5178 results about "Distribution method" patented technology

The invention provides a digital power grid power cloud resource real-time hierarchical distribution method, which comprises the steps of dividing power cloud resources into a plurality of sub-resource pools according to a physical architecture and a topological structure of a digital power grid, setting a special resource pool for key equipment and sensitive loads of the power grid, and dynamically adjusting network topology and a routing strategy. A low-delay and high-reliability network transmission channel is provided through a priority queue and a bandwidth guarantee mechanism; for each sub-resource pool, extracting periodicity, tendency and randomness characteristics of a load by analyzing historical load data and a current system state of power grid equipment, and updating a load prediction result in real time; after incremental resources are migrated, a power grid control request is routed to a corresponding computing node by adopting a consistent Hash algorithm in a sub-resource pool, load balancing is realized by dynamically adjusting the number and distribution of virtual nodes of a Hash ring, and weights of the virtual nodes are dynamically adjusted according to load optimization indexes of the nodes so as to adapt to power grid load changes.

The invention provides an oil depot tank field inspection robot task allocation method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. Constructing a multi-target collaborative scheduling model, extracting features by adopting a space-time attention mechanism, establishing a task priority evaluation and path planning sub-model, and generating an optimal task allocation strategy; and an inspection task instruction is issued, the execution condition is monitored in real time, and when abnormity is detected, the emergency module is started for optimization adjustment. According to the invention, intelligent and collaborative routing inspection task distribution is realized, and the routing inspection efficiency and safety are improved.

The invention provides a live broadcast room content identification and intelligent distribution method and system based on multi-modal fusion, and relates to the technical field of machine learning, and the method comprises the steps: collecting video, audio and bullet screen data of a live broadcast room, and carrying out the feature extraction; performing cross-modal interaction, feature alignment and feature reconstruction on the multi-modal features by using the multi-modal feature fusion model to generate cross-modal fusion features; and carrying out content identification based on a pre-trained content classification model to obtain a category label and a time sequence feature. Furthermore, a live broadcast room content portrait and an association network are constructed, and a live broadcast room real-time score is generated and a live broadcast room display weight and a push strategy are dynamically adjusted in combination with user historical watching behavior data and a deep reinforcement learning model. Through multi-modal data fusion and deep reinforcement learning, accurate identification and personalized recommendation of the live broadcast room content are realized, and the user experience and the platform operation efficiency are effectively improved.

The invention relates to the technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

The invention discloses an intelligent target allocation method and system based on deep reinforcement learning, particularly relates to the field of multi-agent systems, and is used for solving the real-time decision problem under multi-dimensional constraints in a dynamic confrontation simulation scene. Time, space and resource constraints are decomposed into independent models, and the decision complexity of a multi-agent system in a dynamic confrontation scene is reduced; by means of adaptive Monte Carlo sampling, prediction perception path planning and dynamic threshold value resource allocation, diversified high-quality candidate schemes are quickly generated; when the environment changes, the multi-layer influence propagation network and the spatial topological relation graph are used for positioning an affected agent, and scheme fragments are generated through local adjustment, so that global re-calculation is avoided, the overhead is reduced, and real-time response is ensured; meanwhile, an efficient and reliable optimal solution is screened out through resource delay coupling and path efficiency comprehensive evaluation in combination with multi-objective optimization and task consistency verification.

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention provides a visitor identification and multi-dimensional user portrait routing distribution method based on private domain traffic, and relates to the technical field of data analysis, and the method comprises the steps: collecting multi-source data information of a visitor from a private domain traffic pool, constructing a multi-dimensional feature vector, generating a visitor identity fingerprint feature, and building a visitor identity identification model based on the visitor identity fingerprint feature; constructing a multi-level label system based on a recognition result of the visitor identity recognition model; performing feature extraction on historical behavior track data and historical content interaction data of visitors by adopting a transfer learning algorithm, classifying the extracted features based on the multi-level label system to generate user portrait features, dynamically updating the user portrait features in an incremental learning mode, constructing a multi-dimensional user portrait model, and displaying the multi-dimensional user portrait model. And distributing different visitor requests to the corresponding service nodes by adopting a routing distribution strategy in combination with the real-time load states of the service nodes.

