1154results about How to "Efficiency" patented technology

The invention provides a marine pollution tracing and monitoring method based on satellite remote sensing and an unmanned aerial vehicle, and relates to the technical field of marine pollution tracing and monitoring, and the method comprises the steps: configuring a pollution diffusion path prediction model and a data driving model based on range pollution data, precise pollution data and deepwater data after multi-modal data fusion, and obtaining a pollution prediction result and pollution error data, carrying out hybrid drive fusion based on the pollution prediction result and the pollution error data, and tracking an initial source of pollutants to obtain pollution source tracing data. Through preprocessing and fusion of multi-modal data, in combination with range pollution data, precise pollution data and deep water data, a pollution diffusion path prediction model and a data driving model are dynamically adjusted, so that a more precise pollution prediction result and pollution error data are generated, and meanwhile, through reverse diffusion simulation and pollution traceability analysis, a more precise pollution prediction result is obtained. The high efficiency of pollution source positioning and decision support is improved, and an accurate and decision basis is provided for coping with marine pollution events.

The invention relates to the technical field of image processing, and discloses a lightweight image super-resolution reconstruction method based on frequency domain-spatial domain assisted Mama, which comprises the following steps: acquiring an original image data set and carrying out data preprocessing on the original image data set to acquire a training data set and a test data set; introducing a frequency domain-spatial domain Mama layer and an interaction layer based on transpose and self-attention features, and constructing a lightweight image super-resolution reconstruction model based on frequency domain-spatial domain assisted Mama; inputting the training data set into a lightweight image super-resolution reconstruction model based on frequency domain-spatial domain assisted Mama for training to obtain a trained lightweight image super-resolution reconstruction model with weight based on frequency domain-spatial domain assisted Mama for reconstructing a low-resolution image into a high-resolution image; inputting the test data set into a trained lightweight image super-resolution reconstruction model with weight and based on frequency domain-space domain assisted Mama to obtain a reconstructed high-resolution visual image; according to the method, lightweight image super-resolution is realized.

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

The invention relates to the technical field of building engineering, in particular to a steel structure construction quality monitoring system based on an unmanned aerial vehicle and three-dimensional scanning, which comprises a data acquisition module, a data preprocessing module, a construction defect identification and risk assessment module and a quality assessment module, the data acquisition module acquires quality monitoring data of a construction site in real time; the data preprocessing module is used for preprocessing the acquired quality monitoring data; the construction defect identification and risk assessment module identifies steel structure defects in the construction process and assesses the severity of the steel structure defects; and the quality evaluation module performs comprehensive quality evaluation on the preprocessed quality monitoring data and provides corresponding repair suggestions. According to the method, key problem areas can be quickly identified, a construction resource allocation strategy is optimized, and comprehensive and accurate data support is provided for steel structure quality evaluation in a complex construction environment.

The invention discloses a novel electric power system source network load storage coordinated optimization planning method, and relates to the technical field of electric power system optimization, and the method comprises the steps: S1, building a load prediction model based on various user side load dynamic adjustment data, and improving the prediction precision, S2, determining parameters and constraint conditions of each link of source network load storage of an electric power system, s3, carrying out source network load storage collaborative optimization scheduling by utilizing an optimization algorithm; S4, evaluating and verifying an optimization result; according to the novel power system source-network-load-storage coordinated optimization planning method, the precision of load prediction is greatly improved, so that the problem that supply and demand matching is difficult is effectively solved, rapid fluctuation or sudden change of user loads can be rapidly responded, the adaptability and flexibility of a scheduling strategy are remarkably improved, and the scheduling efficiency is improved. The problems of supply and demand matching and scheduling flexibility caused by low load prediction precision under the condition of dynamic adjustment of various user side loads in the prior art are solved.

The invention discloses an unmanned aerial vehicle navigation positioning method and system based on big data, and the method comprises the steps: obtaining the position data, sensor data and environment data of an unmanned aerial vehicle, constructing a real-time map according to the position data and the sensor data through employing an SLAM algorithm, and carrying out the real-time positioning; carrying out sub-region division on the real-time map, and obtaining a risk score by adopting clustering analysis; obtaining path time between the sub-regions according to the environment data and the risk score, establishing a fitness function according to the risk score and the path time, and establishing a path planning model based on the fitness function; obtaining offset data according to the sensor data, establishing a path compensation strategy according to the offset data, and performing path correction; learning the path compensation strategy and the path planning model to obtain an unmanned aerial vehicle navigation model, and optimizing the unmanned aerial vehicle navigation model through a sine and cosine optimization algorithm. The method is suitable for various unmanned aerial vehicle application scenes, and wide application of the unmanned aerial vehicle technology in a complex environment is promoted.

