1098results about How to "Accurate processing" patented technology

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention discloses an efficient flight control system of a low-altitude manned unmanned aerial vehicle based on tilting composite wings, and relates to the technical field of unmanned aerial vehicle control systems. The low-altitude manned unmanned aerial vehicle efficient flight control system based on the tilting composite wings comprises an initialization and pre-check module, a vertical take-off and landing control module, a transition flight control module, an obstacle avoidance and path planning module and a landing control module. Through integration of vertical take-off and landing, smooth transition to cruise, intelligent obstacle avoidance, path planning and other modules, autonomous and efficient flight in a complex environment is realized, and the application scene of the unmanned aerial vehicle is greatly widened. Meanwhile, the accurate flight control and data post-processing analysis capability not only guarantees the safety and comfort of passengers, but also promotes the continuous optimization of the system performance through the real-time performance evaluation and feedback mechanism. In addition, the aerial refueling / charging technology is supported, the cruising ability is prolonged, and the task execution continuity and flexibility are enhanced.

The invention relates to a biped robot complex terrain self-adaptive gait planning method and a biped robot, and belongs to the technical field of robotics.The biped robot complex terrain self-adaptive gait planning method comprises the steps that a fractional order PID controller is constructed, and the fractional order PID controller comprises a multi-sensor network module, a self-adaptive module and a fractional order calculus control module; constructing a reinforcement learning model for biped robot gait planning, setting a state, an action, a decision and a reinforcement learning reward function of reinforcement learning, performing continuous iteration and optimization adjustment on the reinforcement learning model by using a strategy gradient method, updating strategy parameters, and outputting control parameters of the biped robot; and performing decision fusion on control parameters output by the fractional order PID controller and the reinforcement learning model through a comprehensive loss function to obtain globally optimal biped robot control parameters. According to the method, more accurate gait planning can be provided for the biped robot in complex terrains, and better walking flexibility and adaptability are achieved.

The invention discloses a high-precision industrial quality control system based on an AI large model and an application method, which are characterized by being applied to detection and measurement of industrial products. Comprising a product data acquisition module in an industrial production process, a data preprocessing module, an AI large model suitable for industrial quality control, a quality detection module, a result feedback and decision support module, a control module and a system management module, the application of the AI large model suitable for industrial quality control comprises the steps of constructing the AI large model suitable for industrial quality control, calling the AI large model suitable for industrial quality control, executing the control system, carrying out multi-modal data fusion, and carrying out multi-modal data fusion. The product data acquisition module in the industrial production process acquires data of multiple modes for quality detection and measurement, the AI large model suitable for industrial quality control adopts a data fusion algorithm, and the feedback module provides a visual detection result. And visually displaying the detected quality problem to a user in the form of an image and a chart.

The invention, which relates to the technical field of social emotion analysis, discloses a university social negative emotion characterization analysis system comprising a data acquisition and preprocessing module, a feature extraction and anomaly detection module, an intelligent perception and negative emotion characterization module, and an anomaly risk assessment and response module. Emotional feature information is acquired in real time based on a multi-dimensional data source, and the acquired emotional feature information is cleaned and preprocessed. According to the method, by calculating the data consistency offset coefficient and the strong correlation coefficient, the multi-collinearity problem among multi-dimensional data features is solved, and instability of the model regression coefficient is avoided. And the system identifies abnormal correlation in real time and combines risk threshold classification processing to ensure robust model output. An analysis set is established by continuously collecting data, long-term emotion trend analysis is supported, personalized responses such as reminding, intervention or emergency measures are provided according to risk levels, and accurate management and timely adjustment of student emotional states are achieved.

