198 results about "Fusion system" patented technology

The invention discloses a multi-source heterogeneous data fusion system based on an RAG. According to the method, through deep knowledge fusion and a dynamic cognitive evolution mechanism, the decision-making intelligence level in a complex data environment is remarkably improved, and equipment operation parameters, environment indexes and a domain knowledge base are deeply associated to form a panoramic data view with space-time continuity. A generative enhancement mechanism endows original data with a self-evolution characteristic, industry empirical rules and real-time situation awareness are injected while the fidelity of the original characteristic is maintained, so that a decision model can capture micro data fluctuation and follow macroscopic business logic, accurate balance between risk early warning and resource scheduling is realized, and the risk early warning efficiency is improved. The dynamic adaptation characteristic enables the system to autonomously update a knowledge system and optimize a decision path in a complex and changeable industrial environment, and a post response mode of a traditional static analysis model is converted into an intelligent center with prospective pre-judgment and real-time regulation and control capabilities.

The invention provides a dynamic fusion system for digital twin energy management BIM modeling and a method thereof, which are used for improving the intelligent level of energy management and optimizing energy scheduling. The system comprises a data sensing node used for collecting heterogeneous operation data of energy facilities; the local twin processor is used for carrying out real-time digital twin mapping on the data and generating a dynamic feature vector; region twinborn nodes perform local optimization based on edge calculation, and realize decentralized collaboration through a block chain or a knowledge graph; the cloud BIM fusion engine constructs a digital twin BIM model, generates a self-optimization energy scheduling scheme and predicts a long-term trend; and the operation control terminal is used for presenting a real-time view and providing a regulation and control instruction. According to the method, technologies such as multi-source data fusion, streaming aggregation optimization and adaptive twin mapping are combined, and the accuracy and dynamic response capability of energy management are improved.

The invention discloses a multi-source heterogeneous agricultural data element fusion system and method, and relates to the technical field of data fusion processing, and the method comprises the steps: carrying out the data screening of cleaned data; the screened data is preprocessed by the constructed agricultural field knowledge base, the logic consistency of the data is checked through a rule engine, and a corresponding coping strategy is adopted according to a check result; identifying abnormal points in the multi-source data by adopting a Gaussian mixture model and a constructed logic rule combination, and correcting or complementing the detected abnormal points in combination with a specific scene; key features are selected through information gain, multi-source fusion is carried out on the key features and original multi-source data, an abnormal mode library is established after abnormal data modes are classified, and source categories of the abnormal data are matched from a fault matching rule library according to abnormal features of the abnormal data; and the fault matching rule base is established, so that the source category of the abnormal data can be accurately identified, and a targeted solution is provided for processing the abnormal data.

The invention relates to the technical field of distributed target detection, in particular to a distributed target detection fusion system and method based on cooperation of multiple unmanned aerial vehicles, and the system comprises a data collection module, a coordinate system conversion module, a time registration module, a track association module and a filtering estimation module. The method comprises the following steps: collecting detection target data reported by each slave platform through a central platform, and sequentially performing coordinate system conversion, time registration, track association and filtering and state estimation on the detection target data to generate a global unified situation.

The invention provides an intelligent collaborative innovation incubation and campus security fusion management platform, which relates to the technical field of fusion management and comprises a multi-modal data acquisition and fusion system, an intelligent decision and strategy optimization engine, an edge calculation and security communication architecture, a scenarized analysis and prediction module and a security assurance and model adaptation system. The multi-modal data acquisition and fusion system comprehensively collects multi-source data of a campus environment, personnel behaviors and equipment states through a multi-sensor array, realizes efficient fusion of different modal features by using a feature fusion algorithm based on tensor decomposition, provides a comprehensive and refined data basis for subsequent analysis, and improves the accuracy of data fusion. And the intelligent decision-making and strategy optimization engine learns and generates an optimal decision-making strategy for various scenes of campus safety management by virtue of a plurality of advanced algorithms such as a consensus double-Q network algorithm, an artificial fish swarm optimization algorithm based on Levy flight, a particle swarm optimization algorithm based on quantum behaviors and the like.

