475results about How to "Flexible response" patented technology

The invention relates to the technical field of data management, in particular to an intelligent supervision method and system based on big data, and the method comprises the following steps: S1, multi-source data collection: collecting multi-source data in real time; s2, data preprocessing and spatio-temporal feature extraction: preprocessing the collected multi-source data, and extracting spatio-temporal features from the preprocessed multi-source data; s3, performing multi-dimensional risk assessment based on spatial-temporal characteristics: performing risk assessment on behaviors and states of the supervision objects by applying a multi-dimensional risk assessment algorithm; s4, dynamic early warning: in combination with a preset early warning rule, automatically triggering dynamic early warning, and providing a corresponding risk coping suggestion; and S5, supervision strategy optimization: optimizing and updating a supervision strategy through a self-learning mechanism based on a risk assessment result and feedback of dynamic early warning. According to the invention, the accuracy and response speed of risk identification are improved, so that the supervision work is more flexible and efficient.

The invention relates to the technical field of geological disaster monitoring, in particular to a landslide geological disaster monitoring and early warning system based on multi-means fusion, which comprises a data acquisition module, a data fusion module, a landslide prediction module, a landslide disaster intelligent partition and response module and an early warning transmitting module, the data acquisition module acquires landslide related data in real time; the data fusion module is used for fusing landslide related data from different sources; the landslide prediction module is used for predicting the occurrence probability of the landslide; the landslide disaster intelligent partition and response module combines landslide related data and geologic structures monitored in real time, dynamically adjusts post-disaster response measures, and generates early warning information; and the early warning signal sending module automatically sends an early warning signal based on the generated early warning information. According to the invention, potential risks can be found in time in the early stage of landslide occurrence, and the accuracy and timeliness of disaster early warning are greatly improved.

The invention discloses a big data dynamic allocation and optimal scheduling method based on reinforcement learning. The method comprises the following steps: S1, constructing a real-time operation data set; s2, preprocessing the real-time operation data set, and constructing a reinforcement learning environment model; s3, initializing a hierarchical reinforcement learning model; s4, generating an optimal data distribution and scheduling strategy based on the hierarchical reinforcement learning model; s5, adjusting data distribution and task scheduling of each computing node in real time to balance the system load and maximize the resource utilization rate; s6, the hierarchical reinforcement learning model continuously monitors the system state and dynamically updates the strategy, and self-adaptive adjustment is carried out on resource allocation and scheduling in time when the system load or environment changes; and S7, through multi-round iterative optimization, gradually converging the hierarchical reinforcement learning model to an optimal strategy. Through application of reinforcement learning, automatic optimization of resource allocation and scheduling strategies is realized, and the resource utilization rate, the scheduling efficiency and the adaptability of the system are remarkably improved.

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention provides an intelligent access method and system for small accessories of an unmanned aerial vehicle, and relates to the technical field of intelligent access. Generating an action feature vector through an action semantic understanding model; identifying key action nodes and generating a basic action sequence; constructing an action semantic mapping model; performing mapping analysis based on the execution equipment capability; generating an equipment control instruction; collecting action execution state data; establishing an execution evaluation model; generating a compensation instruction and updating the control instruction; and intelligent access operation is completed. According to the method, intelligent storage and taking of the unmanned aerial vehicle accessories are achieved, and the operation efficiency and precision are improved.

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

The invention discloses a big data analysis-based early warning method for abnormal emission of pollution facilities. The method comprises the following steps of S1, collecting data and constructing a data set; s2, storing and preprocessing a data set; s3, constructing a space-time propagation network of the external events by using the graph neural network model; s4, modeling and predicting the time sequence features by using a mixed depth generation model; s5, analyzing the causal relationship between the external event and the facility emission behavior through a causal inference model; s6, adopting a Bayesian optimization algorithm based on uncertainty estimation of a strategy to dynamically adjust an emission early warning threshold value; s7, generating a new abnormal emission mode label by using a self-supervised learning model and a few-sample learning technology; and S8, adopting an interpretable artificial intelligence technology to generate an abnormal source tracing report, and providing coping suggestions and action schemes. According to the method, the early warning of the abnormal emission of the pollution facilities is realized by utilizing a cross-time-space event inference and self-supervised learning method.

