31results about How to "Forward-looking" patented technology

The invention relates to the technical field of communication, and discloses a sea area wireless network coverage method and device and a computer readable storage medium, and the method comprises the steps: obtaining the historical link performance, real-time marine environment data and business load information of a target sea area; constructing a sea surface electromagnetic wave propagation attenuation dynamic prediction model based on a gated circulation unit; and in combination with the prediction result and the service demand, jointly optimizing the base station transmitting power, the antenna beam pointing angle and the subcarrier resource allocation, and issuing a regulation and control instruction. The system comprises a data acquisition module, a propagation modeling module, a resource scheduling module and an instruction execution module. According to the invention, through deep fusion of marine environment physical field perception and wireless communication system control, a dynamic coverage regulation and control mechanism driven by a sea condition is established; the provided sea surface electromagnetic wave propagation attenuation dynamic prediction model can pre-judge the signal attenuation trend under the complex sea condition in advance, so that network resource scheduling has perspectiveness.

The invention belongs to the technical field of new energy vehicles, and particularly relates to a hybrid vehicle approximate optimal energy management strategy integrating prediction and control, and the strategy specifically comprises the following steps: (1) constructing a global prediction domain, predicting a global speed curve through employing a bidirectional long-short-term memory network based on traffic data, and obtaining a global speed curve; obtaining a global approximate optimal reference state-of-charge sequence through a dynamic programming algorithm; (2) constructing a mesoscopic coordination domain, performing mesoscale speed prediction by adopting a method of fusing an encoder and a bidirectional long-short-term memory network, and solving a multi-scale information evaluation factor online and performing dynamic correction in combination with a grey wolf optimization algorithm; and (3) constructing a real-time control domain, introducing the evaluation factor to construct a multi-target optimal real-time control framework, and realizing instantaneous optimal mode decision and power distribution. According to the method, the economy, the smoothness and the NVH performance are comprehensively considered, global reference and real-time control are effectively decoupled, and the adaptability of the vehicle to a complex dynamic driving scene and the fuel economy of the whole vehicle are remarkably improved.

The invention discloses a multi-terminal parallel authentication authorization and equipment perception method for a key task communication system, which belongs to the technical field of key task communication, and specifically comprises the following steps: S1, configuration preparation: a server pre-configures a terminal initial configuration file for a terminal; the initial configuration file of the terminal comprises lt; m < cpt >-UE-idgt; element, lt; m < cpt >-UE-idgt; the elements comprise the next-level lt; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; lt; the method comprises the following steps: carrying out Instance-ID-URNgt; the elements are instantiated to be unique identifiers by the configuration terminal; s2, configuration issuing and effectiveness: the server issues the initial configuration file of the terminal to the terminal, and the initial configuration file of the terminal is restarted to take effect through the terminal; s3, authentication and information reporting: the terminal initiates an authentication and service authorization request, wherein the request carries the result; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; s4, service authorization and configuration issuing: the server receives the request, analyzes the terminal instantiation unique identifier, records analysis information, and determines whether to issue matched user configuration file data to the terminal; according to the invention, the terminal registration authentication process is deeply coupled with the terminal type and capability evaluation, and intelligent decision-making can be carried out based on terminal difference.

This application provides a remote anomaly early warning method and system for switchgear. The method acquires remote monitoring data, historical operating data of each component, and its current service life. Based on the historical operating data, it determines the partial discharge characteristic data of each component. Based on the partial discharge characteristic data, it calculates the partial discharge risk score for each component, selects partial discharge monitoring components from all components, and inputs the remote monitoring data, current service life, real-time temperature, and real-time operating voltage of the partial discharge monitoring components into an insulation material aging rate prediction model to obtain the insulation material aging rate, determine the cumulative aging amount of the insulation material, and determine the anomaly early warning result. This application can promptly determine the risk of partial discharge, has a forward-looking perspective, and avoids the physical limitations of relying solely on sensors to collect partial discharge data, thus improving the accuracy of partial discharge monitoring.

