10772results about How to "Guarantee stability" patented technology

The invention provides a private domain live broadcast data storage and visitor authentication method and system based on a block chain, and relates to the technical field of data storage and access control, and the method comprises the steps: collecting multi-mode biological characteristic data of a visitor, carrying out the feature extraction and fusion through a deep neural network of a space-time attention mechanism, and carrying out the authentication of the visitor; authenticity verification is carried out in combination with an adversarial generative network, a dynamic identity token is generated, and a hash value of the dynamic identity token is written into the block chain. The method comprises the following steps: performing semantic analysis on live broadcast data flow through a self-adaptive content perception model, constructing a double-layer access control matrix according to a data sensitivity level and a credibility score, performing self-adaptive fragmentation and hierarchical encryption based on federated learning, and then storing. After an access request is subjected to identity verification, an optimal edge node is selected according to access authority and network performance parameters to construct multi-path transmission, and a transmission strategy is generated based on a service quality score and a data sensitivity level. And when the service quality is lower than a threshold value, triggering the smart contract to perform fault migration.

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention provides an automobile part spraying control method and system, and relates to the technical field of automobile manufacturing, and the method comprises the steps: collecting spraying environment parameters in real time, generating a comprehensive digital model containing the environment parameters and part characteristics, and carrying out the intelligent partitioning of the surface of an automobile part through a clustering partitioning algorithm, and dividing the surface into a plurality of sub-regions; on the basis of each sub-region, combining with the current environment parameters, generating an optimal spraying parameter for each sub-region by utilizing an intelligent agent trained by a reinforcement learning algorithm, and on the basis of the spatial relationship between the optimal spraying parameters and the sub-regions, optimizing a global spraying sequence by adopting an ant colony path planning algorithm, and generating an optimal spraying path; the spraying robot sprays, a real-time quality evaluation result of spraying is generated, a pre-constructed fuzzy logic controller is adopted, spraying parameters are adjusted, sprayed parts are obtained, a full-period comprehensive data set is integrated, and spraying control parameters are iteratively updated.

The invention discloses a full-effect electric energy optimization rapid detection system and method, and particularly relates to the technical field of electric energy detection. The method comprises the following steps: acquiring voltage, current, frequency, harmonic component and three-phase unbalance degree of a power grid in real time, calculating a power grid impedance value and a load dynamic characteristic ratio, drawing an impedance frequency characteristic curve, analyzing a harmonic amplification abnormal degree and a load dynamic response characteristic, and evaluating the accuracy of a detection instruction generated by the full-effect electric energy optimization device. The error correction is carried out on the inaccuracy detection instruction, the duty ratio is dynamically adjusted by utilizing the PWM technology, the accurate matching of the amplitude and the phase of the output current is realized, the error correction effect is evaluated by comparing the real-time detection value with the theoretical calculation value, and the detection model and the compensation strategy are automatically optimized. The problems of detection precision reduction and error compensation caused by complex power grid conditions in the prior art are solved, harmonic amplification, reactive power over-compensation and three-phase balance damage are avoided, and efficient and accurate electric energy optimization and power grid stability improvement are achieved.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention discloses a fan multi-mode fault diagnosis method based on voiceprint feature and vibration data fusion. The method comprises the steps that S1, voiceprint signals and vibration signals generated in the fan operation process are synchronously collected through a plurality of sensors; s2, preprocessing the collected voiceprint signal and the vibration signal; s3, fusing the preprocessed voiceprint signal and vibration signal through a feature level weighted fusion algorithm; s4, performing coarse-grained fault screening under a multi-level framework; s5, performing fine-grained fault diagnosis under a multi-level framework; s6, after the fine-grained fault diagnosis is completed, the system outputs a corresponding fault early warning signal according to the type, the specific part and the severity of the fault; and S7, the system dynamically adjusts the fault diagnosis model by monitoring the running state and the fault condition of the fan in real time. According to the method, the fault type of the fan can be accurately identified, the severity of the fan can be evaluated, and the overall intelligent level and the real-time response capability of a fault diagnosis system are improved.

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

The invention discloses a substation network security defense system based on artificial intelligence. The system comprises a data collection module for collecting transformer substation network data; the anomaly detection module is used for identifying an abnormal mode and an intrusion behavior; the threat intelligence analysis module is used for identifying potential threat and vulnerability information; the intelligent security monitoring and response module is used for evaluating the network security state; the malicious code detection module is used for detecting, analyzing and isolating malicious software; the intelligent security prediction module is used for predicting a network security event and carrying out early warning prompt; the self-adaptive security policy module is used for automatically adjusting or updating an optimal security policy; the system updating and maintaining module is used for updating and maintaining system modules and functions; the user interface and alarm module is used for providing safety state display and alarm for a user; the data backup and recovery module is used for backing up and recovering key data; and a compliance auditing module which makes operation and configuration accord with security standards. According to the invention, hierarchical, intelligent and active protection of the transformer substation network is realized.

