1348results about How to "Improve good performance" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention relates to the technical field of dispatching automation, and discloses a dispatching automation system application state management method which comprises the following steps: dynamic task state management: monitoring dynamic state indexes of various computing tasks in real time; according to different characteristics of task types, making differentiated priority management strategies; the task priority is dynamically adjusted, and it is ensured that key tasks are preferentially and efficiently executed; dynamic resource state management: continuously monitoring dynamic state indexes of various computing resources, wherein the dynamic state indexes comprise utilization rates and load conditions; dynamically adjusting a resource allocation scheme according to the resource state; dynamically optimizing resource allocation to ensure that the overall resource utilization rate of the system is continuously improved; and optimizing a self-adaptive scheduling strategy: introducing a self-adaptive scheduling algorithm based on reinforcement learning. According to the application state management method of the dispatching automation system, dynamic state management is adopted, a traditional static dispatching mode is abandoned, and the method can better adapt to a complex and changeable computing environment.

The invention provides a power grid fault detection and positioning method and system for an intelligent power grid system. The method comprises the following steps: collecting a fault traveling wave signal when a power distribution line has a fault; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; carrying out traveling wave front edge detection on the target fault traveling wave signal, and determining the signal arrival time of the fault traveling wave signal; fault signal features are extracted from the target fault traveling wave signals through a frequency domain analysis method and a time domain analysis method; estimating the fault line characteristics of the fault line with the fault in combination with the fault signal characteristics and the signal arrival time; outputting the line type and the fault type of the fault line through a feature recognition model; selecting an optimal fault positioning algorithm by using a decision tree algorithm based on the line type and the fault type; and determining the line position of the fault line according to the target fault traveling wave signal by adopting an optimal fault positioning algorithm. The method has the effect of intelligently selecting the optimal positioning algorithm according to the line type.

The invention belongs to but not limited to the technical field of emotion recognition, and discloses a multi-modal classroom emotion recognition method and system based on modal adaptive learning, and the method comprises the steps: building a multi-modal data collection system, collecting the multi-modal data, such as texts, audios and videos, in a classroom teaching process in real time, and carrying out the real-time collection of the multi-modal data; and preprocessing and feature extraction are carried out on data of different modes. For emotion expression feature differences of different classroom scenes, a multi-modal data fusion method based on adaptive modal scoring (AMS) is provided, and a time sequence modeling and emotion classification model is constructed. The multi-modal fusion and time sequence modeling mechanism simulates the multi-dimensional perception of the teacher on the emotional states of the students in the class, and enhances the interpretability of the emotion recognition system. According to the system, weighted fusion is carried out on data features from texts, audios and videos through fusion of multi-modal data, and complete emotion feature representation is formed.

The invention provides an unmanned aerial vehicle autonomous flight path planning and image acquisition method and system, and relates to the technical field of unmanned aerial vehicle inspection. Firstly, through intelligent three-dimensional grid division and dynamic route planning, the coverage rate and efficiency of feature region monitoring are remarkably improved; secondly, due to the integration of real-time meteorological data and historical feature data, the pertinence and adaptability of acquisition are enhanced, and high-quality images can be obtained in a complex and changeable environment; through combination of real-time processing and ground deep learning analysis, the accuracy and efficiency of feature extraction are greatly improved; and on the basis of a self-adaptive supplementary sampling mechanism of the region of interest, quick response and accurate positioning of the suspected feature region are realized, and reliable data support is provided for subsequent detailed analysis and decision making. The method is not only suitable for identification tasks of various preset features, but also has good expansibility and flexibility, and can be adjusted and optimized according to different application requirements.

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention discloses an intelligent advertisement putting processing method and system based on user data, and relates to the technical field of data processing, and the method comprises the steps: putting an advertisement in an advertisement pool based on an edge device, collecting the video information during the advertisement playing period through the edge device, analyzing the stored video information, extracting the user feature information, and storing the user feature information in the advertisement pool; the user feature information is integrated into user feature vectors, the user feature vectors stored in the edge devices are uploaded to the cloud platform, the cloud platform analyzes the user feature vectors uploaded by the edge devices to generate user portrait information, and the user portrait information is displayed on the basis of the user portrait information generated by the cloud platform. And putting corresponding advertisements to the user groups with similar user portrait information, collecting feedback data, and dynamically optimizing the advertisements put by the edge devices and the user groups based on the feedback data. The method has the advantages that the data transmission delay and the cloud computing pressure are reduced through the cloud edge collaboration method.