The application relates to the technical field of vehicles, in particular to a long-time-domain driving behavior decision method suitable for high-speed and loop traffic scenes, which comprises the following steps: acquiring a global planning path of a vehicle, a current motion state of the vehicle and current motion states of all surrounding vehicles in a region; generating an optimal driving behavior sequence of the vehicle according to the global planning path, the current motion state of the vehicle and the current motion states of all surrounding vehicles in the region; planning a driving track of the vehicle based on a first driving behavior of the optimal driving behavior sequence, and regenerating the optimal driving behavior sequence after the vehicle executes the first driving behavior based on the driving track, until the global planning path is completed. Therefore, the embodiment of the application can realize multi-step decision of driving behaviors such as lane-changing gap selection and lane-changing overtaking, has foresight, and simultaneously gives an optimal solution to long-time-domain driving behavior sequence planning based on feasibility discrimination, considers safety and high efficiency, and meets the needs of general driving.

The application provides a two-stage MPC control method and system for power smoothing output, and relates to the field of new energy control. A discrete state space prediction model is constructed; all state variables are obtained in the current sampling period and used as the input of the first-stage MPC control; the mechanical and electrical state prediction sequence and the optimal control input are obtained by combining the constraint condition and the dynamic optimization objective function of the second-stage MPC control feedback in the last sampling period; in the second-stage MPC control, the optimal power trajectory meeting the smoothing requirement is obtained by combining the smoothing optimization objective function, and is mapped to the control constraint interval of the next sampling period and fed back to the first-stage MPC control; the optimal control input is applied to the execution equipment, and after the constraint condition is fed back to the first-stage MPC control, the next sampling period is entered. The application can inhibit the rapid fluctuation of the generated power without energy storage devices, improve the grid connection quality, and enable the renewable energy power generation system to still stably output under the severe changes of the external environment.

The application relates to an air compressor preventive maintenance method, system, equipment and medium based on energy consumption analysis, and relates to the technical field of air compressors.The method comprises the following steps: collecting air inlet environment, equipment operation and energy consumption data; calculating environment stress characteristics according to the air inlet environment and the equipment operation data, and calculating energy efficiency characteristics according to the energy consumption and the equipment operation data; evaluating the comprehensive health degree of the air compressor based on the environment stress and the energy efficiency characteristics, wherein the comprehensive health degree comprises environment and equipment body health degree components; and generating a maintenance strategy according to the comprehensive health degree.The application can reduce unplanned downtime, reduce maintenance costs and material waste, and optimize equipment operation energy efficiency.

The invention discloses an agricultural enterprise credit scoring method and device, and a medium, and relates to the technical field of credit risk assessment. The method comprises the following steps: acquiring multi-source heterogeneous data of an enterprise; based on the historical price data of the agricultural product, determining the current price cycle phase of the agricultural product; obtaining a weight corresponding to the operating characteristic in the price cycle phase from the characteristic weight matrix; and inputting the basic credit features, the operation features and the weights into a credit scoring model to obtain the credit score of the enterprise output by the credit scoring model. Through the mode, the problems of risk identification lagging and insufficient response to periodic market fluctuation of an existing method can be solved.

The application discloses a kind of based on big data visualization's transformer substation intelligent monitoring method, it is related to measurement data visualization technical field, including, the offset degree between the space-time fingerprint base template and real-time behavior fingerprint is calculated, generates offset vector set, obtains the distribution of offset vector set in space by kernel density algorithm, generates abnormal density field and space abnormal density point cloud;According to space abnormal density point cloud setting density threshold and solving the equipotential surface of abnormal density field, generate abnormal three-dimensional equipotential surface group;Extract risk evaluation index of abnormal three-dimensional equipotential surface group and carry out dynamic rendering to abnormal three-dimensional equipotential surface group, output dynamic visualization picture.The application constructs space-time fingerprint base template and calculates offset vector set, and abstract offset behavior is converted into three-dimensional risk form with space boundary, expansion trend and density aggregation characteristics, improves the explainability of transformer substation abnormal structure, visual identification and forward-looking monitoring capability.

This invention discloses a method and system for detecting moisture in soft capsules. Based on water activity, the method improves efficiency and quality by detecting the moisture content of soft capsules. The method includes: selecting a water activity meter; selecting capsules of the same mass as the sample to be tested as the test object, with the quantity being the sample size recommended in the water activity meter's instruction manual; controlling the environmental conditions between the laboratory and the workshop control room; determining the detection time T for the relative humidity of intact capsules to reach equilibrium; during the capsule drying process, sampling capsules at different drying degrees and simultaneously detecting the moisture and water activity values ​​of the samples using a halogen moisture meter and a water activity meter; collecting data from several batches of capsules and obtaining a data model for moisture and water activity values ​​through fitting; calculating the acceptable water activity range for the soft capsules and using this acceptable water activity range as the judgment standard for the sample to be tested.