The invention belongs to the technical field of hypersonic flight vehicle test, and relates to a hypersonic flight vehicle simulation flight test system based on a multi-modal large model, which comprises a data acquisition and fusion system and a control system. Through a data acquisition and fusion system and a cross-modal attention mechanism, efficient alignment and feature representation of time sequence data, environmental data and a historical case library acquired by a ground test system are realized, and the data acquisition and analysis precision is remarkably improved. In combination with a hardware system, effectiveness of test parameters is ensured, and the aircraft design and verification period is shortened. The control system dynamically adjusts test parameters through a natural language instruction, the model generates a structured control parameter vector through semantic understanding, the performance and stability of the aircraft control system are improved, the test operation complexity is reduced, and the user experience is improved.

The invention provides a vehicle-road cloud integrated multi-network integration system data classification and grading method, which realizes automatic data screening and marking through construction of a multi-scene data identification rule base and business process feature analysis, and solves the problem of standardized identification caused by lack of a unified classification framework of multi-source data. Semantic relation systematic modeling is achieved by means of a multi-layer architecture model and multi-layer model construction, and the problems of insufficient vehicle and road cloud cross-domain entity semantic abstraction and model fragmentation are solved; according to the method, quantitative evaluation of importance and sensitivity is achieved in combination with regulation requirements and modeled data, the problems that data classification and grading standards are missing and a safety evaluation system is imperfect are solved, a closed-loop process of data recognition, semantic modeling, model mapping and grading evaluation is formed, and the problems that in the prior art, a static rule is relied on, and safety evaluation is poor are solved. And changes of data types, interaction types and security requirements in a multi-network fusion scene cannot be dynamically adapted.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention discloses a hydropower station computer monitoring operator station fusion system, which comprises a controlled plant computer monitoring system and an operator station fusion system, and is characterized in that a fusion operator workstation is directly connected with a local switch and a controlled plant switch through double independent network interfaces; a man-machine interface module is fused to uniformly manage monitoring and control of cross-plant equipment; and the data server and the switch adopt a dual-computer redundancy architecture and STP / LACP link redundancy, and are combined with AES-256 encryption and instruction bidirectional verification to improve the security. The system fuses multi-source data through an improved adaptive weighted Kalman filtering algorithm, predicts an equipment state trend by using an LSTM model, supports local / controlled priority mode switching, and automatically triggers data caching and instruction degradation when a network is abnormal. An original system does not need to be transformed, through physical direct connection and logic fusion, the framework is simplified, the operation and maintenance cost is reduced, efficient integration and reliable redundancy of multi-plant monitoring functions are achieved, and the system is suitable for large hydropower stations and one-plant multi-station scenes.

The invention discloses an FME-based adjacent pattern spot fusion method and system, and relates to the technical field of adjacent pattern spot fusion, and the method comprises the steps of data processing, parameter setting and rule configuration, adjacent pattern spot fusion execution, result verification and output and feedback and optimization, fusion standard and rule determination, index system unified evaluation, and spatial relationship optimization processing. Manual intervention is reduced through the automatic working process, and the processing efficiency is improved; the invention further provides an adjacent pattern spot fusion system based on FME, the adjacent pattern spot fusion system comprises a data preprocessing module, a data input module, a parameter setting and rule configuration module, a comprehensive correlation index calculation module, a pattern spot fusion module and a result verification and output module, and the adjacent pattern spot fusion method based on FME is executed.

The invention discloses a role playing model fusion system and method based on retrieval and reflection, and belongs to the technical field of natural language processing. The implementation method comprises the following steps: 1, performing data cleaning on original role historical dialogue data, and performing semantic screening by using semantic similarity to form historical dialogue context data; 2, event knowledge associated with the roles is constructed through association degree matching; 3, constructing a role reflection strategy mode by using historical dialogue data, and generating role reflection data; 4, when a user inputs a question, replying by adopting a multi-source fusion mode based on role thinking; compared with the prior art, in a multi-scene task, a dynamic retrieval role knowledge base is utilized to screen task related background knowledge, and a thinking mode modeling framework based on a role reflection mechanism and a context fusion strategy are constructed to generate high-quality answers conforming to the context; and thus, the fusion ability of the role playing model is improved, and the consistency of role words and language styles is realized.