The invention relates to the technical field of artificial intelligence, and discloses a network security situation awareness method based on artificial intelligence, which comprises the following steps: acquiring network security data; performing feature extraction on the network security data by adopting a full-connection neural network based on dynamic group evolution optimization to obtain a first feature; performing feature dimension reduction on the first feature by using a self-encoding neural network based on contrast entropy to obtain a second feature; constructing a classifier model by using a fractional order neural network based on complexity perception, and training the classifier model by using the second feature; and performing network security situation awareness classification by using the trained classifier model. According to the method, the capability of distinguishing the complex network security situation is improved, a more accurate classification effect can be realized, and the applicability of the model in the network security data and the classification accuracy are enhanced.

The invention relates to the technical field of antibacterial peptide application, in particular to an antibacterial peptide for inhibiting drug-resistant acinetobacter baumannii as well as a preparation method and application of the antibacterial peptide. The antibacterial peptide is cd77AHP-2, and the amino acid sequence of the antibacterial peptide is shown as SEQ ID NO.1. The antibacterial peptide cd77 obtained through artificial chemical synthesis is modified, distribution of cationic amino acid is optimized, and the antibacterial activity of the antibacterial peptide is remarkably improved. The antibacterial peptide cd77AHP-2 has a remarkable antibacterial effect on acinetobacter baumannii which are gram-negative bacteria and multi-drug resistant acinetobacter baumannii, and the antibacterial peptide cd77AHP-2 has a remarkable antibacterial effect on the acinetobacter baumannii which are gram-negative bacteria. The antibacterial peptide cd77AHP-2 provided by the invention has relatively low cytotoxicity to human bronchial epithelial cells, and has the potential of preparing a drug for resisting drug-resistant acinetobacter baumannii. The invention provides a novel functional polypeptide for the field of antibacterial peptides, and the novel functional polypeptide has important practical application value.

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 discloses a scaffold steel plate net building method based on BIM, and relates to BIM modeling. The method comprises the steps that feature parameters of a scaffold steel plate net are obtained; according to the characteristic parameters, establishing a three-dimensional entity model of the scaffold steel screen by using a parametric modeling method; performing grid division on the three-dimensional solid model by adopting a self-adaptive grid division algorithm to obtain a finite element grid; identifying a stress concentration area in the finite element grid by adopting a self-adaptive grid encryption algorithm, and performing grid encryption on the stress concentration area to obtain an encrypted finite element grid; performing structure optimization on the encrypted finite element mesh by adopting a topological optimization algorithm to obtain an optimized three-dimensional entity model of the scaffold steel plate mesh; and performing component arrangement optimization on the three-dimensional entity model after structure optimization by adopting a discrete topological optimization algorithm to obtain a final scaffold steel plate net BIM model. In order to solve the problem that in the prior art, scaffold steel plate net erecting redundancy is large, the redundancy design is reduced.

The invention discloses a crowdsourcing acquisition map data desensitization method and system based on deep learning. According to the method, a'high-precision map desensitization rule and target sensitivity level table 'is constructed for grading and classifying a to-be-desensitized target, and a desensitization method is specified and comprises a shape type, a privacy type and a character type. For shape type and privacy type sensitive targets in a high-precision map, the positions of the sensitive targets are detected by using a sensitive target detection algorithm based on Faster RCNN, and different desensitization methods are used according to the sensitive levels of the sensitive targets. For character sensitive targets in a high-precision map, all character areas in the map are detected by using a natural scene text detection model I3CL, the text areas are recognized by using a text recognition model SVTR to obtain text contents of the character areas, sensitive characters in the character areas are matched through a sensitive word matching module, and the sensitive characters in the text areas are matched through a sensitive word matching module; and a sensitive area removing model based on LaMa is used for the sensitive character area, and sensitive characters are removed from the graph.