The invention belongs to the technical field of natural language processing. According to the zero resource framework-based fine-grained large model illusion processing method and system, multi-dimensional anomaly detection is carried out on a preprocessed text through a large language model, and a multi-dimensional anomaly detection result is obtained; performing anomaly correction priority ranking according to a multi-dimensional anomaly detection result; dynamically adjusting a context window range according to a syntactic analysis result and the generated syntactic dependency tree; performing priority correction on high-risk vocabularies and sentences according to an abnormal correction priority ranking result in combination with the adjusted context window range; and evaluating the corrected content, and obtaining a corrected final text when a termination condition is met. According to the method, the excessive confidence, subtle semantics and grammar deviation illusion detection of the detection content is realized, and the illusion detection processing capability of a large language model, especially the detection processing capability of a fine-grained illusion problem, is improved.

The invention relates to an emergency protection circuit device abnormal alarm identification method based on a large model, and the method comprises the following steps: obtaining and analyzing an abnormal alarm record of an emergency protection circuit device, and obtaining structured abnormal alarm data; constructing an abnormal alarm feature vector space; clustering the abnormal alarm data in the feature vector space, and identifying typical alarm modes in different fault modes; dynamically generating induction information aiming at a specific alarm mode in combination with circuit action logic and fault induction factors; screening out unique information with low semantic similarity as an induced action candidate; constructing an induction strategy reinforcement learning model, and finding an optimal induction strategy through iterative optimization; processing real-time abnormal alarms by applying an optimal induction strategy; evaluating a processing effect, and predicting a potential abnormal trend in combination with a maintenance period; and regularly performing incremental training on the large model. According to the method, the processing efficiency and accuracy of the abnormal alarm can be effectively improved, and the reliability and safety of the system are enhanced.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to the technical field of energy storage configuration, in particular to an energy storage configuration method and system for photovoltaic power generation. The method comprises the following steps: acquiring information data of a photovoltaic power generation energy station; performing distributed synchronous energy storage network construction on the photovoltaic power generation energy station information data to obtain a distributed synchronous energy storage network; performing real-time power generation data acquisition on the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; performing photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; performing historical electricity load data acquisition on the distributed synchronous energy storage network to obtain historical electricity load data; and performing load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data. According to the invention, through real-time photovoltaic power generation prediction, load demand dynamic analysis and adaptive energy storage strategy optimization, the accuracy and flexibility of photovoltaic energy storage configuration are improved.

The invention provides a micro robot motion control method and system, and the method comprises the steps: carrying out the synchronization and calibration processing of environment state information, and generating a comprehensive perception image; analyzing the comprehensive perception image to obtain a classification result, and predicting the environment state change influencing the motion track of the micro robot in a period of time in the future based on the classification result; generating a candidate path by using a fast random tree, and optimizing the candidate path in combination with an AI search algorithm to obtain a target path; constructing a three-dimensional dynamic probability graph, continuously updating the three-dimensional dynamic probability graph by using a reinforcement learning algorithm, and generating an instant decision map; and estimating the position of the micro-robot by using a Kalman filter to obtain the position state of the micro-robot, adjusting the speed and steering angle of a driving motor of the micro-robot, and generating trajectory tracking of the micro-robot. According to the technical scheme provided by the invention, the flexibility and intelligence of the micro robot are improved.

The invention relates to the technical field of cross-border e-commerce storage, in particular to a deep learning cross-border e-commerce storage management system, which comprises a data acquisition processing unit, a space-time diagram neural network analysis unit, an alliance management scheduling decision unit and an equipment execution feedback unit, a data acquisition and processing unit acquires and preprocesses storage data, a space-time diagram neural network analysis unit constructs a space-time diagram and predicts task requirements and equipment states, an alliance management scheduling decision-making unit constructs a dynamic alliance and generates a scheduling instruction, and an equipment execution feedback unit receives the instruction and autonomously adjusts a strategy. The problems of unreasonable equipment scheduling, poor collaboration and resource waste in traditional cross-border e-commerce warehouse management are solved, intelligent scheduling and collaborative operation of the warehouse equipment are realized, and the warehouse management efficiency and the resource utilization rate are improved.