The invention discloses a dynamic scheduling method for a cloud computing resource pool in the technical field of cloud computing. Comprising the following steps: S1, analyzing resource demands; s2, performing performance prediction on the resource pool through load prediction; s3, predicting task demand resources; s4, resource allocation decision making; s5, task scheduling; and S6, resource monitoring and feedback. According to the invention, load prediction is carried out through the long-short-term memory network, an adaptive mechanism is introduced, and a load prediction model is updated along with continuous change of data, so that the accuracy is improved; the resource demand of the task is predicted through a machine learning algorithm, a reasonable resource allocation decision is made according to the state of the current resource pool and the resource demand predicted by the task, the utilization rate of the resource is improved, stable operation calculation of the task is guaranteed, efficient task allocation and scheduling are achieved, resource utilization maximization is guaranteed, and the resource utilization rate is increased. And the change of the system load is effectively responded.

The invention provides an innovative deep concealed ore deposit detection method, which comprehensively utilizes geology, geophysics, geochemistry and remote sensing technologies, and realizes effective integration of multi-source geological data by developing an advanced data fusion algorithm. According to the method, data collected by geological survey, wide-area electromagnetic method exploration, hydrocarbon-mercury superposition halo analysis and aviation hyperspectral remote sensing are subjected to standardized preprocessing, so that the consistency and accuracy of the data are ensured; and then, deep analysis is carried out on the fused data by utilizing machine learning and statistical analysis technologies so as to identify and predict mineralization modes and abnormal regions. Finally, a three-dimensional geologic model is constructed, the form and distribution of an ore deposit are visually displayed, and a scientific basis is provided for geological exploration. The method is characterized in that a multi-source data fusion technology and mineralization mode identification automation and precision are realized, so that the efficiency and the accuracy of deep hidden ore deposit detection are remarkably improved.

The invention discloses an automatic penetration test method, system and device based on deep reinforcement learning, and a medium, and the method comprises the steps: carrying out the vulnerability scanning and baseline verification of a device through employing a vulnerability scanning and baseline verification tool, and obtaining the vulnerability information of a tested system; generating an attack graph according to the obtained vulnerability information, determining a vulnerability utilization probability according to the vulnerability information, and assigning values to edges of the attack graph to obtain a Bayesian attack graph; designing an attacker target node reward value on the generated Bayesian attack graph, simulating a possible attack behavior of an attacker, and calculating an attack strategy of the attacker by using a deep reinforcement learning method; simulating potential attack behaviors of an attacker in the attack graph according to an attack strategy, and generating a penetration attack path which is most likely to be adopted by the attacker; and performing an automatic penetration test to generate a penetration test report. According to the invention, the penetration test scheme can be intelligently generated according to the vulnerability of the system, and the precision and efficiency of network security evaluation are greatly improved.

The invention relates to a database query method and device based on a large language model agent and electronic equipment, and the method comprises the steps: obtaining and analyzing query request information, recognizing a query request intention type, carrying out the problem splitting of the query request information according to the query request intention type, and obtaining sub-query request information; searching and obtaining an SQL statement corresponding to the sub-query request information in the target knowledge base according to the category of the query request intention, constructing and generating an SQL statement prompt word and sending the SQL statement prompt word to the large language model when the SQL statement is not searched, executing according to the returned SQL statement to obtain a sub-query result, integrating the sub-query result and obtaining a query result of the query request information, and generating a data visualization code based on the large language model, executing and outputting a query result and a visualization display result thereof. By combining natural language processing, a large language model and a multi-item optimization technology, the efficiency and usability of data query are greatly improved, and non-technical users can conveniently analyze complex data.

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention provides a 5G network intelligent switching management method based on artificial intelligence, and the method comprises the following steps: collecting and processing network related data, and obtaining a network data set; constructing a switching demand prediction model used for predicting network switching time and a target and a switching effect prediction model used for predicting network performance after switching; optimizing the initial switching strategy in combination with reinforcement learning to obtain an optimal switching strategy; deploying a network switching engine model on the edge computing node, and adjusting an optimal switching strategy in real time according to the network environment change; a virtual simulation digital environment is constructed for network simulation, an optimal switching strategy is evaluated according to the simulated virtual network environment, and then a network switching engine model is optimized in real time according to an evaluation result. According to the invention, the intelligent switching decision is realized, the precision and efficiency of the switching decision can be improved, the network adaptability and diversified requirements are enhanced, and the user experience is improved.