The invention discloses a hydropower station equipment state monitoring method and system, and relates to the technical field of hydropower station equipment monitoring, and the method comprises the steps: collecting the hydropower station equipment state through multi-layer data, and dynamically adjusting the collection frequency according to the change of the equipment state; performing signal quality evaluation on the collected data to generate fusion data; inputting the fused data into a trend analysis module, and generating a trend model of the equipment state in combination with historical data and real-time data; according to an analysis result of the trend model, combining the plurality of abnormal states into an event mode, and comparing the event mode with a known fault mode to determine a risk level; and generating a corresponding early warning signal based on the risk level, and performing corresponding response processing on early warning events of different levels. According to the hydropower station equipment state monitoring method provided by the invention, through multi-level data acquisition and dynamic frequency adjustment, the accuracy of data acquisition and the resource utilization rate are effectively improved, so that the system can adapt to different equipment states.

The invention provides a private domain live broadcast room hotspot prediction and adaptive routing method based on swarm intelligence, and relates to the technical field of data processing, and the method comprises the steps: constructing a dynamic interest community through a dynamic graph neural network, extracting group interaction mode features through a multi-layer graph attention network, and carrying out the group interaction mode features; the method comprises the following steps: generating time sequence correlation features in combination with an improved bidirectional gating loop unit, extracting high-order semantic features, performing hotspot prediction through a multi-task learning framework, constructing a multi-level network topology model according to a prediction result, and performing path exploration in combination with an improved heuristic search algorithm to obtain an optimal transmission path. Flow distribution is carried out through a weighted polling algorithm, a deep learning distributed load balancing controller and a congestion control algorithm are combined, the data transmission rate is dynamically adjusted, and self-adaptive routing pushing of live broadcast content is achieved.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention relates to the technical field of storage equipment management, in particular to an intelligent control method and system for storage equipment, and aims to solve the problems of task allocation, path optimization and equipment collaboration in a complex storage scene. According to the method, a storage equipment function model comprising an upper-layer frame model and a lower-layer equipment model is constructed, task allocation and path planning are optimized by using a neural network model in combination with task requirements, and a storage equipment control instruction is generated. In the task execution process, task priorities and path planning are adjusted in real time to adapt to dynamic environment changes, and it is ensured that tasks are completed efficiently. According to the method, a task distribution diagram and a path planning scheme are further optimized through a dynamic task diagram and a dynamic energy field, the problems of equipment congestion, too high energy consumption and task delay are effectively solved, the collaboration efficiency and the resource utilization rate of storage equipment are improved, and the method is suitable for various complex storage scenes.

The invention discloses a key generation and distribution system based on quantum random numbers, and relates to the technical field of quantum key distribution. The system comprises a quantum random number generation module, a light source control module, a quantum key distribution module, a detector module, a data processing module, a communication control module, a network anomaly monitoring module and an identity authentication module. According to the secret key generation and distribution system based on the quantum random number, the quantum random number generator generates a real unpredictable random number sequence by using the randomness principle in quantum mechanics, and the random number sequence is taken as the basis of the secret key, so that the unpredictability and the safety of the secret key are fundamentally improved; compared with a traditional secret key generation method based on a pseudo-random number, the quantum random number has higher randomness and lower predictable risk, various cryptographic analysis attacks can be effectively resisted, the uncrackability of the secret key is ensured, and an unbreakable safety guarantee is provided for information communication.

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are improved.

The invention discloses an e-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, and constructing an original data set; s2, performing data cleaning and characterization processing to generate a feature data set; s3, constructing a knowledge graph and dynamically updating the knowledge graph by using the graph embedding model; s4, generating potential causal paths and weights in combination with the neural relationship reasoning model and the diffusion model, and introducing a microknowledge enhancement layer to generate causal enhancement features; s5, identifying abnormal behaviors in combination with a multi-level anomaly detection mechanism; s6, designing a dynamic game optimization model, determining a collaborative optimal solution under a multi-target conflict through Nash equilibrium, and processing a dominant and subordinate relationship in combination with a Stackelberg game to generate a collaborative optimization strategy; and S7, displaying the monitoring state and the collaborative optimization strategy through a visual interface. According to the invention, big data analysis, a knowledge graph and a dynamic game technology are combined, so that e-commerce intelligent operation monitoring and collaborative decision-making are realized.

