1700results about How to "Reduce resource consumption" patented technology

The invention discloses a hydropower station gate detection method and system, and relates to the technical field of hydropower station gate detection, and the method comprises the steps: collecting environment parameters, building a dynamic environment model, and determining a real-time impact factor on a gate state; synchronously collecting gate vibration and sound wave data to obtain a composite signal; constructing a short-term and long-term trend model, and identifying a potential abnormal trend; automatically adjusting the sampling frequency of the sensor according to an abnormal detection result; performing fault classification on the multi-parameter signals by using an event pattern recognition algorithm; and generating an automatic response strategy according to a fault classification result. According to the hydropower station gate detection method provided by the invention, the accuracy and efficiency of hydropower station gate detection are improved through multi-dimensional parameter fusion and adaptive sampling adjustment. Through monitoring of various parameters, the system can comprehensively analyze the state of the gate, the misjudgment risk caused by a single signal is reduced, and comprehensive recognition of faults is enhanced.

The invention provides an OracIeRAC-based CDC synchronization method and system, and relates to the technical field of OracleRAC, and the method comprises the steps: deploying an incremental log analyzer, a session state collector and a resource monitor, and respectively collecting atomic operation, concurrent transaction information and node performance indexes; and identifying data stream features by using an intelligent classification model, constructing a priority matrix, and performing fine-grained acquisition on high-priority data based on an adaptive sampling strategy. According to the method, multi-dimensional evaluation indexes are further utilized, a time sequence prediction model is constructed to predict node loads, data synchronization tasks are distributed to low-load nodes based on a load balancing matrix, and a two-stage submission protocol is adopted to ensure data consistency. By monitoring the synchronization state in real time and dynamically adjusting the allocation strategy, the CDC synchronization efficiency and stability in the Oracle RAC environment are effectively improved, and the synchronization delay is reduced.

The invention relates to a user question information response method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring question information input by a user; based on semantic retrieval and vector retrieval, retrieving corresponding private domain knowledge information from a preset private domain knowledge base; obtaining a first cue word according to the question information, the private domain knowledge information and a pre-configured agent database; inputting the first prompt word into the large model, obtaining an agent calling link through the large model, and calling a corresponding agent according to the agent calling link; obtaining a second prompt word, inputting the second prompt word and an execution result obtained by calling the corresponding agent according to the agent calling link into the large model, and determining whether the execution result meets a condition or not through the large model; if yes, response information for the problem information is output based on the execution result. By adopting the method, resource loss caused by fixed code development can be reduced.

The invention belongs to the technical field of biodegradable materials, and particularly relates to an environment-friendly degradable modified composite material, a preparation method and application. The material comprises PBAT, PBS, PLA, an inorganic mineral filler, natural plant fibers, PBAT-PTMO, nano-silica, an auxiliary agent and a plasticizer. In the preparation process, PLA is prevented from being hydrolyzed in vacuum drying, calcium carbonate and nano silicon dioxide are preheated and activated, natural plant fibers are dispersed through steam explosion and ethanol treatment, and raw material pretreatment comprises high-speed mixing and agglomeration breaking and medium-speed secondary premixing so as to realize uniform distribution. The co-rotating twin-screw extruder accurately controls the temperature, the speed, the rotating speed and the length-diameter ratio, and ensures melting plasticizing, uniform mixing and stable extrusion molding. According to the invention, by adopting accurate proportioning of multiple raw materials and a modification treatment process, the properties of mechanics, degradation, processing and the like of the material are synergistically improved, and the material is widely applied to multiple fields such as packaging bags, disposable tableware and agricultural plastic mulching films.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence. Comprising a task demand identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module and an emergency task response and environment interference adaptation module. By collecting task requirements and environment parameters in real time, the system can dynamically generate a task priority queue and optimize path planning based on priorities, it is ensured that the unmanned aerial vehicle has efficient response ability when executing tasks such as reconnaissance, monitoring, searching and rescue, the system combines a multi-source data fusion technology, the three-dimensional situation of the task environment is displayed in real time, and the system has good application prospects. Visual support is provided for decision making; the self-optimization capability and the adaptability to emergency situations lay a foundation for long-term intelligent development of unmanned aerial vehicle tasks.