The invention discloses a configuration processing method and device of a hyper-converged system, a storage medium and electronic equipment, and relates to the technical field of computer hardware, and the method comprises the steps that specified configuration information of a server is acquired, and the specified configuration information is configuration information of a basic input and output system of the server; the specified configuration information is analyzed, actual configuration values of a set of specified configuration items are obtained, and one specified configuration item in the set of specified configuration items corresponds to one function provided by the hyper-converged system; in a set of specified configuration items, under the condition that at least one configuration item with the actual configuration value inconsistent with the expected configuration value exists, the actual configuration value of the at least one configuration item is modified, and therefore the difference between BIOS configuration and the hyper-converged system requirement can be automatically detected and corrected; the configuration processing method of the hyper-converged system in the related technology has the problems of low deployment efficiency and poor reliability caused by high deployment complexity.

The invention discloses a sky-ground integrated data fusion management system and a method thereof, relates to the technical field of data processing, and aims to generate high-precision virtual space data by efficiently integrating and processing multi-source data and provide real-time decision support and data sharing functions for users. The system comprises a data access module, a data preprocessing module, a space generation module, a data adaptation module, a space-time alignment module, a space-time coordinate embedding module, a data fusion module, a global supervision module and a data service module. According to the method, accurate alignment and efficient fusion of data of different sources in time and space can be ensured, the system optimizes the data fusion accuracy by utilizing dynamic subdivision and cross-modal data association technologies, and meanwhile, the operation state of each module is monitored in real time through a global supervision mechanism and is dynamically optimized, so that efficient and stable operation of the system is ensured.

The invention discloses a multi-source industrial equipment data acquisition and fusion system and method, and relates to the technical field of data fusion. The method comprises the following steps: carrying out adaptive conversion and edge preprocessing on acquired information of industrial equipment, dividing the acquired information into key information and non-key information, respectively encrypting and transmitting the key information and the non-key information, and constructing a digital twin model of the industrial equipment and an acquisition point topology network of the digital twin model; simulation information, a time correction coefficient and a space correction coefficient of each acquisition point are acquired, correction information of each acquisition point is acquired according to the time correction coefficient and the space correction coefficient, a space-time correction model is constructed, and real-time correction information of each acquisition point is acquired by using the space-time correction model; judging the operation state of each industrial device and generating an abnormal signal for feedback; the data processing amount of the cloud can be reduced, and the operation state of the industrial equipment can be accurately reflected.

The invention discloses a self-adaptive synchronization method, system and platform for a multi-projector wiring-free splicing fusion system based on wireless communication. The method comprises the following steps: generating and acquiring first data corresponding to projectors; wherein the first data is state parameter data of the projector and at least comprises brightness data, color temperature data and picture size data; determining to generate second data corresponding to the first data, and generating corresponding third data based on the second data; wherein the second data is projector state parameter change data; the third data is verification code data; based on wireless communication and in combination with the third data, the second data is dynamically adjusted in real time, and the working states of all the networked projectors are synchronously processed, so that rapid, accurate and self-adaptive synchronization of the states of the multiple projectors under the condition of no wiring is achieved, and the system and the platform are convenient to use. And parameters such as brightness, color temperature and size of the spliced picture are consistent.

The invention discloses an SVG valve hall multi-source monitoring data fusion method and fusion system, and belongs to the technical field of power system equipment monitoring, and the method comprises the steps: carrying out the abnormal value and missing value data cleaning of initial data, outputting the preprocessed monitoring data, carrying out the algorithm matching of the preprocessed monitoring data to complete the time-space unification, and carrying out the fusion of the SVG valve hall multi-source monitoring data. Extracting associated features, forming a multi-source feature parameter set, quantifying data source reliability differences and locking key features by taking the multi-source feature parameter set as a reference, forming a dynamic fusion weight distribution scheme and key feature data, calling the multi-source feature parameter set, and eliminating feature conflicts; and generating a comprehensive fault diagnosis result and a health state evaluation index of the valve hall equipment, outputting early warning information and maintenance decision suggestions, and completing the whole data fusion closed-loop application. And a more efficient monitoring scheme is provided for safe operation of valve hall equipment.