The invention relates to the field of low-altitude economy, in particular to a server communication optimization method and system based on low-altitude economy. The method comprises the following steps: monitoring an unmanned aerial vehicle communication relay node in a low-altitude field; carrying out node space position positioning calculation on the unmanned aerial vehicle communication relay nodes one by one, carrying out distributed relay topology fitting, and constructing a low-altitude relay communication network; collecting low-altitude real-time interaction information based on a low-altitude relay communication network; performing communication link bandwidth calculation on the low-altitude real-time interaction information, performing dynamic communication protocol modulation, and constructing a dynamic low-altitude adaptive communication protocol; identifying real-time communication state parameters of the relay nodes based on the low-altitude relay communication network; performing inter-node communication channel identification on the real-time communication state parameters of the relay nodes, performing optimal channel scheduling decision, and constructing an optimal channel scheduling strategy; current user communication data of the ground server is monitored. According to the invention, low-delay and high-efficiency server communication optimization is realized.

The invention discloses a trackless rubber-tyred vehicle multi-terminal data fusion system, relates to the technical field of data fusion, and is used for solving the problem of poor trackless rubber-tyred vehicle multi-terminal data fusion. Comprising a path quantification module, an equipment health classification module, an alternative optimization module and a consistency verification module. According to the method, multi-terminal data fusion is carried out on a trackless rubber-tyred vehicle, a sensor dependency graph is dynamically constructed, dependency weights are calculated, a critical path is optimized, health indexes are defined according to a signal-to-noise ratio, output fluctuation and response delay, multi-dimensional abnormity is detected in combination with a sliding window and a mahalanobis distance, and a health score is generated to monitor the state of the sensor. A fault sensor is isolated, substitute data is generated through a redundant data source, the substitute data is incorporated into a fusion process after verification, a dependency graph is adjusted, a path is optimized, global consistency verification analysis is performed, different strategy management modules are triggered, a data fusion rule and task allocation are optimized, and the data fusion reliability of the trackless rubber-tyred vehicle in a complex operation environment is improved.

The invention relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention provides a private domain social relation mining method and system based on a graph neural network, and relates to the technical field of social relation mining, and the method comprises the steps: carrying out the entity alignment and relation mapping of historical behavior data of a private domain platform user and an external knowledge base through employing a knowledge graph technology, generating user behavior feature data, and constructing a user relation graph; processing the user data by using a federal map neural network model containing a space-time attention layer to obtain a user node representation vector; and according to the user node representation vector, calculating a social influence index and a relationship strength index between the users, determining a final social circle layer structure by adopting a spectral clustering method and causal intervention analysis, and generating a social relationship analysis report. According to the method, complex user social relations in a private domain platform can be effectively mined, the accuracy and stability of social circle layer identification are improved, and technical support is provided for precise marketing and personalized recommendation.

The invention relates to the technical field of information synchronization, in particular to a cross-platform electronic communication synchronization system, which comprises a network construction and path optimization module for collecting basic network parameters of communication equipment of a plurality of platform networks, obtaining cross-platform network data according to the basic network parameters, selecting an optimal transmission path according to the cross-platform network data, and transmitting the optimal transmission path to a server; and generating real-time optimized path information. In the invention, the basic network parameters of the communication equipment of the plurality of platform networks are collected, the cross-platform network data are acquired, the edge weight is dynamically updated according to the delay and the bandwidth, and the optimal transmission path is selected based on the network state, so that the effect of efficiently utilizing network resources is achieved, and the real-time performance and the accuracy of synchronous data transmission are remarkably improved. The to-be-synchronized data is classified and subjected to priority setting, and the data processing sequence is adjusted in combination with the real-time network data, so that accurate scheduling of the synchronization task is realized, the delay and conflict problems in data transmission are reduced, and the efficiency and reliability of data synchronization are improved.

