portrait are displayed. According to the method, the change and dynamic state of the user portrait are displayed through the chart, more personalized services are provided for an electric power company, and environmental protection and improvement of energy efficiency are promoted at the same time.

The invention relates to the technical field of power loads, and discloses a power consumer daily load data missing value complementing method, which comprises the following steps: acquiring meteorological data and date information data of each consumer in the same consumer aggregation class, integrating the meteorological data of each user, the date information data, the historical power consumption data of the user and the feature vector data to form training data set data corresponding to a user aggregation class; according to the training data set data corresponding to the same user aggregation class, training the data completion model corresponding to the user aggregation class, and after training is completed, outputting the corresponding data completion model and associating the data completion model with the corresponding user aggregation class; and inputting the meteorological information data, the date information data and the feature vector data of a certain user in the user aggregation class into a data completion model associated with the user aggregation class, outputting a missing value of the user, and filling the missing value into the historical power consumption data of the user to form the filled historical power consumption data.

The invention relates to the technical field of electric power, in particular to a line loss abnormity diagnosis method based on electric quantity fluctuation analysis. The method comprises the following steps: acquiring an electricity user and electricity data; calculating a power supply quantity change rate and a power consumption change rate according to the daily power supply quantity of the transformer area and the fluctuation degree of the daily power consumption of the transformer area, and further judging a line loss abnormity type; if the line loss abnormity type is user side abnormity, screening the electricity users, and obtaining a user side abnormity type according to the screened electricity users; if the line loss abnormity type is power supply side abnormity, calculating a power supply quantity multiple, and performing fault attribution to obtain a power supply side abnormity type; and explaining the two exception types through the SHAP so as to generate a line loss exception diagnosis report. In this way, the adaptability and robustness of the diagnosis method in a novel scene containing a distributed power supply and the like can be enhanced, the interpretability of a diagnosis result is further improved, and a clear and credible feature basis is provided for field check.

A Beidou short message communication method and a system for a low-power user terminal are provided. The method includes following steps: by a user terminal, receiving outbound signals broadcast by a central station and forwarded by a satellite transponder, and constructing a short message access frame with a fixed length for transmission; by the central station, receiving and parsing the short message access frame, so as to realize the allocation of time-frequency resource blocks and transmit the time-frequency resource blocks to the user terminal; by the user terminal, transmitting a short message communication frame with a fixed format to the central station; by a communication receiving device of the central station, receiving and sequentially carrying out sampling preprocessing, anti-interference processing, rapid capture and tracking demodulation on the short message communication frame to obtain communication service information, and pushing receipt information to the user terminal to complete an information push.

The invention relates to a power consumption data anomaly detection method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the node feature extraction and decomposition and similarity calculation and grouping of a dynamic heterogeneous network constructed by power consumption data, and obtaining a similar group set; performing feature extraction on each similar group in the similar group set to obtain a dynamic behavior feature of each power user; the abnormal samples are expanded according to the dynamic behavior characteristics, and a comprehensive abnormal sample set is obtained; after a preset anomaly detection model is trained according to the comprehensive anomaly sample set, anomaly detection is carried out on the to-be-detected set, and an anomaly detection result is obtained; according to the method, the similarity is calculated through the power consumption data of the user, so that the dynamic behavior characteristics are obtained, the abnormal samples are expanded, the proportion of the abnormal samples is increased, training and abnormal detection are performed on the preset abnormal detection model according to the expanded comprehensive abnormal sample set, and the accuracy of the preset abnormal detection model is improved.

Techniques for limiting or reducing storage accessibility “hiccups” in active-active clustered systems that perform large writes. The techniques can include executing, by a surviving, failover, or rebooted node of an active-active clustered system, a specialized recovery protocol that includes treating each large write request from a host computer as a plurality of small write requests while execution of the specialized recovery protocol is in progress, draining all dedicated sub-ubers for a primary and secondary node of the active-active clustered system, and, having completed execution of the specialized recovery protocol, resuming normal treatment of large write requests from the host computer. In this way, the need and complexity of managing large writes and maintaining their corresponding sub-uber information during recovery from a forced reboot, crash, or disaster involving the primary or secondary node can be avoided, and storage accessibility hiccups due to performing the large writes can be limited or reduced.