The invention relates to the technical field of charging pile power distribution, and discloses a charging pile dynamic power distribution method and device based on load prediction, and the method comprises the steps: carrying out the system identification analysis of the real-time operation data of a charging pile, and constructing a charging pile system small signal model; training to obtain a low-sensitivity load prediction model and a high-sensitivity load prediction model, and generating charging load prediction data; performing control optimization calculation to obtain a dynamic power compensation control parameter; genetic optimization and multi-stage power distribution are carried out, and a multi-stage power distribution scheme including a conventional load distribution mode and a peak load distribution mode is obtained; power limitation is carried out by reducing charging power of non-priority users, load transfer is carried out through power coordination of adjacent charging piles, an emergency power adjustment control instruction is obtained, a closed-loop optimization mechanism is adopted, and stable operation of a charging pile system under various working conditions is guaranteed through parameter online optimization and dynamic adjustment of a control strategy.

The invention discloses a monitoring point distribution method and device based on industrial park gas monitoring and a medium, and relates to the technical field of carbon monitoring, triangular mesh division is carried out based on DT triangular mesh division, and according to the difference of monitored areas, the DT triangular mesh division method is used for setting point distribution of the monitored areas, so that the monitoring point distribution accuracy is improved. At least one monitoring point is selected in each grid for carbon dioxide concentration monitoring, point distribution setting can be carried out in different area ranges of prevailing wind directions in different states, the optimal monitoring result can be obtained through the least carbon dioxide gas monitoring points, the accuracy and credibility of the final data result are improved, and the method is suitable for popularization and application. The problems that the current industrial park monitoring coverage range is not wide, the device cost is high, and improvement of the carbon monitoring device distribution position affects the accuracy of the carbon ground monitoring technology are solved.

The invention discloses a highly-adaptive distributed data distribution method supporting dynamic extension, which realizes intelligent distribution and efficient processing of data tasks by introducing a task distribution mechanism based on deep reinforcement learning and combining dynamic states of nodes in a distributed system. Specifically, the method comprises the following steps: firstly, collecting real-time state information, including load, bandwidth, delay and the like, of each node in a system, and classifying data tasks according to types and priorities; thirdly, modeling node states and task features through a deep reinforcement learning model, and outputting an optimal task allocation strategy; in the task execution process, the node state is monitored in real time, and the model is dynamically updated through task feedback so as to continuously optimize the distribution strategy. According to the method, distributed task allocation is optimized through a deep reinforcement learning technology, the efficiency and accuracy of data distribution are improved, and meanwhile, the adaptability of the system to a complex dynamic environment is enhanced.

The invention discloses a DC charging pile multi-segment power distribution method and system, and relates to the technical field of DC charging pile power distribution, and the method comprises the following steps: setting an initial monitoring frequency at a first charging stage of a DC charging pile according to historical charging data and monitoring demands, collecting current data when the battery of the electric vehicle is charged at the initial monitoring frequency; on the basis of data acquisition, a time sliding monitoring window is introduced, and current data in the monitoring window form a current data set according to a time sequence. By setting the initial monitoring frequency and combining the sliding window and the neural network model, intelligent monitoring and anomaly prediction of current data are achieved, in the potential anomaly stage, the monitoring frequency is dynamically improved, the charging power is reduced, the abnormal current is accurately captured, the battery performance is protected, and the service life is prolonged. The system efficiency is optimized while the charging safety is guaranteed, and reliable technical support is provided for a high-power charging scene.