The invention relates to the technical field of brain-computer interfaces, and particularly discloses a fusion system combining a brain-computer interface with transcranial electrical stimulation and a feedback stimulation method. The fusion system comprises a main machine part, an auxiliary machine part, a PCB substrate, a terminal integrated receiver and a feedback calculation module. The host part comprises an electroencephalogram signal acquisition device and a transcranial electrical stimulation device which are both installed on a PCB substrate. The auxiliary machine part comprises an MCU and a first wireless device which are both installed on the PCB substrate. The auxiliary machine part further comprises a battery which is connected to the MCU, the electroencephalogram signal collecting device and the transcranial electrical stimulation device. The terminal integrated receiver has a second wireless device. And the terminal integrated receiver is connected to the feedback calculation module. According to the fusion system, the electroencephalogram signal acquisition device and the transcranial electrical stimulation device are combined to acquire brain waves and feed back and adjust transcranial electrical stimulation parameters, so that nervous excitability can be better induced, and an electroencephalogram value is enhanced.

The invention relates to the technical field of deep mineral exploration, and discloses a deep metal mine multi-source geophysical data real-time fusion system. The system comprises a data access module which divides detection dimensions according to physical field types so as to receive original data; the space-time registration module is used for establishing mapping between original data and a unified space-time reference system through timestamps and space coordinates; the feature extraction module is used for extracting key features from the registration data, determining weights according to physical meanings and constructing a feature association network; the fusion decision module identifies an abnormal value and adjusts a network topology structure based on the credibility of the network evaluation data; and the real-time updating module is used for optimizing the fusion priority and algorithm parameters and generating a real-time fusion result library. According to the system, real-time fusion of multi-source geophysical data is realized, and systematicness and timeliness of data processing are improved.

The invention discloses a lightweight bimodal fusion system for RGB-infrared target detection. The lightweight bimodal fusion system comprises a backbone network, a neck module and a detection head network, the backbone network comprises a first feature extraction branch, a second feature extraction branch, a first multi-modal feature fusion layer, a second multi-modal feature fusion layer and a third multi-modal feature fusion layer; the neck module comprises a first up-sampling layer, a third feature fusion layer, a fifth feature decomposition layer, a second up-sampling layer, a fourth feature fusion layer, a sixth feature decomposition layer, a tenth convolution layer, a fifth feature fusion layer, a first self-calibration convolution layer, an eleventh convolution layer, a sixth feature fusion layer and a second self-calibration convolution layer which are connected in sequence; and the detection head network comprises three detection head modules and is used for outputting a target detection result. According to the method, the unmanned aerial vehicle can detect clear target contour information in a low-light environment, so that the success rate of target detection of the unmanned aerial vehicle is remarkably improved.

The invention provides a communication and inductance integrated fusion system, an index determination method, electronic equipment and a medium, and relates to the technical field of communication, the communication and inductance integrated fusion system comprises a fusion base station, a preset terminal and a fusion server, the fusion base station is provided with a passive Internet of Things module, and the preset terminal is provided with an RFID tag; the fusion base station is used for sending a first signal and receiving a second signal through the passive Internet of Things module, the first signal comprises a first sensing signal and an RFID excitation signal, and the RFID excitation signal multiplexes a waveform and a time slot resource of the first sensing signal and sends the second signal to the passive Internet of Things module; the second signal comprises a second sensing signal obtained after the first sensing signal is reflected by the preset terminal and an RFID uplink signal obtained after the RFID excitation signal is backscattered by the RFID tag; and the fusion server is used for performing fusion calculation according to the received second sensing signal and the RFID uplink signal so as to determine a sensing performance index. The perception performance of the system can be evaluated more comprehensively and accurately, and the accuracy of perception performance indexes is improved.

The invention discloses a nine-axis linkage intelligent welding process optimization method based on analogue simulation and digital twinning. An existing welding process mostly depends on artificial experience and single equipment control, and an integrated processing system of simulation prediction, equipment cooperation, virtual-real linkage and iterative optimization is lacked. According to the nine-axis linkage intelligent welding process optimization method, a platform is built based on simulation software, after a three-dimensional model of a to-be-welded component is built, the three-dimensional model of the to-be-welded component is subjected to multi-physics field coupling simulation processing to obtain welding data, the welding data is input into an AI multi-target optimization algorithm to form a simulation data training model, and the simulation data training model is used for training the to-be-welded component. Screening according to the simulation data training model to obtain a processing process of an optimal welding sequence and process parameters; through deep integration of intelligent equipment integration, technological process simulation optimization and digital twin control, the problems of low efficiency, poor quality and large deformation in a traditional manufacturing mode are systematically solved.