The invention provides an intelligent cold chain transportation information management method, system and device and a storage medium, and the method comprises the steps: recognizing the type of transported goods and environment demands through a task recognition module, and determining and configuring a target sensor of a cold chain vehicle through a sensor configuration module; the target sensor collects environment data, the vehicle-mounted GPS module collects position information and a driving state, the RFID tag records a cargo loading state, and all data are sent to the central management platform. The platform constructs a multi-dimensional feature vector, the multi-dimensional feature vector is compressed into a low-dimensional feature representation through an encoder, a reconstruction data vector is obtained through reconstruction of a decoder, and a reconstruction error is calculated. And by analyzing the reconstruction error, the platform generates an analysis result, and the environment control equipment in the vehicle is automatically adjusted according to the result, so that the stability of cargo transportation is ensured.

The invention discloses a smart factory management platform and management method based on digital twinning, and particularly relates to the technical field of digital twinning, and the method comprises the following steps: carrying out the division and preliminary analysis of a digital twinning smart factory, carrying out the feature extraction and anomaly analysis of components with hidden dangers, and carrying out the feature extraction and anomaly analysis of the components with hidden dangers; calculating a data source quality index and a prediction performance quality index, and evaluating the comprehensive operation quality by using a pre-trained machine learning model; based on the evaluation result, dividing the components into low-efficiency and high-efficiency operation components; judging an optimization mode for the low-efficiency component; performing intelligent regulation and control on the efficient component based on the initial sampling frequency; the method can comprehensively evaluate the operation state of the digital twin model assembly, evaluate the adaptation degree of each assembly and the actual factory assembly, intelligently regulate and control the high-efficiency operation assembly, optimize the low-efficiency operation assembly, significantly improve the resource utilization rate, ensure the stability and reliability of the system under various operation conditions, and improve the reliability of the system. And system faults and shutdown are prevented.

The invention discloses a multi-modal large model dialogue method and system based on context awareness and a medium. Relates to the technical field of intelligent dialogues. According to the scheme, the dialogue request information and the perception feature information are generated in the context perception dialogue management model, and the answer strategy is determined according to the dialogue request information and the perception feature information; on the basis of various data of the user, system hot word auxiliary information provides personalized reply for the user and is more suitable for life links, accurate context perception is achieved, and enterprise product operation and development are served.

The invention relates to the technical field of natural language processing, in particular to a model distillation method and system based on a teacher model and situational reasoning. By using a multi-teacher learning framework, the student model can draw knowledge from a plurality of large teacher models, and diversified learning paths are provided for the student model; due to the introduction of a context awareness and example generation mechanism, the understanding and reasoning ability of a student model to a complex situation is enhanced by considering context information of input data and an input example; the reasoning chain strategy can ensure that the student model can generate an accurate and deep reasoning process, and the decision process of the teacher model is simulated; a parameter fine adjustment technology is adopted, only key parameters in the student model are adjusted, efficient migration of teacher model knowledge is achieved, and the number of parameters needing to be adjusted in the distillation process of the student model is reduced; the resource consumption in the model deployment and updating process is remarkably reduced, so that the trained model keeps the performance similar to that of the model.

The invention discloses a detail level rendering method based on Gaussian splash, which can dynamically change the detail level of a model according to the actual needs of a user in an interaction process, realize high-fidelity and low-resource-loss rendering based on Gaussian splash, and reduce the loss required by rendering. The technical scheme of the invention comprises a preparation stage and a real-time rendering stage. In the preparation stage, a multi-view image is acquired, a corresponding three-dimensional model or four-dimensional model is constructed according to the multi-view image, and Gaussian splash models with different detail levels are generated. And in the real-time rendering stage, the Gaussian splash models of different detail levels are selected for real-time rendering according to the real-time data of the user by using the Gaussian splash models of different detail levels.