This invention relates to the field of cloud service technology and provides an intelligent cloud service management method and system, comprising the following steps: collecting hardware performance counter data on each physical node, predicting the memory bandwidth contention intensity and last-level cache contention intensity of each application in the near future, and determining the interference fingerprint based on the number of instruction cycles per second; calculating the interference potential coefficient between each pair of applications, calculating the global co-location interference entropy of nodes, and classifying nodes into different levels based on the interference entropy; when a node is at a low or medium interference level, identifying aggressive applications, and dynamically adjusting the last-level cache quota, memory bandwidth limit, and CPU scheduling priority of aggressive applications; when a node is at a high interference level, determining candidate migration plans: selecting target nodes through a distributed node negotiation mechanism and executing application migration. This invention achieves the optimal balance between performance assurance and system overhead through a two-level strategy of local resource elastic adjustment and proactive migration.

The application discloses a groundwater source comprehensive environmental risk assessment method based on risk control mechanism effectiveness, relates to the field of environmental science and engineering technology, and comprises the following steps: based on the regional comprehensive environmental risk assessment demand, reconstructing the minute-level time sequence by using the global emission record, extracting the synchronous fragment of the energy release peak and the chemical reaction peak, and generating the time peak index list; according to the time peak index list, fusing the wind field data, the flow field data and the thermal field trajectory information, and constructing the multi-dimensional interference spectrum of the peak superposition region. Through the construction of the time peak index list and the multi-dimensional interference spectrum, the application realizes the dynamic perception and early identification of the regional environmental risk, accurately reveals the coupling characteristics of the energy and the chemical process, and through the frequency division breathing phase inversion regulation, the pulse bypass discharge and the cooling infiltration cooperation, the energy release process can keep dynamic balance, the risk resonance amplification can be prevented, and the regional protection and control efficiency can be improved.

The invention relates to the technical field of bridge health monitoring, and discloses a steel pipe-ECC concrete bridge pier health state monitoring system, which comprises a multi-source sensing module, an ECC concrete bridge pier health state monitoring module, an ECC concrete bridge pier health state monitoring module and an ECC concrete bridge pier health state monitoring module, the data acquisition and transmission module is responsible for acquiring sensor data and uploading the sensor data to the cloud; and the health assessment model runs in the cloud, analyzes damage and outputs the health state of the pier by fusing the physical theory and the actually measured data. According to the method and system for monitoring the health state of the steel pipe-ECC concrete pier, the strain sensors, the distributed crack monitoring network and the specially-made miniature slippage displacement meter are arranged in a key area of a steel pipe-ECC interface in a targeted mode, a multi-physical-quantity coupled sensing array is constructed, interface micron-level slippage and concrete internal microcrack initiation can be synchronously captured in real time, and the monitoring accuracy of the health state of the steel pipe-ECC concrete pier is improved. The early monitoring sensitivity and spatial resolution of the specific interface damage evolution process of the composite material are improved, and the technical blind spot that an interface damage origin area is difficult to touch in a traditional monitoring technology is solved.

The invention discloses an artificial intelligence auxiliary decision-making system for ferry drop-and-pull operation, relates to the technical field of intelligent logistics scheduling, and aims to solve the problems of short-view decision-making, cabin capacity fragmentation and channel blockage caused by decoupling voyage number selection and bay position selection in an existing system. Through joint output in the same decision cycle and message vector interaction of a scheduling / stowage unit, coupling optimization of voyage numbers and stowage locations is realized, so that the overall loading feasibility and the continuous operation fluency are improved; in order to solve the problem that the potential influence of current distribution on a future vehicle queue is difficult to quantify in a real-time continuous arrival scene, a future value prediction network is combined with reserved traffic flow information or historical arrival distribution to generate a traffic flow scene, and candidate voyage number actions are subjected to long-term return evaluation and sorting, so that voyage number selection has perspectiveness.

The application discloses a kind of integrated fault prediction comprehensive power supply test method and system, and it is related to power supply test technical field.A kind of integrated fault prediction comprehensive power supply test system, including have: test parameter acquisition module, reference state update module, data fusion module, fault prediction module and retest record module.The application is by setting first prediction channel and second prediction channel, and is combined with initial abnormality judgment result flexible selection model path, while guaranteeing the prediction efficiency, has improved prediction accuracy, adapts to the operation demand under different complexity scene;By setting flow analysis mechanism and dynamic flow transfer limit, and based on the weighting operation of maximum influence data item, the intelligent verification and self-adaptive adjustment of model result consistency are realized, and the stability and robustness of fault judgment process are enhanced.