The invention discloses a process communication method, device and equipment based on an Android / Linux fusion system, relates to the technical field of artificial intelligence, and is used for solving the technical problems of low communication efficiency, poor stability and the like existing in an existing cross-system communication mode. The method comprises the following steps of: sending a calling request to a Binder driver by adopting a Binder adaptation layer of a Linux system; according to a label of a target Binder service in the Android system, adopting a Binder driver to send the calling request to the target Binder service; the target Binder service is adopted to send the calling request to a target process in the Android system for service logic execution, and a calling result is obtained; according to the method, the target Binder service is adopted, and the calling result is fed back to the communication bridge in the Binder adaptation layer through the Binder driver, so that compared with an existing cross-system communication mode, the Binder adaptation layer and the Binder driver are introduced into the Linux system, bidirectional communication between the Android system and the Linux system is achieved, and the problems that communication between the systems is low in efficiency, poor in stability and the like are solved.

The invention discloses an unmanned aerial vehicle flight control system based on attitude information reconstruction. The system aims to realize high-precision attitude information reconstruction by decomposing and re-fusing attitude information provided by a flight controller and an external fusion system. The specific implementation method is as follows: the system firstly uses pitch angle and roll angle data provided by a flight control processor, combines yaw angle data provided by a visual inertia information fusion system, and adopts a weighted fusion algorithm to process the data to reconstruct complete and accurate attitude information; through a weighted fusion algorithm, the system can distribute appropriate weight for each data source according to the precision and reliability of different sensors, and it is ensured that the reconstruction result has high accuracy. In addition, in order to further improve the stability and precision of the system, the system is also combined with calibration parameters, and errors caused by sensor noise and external environment changes are reduced. The reconstructed attitude information is applied to flight control of the unmanned aerial vehicle, and flight stability and control precision are effectively improved. The system can effectively solve the problem of attitude information floating possibly occurring in the starting process of an external fusion system, and therefore control deviation caused by attitude information floating is avoided. The system is especially suitable for unmanned aerial vehicle flight tasks needing high-precision control, such as vertical take-off, precise positioning and hovering, and can meet the task requirements of strict flight control precision requirements.

The invention relates to the technical field of data processing, in particular to a cleaning fusion system for urban traffic multi-source heterogeneous data, which comprises a space-time correlation graph construction module used for creating a unique node identifier for each GPS data record, roadside camera data record and coil data record according to multi-source heterogeneous traffic data, and establishing a spatial adjacent relation edge by judging whether road segment identifiers to which multiple nodes belong are the same or not. According to the method, context awareness evaluation of data source reliability is achieved, meanwhile, when traffic time series data are processed, the data are decomposed into a low-frequency approximation coefficient representing a long-term trend and a high-frequency detail coefficient capturing a sudden change event, and the approximation coefficients of the multi-source data are fused through the obtained context awareness credibility score; meanwhile, a maximum value selection strategy is adopted for detail coefficients, key signal features caused by real traffic events can be reserved, and the fidelity of fused traffic data is improved.

The invention discloses a smart classroom multi-modal fusion system and method based on an AI technology, and relates to the technical field of smart classrooms, the smart classroom multi-modal fusion system comprises an equipment control unit, a real-time monitoring unit and an image acquisition unit, the image acquisition unit acquires three-dimensional image information by deploying various types of image acquisition equipment, a three-dimensional model is synthesized in real time, and the three-dimensional model is displayed on the equipment control unit; holographic images of teachers in local classrooms are captured in real time. The image acquired by the holographic image acquisition system of the teaching classroom is transmitted to a plurality of remote teaching classrooms, and is restored and presented by the holographic image restoration system of the remote teaching classrooms, so that students who listen to a class can not only display a holographic three-dimensional model in a naked eye 3D effect, but also project holographic three-dimensional images of teachers into the remote classrooms, and the teaching effect of the students is improved. The learning enthusiasm and initiative of the students are improved, and the distance between the remote listening students and the teaching teacher can be shortened.