The invention discloses a network threat knowledge graph construction method based on SecBABC. The method can be applied to construction of a network threat knowledge graph. The method comprises the following steps: collecting existing network information, and constructing a network threat domain ontology CTDOM according to an STIX network threat information standard; the method comprises the following steps: constructing a SecBABC knowledge extraction model, capturing a complex semantic relationship between network threat intelligence through a pre-training model SecBert, constructing an adversarial training sample by using PGD, optimizing semantic understanding errors and inaccuracy conditions, updating model parameters through BiLSTM, and finally decoding entities and relationships of the network threat intelligence through CRF; constructing an entity alignment algorithm EAABNA, and performing knowledge fusion from two aspects of entity names and entity attributes; and constructing a network threat knowledge graph by using the fused network threat knowledge. By applying the knowledge extraction model constructed by the method, network threat intelligence can be effectively identified and extracted, and the efficiency and accuracy of knowledge graph construction are improved.

The invention discloses a dynamic load-oriented data processing task resource optimization method, device and system, and the method comprises the steps: constructing an optimization problem taking the total resource overhead of a system as an optimization target through continuously obtaining network time delay, the flow scale of a data processing task, the distribution of the data processing task in a network topology, and the data size of each client, and carrying out the optimization of the data processing task. Routing paths, aggregation positions and computing power resource allocation are continuously adjusted by solving corresponding optimization problems. According to the method, based on a dynamic load environment, resource configuration of a data processing task is optimized by adopting an intra-network aggregation technology, communication delay is reduced, and the overall performance of a system is improved. According to the dynamic load resource optimization system, the flexibility of the programmable switch is utilized, resources can be dynamically allocated and network flows can be adjusted according to changes of network conditions and data flows, and the utilization efficiency of network resources and the performance of data processing tasks are greatly improved.

The invention relates to the technical field of software application testing, in particular to an automatic intelligent testing system and method for software application. The method comprises the following steps: acquiring test environment parameters of software to be tested, calculating a dynamic waiting time reference value, and generating waiting mechanism configuration data; performing element positioning information collection and element positioning strategy generation on a user interface of the software application based on the waiting mechanism configuration data to obtain element positioning configuration data; performing conditional waiting strategy processing according to the element positioning configuration data and the waiting mechanism configuration data to generate conditional waiting mapping data; and performing visual AI auxiliary positioning processing according to the conditional waiting mapping data to generate visual positioning feature data. Through real-time monitoring of test environment parameters, the system automatically calculates and configures appropriate waiting time, so that the test process can flexibly adapt to various operation environments.

The invention discloses an automatic model tuning system based on an AI training intelligent workbench, and relates to the technical field of artificial intelligence, and the system comprises the steps: setting a data collection instrument and an environment monitoring point, collecting model real-time data in a model training process, and carrying out the preprocessing; performing model performance analysis according to the model real-time data and the model original data to obtain final parameter configuration; and performing model training by utilizing the final parameter configuration, dividing the model training process into different stages, dynamically adjusting an intelligent control strategy based on model performance requirements, and performing optimization on the different stages of the model. According to the method, through multi-dimensional performance evaluation, parameter configuration is optimized, and the robustness and generalization ability of the model are improved. Through dynamic adjustment of an intelligent control strategy, fine management of a training process is realized, efficiency and resource utilization are improved, model convergence is accelerated, and finally an efficient, stable and high-adaptability model training effect is achieved.

The invention relates to a CBCT image tooth segmentation method. The method comprises the following steps: generating a two-dimensional oral cavity panoramic film from a CBCT image; carrying out tooth two-dimensional segmentation and tooth ID classification on the two-dimensional oral cavity panoramic film by adopting a deep learning algorithm; calculating a key point coordinate of a two-dimensional single tooth and mapping the key point coordinate to a three-dimensional CBCT image coordinate; respectively cutting the CBCT image to obtain an area of each tooth; segmenting each three-dimensional tooth region by adopting a deep learning algorithm; and assigning ID to all the tooth segmentation results, and splicing to obtain a three-dimensional tooth segmentation result. According to the method, accurate segmentation of the tooth area in the CBCT image can be realized, complex conditions such as tight tooth arrangement, similar shapes and artifact interference can be effectively handled, the problems of tooth segmentation result fusion, ID confusion and metal artifact interference can be effectively solved, and resource consumption is remarkably reduced by determining the tooth key points on the panoramic film, so that the tooth segmentation efficiency is improved. The overall segmentation efficiency is improved, and the segmentation time is shortened, so that the accuracy of the segmentation result and the processing speed are improved.