This invention discloses an intelligent computing center performance evaluation and optimization management system, belonging to the field of intelligent computing center management technology. The system includes a data acquisition module, a data preprocessing module, a performance evaluation module, an optimization decision-making module, an execution control module, and a data storage module. The data acquisition module uses IoT sensors and edge computing nodes to collect real-time operating data from various devices and transmits it encrypted. The data preprocessing module uses the Spark Streaming framework to clean and denoise the data. The performance evaluation module integrates a CNN-LSTM hybrid network and the Analytic Hierarchy Process (AHP) to generate a comprehensive performance score through a formula. The optimization decision-making module uses reinforcement learning and other algorithms, combined with a digital twin engine and energy consumption prediction formulas, to generate the optimal resource scheduling scheme. This system achieves comprehensive data acquisition, accurate performance evaluation, scientific optimization decision-making, and efficient execution control for intelligent computing centers, improving operating efficiency and reducing energy consumption, and has high practical value.

The invention relates to an intelligent patrol method and system for a subway protection district, and the method comprises the steps: carrying out the preprocessing of a real-time image collected by an unmanned plane, carrying out the target detection of each frame of aerial photo in the preprocessed real-time image, and obtaining all target detection results; constructing an environment field corresponding to the moving target based on the bounding box of each moving target, performing environment feature extraction on the environment field, performing self feature extraction on each moving target, and combining the obtained self features with the target environment to generate final detection features; and matching the final detection features with known detection features in a database, carrying out duplicate removal on a target detection result according to a matching result, carrying out track prediction on the moving target, and generating alarm information according to an obtained track prediction result to give an alarm. Therefore, accurate duplicate removal and dynamic tracking of the moving target are realized, the accuracy and efficiency of patrol of the subway protection area are improved, and the safety of the subway protection area is improved.

The application discloses a method and system for regulating the tulip contact of a circuit breaker trolley, and relates to the technical field of electrical equipment regulation, comprising the following steps: extracting contact multi-source features based on the obtained contact operation history data, and predicting a contact expansion state sequence; performing contact simulation control based on the contact expansion state sequence, generating a contact dynamic response; applying disturbance to the contact simulation control, performing adversarial training to update the simulation control process, and generating a contact control strategy; wherein the adversarial training comprises taking the synchronism between the contact fingers as a constraint condition, and the synchronism is obtained based on contact dynamic response analysis. The application is used to solve the problems of poor safety and inaccurate regulation of the predictive control means of the tulip contact.

The application discloses a multi-target computing power scheduling method, device, equipment, medium and program product, and relates to the technical field of computing power networks. The scheduling device of the method receives task carrying willingness scores and resource state information of a plurality of resource pool systems, and splits a computing power task sent by a task party to obtain a plurality of sub-computing power tasks; the task carrying willingness scores, the resource state information and the sub-computing power tasks are matched to obtain candidate resource pool systems, and scheduling information is sent to the candidate resource pool systems. The candidate resource pool systems determine predicted load evaluation results according to the scheduling information and real-time running data and send the predicted load evaluation results to the scheduling device; the scheduling device determines a target resource pool system of each sub-computing power task according to the predicted load evaluation results and the sub-computing power tasks, and sends a corresponding sub-computing power task to the target resource pool system, so that the target resource pool system executes the sub-computing power task. Therefore, the effect of computing power scheduling is improved.

The invention relates to the technical field of intelligent power distribution, in particular to a switch cabinet safety monitoring and control system based on artificial intelligence, and the system comprises an operation situation sensing module which collects the multi-dimensional operation data of a switch cabinet in real time, and carries out the calculation to generate a dynamic correlation intensity matrix; the abnormal traceability and prediction module is used for carrying out time sequence analysis and mode recognition on the dynamic correlation intensity matrix, outputting traceability positioning information of potential faults and predicting risk evolution; the control strategy generation module is used for generating a targeted adaptive control instruction according to the traceability positioning information and risk evolution prediction; and the execution and feedback module is used for executing the self-adaptive control instruction to adjust the operation state of the switch cabinet, collecting state feedback data after the instruction is executed and sending the state feedback data back to the operation situation sensing module. According to the method, a complete closed-loop control chain from sensing, decision making to verification is realized, and the long-term adaptability and the system evolution capability of a control strategy are effectively improved.

