271 results about "Model matching" patented technology

The invention discloses an anomaly detection model selection method and system based on index portraits, and relates to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: collecting historical data of a target monitoring index, extracting multi-dimensional features to construct an index portrait, and classifying the index portrait; screening candidate anomaly detection models from the matching rule base, performing adaptation degree scoring in combination with a model compatibility evaluation mechanism, determining an optimal anomaly detection model to perform anomaly detection, and outputting an anomaly judgment result; when a plurality of models exist, generating a final abnormal result through a confidence-driven arbitration mechanism; for multi-index abnormity, causal reasoning is carried out in combination with an electric power knowledge graph, main alarm indexes are determined, and secondary indexes are processed according to a delay strategy; meanwhile, incremental updating of index portrait features, adaptive adjustment of model parameters and dynamic optimization of matching rules are supported, and a whole-process closed-loop mechanism covering'portrait construction-model matching-result fusion-alarm decision-feedback updating 'is constructed.

The invention particularly relates to a laser point cloud over-break and under-break volume calculation method for tunnel engineering, and relates to the technical field of tunnel engineering construction quality detection. Modeling a tunnel design contour; matching and aligning the point cloud with the design model; carrying out over-break and under-break area division and volume calculation; and result visualization and report output. According to the method, point cloud preprocessing parameter optimization and high-precision registration are achieved through multi-dimensional quantitative analysis, and the reliability of an over-break and under-break calculation benchmark is remarkably improved; in a point cloud denoising link, a voxel evaluation coefficient is introduced to ensure that the filtered point cloud not only keeps key details such as tunnel vault radian and side wall broken lines, but also can simplify the data volume; in a model registration stage, outlier interference is eliminated and a strict iteration stop condition is set, so that a reliable reference is provided for back break deviation calculation.

The invention discloses a rotor punching and stacking welding defect identification system based on edge calculation, and relates to the technical field of rotor welding identification, and the rotor punching and stacking welding defect identification system comprises a data acquisition module which is used for carrying out real-time acquisition on smoke dust data generated in the welding process, and the smoke dust data comprises smoke concentration, suspended particulate matter concentration and smoke infrared thermal imaging temperature; the model matching module is used for comparing the smoke concentration, the suspended particulate matter concentration and the smoke infrared thermal imaging temperature with corresponding preset reference models respectively to obtain abnormal data with an abnormal trend; and the judgment module is used for performing time sequence analysis on the abnormal data, calculating a standard deviation and a mean deviation rate based on a sliding window, and generating a corresponding early warning growth value. According to the method, a dynamic alarm value generation and attenuation mechanism is constructed, so that false alarm caused by continuous influence of historical data is avoided, and the real-time response accuracy of the integrated value is improved. And through a pre-judgment mechanism, it is ensured that alarm value attenuation is executed only on the premise that data are stable or no early warning growth trend exists.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving text submitted with a request. Token values are automatically extracted for selected token types from the text using a machine learning model. Tokens are identified in the text, and for each token, features and a token type of the token are determined, along with a score that indicates a likelihood that the token has the token type. A token value is extracted for the token from the text. Extracted token values are compared to request data values. A request value for the selected token type is identified in the data values for each selected token type. The extracted token value for each selected token type is compared to the request value. An audit alert is generated in response to determining that an extracted token value does not match a corresponding request value.

The invention relates to the technical field of bolt length detection data processing, in particular to a bolt length detection system, which is characterized in that a three-dimensional data acquisition module generates a high-density point cloud and extracts a thread curvature extreme point, and a dynamic model matching module screens an optimal lead angle parameter based on a Hausdorff distance; and constructing an axial expansion model carrying phase offset gradient features. The projection mapping correction module adopts a genetic algorithm to optimize a compensation coefficient matrix, and generates a dynamic weight correction parameter to suppress geometric distortion; and the phase error correction module combines Fourier spectrum decomposition and an exponential decay weight coefficient to construct a frequency domain transfer function to eliminate an axial accumulative error. According to the method, the problems of projection distortion and phase deviation caused by the lead angle are effectively solved through geometric-phase double-domain correction, dynamic parameter iteration and an error collaborative suppression mechanism, and the spatial positioning precision of the feature points at the tail end of the thread is improved.