The invention discloses an arrearage risk management and control method, device and equipment. The method comprises the steps of firstly determining a target risk power consumer; predicting arrearage probability distribution and payment probability distribution of the target risk power consumer through a hybrid density network based on the feature data, the account balance and the step tariff use information of the target risk power consumer; determining an arrearage risk early warning time node corresponding to the target risk power user based on the arrearage probability distribution, and generating a collection prompt strategy corresponding to the target risk power user based on the payment probability distribution; and before the time reaches the arrearage risk early warning time node, performing electric charge collection prompting on the target risk power user based on the collection prompting strategy. According to the technical scheme, based on the arrearage probability distribution and the payment probability distribution predicted by the mixed density network, the occurrence probability of the arrearage behavior and the payment behavior of the user is predicted on the time scale, so that arrearage risk management and control are more finely realized, and the management and control effectiveness of the arrearage risk is improved.

The present invention discloses a method for detecting anomalies of electric power and electricity based on big data, which relates to the technical field of anomaly detection of electric power and electricity. Aiming at the problem of high false alarms and missed alarms caused by dynamic changes in the electricity consumption behavior of power users, the method provides online concept drift detection, model adaptive updating, group anomaly determination and multi-scale fusion recognition. Potential pattern changes are discovered through sliding windows and thresholds, and a list of suspected anomalies is generated. Short-term models are corrected using incremental training and long-term models are periodically updated. Subsequently, multi-scale error metrics are fused into multi-scale anomaly scores, and key anomalies and routine anomalies are determined in combination with group deviation rates. Finally, adaptive adjustments are made through threshold correction indicators and the manual annotation results are fed back to the model library, so as to achieve accurate distinction and continuous learning between normal behavior changes and real anomalies. Through this solution, the false alarm and missed alarm rates can be significantly reduced, and the reliability and refinement of power dispatching and monitoring can be improved.

The invention provides an analysis and identification method for cross-domain characteristics of electric power big data, and the method comprises the steps: extracting daily power utilization behavior characteristics and load curve characteristics of a user according to the real-time collection data of a power generation end and a power utilization end; judging the accuracy of the extracted daily power consumption behavior characteristics of the user; if the daily electricity consumption behavior characteristics of the user are accurate, collecting electric power measurement data and electricity consumption feedback data, and extracting personalized characteristics of the user; identifying different types of power consumer groups according to the personalized features of the power consumers; using a PCA principal component algorithm to extract physical characteristics of generality of different types of power consumer groups; based on the common physical characteristics of the users, designing load control management decision schemes suitable for different power user groups; and designing and customizing a personalized decision-making scheme by combining the common physical characteristics and the difference characteristics of the power consumers.

The invention belongs to the technical field of energy management, and discloses a user-side-oriented source-load-storage integrated comprehensive energy management system, and the hardware architecture comprises a sensing layer, a communication layer and a platform layer. On-site facilities of a source side, a load side and a storage side in the hardware architecture are accessed to the integrated energy management system based on the data collection and calculation unit, and data analysis and display are performed through the server; the software function architecture comprises a data layer, a service layer, an application layer and a display layer; the software function architecture supports the realization of the functions of stable power utilization, flexible scheduling and intelligent management of the source-load-storage integrated comprehensive energy management system; the system further comprises a terminal sensing module, a data collection and calculation unit and an optimization control strategy of the comprehensive energy management system. By adopting the source-load-storage integrated comprehensive energy management system facing the user side, the power consumption cost of the power consumer is reduced, the monitoring, analysis and management capabilities of the source-load-storage comprehensive energy are improved, and the distributed new energy consumption is promoted.

The invention relates to a power consumer price-quantity curve aggregation method based on a Gaussian mixture model (GMM) and Gaussian process regression, which belongs to the technical field of power and comprises the following steps: S1, dividing a price-quantity curve of a consumer into a plurality of key categories according to key features of the price-quantity curve by using a GMM algorithm; s2, performing regression aggregation on the curve of each category by using a Gaussian process regression GPR algorithm; and S3, performing segmented step linearization processing on the obtained regression polymerization curve to obtain a segmented step curve conforming to the power market expression form.