The invention provides a multi-agent cooperation system, which comprises a multi-agent cooperation engine module, a task decomposition and distribution module and a tool calling normal form module, and is characterized in that the task decomposition and distribution module is used for decomposing tasks and distributing decomposed sub-tasks to agents; the multi-agent cooperation engine module is used for managing cooperation and communication between agents; and the tool calling module enables the intelligent agent to dynamically call different tools. According to the multi-agent collaboration system, the collaboration efficiency among the agents, the flexibility of task decomposition, the adaptivity of tool calling and the intelligent level of user interaction are remarkably improved, and remarkable technical advantages and effects are shown in complex task processing and dynamic environment processing.

The invention relates to the field of cloud computing and distributed computing, and discloses a cloud data processing system based on an artificial intelligence algorithm, which comprises a data input module, a task decomposition module, a resource scheduling module, a parallel computing module and a result reconstruction module, the data input module receives high-dimensional data and converts the high-dimensional data into a tensor structure; the task decomposition module decomposes the tensor, generates a core tensor and an orthogonal matrix, and generates a plurality of sub-tasks; the resource scheduling module is used for carrying out resource allocation and scheduling on the sub-tasks, and task execution time and resource consumption are minimized; the parallel computing module distributes subtasks to the computing nodes for execution, and the task execution sequence is guaranteed; and the result reconstruction module combines and verifies the execution results of the subtasks to generate a final calculation result. The cloud data processing efficiency and the resource utilization rate are effectively improved, the accuracy and consistency of calculation results are ensured, and the method is suitable for the fields of artificial intelligence training, large-scale data analysis, scientific calculation and the like.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention discloses an audio stream adaptive decryption method, system and device and a storage medium. The method comprises the following steps: acquiring an MPEG-DASH list file and analyzing the MPEG-DASH list file; the standard DRM interface is initialized; performing feature extraction on the identified audio segmentation information in the audio segmentation mode, and dynamically adjusting a segmentation request and processing logic according to the extracted features to perform adaptive segmentation downloading and preprocessing; extracting the encryption parameter to identify an encryption mark of the target audio stream platform, and selecting a corresponding decryption algorithm according to the encryption mark to decrypt the current audio segment; and performing audio frame reconstruction on the decrypted data to form an updated audio stream, caching the updated audio stream to a preset cache region based on a self-adaptive buffer strategy, and outputting the final audio stream to ensure the continuity of the final audio stream and realize efficient parallel processing of decryption and playing, so that the decryption efficiency is improved, and the user experience is improved. And particularly, in a high-concurrency scene, the user experience is improved.

The invention relates to a wheel type double-arm humanoid robot motion track planning and obstacle avoidance method. The method comprises the steps that a robot system is started for self-inspection; the first sensor obtains environment information and constructs a real-time three-dimensional point cloud obstacle information graph; the second sensor obtains chassis state information and mechanical arm state information; a target task is set, and a moving path of a chassis and a moving path of a mechanical arm are planned based on the state information of the target task; and the system controls the chassis to move according to the planned moving path through the cooperative control model, and the mechanical arm moves according to the planned moving track. Through construction and real-time updating of the real-time three-dimensional point cloud obstacle information graph, the robot can effectively recognize and avoid obstacles, the risk of collision with the surrounding environment is reduced, meanwhile, the robot can adapt to the dynamically changing environment, path planning is rapidly updated through the D * Lite algorithm, and the path planning efficiency is improved. And the path can be adjusted in time when the obstacle moves or a new obstacle appears.

The invention discloses an image generation method based on a mixed quantum classical condition generative adversarial network. The method comprises the following steps: firstly, converting a random condition label and a sample real condition label into one-hot codes, processing input noise and the random condition label by using a quantum circuit, performing measurement to convert a characteristic quantum state into a classical characteristic vector, and obtaining generated image data from the characteristic vector through a classical single-layer neural network. And transmitting the sample real image data and the real condition label into a discriminator to obtain a real image discrimination result, and transmitting the generated image data and the random condition label into the discriminator to obtain a generated image data discrimination result. And calculating discrimination loss based on the two discrimination results, training a discriminator and a quantum condition generator, obtaining a trained mixed quantum classical condition generative adversarial network, and generating a condition image. Based on the above scheme, the problems of unstable classical GAN training and many training parameters are solved, the limitation of the quantum GAN in a condition generation task is broken through, and the application of the quantum generative adversarial network in scenes such as image generation and pattern recognition in the field of real artificial intelligence is further promoted.