The invention discloses a deformed workpiece twin modeling method based on measurement point cloud and three-dimensional CAD model matching. The deformed workpiece twin modeling method comprises the steps that an improved GFO-ICP registration algorithm is adopted to solve the initial coarse registration problem of the measurement point cloud and a CAD model in combination with geometric feature constraint and self-adaptive step length optimization; extracting a key geometric curved surface of the point cloud by adopting a clustering-based plane segmentation algorithm, and extracting a semantic feature vector of a segmented region in combination with a Pointnet + + network; a cosine similarity matching mechanism is constructed by integrating mixed descriptors of geometric features (areas and normal vectors) and semantic features, and accurate binding of a point cloud curved surface and a curved surface of a CAD model is achieved. According to the method, the problem of assembly geometric deviation caused by workpiece deformation due to manufacturing errors in compressor and fan rotor assembly is solved, multi-link error accumulation is effectively processed, digital twin modeling of high-precision deformed workpieces is achieved, and the assembly simulation effect and intelligent manufacturing application are improved.

The invention discloses an automatic auditing method and system based on ship-shore model matching fusion, and belongs to the technical field of data processing.The method comprises the steps that ship-shore matching related documents of a user are interacted, and the ship-shore matching related documents support multiple formats; activating a multi-model fusion processing module to perform preprocessing and multi-modal analysis on the ship-shore matching related document, extracting a text, a visual element and structured data, and generating a structured data object; a preset rule engine and the large language model are called to process the structured data object in parallel, the rule engine executes deterministic rule verification, and the large language model processes uncertainty semantic recognition determination; and fusing output results of the rule engine and the large language model to generate an audit result and a risk prompt. The problems that in the prior art, ship-shore matching auditing depends on labor, the efficiency is low, errors are large, multi-format documents are difficult to process through a single technology, rule and semantic coverage is incomplete, information fragmentation is caused, and auditing accuracy and efficiency are difficult to consider at the same time are solved.

The invention discloses a workpiece grabbing and releasing system and method based on multi-modal data fusion and Kalman filtering tracking. Comprising a hardware grabbing and releasing module, a mechanical arm control module, a target recognition module, a dynamic tracking module and a pose estimation module. Firstly, a depth camera collects RGB images and depth point cloud data of the surface of a conveying belt, then the position and the type of a workpiece are recognized, and three-dimensional point cloud of a target workpiece is extracted from the point cloud data according to the position of the workpiece. And the pose estimation module searches the stored model library for the point cloud of the template model matched with the target workpiece, takes the point cloud as an initial iteration value of the ICP algorithm, and outputs the optimal grabbing pose of the target workpiece. And the dynamic tracking module dynamically compensates the position of the target workpiece in the pose estimation process through a Kalman filtering algorithm. And the mechanical arm control module drives the mechanical arm to perform position adjustment according to a prediction result of the dynamic tracking module, and calculates an instruction for controlling the mechanical arm to complete the grabbing action according to the optimal grabbing pose.

The invention discloses an intelligent task allocation method and related equipment. The method comprises the steps of receiving multi-format task description information, extracting multi-dimensional attributes to generate a task vector, constructing an executive vector containing multiple capability dimensions, matching an optimal main executive through a multi-layer perception model, and performing real-time monitoring and adjustment. According to the scheme, the existing defects are specifically solved: multi-format information is analyzed to extract multi-dimensional attributes, so that a system comprehensively understands the context of a task, and the understanding insufficiency of a traditional method is avoided; constructing a multi-dimensional executive capacity vector and combining a scoring model to realize accurate matching of a task and an executive, and solving the problem of unreasonable distribution caused by asymmetry of man-machine capacity; the execution state is monitored in real time, redistribution is triggered, a dynamic feedback closed loop is formed, and the defect that the dynamic performance of a labor division mechanism is insufficient is overcome, so that the task execution flexibility and reliability are improved, resource waste and decision errors are reduced, and the execution efficiency is improved.

The invention belongs to the technical field of data processing, and discloses a deep learning optimization system and method for intelligent equipment. Comprising the steps of collecting data and performing data cleaning to obtain accurate equipment parameters; identifying a target intelligent device resource level based on the accurate device parameters; performing energy consumption scheduling on the target intelligent device based on the resource level to obtain a scheduled intelligent device; performing disturbance simulation on the accurate equipment parameters, and outputting a disturbance stability index; appropriate parameter screening is carried out on the accurate equipment parameters based on the disturbance stability index, and stable equipment parameters are output; performing risk assessment on the stable equipment parameters to obtain parameter risk indexes; performing model matching on the scheduling intelligent equipment based on the parameter risk index to generate a model adjustment strategy; performing scene simulation based on the model adjustment strategy, performing loading optimization on the model adjustment strategy based on a simulation effect, and outputting an optimization model strategy; the efficient deep learning optimization method for the intelligent equipment is realized.