The invention discloses a robot grinding real-time defect detection and self-adaptive correction system based on deep learning. The problem that in the prior art, real-time detection, defect correction and accurate path optimization are difficult to achieve in the grinding process is solved. The image, the three-dimensional point cloud and the moment data are fused through a multi-modal sensing system, and data fusion processing is performed by adopting a time synchronization mechanism, so that high-precision defect detection is realized. And the self-adaptive correction module dynamically adjusts the pressure, the speed and the angle of the grinding tool according to real-time feedback by using a reinforcement learning algorithm, so that accurate grinding for different workpiece surfaces is ensured. The system is further combined with closed-loop feedback control, the grinding path is continuously optimized, and the consistency and stability of the grinding quality are improved. Compared with the prior art, efficient and automatic defect detection and self-adaptive correction can be achieved on the complex and irregular workpiece surface, and the production efficiency and the surface treatment quality are remarkably improved.

The invention provides double-arm picking equipment for shelf-type planted shiitake mushrooms and an operation method thereof. The double-arm picking equipment comprises a moving platform, the upper portion of the moving platform is connected with a chassis, and a lifting device, a shiitake mushroom storage device and a shiitake mushroom picking device are arranged above the chassis. Through mechanical automatic mushroom stick fixing and mushroom picking, intelligent identification, picking and placing of mushroom sticks on a shelf and identification and picking of mushrooms on the mushroom sticks can be carried out at the same time, planting areas can be traversed automatically, the mushroom picking speed is increased, and labor and time cost is saved. Three-dimensional scanning is carried out through the depth camera vision system, the position and size of the mushroom sticks and the maturity of mushrooms can be accurately recognized, and it is ensured that the quality of picked mushrooms is high. The lifting device adjusts the height of the mechanical arm and cooperates with a visual system, picking and mushroom stick processing are more accurate, damage to mushrooms and mushroom sticks is reduced, mushroom picking automation is achieved, operators do not need to put physical labor heavily, and the workload is relieved.

The invention belongs to the field of image processing, and particularly discloses an imaging simulation method and device based on a point spread function and electronic equipment, and the method comprises the steps: building a light source model corresponding to an image shot by an optical system, and obtaining the point spread function of a point light source of the light source model after the action of the optical system; obtaining error point spread functions under different optical system models; performing weighted averaging on each error point spread function according to a preset weight to obtain a comprehensive point spread function; forming an array diagram by the comprehensive point spread function according to light distribution, and carrying out convolution on the array diagram and a shot image to obtain a degraded image simulating a blurring effect caused by aberration; and applying the distortion model of the optical system to the shot image to obtain a distortion-simulated deformation effect image, and realizing imaging simulation of the optical system based on the deformation effect image and the degraded image. According to the invention, the imaging accuracy and efficiency can be improved.

The invention discloses a standardized electrical switch cabinet detection method based on the Internet of Things, and relates to the technical field of electrical switch cabinet detection, and the method specifically comprises the following steps: obtaining the core information of all abnormal data generated by all sensors in an electrical switch cabinet in real time, and carrying out the analysis after obtaining, evaluating the priority of each abnormal data in the electrical switch cabinet with the high current leakage phenomenon, and dividing each abnormal data into low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data according to an evaluation result; and according to the division result of each abnormal data, constructing an abnormal data hierarchical sorting mechanism, and respectively adopting different sorting and processing measures for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data. According to the method, the problem of unreasonable priority processing of the abnormal data in a high-current leakage scene is solved, quick response and accurate detection are realized, and the fault processing efficiency and the system reliability are improved.

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention discloses an operation site safety management and control method and system based on a multi-modal large model, and the method comprises the steps: collecting site violation data, and carrying out the preprocessing, and obtaining multi-modal data; constructing a multi-modal large model; carrying out training and optimization on the multi-modal large model by utilizing the preprocessed multi-modal data; and using the optimized multi-modal large model to realize operation site safety management and control. According to the invention, a visible light image, a safety violation knowledge base and other modal data are fused, and a comprehensive safety monitoring and management platform is constructed by using a large model and a big data processing technology, so that real-time monitoring, early warning and intelligent response of potential safety hazards of a complex operation site are realized, and the safety management level of the operation site is improved.