The invention discloses an employment talent supply and demand distribution method and system based on data analysis, and belongs to the technical field of data analysis, and the method comprises the steps: obtaining talent basic information and post basic information; cleaning and sorting the talent basic information and the post basic information, identifying and deleting repeated data, and processing missing values and abnormal values; key features are extracted from the talent basic information and the post basic information by using a deep learning technology, and the key features are weighted or screened; establishing a matching model between the talent basic information and the post basic information by using a machine learning algorithm; a matching result is analyzed, model optimization is carried out according to the result, parameters or feature weights are adjusted, and the matching accuracy and efficiency are improved; the generalization ability and accuracy of the matching model are evaluated through cross validation and confusion matrix methods; and continuously optimizing the matching model, and adjusting the matching model according to feedback and experience to improve the matching effect. The method has the effect of helping to reasonably configure human resources.

The invention relates to the technical field of power management, and provides a power distribution method based on BMS power control and related equipment thereof. The method comprises the following steps of: performing multi-mode synchronous acquisition and self-adaptive preprocessing on detected battery pack global state data to obtain a battery state tensor, and performing unsteady-state signal decomposition and time-space feature extraction on a received load power demand signal to obtain a power component matrix; performing multi-constraint dynamic planning on the battery state tensor and the power component matrix to obtain an initial power distribution strategy, performing dynamic correction in combination with a deep reinforcement learning model to obtain an adaptive distribution strategy, and performing multi-layer circuit equalization optimization on the adaptive distribution strategy to obtain a power regulation instruction set; and finally, performing multi-physics field coupling verification and fault-tolerant reconstruction on the power regulation instruction set to obtain an execution signal sequence. According to the invention, through data fusion, demand prediction, strategy optimization and deep learning, power distribution is accurately regulated and controlled according to the battery state and the load power demand.

The invention relates to the technical field of power distribution, and discloses a power distribution method and system for a liquid cooling string PCS energy storage converter, and the method comprises the steps: constructing a multi-dimensional state matrix of each energy storage unit in the liquid cooling string PCS energy storage converter; establishing a thermal coupling model among the energy storage units according to the temperature parameters in the multi-dimensional state matrix, and constructing a temperature constraint projection mapping function; constructing a power optimization objective function considering the thermal coupling effect; calculating temperature change caused by disturbance through a thermal coupling model to obtain a temperature sensitive gradient vector; the power distribution weight of each energy storage unit is dynamically adjusted, and when the iteration change value of the power optimization objective function is smaller than a preset threshold value and the thermal coupling model is stable, a target power distribution strategy is output, collaborative optimization of power distribution and temperature control is achieved, the problem of thermal interference between the energy storage units is effectively solved, and the reliability of power distribution and temperature control is improved. And the overall operation efficiency of the liquid cooling string PCS energy storage converter is obviously improved.

The invention discloses a natural gas station intelligent distribution method based on the Internet of Things, and relates to the technical field of natural gas station distribution design. Flow, pressure and valve state data are obtained in real time through an Internet of Things sensor, and a weighted average method and Kalman filtering are used for data fusion and optimization; meanwhile, the relative position relation between the flow meter and the adjusting valve is judged in real time, branch configuration changes are dynamically recognized, self-adaptive branch matching logic is triggered, and therefore control logic is adjusted; meanwhile, the opening degree of the valve is controlled through PID, self-adaptive dead zone compensation is conducted in combination with historical operation data of the valve and the current state, valve response delay is eliminated, the adjusting precision is improved, and flow fluctuation is reduced; in addition, in the feedback adjusting stage, the adjusting result is continuously monitored, the actual flow and the pressure value are compared with the target value, the opening degree of the valve is dynamically adjusted through real-time error calculation, and the output is kept consistent with the target.

The invention is suitable for the technical field of charging piles, and particularly relates to a charging pile power distribution method and device, equipment and a medium. According to the method, the current charging demand of a target charging pile is collected in real time, when the current charging demand is larger than the charging demand at the last collection moment, the first target number of power modules needing to be added is determined, a first path set of the target charging pile and each remaining available power module is obtained, and according to a preset limiting condition, the first path set is determined; screening the first path set to obtain a screened path set, determining the weight of the corresponding path according to the path parameter of each path in the screened path set, determining a target path according to the first target number and the weight of each path in the screened path set, and controlling the target path to be conducted, according to the invention, the power distribution is dynamically optimized according to the actual charging demand and the power supply condition, so that the charging efficiency can be effectively improved, and the power loss is reduced.