The invention discloses a traditional Chinese medicinal material authenticity identification platform, and relates to the technical field of traditional Chinese medicinal material authenticity identification, the traditional Chinese medicinal material authenticity identification platform comprises a data acquisition module, the data acquisition module is connected with a data analysis module, and the data analysis module is connected with a result display module; and the data acquisition module is used for acquiring appearance images, spectral data and background information of the traditional Chinese medicinal materials. According to the traditional Chinese medicine authenticity identification platform, traditional Chinese medicines are comprehensively analyzed from multiple angles, limitation of a single method is avoided, and identification accuracy is improved; the characteristics of the traditional Chinese medicinal materials are deeply analyzed to accurately judge authenticity and quality; the quality of the traditional Chinese medicinal materials is accurately evaluated by establishing a quantitative analysis model; the model is verified by adopting a cross validation method, so that the accuracy and stability are ensured, the identification accuracy is improved, and the problem that the traditional identification method is difficult to meet the requirement of quickly and accurately identifying the authenticity of the traditional Chinese medicinal materials is solved.

The invention relates to the technical field of electromagnetic compatibility optimization design methods, in particular to a system intelligent electromagnetic compatibility optimization design method and system, and the method comprises the following steps: building an electromagnetic simulation unit model of a component, and constructing a component model library; performing interference path analysis on the component model through a path optimization algorithm, identifying key nodes and interference source nodes, and constructing an electromagnetic interference path model; according to the electromagnetic interference path model, carrying out adaptive adjustment on electromagnetic radiation and immunity parameters of system components so as to optimize electromagnetic compatibility; carrying out dynamic prediction and parameter optimization adjustment on electromagnetic compatibility parameters of the system through a deep learning model; the electromagnetic compatibility of the system and the sub-modules thereof is integrally optimized by adopting a progressive feedback adjustment mechanism, and the technical contradiction in the traditional method is effectively solved by introducing innovative mechanisms such as path weight calculation, nonlinear adaptive adjustment and multi-level non-uniform optimization.

The invention discloses a power equipment fault prediction method based on support vector regression, and aims to improve the prediction accuracy. The method comprises the following steps: firstly, collecting multi-dimensional operation parameter data of power equipment, and constructing an original monitoring data set; a data set is segmented through a sliding time window, wavelet transform is used for noise reduction, and then a normalization method is applied to generate a standardized data set. Time domain and frequency domain features are further extracted, a feature vector space is constructed, an optimal feature subset is screened through a mutual information method, and a training data set is generated; performing preliminary training on a support vector regression model by using the training data set to generate candidate support vectors, and screening a support vector set with the maximum contribution through an optimization algorithm; a grid search method is adopted to adjust kernel function parameters and penalty factors, and a support vector set is further optimized. And evaluating the performance of the model through cross validation, applying the optimized support vector regression model to power equipment fault prediction, and generating a fault occurrence probability prediction value.

The invention relates to the field of unmanned aerial vehicle route acquisition, in particular to an unmanned aerial vehicle dynamic inspection route acquisition method and system based on laser point cloud. The method comprises the steps of comprehensive processing of three-dimensional point cloud data, marking of equipment inspection point positions to generate waypoints, safe distance collision inspection of the waypoints, position adjustment of the waypoints, planning of inspection paths and the like, through comprehensive processing of the three-dimensional point cloud data, including denoising, classification and thinning, the quality of the point cloud data can be remarkably improved, and by means of the laser point cloud technology, the quality of the three-dimensional point cloud data is improved. The terrain and equipment characteristics of the to-be-inspected area can be accurately captured, so that the accuracy and reliability of inspection are improved. The steps of waypoint safe distance collision inspection and waypoint position adjustment further enhance the autonomous flight ability of the unmanned aerial vehicle, and ensure the safety and stability of the inspection process. When the routing inspection path is planned, an optimal safe path algorithm is adopted, the path between two waypoints is searched, a local route is generated, and finally the local route is connected to form a complete route, so that the high efficiency and integrity of routing inspection are ensured.

The invention relates to the technical field of drying process control, in particular to a medicinal material drying process control method and system for traditional Chinese medicinal material processing, and the method comprises the following steps: calling a temperature set value, a humidity discharge rate and heating power based on the operation condition of a drying chamber, and analyzing the difference value between the medicinal material moisture content and the target moisture content; combining the medicinal material density with the water content change rate, matching the coupling relationship between the humidity discharge rate and the heating power, and establishing an initial drying process control value; according to the method, through collaborative analysis of the temperature set value, the humidity discharge rate and the heating power, accurate matching of initial drying conditions and material characteristics is achieved, excessive drying is avoided, linkage of the air speed and the humidity discharge rate is optimized, humidity distribution uniformity and environmental stability are improved, the evaporation rate is dynamically matched, and the drying efficiency is improved. Drying uniformity and quality stability are improved, termination control parameters are optimized in real time, drying time is shortened, energy consumption is reduced, and active ingredients and physical characteristics of medicinal materials are reserved to the maximum extent.