The invention relates to the technical field of intelligent recruitment management, and discloses an intelligent matching recruitment management system based on a large model. The system comprises a talent database construction module which collects multi-dimensional ability characteristics of job seekers and enterprise post demand data and generates a three-dimensional recruitment data set containing ability, post and time dimensions; the personnel post adaptation field modeling module performs dynamic integrating degree analysis on the data set, constructs a three-dimensional personnel post adaptation field, and identifies adaptation field critical point distribution and a flowing path; the key feature screening module extracts core adaptive features meeting a threshold value; the large model matching engine module inputs the core adaptive features into a pre-training large model generator, introduces adaptive field topology and feature continuity constraints, and outputs optimized dynamic adaptive field distribution; the environment variable compensation module fuses the dynamic indexes of the labor market to generate compensation parameters, and corrects the core adaptation features; and the strategy generation module generates a post matching strategy set and a talent optimization path to adapt to the requirements of the recruitment market.

The invention discloses a high-altitude track AGV accurate positioning and map building method and system. The method comprises the following steps: collecting data through multiple sensors; constructing a fusion model, and dynamically distributing fusion weights of laser and visual SLAM based on visual image information entropy and laser point cloud geometric feature distribution; semantic features such as track planes and edge lines are extracted and matched with the prior model; and jointly inputting the weight fusion pose, the UWB constraint and the semantic constraint into a back-end graph for optimization, solving an optimal pose and updating the map. The system comprises corresponding modules. Through cooperation of dynamic weight distribution and semantic auxiliary optimization, the problems of low AGV positioning precision and poor robustness in a high-altitude orbit environment are solved, dynamic closed-loop updating of a map can be realized, and the automation and intelligence level of an intelligent detection system is remarkably improved.

The invention relates to the technical field of multi-cloud operation and maintenance, in particular to a cloud platform operation and maintenance method and system and a storage medium, and the method comprises the steps: collecting multi-modal data of a plurality of cloud platforms; a multi-objective optimization decision is generated through an analysis decision model, the analysis decision model comprises a deep learning model, a pre-training language model, a neural network model and an evolutionary algorithm model, and a resource demand range in a future preset time period is predicted through the deep learning model; matching a historical fault library through a pre-training language model to generate a similar case recommendation list; calculating a fault propagation probability through a graph neural network model; generating a multi-objective optimization decision by the evolutionary algorithm model in combination with a resource demand, a similar case recommendation list and a fault propagation probability; and executing a repair operation according to the multi-objective optimization decision. According to the method, global optimization decision and closed-loop operation and maintenance can be realized, and efficiency and stability are improved.

The invention discloses an intelligent equipment fault diagnosis method and system based on cloud side-end cooperation. The method comprises the steps that a data acquisition terminal collects equipment operation data and sends the equipment operation data to an edge side; the edge side carries out diagnosis model matching and data classification marking based on working conditions, and gives an alarm when an identification result needs to be alarmed; the edge side transmits the preprocessed data to a cloud end, the cloud end analyzes the data, optimizes and subdivides a corresponding model, stores the data, and pushes the optimized model to the edge side for upgrading; and when the cloud receives the prediction result of the edge side needing to be alarmed, giving an alarm. According to the method, different operation working conditions of the equipment are automatically identified through the edge nodes, corresponding diagnosis models are selected for data processing, and classified and marked data are transmitted to the cloud for model training of subdivided working conditions, so that intelligent diagnosis of equipment faults is realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention relates to the technical field of computer vision and edge computing, and discloses a multi-computing-power collaborative video structured analysis method, which comprises the following steps of: acquiring a plurality of paths of original video streams, and synchronously decoding the plurality of paths of original video streams; gradient analysis is adopted to evaluate the decoded multiple paths of original video streams, and a preprocessing algorithm combination is selected based on a quality feature vector; establishing a data transmission channel, and adopting an asynchronous communication protocol and a multi-level cache system; performing content complexity analysis, and performing model matching according to the content complexity analysis to obtain a scheduling decision; carrying out distributed deployment, and carrying out pipeline reasoning on the deployed model; pipeline reasoning results are fused, and consistency constraint verification is carried out on fusion results; optimizing system configuration parameters according to a video structured analysis result to realize video structured analysis; the real-time performance, the accuracy and the system stability of video analysis are improved.