The invention discloses a real-model experiment distribution line fault positioning method and system. The method comprises the following steps: acquiring a topological structure diagram of a target power distribution network in a real-model experiment; based on the topological structure diagram of the target power distribution network, combining a preset diagram input feature selection logic, and obtaining target power distribution network node and edge features as input of a power distribution network fault model to be established; according to the input of the to-be-established power distribution network fault model and the output of a preset power distribution network fault model, training the power distribution network fault model based on the improved graph neural network, the output of the preset power distribution network fault model being the probability of the line fault in the power distribution network; and according to the trained power distribution network fault model based on the improved graph neural network, real-type experiment power distribution line fault positioning is completed. The system and the method have good expansibility and adaptability, can be suitable for power distribution networks of different scales and structures, and have wide application prospects.

The invention provides a cross-platform low-code application development system based on a Flutter framework. Comprising a login registration module, a project management module, an interface design module, a state management module, a route management module, a team management module, a help document module, an AI page design module, a development document generation module, a hardware integration module, a code generation module, a multi-user collaborative development module, a communication module, an online simulator module, an AI generation application module and an interface management module. An asset management module, a developer forum module, an application market module and an application packaging module. The communication module supports various communication protocols and secure transmission modes, and ensures that data exchange applied to cross-platform and cross-system environments is secure and reliable. Through component dragging and graphical configuration, the low-code platform automatically analyzes configuration data to generate an App, a development document and a source code. And meanwhile, multi-person collaborative development is supported, so that the development threshold is reduced and the development speed is increased.

The invention relates to an intelligent integrated modular cabinet control method, system and device and a medium, and the method comprises the steps: monitoring the external environment parameters of a cabinet in real time through a sensor group in the cabinet, and carrying out the preprocessing of the external environment parameters through an edge computing device, and obtaining multi-dimensional environment parameters; integrating and analyzing the multi-dimensional environmental parameters and the internal operation data of the cabinet by using a multi-modal data fusion algorithm, and outputting a fusion analysis result; according to a fusion analysis result, generating a digital twinborn model of the current environment and the equipment state of the cabinet in the virtual environment, and performing rapid fusion simulation analysis on different adjustment schemes through simulation drift and a genetic algorithm to obtain an optimized adjustment scheme; and adjusting the resource configuration of each module in the cabinet and the operation state of the heat dissipation equipment based on the optimization adjustment scheme. The equipment cabinet has the effect of improving the operation efficiency of the equipment cabinet.

A low-altitude aircraft intelligent identification and emergency countering system relates to the technical field of low-altitude aircraft safety management and comprises a detection unit, an identification unit, a command decision platform, a scheduling unit and a countering unit. The detection unit is used for detecting all low-altitude aircrafts in a prevention and control area and obtaining flight data of all the low-altitude aircrafts; the identification unit is used for receiving the flight data and analyzing and judging the threat level of the low-altitude aircraft based on a fusion algorithm; the command decision platform is used for receiving the threat level of each low-altitude aircraft and sending a disposal instruction to the countering unit; the scheduling unit is used for giving a specific countering equipment scheduling instruction through an intelligent optimization scheduling algorithm according to the disposal instruction; the countering unit controls different countering devices to accurately strike low-altitude aircrafts with corresponding threat levels according to the scheduling instruction; according to the method, the malicious unmanned aerial vehicle can be accurately identified and can be countered in time.

The invention discloses a smart city street lamp intelligent control system and method. The method comprises the following steps: performing multi-aspect inspection on each hardware device through a comprehensive intelligent control module and a communication module; the environment monitoring module is used for collecting multi-source data in real time and integrating and analyzing the multi-source data through the data fusion module; generating a dynamic dimming strategy of the common road section and the high-priority road section according to the collected data and regional requirements; brightness adjustment and on-off control are executed one by one through the single lamp control module, and equipment operation parameters are collected through the real-time feedback and monitoring module. The remote management platform is used for monitoring the running state of the street lamp in real time and dynamically adjusting strategy optimization analysis; different operation scenes are simulated through a system simulation test, actual deployment is carried out in a pilot area, and parameters and operation logic are optimized. Through modular design, intelligent decision making and data driving modes, the management efficiency and the energy-saving level of urban street lamps are remarkably improved, and reliable technical support is provided for smart city construction.

The invention discloses a multi-object and multi-model deployment and control early warning system. The multi-object multi-model deployment and control early warning system comprises a multi-source data fusion module, an efficient algorithm and model optimization module, a real-time data processing and analysis module, a multi-model cooperation and decision module, an adaptive learning and evolution module and a security and privacy protection module. And a more comprehensive and deep judgment result is provided, so that the police officers can make more accurate decisions in the operation process. The multi-model structure of the system realizes cooperative work through the support of each module, and finally achieves the goals of accurate data, timely analysis and reliable early warning. And due to the close relationship between each module and multiple models, the system has higher decision-making ability and adaptability when facing a complex dynamic environment.