The invention relates to the field of electric digital data processing, in particular to an intelligent management and control method and system for building water. The method comprises the steps of collecting real building water consumption data, training a data expansion model based on a conditional generative adversarial network algorithm adopting a dynamic recoding strategy, training a feature extraction model, training a classifier of a deep neural decision tree algorithm based on local structure entropy, and collecting new real building water consumption data. And performing feature extraction on the new real building water consumption data, inputting the new real building water consumption data after feature extraction into the trained classifier, identifying the energy consumption level of the new real building water consumption data, and performing corresponding behaviors. An existing building water intelligent management and control system and method have the problem of poor data analysis accuracy. The building water intelligent management and control method and system provided by the invention are high in data analysis accuracy.

The invention discloses a packaging tin material proportioning optimization method based on a particle swarm optimization algorithm, and the method comprises the following steps: initializing a particle swarm, dynamically adjusting the positions and speeds of particles through a fuzzy logic control system, and generating an initial material proportioning scheme; a fitness function is constructed based on a non-Euclidean geometric method, multiple indexes such as strength, toughness, corrosion resistance and cost are comprehensively considered, and fitness values of all the schemes are calculated; gradually optimizing a material proportioning scheme by using a biologically inspired particle swarm optimization algorithm; a dynamic clustering mechanism is introduced, and different material matching schemes are analyzed and optimized; dynamically switching between global search and local search through a hybrid search strategy, and further optimizing a selected scheme; and finally, outputting an optimized optimal material proportioning scheme, and evaluating the reliability of the optimal material proportioning scheme through experimental verification and simulation. According to the invention, the optimization efficiency and performance of the packaging tin material proportioning are improved, and the method is suitable for the requirements of production automation and high efficiency.

The invention provides a smoothed particle fluid dynamics simulation wave impact structure boundary force processing method, which comprises the following steps: S1, initializing the speed, density and position of particles by establishing a numerical model; s2, dividing fluid particles and boundary particles; s3, calculating and correcting the density and speed variation of the particles according to the hydrodynamic equation and the boundary rejection effect; s4, calculating the pressure and the speed of boundary particles through a Schelerd interpolation method, and reflecting the interaction between the fluid and the solid; s5, based on the pressure and the position of the boundary particles, calculating the pressure intensity, obtaining the stress and the torque of the structure, and updating the degree of freedom; and S6, updating the particle state and entering the solution of the next time step. According to the method, when the influence of the boundary force is determined, the influence of the relative speed of fluid particles and wall surface particles is considered, meanwhile, the maximum speed in a flow field is approximately replaced by the artificial sound velocity, and the purposes of accurately simulating the interaction process of the ocean wave-ocean engineering structure and ensuring the stability of numerical simulation calculation are achieved.

The invention discloses an intelligent interactive Agent system based on Langchain and an implementation method, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps of 1, establishing an intelligent interactive Agent system by using an interface and an integrated tool provided by a Langchain framework, 2, managing calling of various tool modules and services through a central scheduling module, and dynamically registering and unbinding the tool modules by using the interface provided by the Langchain framework, and 3, processing user input by using a large language model through a process management module, and establishing an intelligent interactive Agent system. 4, a series of tool modules are integrated through the integrated tool management module, the tool modules comprise a text search tool, a weather query tool and a video information acquisition tool, each tool module works independently or is in linkage with other tool modules, and the tool modules are connected with the integrated tool management module. And 5, managing a user interaction interface through a front-end management module, supporting a user to query and control in a natural language, displaying a processing progress and a processing result in real time, and providing feedback and help information.

The invention relates to the technical field of television image communication, in particular to a user information input method and system in an intelligent television all-in-one machine. The method comprises the following steps: collecting a user image, carrying out white balance correction and focusing processing, generating a moving object focusing image, carrying out identification and attention tendency analysis on a character in the focusing image, obtaining character tendency data, analyzing a historical interaction image, identifying an interaction mode and constructing a user intention prediction model, and obtaining a user intention prediction result. And when the tendency data shows that the user preferences the television content, intention drilling and auxiliary adjustment are performed on the current image based on the prediction model, predicted user input information is generated, logic conflicts in the predicted information are detected, feedback optimization is performed, and final user input information is output. According to the invention, a more accurate and more flexible user information input method is realized.