The invention discloses an adversarial agent-based number-real consistency error adaptive prediction method and system, and belongs to the technical field of information physical system and artificial intelligence crossing. According to the method, a destructive agent for maximizing a number-real consistency error and a prediction agent based on a pre-training language model are constructed for continuous confrontation, in the interaction process, the destructive agent intelligently generates disturbance through reinforcement learning so as to explore a weak link of a system, and the prediction agent predicts a disturbance result; after the system obtains the real error, on one hand, a strategy of destroying the agent is awarded and updated, and on the other hand, the real error is utilized to carry out supervised learning type increment fine adjustment on the prediction agent. Through the closed-loop mechanism of double-agent coevolution, the prediction precision and generalization ability of the data-real consistency error can be significantly improved, and the system is endowed with efficient and online adaptive learning ability, so that the reliability and safety of the data-real fusion system can be comprehensively improved.

The invention relates to an intelligent optimization method for a non-cellular station address for sensing fusion, and the method comprises the steps: S1, building a mathematical relation model between communication performance and an access point station address based on a logarithmic distance path loss model; s2, on the basis of a bistatic system path loss model, constructing a mathematical relationship model between the sensing performance and the access point site; and S3, modeling an access point site optimization problem in the cellular-free sensing fusion system as a Markov decision process, constructing a state space and an action space, and calculating a state of the access point site according to a mathematical relationship model between communication performance and the access point site and a mathematical relationship model between sensing performance and the access point site. Fusing to obtain a reward function of a Markov decision process; and S4, solving the Markov decision process by adopting a flexible action-evaluation deep reinforcement learning algorithm to obtain an optimal access point deployment strategy. Compared with the prior art, the method has the advantages of improving the site selection accuracy, widening the application range, improving the site selection effect stability and the like.

The invention discloses an energy structure planning method and system based on an entropy flow dynamic model, and relates to the field of comprehensive energy planning, and the method comprises a first model building module which is used for building a decision matrix and a state matrix, and building an evolution model based on a system dynamics principle; the second model construction module is used for constructing a target function which takes system entropy generation as an optimization target and takes policy implementation cost and system orderliness as constraint conditions according to the entropy balance equation and the thermodynamic force-flow relationship, and constructing a maximum entropy generation model based on the target function and an evolution model; and the path output module is used for performing iterative calculation by utilizing an outer layer nested optimization algorithm based on the maximum entropy generation model to obtain an optimal policy combination of each time node in a preset planning period, and outputting an energy planning maximum entropy path. According to the method, system dynamics and an entropy theory are fused, source-network-load-storage full-link interaction is described, and a scientific and reliable benchmark reference is provided for low-carbon transformation of an energy system.

The present invention relates to a spatio-temporal fusion system and method for time-series integration of multimodal data in which numerical data, image data and the like are combined. Provided are the spatio-temporal fusion system and an operating method therefor, the system comprising: a numerical data reception unit for receiving time-series numerical data for a plurality of variables; an image data reception unit for receiving sequentially captured image data; a sequence encoder, which encodes the time-series numerical data so as to generate numerical tokens; an image encoder, which encodes the image data so as to generate image tokens; a time encoder, which encodes data obtained by combining the time-series numerical data and the image tokens, so as to generate first intermediate tokens; a spatial encoder, which encodes data obtained by combining the image data and the numerical tokens, so as to generate second intermediate tokens; and a fusion unit for deriving integrated characteristic data by using the first intermediate tokens and the second intermediate tokens.

The application discloses a multi-radar point cloud weighted fusion system and method based on an entropy weight method, belongs to the technical field of intelligent stereoscopic warehouse perception and point cloud fusion, and aims to solve the problems of low fusion precision, poor real-time performance and unreasonable weight distribution of existing multi-radar point cloud fusion. The method comprises the following steps: synchronously collecting and pre-processing multi-radar point clouds to generate a set; separating dynamic and static points to obtain static point clouds; dividing fusion voxels and detecting changed voxels; extracting features and standardizing; combining prior weights and entropy weight methods to calculate fusion weights; and weightedly fusing to generate a fusion point cloud map. The application can significantly improve the precision and real-time performance of intelligent stereoscopic warehouse point cloud fusion, and provides a reliable three-dimensional perception basis for upper-layer operations of a warehouse.