The invention discloses a blockchain and homomorphic encryption-based power data privacy protection method and system, and belongs to the technical field of power data privacy protection, and the method comprises the steps that a data provider encrypts power data by using Paillier homomorphic encryption, uploads a ciphertext hash value to a blockchain, carries out the digital signature of a ciphertext, divides a key, and carries out the encryption of the key; enabling each data demander to receive the ciphertext, the signature and the key fragment; and the data demander performs cooperative decryption after performing data source and integrity verification to obtain a plaintext. Through distributed storage of a key sharing scheme, the security of the key is remarkably improved, single-point faults are avoided, the fault tolerance of the whole system is improved, and the flexibility of data transaction is optimized; for a mass data transaction process, the encryption and decryption efficiency is improved, the data processing speed and performance are improved, the real-time performance of data transaction is ensured, the resource consumption is reduced, and the user experience is improved.

The invention provides a hysteresis loop prediction model establishment method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out a hysteresis characteristic experiment on a ferromagnetic material, setting hysteresis loop related characteristics comprising magnetic induction intensity waveform data and magnetic field waveform data under the excitation of preset sine waves under different magnetic induction intensity peak values, establishing a hysteresis loop prediction model comprising an improved convolutional neural network model and a K-nearest neighbor algorithm model, training the improved convolutional neural network model, inputting a magnetic induction intensity peak value and magnetic induction intensity waveform data, and outputting magnetic field waveform data; and adjusting the magnetic field waveform data based on the feature data by using a K-nearest neighbor algorithm model to obtain a final magnetic field waveform result. According to the method, the hysteresis loop prediction model is established based on the improved convolutional neural network in combination with the K nearest neighbor algorithm, the overfitting phenomenon can be prevented, the training speed is higher, the resource consumption is lower, and rapid iteration and adjustment in practical application are facilitated.

The invention discloses a time-frequency domain cooperative industrial Internet of Things anomaly detection method under a full MLP lightweight architecture, and the method only employs four parallel two-layer MLP networks to construct a lightweight full MLP architecture with high timeliness and low resource consumption, and guarantees that the lightweight full MLP architecture can be flexibly applied in an industrial scene with limited resources. According to the architecture, a double-branch reconstruction learning process is constructed from two angles of'local to global 'and'global to local'; in order to further improve the accuracy, the reconstruction learning process of each branch utilizes two two-layer MLP networks to carry out reconstruction learning in a time domain and a frequency domain respectively, and the time domain and the frequency domain are aligned by utilizing reconstruction consistency, so that the feature capture capability of the full MLP architecture is improved; and finally, designing an anomaly scoring scheme based on a time-frequency reconstruction error, fusing the time-frequency domain reconstruction error obtained in double-branch reconstruction learning by using different weights, thereby generating a final anomaly score for each timestamp, and further judging whether the timestamp is abnormal or not.

The invention provides a natural resource map completion method based on a large model, which relates to the technical field of natural resources, and comprises the following steps: firstly, inputting a query triple, and carrying out retrieval in a natural resource knowledge map according to the query triple to obtain ordered candidate entities; secondly, performing knowledge prompt processing on the query triad to obtain a prompt text; extracting structure information of the natural resource knowledge graph, and performing knowledge prefix adapter processing on the structure information to obtain a virtual knowledge mark; and finally, taking the virtual knowledge mark and the prompt text as an input prompt, reordering the candidate entities by utilizing a preset large language model, and generating reply content for complementing the natural resource knowledge graph. According to the method, the capability of complementing the long-tail entity is effectively enhanced, the resource consumption of fine adjustment is reduced, and the knowledge graph complementing effect is improved.

The invention relates to a network security situation awareness method based on a deep neural network. The method comprises the following steps: acquiring and preprocessing a network security data set to obtain an initial data set; constructing a situation element extraction model, and inputting the initial data set into the situation element extraction model to obtain network security attack elements; carrying out feature extraction based on the network security attack elements, and carrying out feature-level data fusion to obtain a fused feature vector; constructing a network security situation assessment model, and inputting the fusion feature vector into the network security situation assessment model to obtain a situation assessment result; forming a multi-dimensional time sequence data set based on the network security attack elements and the situation assessment result, and training a deep neural network security situation prediction model based on a sample convolution and interaction module based on the multi-dimensional time sequence data set; and inputting the current situation data into the trained deep neural network security situation prediction model, and outputting a network situation prediction result. And the network security situation awareness evaluation and prediction accuracy is improved.