The invention discloses a big data privacy aggregation encryption method based on polymorphic fuzzy logic. The method comprises the following steps: S1, generating a to-be-encrypted data set containing privacy sensitive information; s2, constructing a fuzzy logic rule base based on polymorphic fuzzy logic; s3, generating a dynamically optimized fuzzy logic rule; s4, forming a fuzzy data set; s5, forming an optimized aggregated data block; s6, generating an encrypted data block, and generating index information corresponding to the encrypted data block; and S7, storing the encrypted data block and the index information to a distributed storage system, and dynamically controlling the access of the distributed storage system based on a data access permission rule, so that access users with different permissions can only obtain data matched with the permissions in the encrypted data block. According to the method, the privacy protection strength and efficiency are effectively improved, and a safer and more reliable solution is provided for a big data privacy protection scene.

The invention provides an intelligent scheduling method, system and device for concrete and a storage medium. The method comprises the following steps: acquiring current state information; when a scheduling instruction is received, constructing a pre-selected batch list in combination with the current state information and the dispatching strategy; the pre-selected batch list comprises a plurality of pre-selected task batches; sequentially associating scheduling information for the pre-selected task batches based on the current state information and a scheduling strategy to generate a target scheduling scheme; by combining the dispatching strategy and the scheduling strategy, the production and transportation process of the concrete is optimized, the unnecessary waiting time and resource waste are reduced, and the scheduling continuity and stability are improved.

The invention relates to a power equipment self-repairing surge protection system based on artificial intelligence, and the system is characterized in that a surge detection module is connected between a live wire and a protection ground wire of power equipment, collects voltage and current data, and transmits the data to an artificial intelligence analysis unit; and the artificial intelligence analysis unit extracts the surge characteristic parameters, generates protection strategy parameters through a deep neural network, and controls the trigger thresholds of all levels of protection devices in the programmable protection circuit. The programmable protection circuit comprises a gas discharge tube capable of adjusting trigger voltage, a metal oxide varistor and a transient voltage suppressor diode, and is used for discharging surges with different energies step by step. The resettable fuse cuts off the circuit when the temperature exceeds a threshold value and recovers conduction after the temperature is reduced. And the controller receives and applies the protection strategy parameter output by the artificial intelligence analysis unit, and dynamically adjusts the trigger threshold value of the protection device, so that the response and protection performance of the system to the surge is improved, and the self-repairing and stable operation of the power equipment are realized.

The invention relates to the technical field of automatic inspection, in particular to an automatic road surface inspection robot control method and equipment, and the method comprises the steps: collecting environment data and robot motion state data through integrating a plurality of sensors, carrying out the preprocessing, carrying out the data fusion through employing a Kalman filtering algorithm, and generating a comprehensive road surface state map; a disease area is accurately identified and positioned through a convolutional neural network, dynamic path planning and polling strategy optimization are performed through a reinforcement learning algorithm, adaptive control of a complex road surface environment is realized through a fuzzy control algorithm, and meanwhile, a targeted road surface maintenance suggestion and an emergency processing scheme are generated according to real-time polling data, so that the real-time polling of a road surface is realized. According to the method, the scientificity and timeliness of road surface maintenance and traffic management are effectively improved, the road surface diseases can be accurately recognized and the inspection path can be optimized in a complex environment, and the road surface inspection efficiency and the intelligent level of road maintenance are remarkably improved.