The invention discloses a non-intrusive power user identification method based on self-supervised comparative learning, and relates to the technical field of non-intrusive power monitoring and user identification, and the method comprises the steps: input data are electricity utilization power time sequence data recorded by equipment such as a household intelligent electric meter; inputting the data into a double-layer contrast learning framework based on clustering guidance, and completing power user identification in combination with an adaptive time-frequency feature enhancement mechanism; the method comprises the following steps: constructing a double-layer contrast learning module, introducing a CEEMDAN algorithm to decompose IMF components of power consumption data of different dates, mapping distribution characteristics of the IMF components into selection probabilities of convolution kernels with different widths by a training network, completing adaptive selection of the convolution kernels by means of a Gumbel-softmax mechanism, removing accidental fluctuation of data through convolution in a time domain, and obtaining a time domain; and finally, fusing features of different dates through an attention module, mapping the features into probability distribution through a full connection layer, and classifying the probability distribution, thereby finally realizing accurate identification of non-intrusive power consumers.

The invention discloses a cloud-side cooperation-based power consumer power consumption data abnormity monitoring method and system, and relates to the technical field of cloud-side cooperation, and the method comprises the steps: obtaining the position of equipment through a GPS, constructing a basic portrait of a power consumer, optimizing the deployment of edge equipment, and achieving the real-time collection of basic power consumption data and environment data. And then de-noising the data and extracting features, identifying an equipment type by using a prediction model, constructing an interference model in combination with a timestamp, environmental data and the equipment type, correcting a basic power utilization function matrix to obtain a standard matrix, and further realizing anomaly monitoring and early warning through deviation comparison. The system iteratively optimizes an interference model through regional convergence data to form an acquisition-analysis-correction-monitoring closed loop. According to the method, the monitoring real-time performance is improved through a cloud edge collaborative architecture, the cost is reduced by utilizing a lightweight model and space optimization deployment, and the problems of high delay, poor adaptability and the like of traditional centralized monitoring are solved.

The invention discloses a block chain enabling smart power grid transaction system based on a game theory, and relates to the technical field of regional chains, and the system comprises a strategy management module which is used for making a buying and selling strategy based on the optimal utility through the game theory according to the electric meter storage space of a user, power utilization historical information and transaction record information, the electric equipment of the whole network is dispatched through the block chain; the transaction contract module is used for creating and updating a transaction contract based on the operation strategy formulated by the strategy management module; and the user management module is used for managing the information of the power users and the historical electricity utilization records. According to the invention, an efficient, fair and transparent power transaction mode is constructed by using the security of the block chain technology and the real-time response characteristic of the smart power grid, and the method aims at fully utilizing the distributed power storage units to achieve power utilization peak regulation so as to reduce the energy loss.

The invention discloses an authority management method and system based on user behaviors, and belongs to the field of artificial intelligence, and the method comprises the following steps: S1, obtaining behavior data of a power user, and carrying out the preliminary data cleaning of the data; s2, performing multi-dimensional feature extraction on the cleaned behavior data, and generating a user behavior portrait in combination with a user identity tag; s3, inputting the user behavior portrait into a behavior scoring model, and outputting a behavior score and a risk coefficient; and S4, dynamically configuring an authority level by an authority control engine according to a scoring risk mapping rule. By introducing a dynamic permission adjustment mechanism based on the user behavior score and risk assessment, the behavior mode of the user can be monitored and analyzed in real time, the permission level of the user can be dynamically adjusted according to the real-time demand and risk level of the user, the permission demand of the user can be judged according to the actual operation behavior of the user, and the user experience is improved. Excessive or insufficient authority authorization is avoided, and intelligence and dynamics of authority management are improved.

The invention provides a payment prompting method for a power consumer and related equipment. The method comprises the following steps: acquiring a credit evaluation grade, user characteristics and a user risk grade of a power user; wherein the user features have a plurality of dimensions; determining a corresponding prompt strategy based on the credit evaluation level, the user characteristics and the user risk level; prompting the power user based on the prompting strategy and the payment state of the power user; and stopping prompting the power user in response to the condition that the payment state is paid.