The invention provides a large model fine tuning method, system and device for electric power operation and maintenance, and a medium. The method comprises the following steps: taking each operation and maintenance data sample in an obtained to-be-processed electric power operation and maintenance data set as a to-be-optimized problem sample; performing data feature extraction on the to-be-optimized problem sample according to an operation and maintenance problem type identified by a preset keyword library and a first preset large language model to obtain a problem feature text, expanding the problem feature text according to a preset data type knowledge library, and summarizing the problem feature text according to the same type to obtain a problem fine-tuning training sample set; dividing the problem fine tuning training sample set into a problem instruction fine tuning sample subset and a problem increment fine tuning sample subset, performing fine tuning on a second preset large language model to obtain operation and maintenance problem large models, and combining all the operation and maintenance problem large models to obtain a power operation and maintenance vertical domain large model. According to the method, the information utilization efficiency, adaptability and expression ability of model fine tuning on complex power operation and maintenance data can be effectively improved, and then the efficiency and accuracy of fine tuning training are improved.

The invention discloses a multi-link fusion method and system based on an SD-WAN (Secure Digital Wireless Area Network) technology, which can timely discover and deal with the condition of network failure or performance reduction by monitoring network state parameters of a plurality of network links in real time, and is helpful for ensuring reliable network connection at any time point, thereby improving the reliability and the stability of the network, and improving the reliability and the reliability of the network. By dynamically adjusting distribution of data traffic, the optimal link combination can be selected for data transmission, so that congestion is avoided, delay is reduced, the packet loss rate is reduced, the utilization rate of network bandwidth is maximized, the data transmission efficiency is remarkably improved, the routing optimization step can quickly respond to service changes by utilizing flexibility and programmability of the SDN technology, and the service quality is improved. According to the method, the continuity and the high efficiency of services are ensured by optimizing data transmission routes, existing network resources can be fully utilized through multi-link fusion and intelligent scheduling, excessive dependence on a single link or an expensive high-performance link is avoided, and reduction of network construction and operation and maintenance cost is facilitated.

The invention discloses a wide-speed-domain position-sensorless control method for a permanent magnet synchronous motor, and the method comprises the steps: building an extended state observer of the permanent magnet synchronous motor, and obtaining the counter electromotive force of the motor through the extended state observer; a self-adaptive vector filter is adopted to filter out higher harmonics in the back electromotive force obtained by the extended state observer, estimated back electromotive force is obtained, the obtained estimated back electromotive force serves as input, position information contained in the estimated back electromotive force is demodulated through a phase-locked loop position observer, and the rotating speed and the position angle of the motor are obtained; and controlling the permanent magnet synchronous motor according to the obtained rotating speed and position angle. The position observer is used for extracting position information from the rotor back electromotive force, a mechanical position sensor is omitted, the system structure is simplified, cost is reduced, the self-adaptive vector filter is used for filtering harmonic waves for estimating the back electromotive force, and estimation errors of the rotating speed and the position angle of the motor are reduced. And the high efficiency and the stability of position-sensorless control of the motor in a wide speed range are improved.

The invention relates to the technical field of power equipment monitoring, in particular to a power equipment state monitoring data acquisition method based on a Beidou navigation system, which comprises the following steps: S1, equipment state data acquisition: acquiring state data of equipment in real time; s2, establishing an inter-equipment collaborative learning mechanism: establishing an inter-power equipment collaborative learning mechanism; s3, data fusion and standardization: performing standardization processing and fusion on the state data acquired by each device; s4, performing equipment state correlation analysis: identifying state correlation and potential fault modes between the equipment; and S5, a state evaluation and feedback mechanism: according to an analysis result associated with the equipment state, evaluating the equipment state, and feeding back an evaluation result to an inter-equipment collaborative learning mechanism. According to the invention, equipment monitoring and maintenance strategies can be dynamically optimized, the accuracy and efficiency of equipment health management are improved, the risk of fault diffusion is reduced, and the service life of equipment is prolonged.