The invention relates to a vehicle track prediction method based on interactive perception and map adaptation, and belongs to the technical field of automatic driving. The method comprises the steps of collecting a historical track and geographical semantic information of a vehicle; the method comprises the following steps of: modeling interaction between dynamic objects by utilizing Transform, and fusing map information when a state sequence is decoded; a hierarchical attention mechanism is adopted, the future state of the vehicle is considered, and the consistency and accuracy of prediction are ensured; in addition, a multi-resolution map coding module is also constructed, the module fully utilizes a Vision Transform module to efficiently capture local and global scene context information, and then map representation is closely associated with a vehicle state to accurately model an interaction process of a vehicle and a scene, so that a more reasonable future trajectory prediction result is generated. The method shows wide application potential, can effectively improve the calculation performance, and is suitable for a more complex traffic environment.

The invention discloses a high-precision three-degree-of-freedom attitude adjustment platform for a spectrometer, which is based on a simplified design of a Stewart platform and comprises a movable platform, a static platform and a three-degree-of-freedom driving mechanism connected between the movable platform and the static platform. The driving mechanism is composed of three sets of servo electric cylinders, telescopic rods and servo motors and is driven by a ball screw, and posture adjustment of the platform is achieved. According to the platform, a laser interferometer is adopted as a high-precision sensor, a closed-loop feedback control system is combined, and submicron displacement measurement and attitude control are achieved. The platform can achieve translation in the Z-axis direction and rotation around the X axis and the Y axis, the rotation angle can reach + / -15 degrees, and the precision can reach 0.02 mm. According to the invention, the device ensures high precision and stability in a complex working environment, has the characteristics of stable movement, quick response, easy maintenance and the like, and is suitable for the fields of optical experiments, spectral analysis and attitude adjustment and error compensation of precise instruments.

The invention provides an ultrafast laser heat dissipation state real-time monitoring system based on cloud computing, and relates to the technical field of laser heat dissipation, and the system comprises a laser monitoring module which is used for transmitting ultrafast laser and is internally provided with a temperature sensor so as to monitor the working temperature of a laser in real time; the data acquisition module is used for being connected with the laser monitoring module, acquiring temperature data and key parameters, including laser power and working current, of the temperature sensor in real time, and uploading the temperature data and the key parameters to the cloud server; and the cloud analysis module is used for receiving and analyzing the temperature data and the key parameter data, identifying an abnormal temperature mode, predicting a potential overheating risk and calculating heat dissipation efficiency to obtain an analysis result. According to the invention, comprehensive, real-time and intelligent management of the heat dissipation state of the laser is realized, the operation stability and safety of the laser are effectively improved, the overheating risk is reduced, the heat dissipation efficiency is optimized, and remote monitoring and data analysis services are provided for users.

The invention discloses an automatic test method based on natural language processing, which comprises the following steps: S1, generating a test case: performing semantic analysis on a software requirement document or a test requirement by utilizing a natural language processing technology, extracting key information, and combining the extracted test information and a preset test case template to generate a test case; a preliminary test case is automatically generated; the key information comprises a test purpose, an operation step and an expected result; s2, analyzing the test case; s3, generating a test script; s4, executing the test script; and S5, analyzing a test result. According to the automatic test method based on natural language processing, the test requirements described by the natural language can be automatically analyzed, the test case is generated, the test is automatically executed, and the test report is finally generated, so that the software test efficiency is improved, the workload of manually compiling and executing the test case is reduced, and the test efficiency is improved. And the coverage rate and the accuracy of the test are improved, more software defects can be found, and the quality of software products is guaranteed.

The invention discloses an unmanned warehouse shelf-oriented automatic cargo checking method and system, and the method is characterized in that the method comprises the following steps: employing a target detection algorithm to extract a cargo boundary frame from captured continuous cargo images; for each cargo bounding box, calculating shape features, color features, texture features, size features and local features of the cargo in the cargo bounding box, and splicing feature vectors of the cargo according to the features; pre-defining cargo templates of different types of cargos, matching the cargo templates with the feature vectors of the cargos so as to obtain a category label of each cargo bounding box, and iteratively optimizing the target detection algorithm through a loss function; and tracking the goods extracted by the target detection algorithm by using a Kalman filtering target tracking algorithm, and obtaining the position and quantity information of the goods from the continuous goods images.