The present invention belongs to the field of power safety, and specifically relates to a method, system and device for warning of electricity theft behavior by integrating digital and graphical data. The method combines the topological graph of each power metering node in the transformer area with the metering data, and separates the power nodes of commercial users and residential users on the topological graph to obtain a merchant sub-graph and a residential sub-graph. Then, by combining the metering data analysis, the fluctuation similarity and growth similarity of the power consumption data changes of the power users on the two sub-graphs are compared, and the nodes with higher similarity are regarded as safe nodes without the risk of electricity theft. Next, according to the remaining topological graph, each independent risk area is generated, and the corresponding feature sequences are generated by combining the power information of the nodes in the risk area. Finally, using machine learning algorithms, the classification results of the presence of electricity theft behavior are generated for each area according to the input feature sequences. The present invention solves the problems of poor accuracy and insufficient real-time performance existing in the existing electricity theft behavior detection methods based on big data economy.

The invention discloses a power consumer group portraying method and system based on a depth map neural network, and relates to the technical field of power consumer portraying, and the method comprises the steps: collecting first data, and carrying out the preprocessing of the first data; constructing a first relation graph based on the second data, and performing feature processing on the first relation graph through a first model; and generating a second group portrait through a first algorithm based on the first group feature, dynamically adjusting the first relation graph based on the first data, and adaptively adjusting the second group portrait through a first machine learning method. According to the method, a user group portrait is updated in real time through an incremental updating mechanism of a graph structure and a GNN model; by fusing multi-source heterogeneous data, the accuracy and integrity of the group portrait are improved; modeling is performed on a complex relationship between users by using a graph structure, so that the accuracy of a group portrait is improved; the user group portrait is continuously optimized through reinforcement learning, and the effectiveness and accuracy of the group portrait are ensured.

The invention provides a power consumer power consumption behavior portraying method considering power consumption sensitivity, belongs to the technical field of industrial data processing, and solves the problem that in most power consumer portraying methods, sensitive loads influenced by other factors are mixed with basic loads for analysis. The method comprises the steps that a power load data set is acquired, a load curve is drawn, preliminary clustering is carried out, and a preliminary clustering cluster is labeled; inputting the load data set after preliminary clustering into an STL model for decomposition and dividing the load data set into a basic load and a sensitive load; respectively carrying out clustering analysis on the basic load and the sensitive load, and respectively defining a tag library of the basic load and a tag library of the external factor sensitive load according to a clustering result; and marking two types of tags for each user according to the tag library of the basic load and the tag library of the external factor sensitive load, and generating an accurate portrait of the power user by combining the tag of the initial clustering result.

The invention relates to the technical field of data security protection, in particular to a security and privacy protection method for data service of a regional power marketing management information platform, which comprises the following steps of: inputting original data of a power user into a trusted execution environment, adding dynamic noise to the data in the trusted execution environment and generating irreversible anonymous variation data; generating a dynamic access strategy based on the consensus verification result of each computing node, wherein the dynamic access strategy comprises a data access permission threshold value and an abnormal access consensus mark; and segmenting the anonymous variation data into a plurality of dynamic fragments, adding a unique recombination verification code to each fragment, and transmitting the fragments to a target terminal through an independent communication link. According to the invention, the risk of cross-regional joint node collusion tampering or cheating access strategies is effectively resisted; and furthermore, an abnormal access consensus result is used for driving an authority threshold index to decay, so that fine-grained and dynamic access control is realized.

The invention discloses a method and a system for identifying, early warning and disposing potential safety hazards of power utilization of power consumers. The method comprises the following steps: acquiring multi-source power data; according to the multi-source electric power data, based on a hidden danger recognition model, spatio-temporal data association and a device-line-user three-level positioning mechanism, performing recognition, early warning, anomaly backtracking and positioning on the hidden danger of electricity utilization of the user; and inputting potential safety hazard identification early warning and positioning related information into the power utilization safety risk disposal large model to generate a disposal strategy. According to the invention, through adoption of a method of processing data through multi-source data fusion and a three-level positioning mechanism, the data treatment capability and the hidden danger disposal capability are enhanced, the risk identification and classification accuracy is improved, and the early warning response time is shortened.