The invention relates to the technical field of wireless communication and data transmission, in particular to a data transmission method, system and device based on a portable WiFi network. The method comprises the following steps: acquiring initial multipath channel signal data; performing channel feature extraction on the initial multipath channel signal data to obtain antenna channel feature data; performing phased-array antenna beam calculation on the antenna channel characteristic data to obtain an antenna initial beam directional diagram; performing spatial filter interference suppression on the antenna initial beam directional diagram to obtain antenna optimization beam form data; performing digital beam forming on the antenna optimization beam form data to obtain antenna beam configuration parameters; performing network topology node analysis on the antenna beam configuration parameters to obtain a portable WiFi node distribution diagram; and carrying out improved on-demand routing calculation on the carry-on WiFi node distribution map to obtain an initial routing topological structure. According to the invention, the reliability of portable WiFi network data transmission is improved.

The invention relates to the technical field of grounding monitoring, and discloses a multipath grounding monitoring method for an intelligent station electrostatic monitor, and the method comprises the steps: carrying out the infrared scanning of a station region, comparing a station layout template, obtaining the station configuration data, and reading an electrostatic monitoring parameter; performing time division multiplexing matrix design on the multiple monitoring channels to obtain a time division multiplexing configuration scheme; performing on-off control time sequence compilation on the high-speed solid-state relay to obtain an on-off control time sequence table, and executing intelligent on-off control to realize monitoring channel switching; inputting the electrostatic signals of the plurality of monitoring channels into a high-precision analog-to-digital converter for parallel sampling and de-noising processing to obtain pre-processed electrostatic data; according to the invention, the monitoring strategy can be dynamically adjusted according to the station characteristics, the efficient collection and processing of multichannel data are realized, and an accurate static risk assessment and early warning mechanism is provided.

The invention discloses an infrared image super-resolution reconstruction method based on comparative learning, and relates to the technical field of image processing. Comprising the following steps: preparing a data set; constructing a degradation pool; training a comparison encoder; constructing a network model; and training the network model. According to the technical scheme of the invention, the structure and the training mode of a comparison encoder are designed, discriminative feature representations are generated from the two dimensions of structure and degradation respectively, and the modulation infrared super-resolution network is represented to realize a specific super-resolution task according to the feature representations. The capabilities of the existing super-division network in the aspects of degradation adaptability, structural detail recovery, generated image quality and feature expression discrimination can be effectively improved, and the bottleneck of the traditional super-division network in the aspects of coping with complex degradation and insufficient detail recovery is solved; according to the method and the device, super-division of a real-world low-quality infrared image is realized, so that the quality of the infrared image is improved, and particularly, the details and the contrast ratio of the infrared image are enhanced.

The invention discloses a system for constructing a medical accelerator quality control knowledge base by using a large language model. The system comprises an acquisition module, a two-stage preprocessing module, an abnormal value detection module, a large language training module, a knowledge graph module and a knowledge base indexing and querying module. According to the method, related data of the medical accelerator are collected through multiple channels, information of the medical accelerator in multiple aspects of equipment performance, faults, quality control and the like can be covered, and the constructed knowledge base has comprehensiveness. Intelligent screening and hierarchical processing are performed on the data, so that more accurate processing of the data in different states is facilitated; the preset rule set comprises an equipment parameter range rule, a data format and integrity rule, a data frequency rule, a historical data statistical rule and the like, various characteristics of the medical accelerator data are comprehensively considered, and the accuracy of data screening and blocking is improved; and the method can better adapt to the complexity and specialty of medical accelerator data.