The invention relates to the technical field of data processing, and discloses a three-dimensional drilling data integration and display system for safety evaluation of an earthquake area. Comprising a mode selection module for selecting a point location marking mode of an earthquake region, a point location marking module for marking evaluation data point locations distributed at intervals, a data acquisition module for acquiring three-dimensional drilling data, a model construction module for constructing a region simulation model matched with the earthquake region, and an interactive display module. The dividing module is used for dividing a regional simulation model into sub-models and establishing regional channels between adjacent sub-regions; according to the invention, dynamic integration and display effects of three-dimensional drilling data at adjacent positions of an earthquake area can be realized, and an independent and reliable interactive display channel can be provided for integration and display of three-dimensional drilling data at different time lines and different geographic positions; the targeted integration and display effect of the local position of the earthquake area on the timeline is realized, and the accuracy of safety evaluation of the earthquake area is improved.

The invention discloses a mechanical arm intelligent grabbing system and method based on three-dimensional vision. The system comprises a three-dimensional vision system, an image preprocessing module, a model matching module, a grabbing point generation module, a grabbing control module and a dynamic environment self-adaption module. The method comprises the steps of multi-modal reference model collection and feature library establishment, precise hand-eye calibration and grabbing strategy self-learning and multi-modal fusion recognition, and dynamic prediction and self-adaptive grabbing. By adopting the three-dimensional visual recognition system and combining algorithms such as point cloud processing, point cloud method phase generation, point cloud downsampling, rough model matching and fine model matching, the limitation that a traditional mechanical arm system depends on two-dimensional image training and position coordinate point matching is overcome; by generating the grabbing points and calculating the pose coordinates of the grabbing points according to the hand-eye calibration matrix, the problem that the estimated pose of the grabbed object under the camera coordinate system is inaccurate in the prior art is solved. The system is easy to implement and has high practicability and popularization value.

The invention belongs to the technical field of data processing, and particularly discloses a computer vision model service scenarized configuration generation method, which comprises the following steps: constructing a basic information database comprising a plurality of computer vision models, the database being used for storing model identification information and feature parameters; extracting structured features of the scene according to a scene analysis engine, wherein the structured features are used for describing operation conditions of the scene; calculating a model matching score according to the structured features and the feature parameters, and obtaining a recommendation model through a multi-condition weight matching function; executing a model selection strategy based on the matching score, and obtaining a model matched with the scene; and generating a configuration file conforming to a predetermined specification according to the recommendation model. The objective of the invention is to solve the problems of low efficiency and error proneness caused by the fact that a computer vision model in the prior art depends on a manual selection model and cannot quickly select and adapt to an optimal model in diversified scenes.

Provided in the embodiments are a machining monitoring method, a machining monitoring apparatus, a computing device and a computer-readable storage medium. By acquiring first information of a first machining process in real time, retrieving a model in a model library on the basis of the first information, and matching a physical model having second information identical to the first information, the matched physical model characterizes a second relationship between material removal characteristics and specific cutting energy, thereby eliminating the influence of tool types and workpiece materials, and reflecting the machining characteristics of a second machining process; by comparing a first relationship with the second relationship, the differences between the first machining process and the second machining process are obtained to provide monitoring suggestions; and since the first relationship and the second relationship are acquired on the basis of the material removal characteristics and the specific cutting energy, and a monitoring threshold value is generated without using a signal strongly correlated with process conditions to perform learning, the method is well-suited for various machining scenarios.

The invention discloses a new energy automobile charging process inspection, diagnosis and charging integrated system, and relates to the technical field of new energy automobile charging control. The system comprises a central analysis module and a plurality of chargers connected with the central analysis module. The method comprises the following steps: collecting environmental data in real time through a charger, combining the environmental data with a battery state by a battery management module, and matching a power correction coefficient based on a lookup table or an artificial intelligence model to determine a target power upper limit; meanwhile, the central analysis module groups the chargers according to target power upper limit change and distributes the surplus capacity of the power station in proportion; according to the method, power is corrected through two dimensions of environment and battery state, limitation of single parameter correction is avoided, safety risks of coupling of an extreme environment and battery aging are effectively avoided, and charging safety is guaranteed; power is distributed according to dynamic grouping and proportion, the problem of capacity waste or overload of traditional unified distribution is avoided, and the capacity utilization rate of the charging station is improved.