The invention provides a low-altitude logistics multi-unmanned aerial vehicle cooperative distribution method based on deep reinforcement learning, and the method comprises the steps: obtaining a distribution task and multi-level dynamic airspace information, and distributing the distribution task to idle unmanned aerial vehicles according to a task distribution mechanism; based on the multi-level dynamic airspace information, an airspace scheduling mechanism is constructed, and the airspace scheduling mechanism comprises an airspace scheduling height level selector and a multi-unmanned aerial vehicle cooperative path-finding solver; the airspace scheduling height level selector determines the flight height level of the unmanned aerial vehicle according to the distribution task and the multi-level dynamic airspace information to obtain a height level decision; the multi-unmanned aerial vehicle cooperative path-finding solver performs cooperative path planning in a horizontal range at each height layer to obtain a path decision; and the unmanned aerial vehicle executes a distribution task according to the height layer decision and the path decision. According to the method provided by the invention, the high-throughput and low-delay delivery target is realized in a complex urban scene, and the operation risk caused by airspace conflicts is reduced.

The invention discloses an intelligent target distribution method and system for collaborative interception, particularly relates to the field of radar networking, is used for solving the problem of correction of phase noise accumulation and array error propagation, and comprises the following steps: establishing a high-dimensional node phase distribution model, determining a noise mode and extracting key features; in combination with calculation of a phase drift index and a signal distortion index and construction of a multi-dimensional noise matrix, fine quantization of node errors is realized; on the basis, the array surface information is corrected in real time by using a dynamic interpolation and phase offset correction technology, node cooperation is optimized in combination with an energy measurement algorithm, and the stability and precision of the array surface information are improved; and finally, through a multi-dimensional decision matrix, target features and node states are comprehensively analyzed, and intelligent allocation and optimization of resources are realized. Therefore, the observation precision and the cooperation efficiency of radar networking are remarkably improved, and reliable technical support is provided for high-precision target monitoring and resource optimization allocation in a complex environment.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

The invention is applicable to the technical field of computing power distribution, and provides a power equipment edge gateway computing power dynamic distribution method, which comprises the following steps of: determining a system state representation vector based on multi-source data of power equipment; the system state representation vector comprises a real-time computing power occupancy rate, a task queue length, a network bandwidth utilization rate and an equipment energy consumption index; constructing target parameters and constraint conditions of the calculation power dynamic distribution model; taking the system state representation vector as input, solving the computing power dynamic allocation model through a reinforcement learning algorithm, and generating a task scheduling path and a resource allocation proportion; and optimizing a preset hierarchical buffer queue based on the task scheduling path and the resource allocation proportion, and generating a target dynamic allocation strategy. According to the method, the high-real-time task response speed and the resource utilization rate are improved, energy consumption is reduced, meanwhile, backlog tasks are efficiently processed in the idle period through an event-driven mechanism, and system load balance is guaranteed.

The invention provides a task allocation method, device and equipment based on a distributed cluster and a computer readable storage medium. The method comprises the following steps: acquiring to-be-allocated tasks and determining priorities among the to-be-allocated tasks; selecting a to-be-allocated task node from the low-load nodes according to the task processing capability and the task processing type of the low-load nodes; according to the task number and the priority of the to-be-allocated tasks and the task processing capability and the task processing type of the to-be-allocated task nodes, estimating whether the load of the to-be-allocated task nodes after task allocation exceeds a preset load threshold value or not; if the load does not exceed the preset load threshold, establishing a distribution relationship between the to-be-distributed task and the to-be-distributed task node; and distributing the to-be-distributed task according to the distribution relation. According to the embodiment of the invention, tasks can be distributed more accurately, node overload and software crash are avoided, and load balancing is realized.