The invention belongs to the field of time sequence anomaly detection, and discloses a time sequence anomaly detection method and system based on frequency enhanced convolution Transform, and the method comprises the steps: carrying out the sequence-level preprocessing of an input sequence, including normalization and block generation; encoding the input sequence by adopting a multi-head self-attention mechanism; extracting interrelationships in the sequence by a full convolution module having a plurality of stacked 1D convolution layers of different kernel sizes; s2 and S3 are repeated, and multi-layer coding is carried out on the sequence; converting the encoded embedding into a frequency signal by using a Fourier transform method; the KL divergence and the absolute error are respectively used for calculating the consistency loss of the time domain and the frequency domain, calculating the point-by-point anomaly score, and identifying whether the point is abnormal or not. Wide experiments on five common data sets show that the method is superior to an existing method in the aspect of abnormal recognition.

The invention relates to the technical field of power electronics and energy management. The invention provides an AC / DC power supply system monitoring device and method. The method comprises the following steps: constructing a historical data set; preprocessing the historical data set to obtain a preprocessed data set; based on the preprocessed data set and a machine learning algorithm, constructing a system monitoring model, and presetting a voltage anomaly judgment threshold value of the system monitoring model; acquiring real-time power data of the AC / DC power supply system and real-time operation state and load change data of the power grid, inputting the data into the system monitoring model, and identifying the mode, trend and potential abnormity of voltage fluctuation of the AC / DC power supply system; and dynamically adjusting a preset voltage abnormity judgment threshold value, and giving a corresponding AC / DC power supply system adjustment strategy and early warning information. The problems that in the prior art, an alternating-current and direct-current power supply system monitoring method is low in efficiency, frequent in false alarm and missing alarm conditions, difficult to adapt to a complex and changeable power grid environment and lack of intelligent analysis and early warning functions are solved.

The invention discloses an intelligent agricultural weeding track control system, which relates to the field of agricultural related technologies and comprises an identification and positioning module, a track planning module, a navigation optimization module, a collaborative operation module and a man-machine interaction module. The invention further discloses an intelligent agricultural weeding track control method which comprises the steps that the unmanned aerial vehicle or the ground robot cruises above a farmland or on the ground, the depth camera collects farmland image data in real time, the image processing unit conducts denoising, filtering and edge detection processing on the original image data collected by the depth camera, and the weeding track is obtained. The two-dimensional image and the three-dimensional point cloud data are registered, so that the two-dimensional image and the three-dimensional point cloud data are consistent in space, and weeds and crops are distinguished through an image recognition technology. Through the path planning algorithm, the optimal weeding path can be calculated according to the actual terrain, weed distribution and obstacle conditions of the farmland, blind walking and repeated operation of the robot in the operation process are avoided, and therefore the weeding efficiency is greatly improved.

The invention discloses a multi-task intelligent coordination execution method and system based on a pension accompanying robot, and belongs to the technical field of multi-task intelligent coordination execution, and the method comprises the steps: S1, carrying out the initialization of the system according to a current environment and preset parameters; s2, collecting information of the user and the surrounding environment thereof through an integrated sensor; s3, performing fusion analysis processing according to the collected information, predicting the behavior trend of the user, identifying the current demand of the user according to a data analysis result and a prediction result, and automatically generating a task; by integrating advanced data fusion, behavior pattern recognition and prediction, demand recognition and task evaluation, the intelligent level and service quality of the system are remarkably improved, and more accurate behavior pattern recognition and trend prediction are realized through membership function and sliding window prediction; and in combination with the behavior pattern and the trend prediction result, tasks meeting user requirements are generated, and priorities are set through a multi-dimensional evaluation model, so that the efficiency and rationality of task execution are ensured.

The invention relates to the technical field of transformer detection, in particular to a millimeter wave radar-based transformer vibration anomaly detection method, which comprises the following steps of: acquiring an original reflection signal on the surface of a transformer, performing time domain and frequency domain analysis on the original reflection signal, and constructing a vibration feature vector; based on the constructed vibration feature vector, unsupervised learning is carried out on the vibration mode of the normal transformer through a deep self-encoding network, and potential features of the normal vibration mode are automatically learned; converting the new reflected signal into a new vibration feature vector, reconstructing the new vibration feature vector by using a deep self-encoding network, calculating a reconstruction error, and if the reconstruction error exceeds a predetermined threshold, judging that the vibration mode is an abnormal vibration mode; a self-adaptive threshold strategy is adopted, and a preset threshold is automatically adjusted so as to adapt to the change of the operation states of different transformer devices along with time. According to the invention, a self-adaptive threshold strategy is introduced, so that running characteristics of different transformer devices and fluctuation of an external environment can be adapted in real time.