The invention relates to the technical field of tourist route recommendation, in particular to a tourist route recommendation system and method based on big data. The method comprises the following steps: collecting travel demand data and basic sign information of a user, and performing hierarchical analysis on a demand to obtain master / slave demand data; then, through demand behavior intersection analysis, muscle soreness intensity is simulated in combination with user physical sign data, and the physical degeneration rate is further estimated; based on the physical decline rate, making a rest strategy, and optimizing distribution and adjustment of tourist routes; and finally, transmitting the adjusted tourist route data to a user terminal. According to the invention, the tourist route recommendation technology is improved, so that the tourist route recommendation technology is more perfect.

The invention discloses a feature recognition method for a wireless communication digital signal, and relates to the field of wireless communication, and the method comprises the steps: further calculating an extracted feature vector F (t) through constructing a comprehensive quality algorithm formula, outputting a quality score Qs of the wireless communication signal, and carrying out the preliminary comparison and evaluation of the quality score Qs and a preset signal quality threshold value Q. And meanwhile, dynamic characteristics are introduced, secondary comprehensive calculation is carried out on the signal quality, and a second wireless communication signal quality score Qs2 is output. According to the optimization process, the dynamic change characteristics of the signals can be more comprehensively analyzed, the signals are accurately classified through the classifier, the signal quality is divided, an adjustment mechanism can be automatically triggered according to the signal quality grade, and the transmission efficiency is improved by means of adjusting the communication channel frequency, optimizing redundant coding, dynamically adjusting the transmission power and the like. The communication reliability and the anti-interference capability of the system in a complex environment are improved, and the safe operation of the rail transit system is effectively guaranteed.

The invention discloses a long text analysis method and device, a storage medium and a terminal. Identifying at least one title in the to-be-analyzed long text, and generating directory information of the to-be-analyzed long text based on each title; dividing the long text to be analyzed into a plurality of text paragraphs according to the titles and the input length requirement of the vector model; determining a target superior directory corresponding to each text paragraph from the directory information, and respectively integrating each text paragraph and the target superior directory corresponding to each text paragraph; and inputting the integrated text paragraphs into a vector model to obtain vector features output by the vector model for the text paragraphs. Due to the fact that the whole document is divided into the text paragraphs through the title of the long text, the text paragraphs are divided more reasonably. And continuously integrating the long text information according to the context information contained in the superior directory, so that the semantic information of the text paragraphs is richer, and the model can more accurately understand and process the text paragraphs on this basis.

The embodiment of the invention provides an information retrieval method and device, electronic equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that obtained intention description information is input into a preset intention recognition model, and an intention map is obtained; performing modal feature extraction processing on the intention description information to obtain at least one modal feature representation; performing modal feature extraction processing on the intention graph to obtain graph structure feature representation; inputting the modal feature representation and the graph structure feature representation into a pre-trained multi-modal fusion model to obtain a multi-modal semantic representation; generating a query statement through a preset large language model according to the multi-mode semantic representation and mode information of a database to be queried; and retrieving the data in the database according to the query statement, and outputting a retrieval result. According to the embodiment of the invention, the accuracy of an information retrieval result and the use experience of a user can be improved.

The embodiment of the invention discloses a network security alarm processing method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a network security alarm; retrieving a target text vector associated with the network security alarm in a network security domain knowledge base; the network security domain knowledge base is a knowledge base constructed after vectorization processing is carried out on network security corpora; based on the target text vector, an alarm vector corresponding to the network security alarm, a preset tool library and a large language model, obtaining a research and judgment processing flow of the network security alarm; executing a research and judgment processing flow; and obtaining a research and judgment result of the network security alarm based on an execution result of the research and judgment processing flow.

The invention relates to an intelligent ventilation system in a tunnel and a control method thereof, and belongs to the technical field of tunnel ventilation. The intelligent ventilation system in the tunnel comprises a data acquisition unit, a ventilation unit, a control unit, a data storage unit and a data analysis unit, wherein the data acquisition units are mounted in detection sections in a tunnel, and each data acquisition unit comprises a COVI detector, a wind speed and direction detector and a traffic flow detection module; when the CO concentration and the VI exceed threshold values, the ventilation unit is controlled to enter a corresponding working mode; the data analysis unit is used for generating an early warning model according to the data of the data storage unit, and the early warning model is used for obtaining CO concentration and VI data in the future # imgabs0 # time according to the wind speed and wind direction data, the traffic flow, the vehicle type, the fuel oil type data, the CO concentration and the VI change. According to the ventilation system, intervention can be conducted before the pollution concentration is increased, the period of insufficient ventilation is effectively avoided, and it is guaranteed that good air quality is always kept in the tunnel.