The invention discloses a talent evaluation and post matching system and method, and relates to the technical field of information, and the system comprises a personality evaluation module, a post personality demand modeling module, a matching calculation module, a feedback optimization module and a data storage module. The personality evaluation module is used for acquiring a nine-dimensional personality feature vector of an employee based on a nine-type personality theory; the post personality demand modeling module is used for constructing and storing a nine-type personality weight vector model of a post; the matching calculation module calculates matching degree scores between the employees and the posts and generates a recommendation sequence based on a rule algorithm and a K-nearest neighbor algorithm; a feedback optimization module collects performance feedback data after employees enter the job, and automatically updates a matching model to improve prediction accuracy; and the data storage module is used for centrally managing evaluation data, post models and feedback records. According to the method, personalized matching and dynamic optimization between the employees and the posts are realized, and the organization configuration efficiency and the man-post adaptation quality can be improved.

The invention relates to the field of automobile financial consumption, and provides a method, a system, equipment and a medium for intelligently and automatically recommending an automobile loan scheme. According to the method, a two-dimensional portrait is constructed by obtaining user data and fund party data, the user data is processed by feature crossing and a portrait algorithm to generate a user portrait, and the fund party data is processed by normalization and feature engineering to generate a fund party portrait. The method is advantaged in that the user and fund party portraits are matched based on the machine learning model, the matching degree is calculated, the personalized car loan scheme is generated according to matching degree sorting, interest rate, deadline and approval requirements are considered, accurate matching of the user and the fund party is realized, and recommendation success rate and user satisfaction are substantially improved. Meanwhile, the method supports dynamic adjustment of the model, a matching algorithm is optimized in real time through performance comparison of the challenger model and the champion model, efficient extension and multi-fund-party access are achieved, and the method has industrial popularization value.

The invention discloses a power grid alarm sensing system, method and device based on artificial intelligence and a medium, and relates to the technical field of computers, and the system comprises a power grid alarm sensing middle station, a data acquisition module, a feature extraction module, a model matching module, a model prediction module and a current imbalance alarm sensing module. The data acquisition module is used for acquiring three-phase current to obtain current time sequence data; a feature extraction module performs feature extraction on the current time sequence data to obtain multi-dimensional feature extraction; a model matching module performs matching in a preset model library based on the electrical physical characteristics to obtain a target current imbalance sensing model; the model prediction module inputs the electrical operation state characteristics into a target current unbalance perception model to obtain a current unbalance perception result; and the current imbalance alarm sensing module carries out current imbalance alarm based on the current imbalance sensing result. According to the invention, the working condition of the power grid is described more comprehensively, and current imbalance alarm sensing is accurately carried out on the power grid.

The invention relates to the technical field of die casting intelligent manufacturing, and discloses an online monitoring and process compensation method and system for residual stress and deformation of a die casting. The method comprises the following steps: embedding a distributed temperature-stress composite sensor array in a mold cavity, and synchronously acquiring temperature and stress signals; after the signal is purified, a space-time correlation matrix is constructed to quantify a thermal-mechanical coupling relation; identifying a stress distribution mode through a support vector machine model, and positioning a fluctuation abnormal region; analyzing a defect mechanism based on mutual information and Granger causality test, and calculating a pore formation probability in combination with fluid dynamics simulation data; matching the pore high-risk location with a historical crack correlation model to generate a crack prediction index; and performing inversion optimization on the mold filling speed and pressure parameters by using the potential quality hazard evaluation function. Real-time monitoring of residual stress and deformation in the die-casting process, defect dynamic traceability and process online optimization are achieved, and air hole and crack defects are effectively restrained.