The invention discloses a power distribution network regulation and control method and system. The method comprises the steps that new energy output data, obtained through prediction, of a target power distribution network are acquired; inputting the new energy output data into the power distribution network operation model to obtain power flow data of each node; sensitivity analysis is carried out on the power flow data, clustering is carried out based on a sensitivity analysis result, and nodes are divided into a plurality of initial node groups; correcting the initial node group based on the topological structure information to obtain a plurality of target node groups; respectively inputting the power flow data into a pre-constructed mobile emergency power supply scheduling model and a hybrid intelligent soft switch regulation and control model, and generating a primary regulation and control scheme according to an output result; calculating a reactive power demand of the target node group according to the power flow data and the real-time voltage data, and generating a secondary regulation and control scheme according to the reactive power demand; and executing the primary regulation and control scheme and the secondary regulation and control scheme. According to the method provided by the invention, the real-time voltage regulation and control requirement of the power distribution network is met.

The invention discloses a radar signal feature representation method based on self-supervised contrast learning. The method comprises the steps of obtaining radar signal time sequence data and performing preprocessing; performing data enhancement to obtain a strong enhancement view and a weak enhancement view, inputting the strong enhancement view and the weak enhancement view into an encoder to obtain a potential embedding representation, inputting the potential embedding representation into a time comparison module to perform a task of cross-view time step prediction, outputting a corresponding context vector, and calculating time comparison loss; inputting context vectors corresponding to the strong enhancement view and the weak enhancement view into a context comparison module to carry out self-supervised comparison learning and calculate context comparison loss; a total loss function is obtained through integration, a model formed by the time comparison module and the context comparison module is trained, a radar signal time sequence feature representation model is obtained, and time sequence dependence, context relation and cross-view consistency in a radar signal time sequence can be efficiently and accurately captured; therefore, the ability to understand radar signals is enhanced, and a complex radar application scene can be dealt with more flexibly.

The invention discloses a high-rise shear wall structure anti-seismic effect evaluation method and system, and particularly relates to the technical field of high-rise shear walls. The method comprises the following steps: accurately collecting low-cycle fatigue parameters, constructing a concrete and steel bar degradation model considering a fatigue effect, applying the model to anti-seismic analysis of a shear wall structure, simulating the influence of main shock and multiple aftershock loading on structural performance, drawing a bearing capacity degradation curve according to multiple loading results, and analyzing a degradation speed abnormal state. The risk that the structural rigidity and the bearing capacity are abnormal is judged by combining the change of the residual rigidity and the bearing capacity, the risk is divided into high, medium and low levels, and proper reinforcement or prevention measures are provided for different risk levels, so that the defect that the material fatigue effect is not fully considered in the existing anti-seismic evaluation is effectively overcome, and the anti-seismic evaluation accuracy is improved. According to the method, the damage accumulation condition of the high-rise shear wall structure in frequent aftershocks can be predicted more accurately, so that the risk of premature failure of the structure is reduced, and the durability and safety of a building in an earthquake are improved.

The invention discloses a chat secure communication method and system based on a dynamic key, and particularly relates to the technical field of communication, and the method comprises the steps: judging whether the dynamic key needs to be updated or not according to an interaction mode between nodes and an abnormal behavior between the nodes; determining a dynamic key updating time window by establishing a dynamic key updating evaluation model; analyzing the stability of node connection and the historical data transmission quality, and adjusting the weight of the node in the dynamic key distribution process so as to confirm the priority of the node; according to the node priority and the updated dynamic key, the system of the distributed chat tool encrypts the original information sent by the information sender and decrypts the encrypted information received by the information receiver to realize information transmission; and the encrypted message cannot be decrypted by verifying the dynamic key before updating, so that the security of chat communication is ensured.