The invention, which relates to the technical field of artificial intelligence, discloses a warehouse-in and warehouse-out path planning method and system for intelligent warehousing, and the method comprises the steps: generating an optimal operation path of a to-be-operated carrier through a path planning model; the method comprises the following steps: predicting whether a current carrier has a collision risk with all carriers in a warehouse in a time dimension or not through a time block, predicting whether the current carrier has a collision risk with all carriers in a warehouse in a space dimension or not through a space block, and when the time block and the space block simultaneously detect that the current carrier has the collision risk at a certain moment, judging whether the current carrier has the collision risk; the decision block judges that the current carrier has a collision conflict, and determines an optimal avoidance strategy through an action value function; according to the optimal avoidance strategy, the avoidance waiting carrier is determined, the total waiting time and the waiting space position of the avoidance waiting carrier are calculated, and the avoidance strategy is executed. According to the technical scheme, the problem that in-out garage path planning and collision processing cannot be intelligently and accurately carried out in the prior art can be solved.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention relates to a digital human interaction method and system based on multi-modal understanding, and belongs to the technical field of digital human intelligent interaction. The method comprises the following steps: acquiring multi-modal data, and obtaining multi-modal features through a feature extraction model according to the multi-modal data; obtaining enhanced modal features through a knowledge enhancement model according to the multi-modal features; according to the enhanced modal features, a modal fusion feature vector is obtained through a graph representation learning model; and the big language model learns modal fusion feature vectors through a multi-head attention mechanism to obtain natural language output. The digital human understanding of multi-modal data is realized, and the smoothness, naturalness and intellectualization in the digital human interaction process are improved.

The invention belongs to the technical field of point cloud semantic segmentation, and discloses a power corridor laser point cloud semantic segmentation method and system collected by an unmanned aerial vehicle, and the method comprises the steps: collecting laser point cloud and image data; reconstructing point cloud data based on the image data to obtain an image point cloud, performing spatial registration on the laser point cloud and the image point cloud, and projecting the registered laser point cloud onto the image; operating the image data to obtain a depth image and a normal image, and performing fusion operation on the depth image, the normal image, the image and the projected laser point cloud to obtain multimode fusion data; and carrying out two-dimensional semantic segmentation on the multi-mode fusion data, and carrying out back projection operation on a segmentation result to obtain a three-dimensional point cloud semantic segmentation result. According to the method, the visible light image of the power corridor and the laser point cloud data are fused, high-precision semantic segmentation of the laser point cloud is realized by using the appearance information contained in the visible light image, and the accuracy, efficiency and fineness of semantic segmentation of the laser point cloud of the power corridor are improved.

The invention discloses a data model training method and system based on a display scheme. The method comprises the following steps: S1, constructing a multi-source situation data set; s2, preprocessing the multi-source situation data set, and integrating the multi-source situation data set into a single situation vector; s3, generating a user preference portrait, and fusing the situation vector and the user preference portrait to generate a dynamic situation preference portrait; s4, inputting the dynamic situation preference portrait into a conditional generative adversarial network to generate a display scheme; s5, embedding an adaptive neural regulation and control module, and optimizing a display scheme generation effect; s6, introducing a hierarchical situation control mechanism; s7, iteratively updating the user preference portrait based on historical interaction feedback; and S8, analyzing the similarity between the current situation and the historical situation, judging the applicability of the display scheme by applying a meta-learning algorithm, and migrating the effective display scheme in the historical situation to the current situation. According to the method, a multi-dimensional context awareness and dynamic feedback optimization model training method is adopted, and context self-adaptive generation of the display scheme is achieved.

The invention provides a battery state prediction method and system, computer equipment and a storage medium, and the method comprises the steps: randomly selecting a preset proportion of batteries from a target batch of batteries as a test sample set; performing current switching cycle life test on each battery sample to obtain a plurality of groups of current sudden change data comprising current time sequence data, voltage time sequence data and battery health state corresponding to the preset charge state segment point sequence; obtaining a current abrupt change point battery impedance spectrum according to the current time sequence data and the voltage time sequence data in each group of current abrupt change data, and constructing a battery state prediction model according to all the current abrupt change point battery impedance spectrums and the corresponding battery health states; and obtaining a battery state prediction value according to the obtained current mutation battery impedance spectrum of the to-be-evaluated battery and the battery state prediction model. According to the invention, the high efficiency, accuracy and operation convenience of online monitoring of the health state of the battery can be effectively improved under the low-cost condition of not additionally adding measuring equipment.