The invention discloses a power consumer group intelligent identification method, system and device based on a PSO-KMeans algorithm, and a medium, and belongs to the technical field of power big data analysis, and the method comprises the steps: collecting original load data of power consumers, carrying out the preprocessing of the original load data, obtaining standardized load data, extracting multi-dimensional features, and constructing a weighted feature matrix based on an entropy weight method; performing coarse clustering by utilizing spectral clustering to generate an initial clustering center set, taking the initial clustering center set as an initial particle position of an improved adaptive inertia weight PSO algorithm, optimizing a K-Means clustering center, and outputting a global optimal clustering center; and performing clustering by taking the result as a K-Means initial center to obtain a final user group, and identifying abnormal power utilization suspected users in combination with a contour coefficient and a local outlier factor. According to the method, high-quality and high-stability user grouping and accurate anomaly detection are realized, and efficient technical support is provided for power user management and demand side response.

The invention discloses an operation management and control method and device for shared energy storage power stations and a medium. The method comprises the steps that multiple shared energy storage power stations, multiple wind power clusters and associated online market platforms included in an operation management system are determined; establishing a corresponding shared energy storage operation model and a corresponding wind power cluster operation model; generating a cooperative operation model between the shared energy storage power station and the wind power cluster; receiving an electric energy order sent by a power user through the online market platform, and selecting an adaptive specified power station from the plurality of shared energy storage power stations; and taking the specified power station as an executor of the electric energy order. A shared transaction environment is constructed for the shared energy storage power stations, the wind power cluster and the power users, the users can quickly and accurately select the most suitable shared energy storage power station from the multiple shared energy storage power stations, and finally the win-win situation of the three parties is achieved.

The present application belongs to the technical field of electrical equipment state monitoring and fault diagnosis, and particularly relates to an automatic electricity stealing identification method based on data mining technology.The present application constructs an "electric-thermal-acoustic" multi-physical field coupling benchmark physical model through a BP neural network, mines the dynamic correlation law between the current and the temperature, acoustic emission physical quantities through the benchmark physical model, effectively captures the multi-dimensional physical quantity abnormal coupling characteristics caused by electricity stealing behavior, and through the model residual blood sequence output by the benchmark physical model, the deviation degree between the temperature measured value and the acoustic emission measured value of the actual collected power user equipment under the current power utilization state and the predicted value output by the BP neural network can be reflected, so as to strengthen the identification accuracy of electricity stealing behavior.

The present application relates to the technical field of power dispatch, in particular to a non-uniform power supply power data acquisition calculation method, comprising: each non-uniform power supply concentrator collecting active power sampling data of each electric energy meter in real time, processing and storing to obtain non-uniform power supply real-time active power data; each non-uniform power supply concentrator transmitting non-uniform power supply active power data to the power information acquisition system master station of the management information district in real time through the boundary firewall between the wireless public network and the power user power information acquisition system; and accumulating and calculating the non-uniform power supply power data of each region and the non-uniform power supply power data directly acquired by the dispatching data network to obtain real-time data of the total non-uniform power supply power of the dispatching range. The non-uniform power supply power is acquired by the power information acquisition and regulation and control cloud interaction mode, the safe and stable operation of the equipment is preferentially ensured, and the real-time and stability of the active power data transmission are effectively ensured.

The invention relates to a power market-oriented power selling data analysis method and system, and the method comprises the steps: analyzing the historical purchase data and actual power consumption of a plurality of power users in the power duration, obtaining the power purchase deviation inertial characteristics corresponding to a user group, and enabling the power purchase deviation inertial characteristics to reflect the power purchase strategy of the power users. And then predicting the electricity consumption predicted quantity of the power consumer in the next time period by adopting an electricity consumption variable quantity prediction model based on the long-short-term memory network, carrying out differencing on the electricity consumption predicted quantity and the agreed quantity in the next time period, if the difference value can be matched with the electricity purchase deviation inertial characteristic, indicating that the difference value is an inertial difference value based on a user strategy, and if the difference value is not matched with the electricity purchase deviation inertial characteristic, indicating that the difference value is not an inertia difference value based on a user strategy. And if not, it is indicated that the power utilization plan is changed, and the predicted power utilization amount can be corrected by using the result, so that the predicted power utilization amount is more accurate.