The invention relates to the field of heat supply heat load operation adjustment, and provides a continuous optimization heat supply load prediction method and system based on big data analysis learning, and the method comprises the steps: S1, configuring a data interface on a real-time library service module to receive real-time and historical operation data, the operation data comprises meteorological information data, heat exchange station heat load and room temperature qualification rate; and S2, preprocessing the collected operation data through a data processing module integrated on the heat supply platform service module, and analyzing the preprocessed operation data through an analysis algorithm stored in a relation module to determine a predicted heat load value of a next control period. The heat supply platform is combined, the big data analysis technology is used for learning and analyzing historical heat supply network operation data, proper correction and adjustment are carried out, the heat load of the heat supply system is effectively predicted, the method is closely combined with actual operation, prediction accuracy is remarkably improved, the operation process is simplified, energy use is optimized, and the operation efficiency and the system response speed are improved.

The invention relates to the technical field of crack detection, in particular to a bridge concrete crack intelligent detection method based on a deep neural network. The method comprises the following steps: firstly, acquiring images of a bridge crack high-incidence area through an unmanned aerial vehicle, constructing an inspection data set, and performing preliminary classification on the images by using a mutual information clustering algorithm to distinguish crack images from non-crack images; acquiring position information of a crack area based on the crack image, and acquiring ultrasonic data of the crack area by using an ultrasonic probe; establishing a multi-modal crack data set by fusing ultrasonic data and crack image data, detecting the multi-modal crack data set by using a multi-modal fusion network, and distinguishing true cracks from false cracks; the method comprises the following steps: carrying out 3D modeling on true cracks to generate a visual crack model, grading the cracks based on a damage evaluation method, and formulating corresponding repair measures for mild, moderate and serious damages to ensure the safety and stability of a bridge structure.

The invention provides an elevator risk early warning method based on local patch and enhanced position coding, which comprises the following steps of: firstly, collecting original data of an elevator of the internet of things, and converting the original data into multivariable time sequence characteristics after cleaning the original data; secondly, performing patch operation on the multivariate time sequence, namely, firstly dividing each input univariate time sequence into patches to serve as input tokens of Transformer, so that local semantic information is reserved, and a longer review window can be obtained under the condition that the number of the input tokens is the same; after relative position coding is carried out on the token, the token is input into a Transform model, and in the model, a self-attention mechanism and a convolution pooling layer are adopted to carry out extraction and intersection on patches, so that the feature relation between the patches is concerned, and meanwhile, the time feature relation in the patches is concerned; performing fusion operation on the global time sequence characteristics and the in-patch time sequence characteristics, and then handing over to a full connection layer for prediction to output a prediction result; during model training, the model is adjusted through an optimization algorithm to improve the performance.

The invention discloses a spatial matching method and system based on natural resource monitoring and demographic data, and the method comprises the steps: obtaining residential land and house building data in regional natural resource survey monitoring data, employing a spatial position and attribute matching technology, and combining a random forest algorithm to carry out the spatial matching of the residential land and the house building, thereby achieving the spatial matching of the residential land and the house building. Extracting residential building pattern spots and attribute information thereof; establishing attribute feature mapping between a residential building pattern spot level scale and an administrative division unit scale, and converting administrative division demographic data to residential building pattern spots by using a random forest hyper-parameter search method; further integrating multi-source data in the natural resource survey monitoring data, constructing a population geographic blot index taking a water area as a mask, and correcting the population of the residential building; and finally, calculating a population distribution fitting coefficient, iteratively correcting a population distribution error, and realizing spatial accurate matching of the residential building and population distribution through a partition adjustment strategy.