The invention provides an unmanned aerial vehicle conflict resolution method based on reinforcement learning and a self-attention mechanism, and the method comprises the steps: firstly, modeling a dynamic relation between an unmanned aerial vehicle and an airspace as an interaction process between an intelligent agent and an environment in reinforcement learning; then, designing an action space, an observation space and a reward function of the intelligent agent, and building a conflict resolution training framework based on reinforcement learning; and finally, designing an unmanned aerial vehicle attention model based on a self-attention mechanism, which comprises an encoder for extracting unmanned aerial vehicle positions and conflict characteristics in a dynamic environment and a decoder for generating unmanned aerial vehicle conflict resolution actions by using an autoregression mode. According to the novel unmanned aerial vehicle free flight conflict resolution method, reasonable combined action distribution can be carried out on multiple unmanned aerial vehicles flying in the airspace, conflict resolution is completed, and flight safety is guaranteed.

The invention discloses a security risk evaluation system for cloud printing data transmission, and relates to the field of cloud printing data transmission security, and the system comprises the following components: a data acquisition module which establishes connection with a server or an interface related to cloud printing data transmission according to configuration information, and performs data acquisition according to a set acquisition frequency; according to the invention, analysis and risk assessment are carried out on a real-time data set through an anomaly detection algorithm and a risk assessment model adopted by the data analysis module, multiple risk factors and indexes are comprehensively considered, quantitative assessment is carried out on abnormal conditions, and the risk assessment accuracy is improved. Therefore, the risk level and possible loss can be judged more accurately, so that the system can discover potential safety risks in time, and the accuracy and efficiency of risk identification are greatly improved.

The invention provides an insulator defect detection method GAC-YOLO based on YOLOv7, and relates to the field of identification and detection of insulator targets. Comprising the steps that a GSConv module is optimized, and the calculation amount and the memory requirement are reduced while the precision is guaranteed; an ACmix attention mechanism is introduced, convolution and self-attention are fused, the prediction ability of the model is enhanced, and then the detection effect is improved; the centralized feature pyramid is fused, information exchange and integration can be achieved through feature maps of different scales, more comprehensive insulator features are further obtained, and therefore the insulator detection precision is improved. And performing an ablation experiment on each module of the model, and performing a contrast experiment with a mainstream deep learning model to obtain a final insulator detection model. According to the model realized by the invention, on the basis of ensuring the original detection rate of the YOLOv7, the accuracy of the model in insulator defect detection is improved.

The invention discloses a visual detection method and device for the straightness of a pipe, and relates to the technical field of pipe straightness detection.The straightness of the pipe is detected in the process that the pipe moves forwards along the axis, two cameras with imaging planes perpendicular to each other are arranged on the circumference of the pipe, and the main optical axes of the two cameras are both perpendicular to the axial direction of the pipe; obtaining internal and external parameters of the two cameras through calibration and establishing a three-dimensional coordinate system; determining the image taking frequency of a camera according to the pipe length and the movement speed, so that an overlapping area exists between adjacent frames of the camera; the two cameras shoot two pipe images at the same time according to the image taking frequency, feature extraction is conducted on the two pipe images respectively to obtain the pixel coordinates of the central axis of the pipe, and a three-dimensional coordinate point set of the central axis of the current section of the pipe is obtained through calculation; calculating a splicing matrix between the adjacent frames according to an overlapping area between the adjacent frames, and splicing the three-dimensional coordinate point sets of the central axes of the multiple sections of pipes to obtain a three-dimensional coordinate point set of the central axis of the whole pipe; and performing straight line fitting on the three-dimensional coordinate point set of the central axis of the whole pipe, and evaluating the straightness of the pipe by using a straight line fitting error. The device has the advantages of being simple in structure, low in cost, capable of achieving online detection, high in precision, good in universality and the like, and can be rapidly applied to straightness detection of various pipes.