The application discloses a kind of based on time series method and AHP-SLP's logistics center warehousing system layout method, specifically includes the following steps: first, time series prediction model is constructed, the material flow of logistics center warehousing system is predicted;Second, the function area of logistics center warehousing system is divided, and the scale of each function area is calculated.Then analyze its logistics relationship, then obtain the data of each index in index system, based on AHP, the subjective weight of the influence factor of non-logistics relationship is calculated and consistency test is carried out, and the comprehensive logistics relationship is obtained;Finally, using the relationship table method, the comprehensive logistics relationship of logistics center warehousing system is charted, and the alternative scheme set is obtained, then the system is analyzed, and the optimal layout scheme is obtained.The application constructs logistics center warehousing system layout method, realizes the quantification and qualitative of logistics center layout, can reduce the capacity loss of goods in the process of warehousing transfer, improve the operation efficiency of logistics center warehousing system.

The embodiment of the invention discloses a network energy-saving control method and system, electronic equipment and a storage medium, and relates to the technical field of communication, and the network energy-saving control method comprises the following steps: obtaining network state data of each network equipment in a target network; performing load prediction on each network device based on historical load data of each network device in the network state data to obtain predicted load data of each network device; and based on current network state data in the network state data and a preset service level agreement (SLA) constraint, performing SLA resource division on each network device, and determining a resource division result required by each network device to meet the SLA constraint; performing resource scheduling calculation on each network device based on the current network state data, the SLA resource division result and the predicted load data by taking reduction of the energy consumption of the network device and meeting of the SLA constraint as targets to obtain a target scheduling decision; the target scheduling decision is used for indicating start and stop of the resource module of each network device.

The application belongs to the field of robot positioning, and relates to a self-adaptive layered robot positioning method and device for degenerative scenes, which comprises the following steps: monitoring the data quality obtained by a heterogeneous sensor in real time, mapping the state of the heterogeneous sensor to a unified quantization space, and outputting a unique degenerative level identifier D1-D4; for a D1 environment, the uncertainty of feature matching is quantified, and the most contributive feature subset is selected; for a D2 environment, a singular value decomposition is used to locate a degenerative subspace; for a D3 environment, an expected error of the heterogeneous sensor engine is estimated through a performance prediction network, and a continuous confidence weighting is used instead of hard switching; for a D4 environment, a pre-training-fine-tuning-online adaptation three-level architecture is used to provide a cross-platform generalized pure inertial odometer; and historical events are stored as experience data. The application realizes fine differentiation and directional processing of degenerative scenes, and builds an experience-driven closed-loop optimization mechanism.

This invention relates to the field of production line automation technology, and particularly to an integrated die-casting workshop robot energy-saving prediction and analysis method, device, equipment, and storage medium. This method predicts production line energy consumption and identifies energy-saving potential ranges by comprehensively analyzing production plans and real-time operating data. Simultaneously, it intelligently generates differentiated production line hibernation strategies by combining equipment status data. Finally, by optimizing and integrating energy-saving potential and hibernation strategies, an optimal hibernation scheme that balances efficiency and energy saving is formed. This scheme constitutes a complete closed loop from prediction and evaluation to optimized execution, making energy-saving decisions forward-looking and avoiding blind decisions. By comprehensively considering the differentiated hibernation strategies generated from equipment status, the feasibility of the strategies and equipment safety are ensured. Therefore, this method can significantly improve energy utilization efficiency and reduce ineffective energy consumption without affecting production plans, thereby achieving precise and intelligent energy saving and consumption reduction.

The invention relates to the technical field of territorial space planning, in particular to a territorial space planning data acquisition and analysis system. Comprising a data acquisition module, a data preprocessing module, a data analysis module, a data storage module and a result display module. Various data sources are integrated, data timeliness is improved by combining various data acquisition modes, territorial space planning is ensured to be performed based on newest information, and planning errors are avoided; a plurality of analysis methods are introduced, including spatial overlay analysis, buffer analysis, statistical analysis and model prediction analysis based on machine learning and deep learning, so that the prediction accuracy is improved, and the planning has perspectiveness and adaptability; the mode of combining the relational database and the non-relational database is adopted, the respective advantages are exerted, the non-relational database adopts a distributed storage architecture, the data storage reliability and expansibility are improved, the system stability is ensured, and the increased data storage requirement is met.