The invention belongs to but is not limited to the technical field of aluminum alloy preparation, and discloses a high-plasticity aluminum alloy prepared from secondary aluminum and a preparation method, high-quality secondary aluminum waste is selected, and obvious impurities are removed; carrying out chemical or mechanical cleaning on the secondary aluminum, and sorting; the pretreated secondary aluminum waste is added into a smelting furnace to be smelted; a purifying agent is added for melt purification; the melt is further purified through inert gas stirring and standing; adding a proper amount of alloy elements into the melt; under the protection of inert gas, uniformly stirring the melt; casting the melt into a cast ingot by adopting a semi-continuous casting or continuous casting process; controlling the cooling speed; carrying out homogenizing heat treatment on the cast ingot to eliminate composition segregation in the cast ingot; and extruding and rolling to obtain the high-plasticity aluminum alloy material. According to the method, the purity of the aluminum alloy is improved, the performance uniformity is enhanced, and the aluminum alloy material with high plasticity and excellent mechanical performance is obtained and is suitable for industrial application with high requirements.

The invention relates to the technical field of software, in particular to a timed task execution method and device, computer equipment and a readable storage medium. The method comprises the following steps: generating different timed tasks according to received different task description information through a task configuration thread of a timer; and processing the different timed tasks generated by the task configuration thread in sequence through the task execution thread of the timer.

The invention belongs to the technical field of waste plastic recovery, and particularly relates to a standardized preparation method of a regenerative cycle material. The sand blasting stripping machine is used for efficiently removing attached layers such as labels, glue, paint and electroplated coatings on the surface of the waste plastic, then the shredder and the crusher are used for treating materials into crushed pieces with the diameter of 5-30 mm, continuous crushing operation is formed, the size uniformity is ensured, the sand blasting stripping machine adopts the physical impact principle, use of chemical reagents is avoided, and the production cost is reduced. The environmental pollution is reduced, and the high responsibility to the environment is reflected. Through density difference, effective recovery and reutilization of the grinding material are realized by utilizing a solution density sedimentation method, so that the cost is reduced, the resource consumption is reduced, and the concepts of circular economy and low-carbon economy are met.

The invention discloses an encryption method and device for industrial internet data exchange, equipment and a medium, and relates to the technical field of industrial information security, and the encryption method comprises the following steps: generating a pair of public key and private key for industrial equipment or nodes preliminarily participating in data exchange by using an elliptic curve ECDH protocol, and carrying out initialized key exchange; generating a temporary additional key, and calculating a temporary session key; encrypting the temporary session key by using a public key of a receiver to obtain a session key, and processing data according to different stages of data exchange; and when each session ends or a preset time / data volume threshold is reached, automatically discarding the old key and renewedly negotiating a new session key. According to the method, resource consumption in the key exchange process is reduced, potential safety hazards possibly caused by key reuse or long-term key fixation are reduced, the attack resistance of the data exchange process is improved, the confidentiality and integrity of data exchange are guaranteed, and a continuous and safe environment is constructed for data exchange of the industrial internet.

The invention belongs to the field of optical communication, and particularly discloses a hollow-core optical fiber connector and a manufacturing method thereof.The connector comprises a connecting piece, the connecting piece is fixedly installed at the end of a hollow-core optical fiber body, and an insertion hole for the hollow-core optical fiber body to penetrate through is formed in the connecting piece; and the plugging structure is arranged at one end of the connecting piece to physically plug the central channel of the hollow-core optical fiber body, and the plugging structure can allow optical signals transmitted in the central channel to pass through. Based on the structural design of the hollow-core optical fiber connector, butt joint of the two hollow-core optical fibers can be achieved by butt joint of the plugging structures on the two hollow-core optical fiber connectors, it is guaranteed that optical signals are transmitted in the two hollow-core optical fiber bodies, no special equipment needs to be adopted for fusion welding, the connection mode is simpler and more convenient, and the cost is lower. Meanwhile, due to the design of the plugging structure, the problem that water vapor and impurities enter the central channel of the hollow-core optical fiber body to affect optical signal transmission can be effectively avoided.