The invention discloses a ground unmanned equipment cluster path planning and formation control method. The method comprises the following steps: step 1, constructing a grid map: constructing the grid map of an operation area of a ground unmanned equipment cluster according to map information; 2, completing path planning by adopting a bidirectional A * algorithm: simultaneously searching from a starting point and an ending point, estimating the cost from a current node to a target node through an evaluation function, and completing path planning; 3, a dynamic step length adjustment mechanism: dynamically adjusting the search step length according to the environmental complexity and the state of the unmanned equipment; 4, performing formation control based on the main path: performing formation control by adopting a Leader-Follower mode; and 5, generating a cluster path: generating a final action path by using the path planning and the formation control. According to the invention, the efficiency and adaptability of the multi-ground unmanned equipment system are improved, and the stability and safety of formation control are improved.

The invention provides a target submarine path prediction and tracking method for an unmanned ship, and belongs to the technical field of unmanned ship dynamic tracking. Track data are collected in various sea areas through a sonar radar array sensor of the underwater unmanned ship, and the comprehensiveness and robustness of the data are ensured. The core of the method is that a multi-head attention mechanism and a target submarine trajectory prediction model of an LSTM network are combined, trajectory features can be accurately captured, the moving trajectory of a future target submarine can be predicted, particularly, an environment sensing module is introduced, the influence of obstacles around the target submarine on target submarine trajectory prediction is considered, and the target submarine trajectory prediction accuracy is improved. And the prediction accuracy is improved. In the training process, a model interaction strategy is adopted to optimize the model performance and robustness. Finally, the system is successfully deployed in the unmanned ship, collects data in real time, predicts a target trajectory, guides the unmanned ship to accurately track, and provides powerful technical support for the fields of ocean exploration, resource development, safety maintenance and the like.

The invention relates to the technical field of intelligent control, in particular to a traditional Chinese medicinal material extraction intelligent control system which comprises a heat energy distribution control module, a pressure environment optimization module, a solvent dynamic adjustment module, an extraction abnormity intervention module and a component extraction quality improvement module. According to the invention, by dynamically adjusting heat energy output, pressure distribution and solvent flow velocity, accurate control of key parameters in the extraction process of traditional Chinese medicinal materials is realized, uniform distribution of heat energy is ensured, extraction efficiency and quality are optimized, the system is adjusted according to the change of pressure gradient, the adaptive capacity to the fluctuation of extraction conditions is enhanced, and the extraction efficiency is improved. The flexibility and overall stability of the system are improved, the use efficiency of the solvent is improved through fine solvent adsorption and diffusion regulation and control, the solvent is promoted to uniformly act on the traditional Chinese medicinal materials, the extraction rate and uniformity of effective components are improved, the solvent distribution and concentration are rapidly adjusted when temperature fluctuation or pressure deviation occurs, and the abnormal response capability is enhanced. And the continuity of the extraction process and the product consistency are ensured.

The invention provides a cable insulation performance detecting and repairing device and method. The cable insulation performance detecting and repairing device comprises a cable laying device, a detecting mechanism, a repairing mechanism and a cable collecting device. The cable laying device is used for stably releasing a to-be-detected cable. The detection mechanism is used for detecting cable insulation performance and comprises a detection ring, a first driving device and an electric leakage detection device, and insulation defects are positioned by rotating the detection ring and acquiring electric leakage current signals; the repairing mechanism comprises a gluing mechanism and a drying box, the gluing mechanism is used for coating glue to the cable through the glue injection device and the gluing device after the damage point is detected, and the drying box accelerates repairing and solidifies the glue; and the cable take-up device is used for taking up the repaired cable. The automatic and cooperative work of all mechanisms in the whole device greatly improves the speed and quality of cable insulation performance detection and repair, avoids the problem of uneven repair or leak detection caused by misoperation or low efficiency in traditional manual operation, and remarkably improves the safety, reliability and construction efficiency before cable construction.