The embodiment of the application provides a kind of based on dynamic knowledge graph and multi-source data fusion's marine pasture intelligent site selection decision system and method, it is related to the technical field of marine information technology and intelligent decision-making technology.Its method includes: obtaining the multi-source heterogeneous data covering candidate sea area, and generating dynamic metadata label for data unit in the multi-source heterogeneous data;Based on the multi-source heterogeneous data and external knowledge source, the knowledge graph of the field of marine pasture site selection is constructed;Based on the knowledge graph, a dynamic association rule base is constructed;According to the site selection constraint condition input by the user, the knowledge graph and the dynamic association rule base, the recommended site selection area of the candidate sea area is generated.Through the application, the problem of low site selection decision accuracy is solved, and the effect of improving site selection decision accuracy is achieved.

The invention provides a dangerous chemical storage temperature and humidity linkage control system, method, equipment and medium, and the method comprises the steps: respectively determining the sensitivity coefficient of a target dangerous chemical to the temperature and humidity and the coupling relation between the temperature and humidity through the physicochemical property data of the dangerous chemical and the linear regression trend of the response of the target dangerous chemical to the temperature and humidity; determining the steady-state adjustment boundary of the humidity in different temperature states according to the sensitivity coefficient and the coupling action relationship; based on the real-time temperature and humidity distribution information of the target dangerous chemical and all the steady-state adjustment boundaries, determining a plurality of risk overrun points of the temperature and humidity under the current environment trend; and carrying out temperature and humidity multi-point linkage control on the storage environment of the target dangerous chemicals according to multi-point temperature and humidity linkage regulation and control parameters generated according to the dynamic association rules of all the risk over-limit points and the temperature and humidity. According to the technical scheme provided by the invention, collaborative optimization regulation and control of temperature and humidity parameters can be realized in the dangerous chemical storage process.

The application discloses a kind of unmanned aerial vehicle three-dimensional dynamic barrier control method based on risk trigger and energy optimization, and the method steps are as follows: (1) establish unmanned aerial vehicle motion model and barrier environment model;(2) construct multi-source comprehensive risk index and event trigger mechanism with hysteresis and refractory period;(3) define energy-aware local prediction optimization objective function and hard constraint set;(4) local trajectory generation based on nonlinear programming transcription and numerical solver;(5) execute dual-mode switching control logic and unmanned aerial vehicle state update.The application aims to realize the real-time, efficient scheduling and optimization of the barrier behavior of unmanned aerial vehicle in complex three-dimensional dynamic environment based on risk trigger and energy optimization on the resource-limited airborne computing platform, while ensuring high maneuvering safety, significantly reducing the computing load of the controller, and realizing energy-aware trajectory generation for the asymmetric three-dimensional flight characteristics of unmanned aerial vehicle.

This invention relates to a safety early warning method for coal mining equipment based on real-time industrial data, belonging to the field of fault diagnosis technology. The method includes: acquiring real-time frequency domain feature sequences at multiple monitoring times and obtaining reference frequency domain feature vectors; obtaining real-time potential feature vectors for each monitoring time using a SafetyCAE feature extraction network; calculating the Mahalanobis distance between the real-time potential feature vectors and the reference frequency domain feature vectors; calculating the prediction uncertainty factor for each monitoring time; calculating a comprehensive safety early warning factor based on the Mahalanobis distance and the prediction uncertainty factor; performing component-level and whole-machine-level risk diagnosis on the coal mining equipment based on the comprehensive safety early warning factor; inputting the comprehensive safety early warning factor into the fault diagnosis isolated forest iForest model, and outputting the anomaly score and anomaly label for each monitoring time. This invention has stronger forward-looking capabilities, realizing a shift from traditional post-event diagnosis to pre-event safety early warning.

The invention discloses an unmanned aerial vehicle image detection confrontation sample generation method and system based on a black box normal form. The method comprises the steps of S1, candidate region generation: extracting multi-scale features from an input unmanned aerial vehicle image, and generating a candidate region set containing potential target frame coordinates through feature fusion and transformation; s2, target category retrieval and allocation: allocating a target category with the highest attack efficiency to each candidate region in the candidate region set based on pre-constructed object category correlation prior information to form a target category set; and S3, adversarial sample generation: combining the candidate region set with the target category set, generating a virtual instance set, and adding the virtual instance set into the original input image to obtain a final adversarial sample image. According to the method, efficient black box attacks are realized, calculation delay and energy consumption are innovatively induced, the attack pertinence is high, the concealment is high, the attack success rate is improved, the adaptability is high, and a new view angle is provided for security assessment and defense.