The invention discloses an integrated construction method and system for human settings, images, sounds and life tracks of a digital human, and belongs to the technical field of the Internet. The method comprises the following steps: generating a virtual digital human; constructing a life track, an image and voice of the virtual digital human; and performing behavior analysis on the virtual digital person, and updating the life track, image and voice of the virtual digital person. According to the invention, the defects of too long time and low efficiency of traditional digital human creation are overcome; the method solves the problems that the personality of the digital person in the traditional digital person creation depends on a model too singly, so that the characteristics of the digital person are too single, and the digital person cannot automatically learn and grow.

The invention, which relates to the technical field of new energy power generation, discloses a new energy power generation scheduling optimization system comprising a multi-dimensional evaluation module, a dynamic weight adjustment module, a priority calculation and determination module, and a scheduling decision module. By establishing a multi-dimensional evaluation grade and a dynamic weight adjustment mechanism, the method can comprehensively and accurately evaluate the factors of the emergency degree of the energy demand, the power generation capacity of the power generation equipment and the operation state of the equipment, dynamically adjust the priority of each power generation equipment according to the real-time monitored data in different time periods, and ensure that the power generation capacity of the power generation equipment is greatly improved in the peak time period of the energy demand. Equipment which is high in power generation capacity, stable in operation and capable of responding quickly is scheduled preferentially, emergency energy requirements are met, power outage accidents are effectively avoided, new energy power generation can better adapt to complex and changeable energy requirements and power generation conditions due to the intelligent and accurate scheduling capacity, and more reliable energy supply is provided for users.

The invention provides a process optimization method for coloring an artificial fur fabric by using cations, which belongs to the technical field of artificial fur fabric coloring processes, and comprises the following steps: collecting various process parameters and quality index data required by artificial fur fabric coloring, including grey cloth specification, squeezing treatment, dye formula, dyeing process, post-treatment and the like. Establishing a coloring effect empirical model considering each process parameter, wherein the coloring effect empirical model comprises equations of dye diffusion, fixation, fiber water absorption, fluff shaping and the like; substituting the collected parameter data into the model to obtain a preliminary process parameter-effect relationship; a large number of technological parameter combinations are generated according to the parameter range, and clustering analysis is carried out by utilizing a model calculation theoretical effect. And selecting process parameters of the typical clustering center to carry out pilot test verification, and optimizing the process parameter-effect relation model. And adopting a genetic algorithm to obtain an optimal process parameter combination on the basis of the optimization model, carrying out pilot scale production verification, and finally determining an optimization process scheme.

The invention provides a single-channel speech enhancement method based on an improved convolutional recurrent network, and the method comprises the steps: 1, downloading a data set for model training and testing, carrying out the preprocessing of the speech of the data set through employing a VoiceBank + DEMAND data set, and extracting the amplitude spectrum of a speech signal through short-time Fourier transform (STFT); 2, inputting the amplitude spectrum into an encoder, and extracting high-dimensional features from the amplitude spectrum; step 3, using the Channel-S4D block as a recursion module of the model, inputting a result output by the Channel-S4D block into a decoder, and recovering the result to an original dimension; 4, constructing a joint loss function; and 5, reconstructing and evaluating the enhanced voice signal. According to the invention, the Channel-S4D module constructed based on the diagonalization state space model efficiently processes a voice sequence, captures channel-level voice key information, better models the time dependence relationship of voice signals, and improves the adaptability of the model to a complex noise environment while reducing the parameter quantity and the calculation quantity of the model.

The invention relates to a fall detection method based on spatial point cloud attitude analysis and a computer system, and belongs to the technical field of action and behavior recognition. According to the scheme, a human body target is detected according to a point cloud video, and the target is tracked; for each point cloud video frame, firstly, target detection is carried out according to point cloud data of a point cloud frame image, dynamic point clouds meeting conditions are clustered to form a clustered target, and the clustered target and a tracking target of a target tracking algorithm are matched and associated according to spatial positions; after matching association, adding tumble data used for tumble detection and obtained according to the clustering target into a class of the tracking target corresponding to the matching association; and judging whether the target falls down or not according to the continuous multi-frame falling data of the corresponding target. According to the algorithm, a target which is static for a long time can be identified through a target strong repair algorithm, and a falling state is accurately identified.