An aspect of the invention relates to a moving image distribution system. The moving image distribution system may include one or more processors. The one or more processors may execute a computer-readable command to register a distribution user that has made a registration request among a plurality of distribution users that distribute moving images including character objects generated based on their own movements, as a first co-starrable user, to select a first co-starring user from among a plurality of first co-starrable users in response to reception of a first co-starring application for co-starring with any of the plurality of first co-starrable users from a co-starring application user, and to distribute a co-starring moving image in which the co-starring application user and the first co-starring user co-star.

The invention discloses an intelligent wireless charging power distribution method and system, and aims to improve the efficiency and safety of simultaneous charging of multiple devices, and the system design comprises a plurality of key modules: a device detection and recognition module automatically recognizes the charging devices and collects the power demands of the charging devices; the power demand analysis module calculates the power demand of each device according to the device electric quantity and the charging mode, and the power distribution control module dynamically adjusts the power output according to the priority, the distance and other factors; the real-time monitoring and feedback module ensures the stability and safety of the charging process, and the data recording and learning optimization module optimizes the charging strategy by analyzing historical data. Through cooperative work of the modules, the system can adjust the charging power in real time, optimize the charging efficiency, improve the user experience, and ensure the safety of the charging process. The system shows good adaptability in a multi-device environment, and important reference and application prospects are provided for development of a wireless charging technology.

The invention relates to a large model workflow arrangement method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the logic arrangement and configuration of a task decomposition tree structure through a preset LiteFlow, and obtaining a candidate execution scheme; performing performance evaluation on the candidate execution schemes to obtain a performance evaluation table; determining performance evaluation data based on the performance evaluation table, and judging whether the performance evaluation data is within a preset performance range, and if not, performing scheme defect analysis on the candidate execution scheme based on the performance evaluation table to obtain defect analysis data; and performing dynamic activity planning on the candidate execution scheme based on the defect analysis data through the LiteFlow to obtain a workflow arrangement scheme, thereby solving the problems that a conventional workflow arrangement method often adopts a static and fixed task allocation mode and cannot effectively adapt to dynamic change in a large model operation process, so that the system performance is reduced, and the system performance is influenced. And the resource utilization efficiency is low.

The invention relates to a liquid cooling cabinet cooling liquid intelligent distribution method and system, and belongs to the liquid cooling field, the method comprises the following steps: S1, data collection: monitoring server equipment temperature information and cooling system information in real time; s2, judging the running state: comparing the monitoring information in the above step with a preset parameter threshold value, and judging the current cooling state; s3, adjusting the flow of the cooling liquid: dynamically adjusting the flow of the cooling liquid of the liquid cooling system according to the cooling state; and S4, performing abnormal early warning: continuing to perform data collection in real time after the flow is adjusted, judging the cooling state according to the collected information, returning to the step S3 until the temperature of the server returns to normal, and performing early warning when normal cooling cannot be performed. According to the method and the device provided by the invention, the stable and efficient operation of the server equipment is ensured through the steps of real-time monitoring, state judgment, flow adjustment, cooling state judgment, abnormal early warning, record feedback and the like.

The invention discloses a micro-grid virtual inertia optimal distribution method, and relates to the technical field of micro-grid inertia optimal distribution. Establishing a micro-grid transient model containing photovoltaic, energy storage and controllable load to quantitatively describe virtual inertia support requirements of the maximum frequency deviation and the maximum frequency change rate of the micro-grid, and obtaining the critical inertia of the micro-grid; setting constraint conditions based on the critical inertia of the micro-grid, and establishing a virtual inertia optimal distribution model of the micro-grid by taking the minimum maximum frequency deviation as an optimization target; and solving the micro-grid virtual inertia optimal distribution model to obtain a village-level micro-grid internal virtual inertia optimal distribution scheme. The invention further discloses micro-grid virtual inertia optimization sub-configuration, and a photovoltaic, energy storage and controllable load virtual inertia distribution scheme is obtained on the premise of guaranteeing that the micro-grid inertia is at a safety level and photovoltaic, energy storage and controllable load stable operation.