The invention belongs to the technical field of machine vision and deep learning, and discloses an engineering structure crack detection and measurement method based on machine vision, and the method comprises the steps: collecting original image data of an engineering structure, discretizing the original image data into image blocks, carrying out the preprocessing, building a crack classification model through a Resnet-50 neural network, and carrying out the training, the method comprises the following steps of: firstly, constructing an image block, identifying a crack image in the image block, then constructing a SegFormer segmentation model based on a Transformer framework to segment the crack image to obtain a crack mask graph, evaluating the performance of the two models, then analyzing the two models by adopting an orthogonal skeleton line method, obtaining a contour line and a skeleton line of a crack, and calculating the length of the crack. According to the method, the ResNet-50 image classification model and the SegFormer segmentation model are combined to realize high-precision crack identification and segmentation of the building structure crack, the contour line and the skeleton line of the crack are obtained and the length of the crack is measured by means of the orthogonal skeleton line method, and the crack of an engineering structure is well detected and subjected to risk assessment.

The invention relates to the technical field of heterogeneous cross-modal fusion, and discloses a pulmonary embolism auxiliary diagnosis method and system based on heterogeneous cross-modal fusion, and the system comprises the following modules: an image module which combines Swin Transform and an improved 3D CNN model to extract features; the polarization self-attention module is used for efficiently processing high-resolution 3D input features and reducing calculation overhead; the multi-scale feature fusion module integrates SE module channel feature extraction, ASPP module multi-scale feature extraction and 3D convolution space feature extraction; the EHR module is used for extracting features from the electronic health records; and the fusion module is composed of a multi-modal fusion module MAFM and is responsible for extracting key features from the image and the EHR module and fusing the key features so as to improve the diagnosis effect. Through a parallel network and a multi-modal fusion mechanism, the diagnosis precision and robustness of pulmonary embolism are remarkably improved.

The invention provides a detection early warning device for a photovoltaic power generation panel, and relates to the technical field of detection alarm, the detection early warning device comprises a detection early warning device body and a photovoltaic panel, the detection early warning device body comprises a detection plate, a driving mechanism, a detection assembly and an electric leakage detection mechanism, the detection plate is welded at the end part of the photovoltaic panel, and the driving mechanism is welded at the end part of the photovoltaic panel; the driving mechanism is in threaded connection with one end of the detection plate, a plurality of electric leakage detection mechanisms are embedded in the surface of the detection plate, a lead screw is inserted in the driving mechanism, the surface of the lead screw is sleeved with the detection assembly, and the detection assembly comprises a lifting mechanism and an attaching mechanism; according to the detection early warning device, the whole detection assembly can be controlled through the driving mechanism to carry out the switching process of the inner area and the outer area, so that the probability that a detection sensor part is damaged due to direct drying is reduced, and more comprehensive temperature data and electric leakage abnormal data of the surface and the bottom of the photovoltaic panel can be obtained only through a single monitoring module; and the detection efficiency and accuracy are improved.

The invention discloses a multi-microgrid operation optimization method considering supply and demand flexible double responses. The method comprises the following steps: establishing a cost optimization model of multi-microgrid electric energy cooperation sharing; a gas turbine, a waste heat boiler and an organic Rankine cycle power generation device are introduced to construct a supply side response model for flexible output of electricity and heat energy of the cogeneration unit; a demand side establishes a demand side flexible response model by considering transferability and replaceability of the electric heating load; and a multi-microgrid electric energy sharing cooperative operation model is established based on the Nash negotiation theory, and then the model is decomposed into a microgrid alliance benefit maximization sub-problem and a benefit distribution sub-problem. And in the cooperative income distribution sub-problem, the micro-grids negotiate with each other by taking the electric energy contribution and the marginal contribution degree of the micro-grids in cooperation as bargaining capabilities, so that fair distribution of cooperative income is realized. According to the optimization method provided by the invention, the advantages of flexible supply and demand and resource coordination and complementation can be brought into full play, and the condition of unreasonable income distribution of each micro-grid in the multi-micro-grid cooperation system is improved.

The invention relates to a data processing method, device and system, electronic equipment and a computer readable storage medium, and relates to the technical field of data processing. The data processing method is executed by a first node and comprises the steps that data are collected, and the data comprise a plurality of data blocks; constructing a Merkel tree according to the plurality of data blocks; each data block is encrypted by using the Merkel tree; each encrypted data block and the Merkel tree are transmitted to a second node, and the Merkel tree is used for the second node to decrypt each encrypted data block and perform data integrity verification on each decrypted data block.