The invention relates to the technical field of asset assessment. The asset evaluation intelligent system based on the AI technology is provided, and the system comprises the steps that web crawler capture processing is conducted on a data source of a public network channel, and a first type of heterogeneous data is obtained; aPI interface docking processing is carried out on the data source of the enterprise internal system, and second-class heterogeneous data is obtained; performing logic splicing processing on the first type of heterogeneous data and the second type of heterogeneous data to generate multi-source heterogeneous asset evaluation data; performing model matching processing on the asset value characteristic matrix based on the asset type, and selecting an estimation model architecture; performing interpretability analysis processing on the dynamic evaluation parameters to generate a key factor contribution degree and an interpretable evaluation conclusion; and performing document processing on the contribution degree of the key factor and the interpretable evaluation conclusion to generate an interactive evaluation document so as to improve the multi-source data integration capability, enhance the risk dynamic evaluation adaptability and strengthen the real-time relevance and interpretability of the evaluation document.

A photolithography machine with an alignment system is described. The photolithography machine can identify a fiducial pattern provided on a substrate and use the location of the fiducial pattern to align the substrate before exposure. The fiducial pattern can include a plurality of fiducial markings arranged in a specified pattern. A model matching technique can be applied to match the respective individual fiducial markings. A shaping technique can then be used to identify the fiducial pattern based on the matched fiducial markings.

The invention relates to the field of engineering cost and technical and economic review, in particular to an intelligent technical and economic review method and system based on parameter fuzzy matching. And an intelligent process of equipment identification, model matching, unit conversion and quota review is designed, so that full-process automation of technical and economic review is realized. The system solves the problem of equipment name expression difference by utilizing fuzzy matching and a synonym library, the most adaptive equipment model is accurately selected through a mutual exclusion and scoring sorting mechanism, the accuracy and efficiency of review are remarkably improved, the problems of manual missed review, wrong review and different standards are effectively avoided, and in the quota matching process, the review efficiency is greatly improved. By applying keyword and antonym matching logic, a quota adjustment condition is automatically judged, a human-material-machine cost adjustment coefficient is calculated, and conversion from'experience judgment 'to'rule driving' is realized.

This invention relates to the field of meteorological services and provides a method and system for correcting subseasonal model precipitation based on climate zoning and sea surface temperature (SST) background constraints. The method includes: climate zoning, adding SST background constraints, reanalysis data modeling, model historical return data modeling, real-time prediction model matching, and prediction result correction. The system includes: a data preprocessing unit, a climate zoning unit, an SST background constraint unit, a reanalysis data modeling unit, a model historical return data modeling unit, and a real-time model matching and prediction result correction unit. This invention considers the importance of regional differences in precipitation and SST background constraints, solving the problems of significant regional differences in daily summer precipitation in my country, the tendency for overfitting in single-point modeling, and the lack of physical constraints that make correction methods unsuitable. It effectively utilizes physically meaningful predictability sources to modify the cumulative probability density correction method based on percentile mapping, thereby improving my country's summer subseasonal precipitation prediction capabilities.

The invention discloses a railway roadbed settlement prediction method and system, and belongs to the technical field of railway engineering. According to the method, basic data such as a roadbed structure, filler parameters, geological conditions and historical monitoring data are collected, a differential prediction model is matched according to roadbed strength grades, risk grading and monitoring resource dynamic allocation are carried out after parameter optimization and multi-index precision evaluation, and iterative optimization is realized based on an empirical database. The system comprises a data acquisition module, a model matching module, a settlement simulation module, a precision evaluation module, a post-processing module and a data storage module, and all the modules cooperate to achieve full-process automation. According to the method and the system, the problem of insufficient adaptability of a traditional single model is solved through accurate model adaptation, the prediction efficiency and the data reliability are improved by means of automatic processing, the safety and the cost are balanced through risk hierarchical management and control, and the adaptability of the system to complex working conditions is continuously enhanced through an iteration mechanism.

The invention provides an electric power prediction system and method for a power distribution network containing multiple source loads. The system comprises a power distribution network management middle platform, a data acquisition module, a feature extraction module, a correlation analysis module, a model matching module and an electric power prediction module. The data acquisition module acquires power path complexity, power transmission quantity, operation data of a multi-element power supply and load data of a multi-element load; the feature extraction module extracts the operation data and the load data to obtain a power feature vector and a load feature vector; the correlation analysis module analyzes and obtains time correlation feature information and space correlation feature information based on the power supply feature vector and the load feature vector; the model matching module is used for matching a target power prediction model based on the power path complexity and the power transmission quantity; and the power prediction module inputs the time correlation feature information and the space correlation feature information into a target power prediction model to obtain a power prediction result. The power prediction precision of the power distribution network is improved, and safe and stable operation of the power distribution network is guaranteed.