The invention relates to the technical field of energy storage systems, and discloses a three-phase parallel operation system and a power distribution method and device thereof. The method comprises the following steps: acquiring initial distribution power of each phase of each energy storage inverter; determining a locked phase, a non-locked phase, a locked energy storage inverter and a non-locked energy storage inverter according to the initial distribution power of each phase of each energy storage inverter; determining missing power of the locked phase of the locked energy storage inverter according to the locked energy storage inverter and the locked phase; according to the initial distribution power of the non-locked phase of the locked energy storage inverter, the charging margin is obtained; according to the initial distribution power of the non-locking phase of the non-locking energy storage inverter, obtaining a discharge margin; and obtaining the target distribution power of each phase of each energy storage inverter according to the missing power, the charging margin and the discharging margin. Through overall calculation of the global margin, the problems that interphase power of a traditional parallel operation system is rigid and the overall performance is restricted by a single-point bottleneck are solved, and the load adaptability and the operation reliability of the parallel operation system are remarkably improved.

The invention discloses a charging pile direct current power distribution method based on chaos topology, and the method comprises the steps: building a charging pile power initial chaos topology distribution network based on a matrix topology network according to user demands; expanding the dimension of the charging pile power initial chaotic topological distribution network to a high-dimensional curved surface through a Gaussian kernel function to obtain a charging pile power chaotic topological distribution network with a plurality of charging nodes; on the basis of the charging pile power chaos topology distribution network, the required power of each charging gun and a power distribution strategy, controlling on and off of a contactor switch unit so as to control guiding of power output by a power conversion unit; therefore, efficient power flexible scheduling can be realized, the degree of freedom of all nodes in the power topology can be customized at will, and it is ensured that each node can reach the efficiency expected by a user.

The invention discloses a resource allocation method and device, equipment and a storage medium, and the method comprises the steps: obtaining a first task type, a first task data amount and deadline of a target to-be-executed task, an estimated resource amount for completing the target to-be-executed task, and an available resource amount of a server cluster; inputting the first task type, the first task data quantity and the estimated resource quantity into a duration prediction model to obtain a first duration; acquiring current time; processing the current time, the estimated resource quantity, the deadline, the available resource quantity and the first duration by using a preset function to obtain the priority of the target to-be-executed task; and allocating resources to the target to-be-executed task based on the priority, the estimated resource quantity and the available resource quantity. And determining the priority of the task by using the information related to the target to-be-executed task, and allocating resources for the task by using the priority. Therefore, the accuracy of the priority is improved, the resources allocated to the tasks are more reasonable, and the resource utilization rate of the server cluster is improved.

The invention relates to the field of power equipment data processing, in particular to a multi-target dynamic optimization unit load intelligent distribution method, which adapts a micro-service cluster through a containerization protocol, adopts an OPC UA protocol and an MQTT protocol to respectively collect thermal power and new energy data at different frequencies, dynamically analyzes an IEC 61850 power grid instruction, converts and stores the IEC 61850 power grid instruction into a Kafka message queue, and sends the IEC 61850 power grid instruction to a network server. The problem of communication protocol heterogeneity is solved. Based on an IEEE 1588 protocol, sub-millisecond clock synchronization of edge nodes is realized, a cubic spline interpolation algorithm is adopted to align multi-frequency data, an interpolation window is dynamically adjusted in combination with a genetic algorithm, and a spatial-temporal characteristic matrix is generated and stored in a Redis database. And performing multi-objective optimization solution through an NSGA-II algorithm, and dynamically adjusting the weight of an objective function to adapt to the frequency modulation requirement of the power grid. A closed-loop feedback mechanism corrects model parameters through Kalman filtering, actual data are written back to a feature matrix, the robustness of the system is improved in combination with a hierarchical fault-tolerant strategy, the problem of space-time mismatch of data is effectively solved, and the economical efficiency and safety of load distribution are improved.