95results about How to "Realize dynamic adaptation" patented technology

The invention relates to a low-altitude economic development form analysis method and system based on a high-density city, and the method comprises the steps: obtaining task address information and task type information of each aircraft, and carrying out the path planning in a preset airspace range according to the task address information and the task type information, obtaining flight path information of each aircraft; acquiring real-time traffic flow and airspace condition information in a preset airspace range, and performing real-time optimization adjustment on the flight path information according to the real-time traffic flow and the airspace condition information; monitoring the activities of the aircrafts in the airspace in real time, obtaining the flight information of each aircraft, and judging whether the corresponding aircraft has a flight violation condition or not according to the flight information; the flight task state is tracked and fed back in real time, the aircraft state and the equipment operation condition in the airspace are monitored, equipment alarm is triggered under the abnormal condition, and preset emergency treatment measures are taken. The method has the effect of improving the low-altitude aircraft management and control efficiency.

The invention discloses a vehicle multi-domain fusion control method, and relates to the technical field of vehicle multi-domain fusion control, and the method comprises the steps: collecting and analyzing the data of each domain in real time through a central calculation unit CCP, rapidly judging whether the configuration parameters need to be updated or not when the working conditions change, and rapidly responding to various complex working conditions. In addition, an A2L file is automatically called through a CCP to be compared with a newest project configuration file, parameter items needing to be updated are automatically recognized, redundant data are eliminated, and then synchronous updating of configuration parameters of all control domains is guaranteed through centralized distribution of configuration updating files and a consistency scoring mechanism, so that the control effect and safety under automatic driving are improved. According to the method, the control deviation caused by desynchrony under the traditional architecture is avoided, the configuration requirements of each domain are automatically detected based on the CCP, parameter comparison and redundancy check are carried out through an automatic A2L file matching mechanism, the configuration file is quickly identified and updated, and the calculation burden of the system is reduced through redundancy elimination.

The application discloses a spunlace non-woven fabric rewinding equipment collaborative control method and system based on the Internet of Things, and relates to the field of electric digital data processing.The method comprises the following steps: obtaining historical data and preprocessing to obtain a plurality of three-dimensional data sequence sets of roll diameter-torque-tension under normal working conditions; dividing the plurality of three-dimensional data sequence sets into a plurality of roll diameter slice sample groups and screening to obtain a non-woven fabric flat data set; performing nonlinear fitting on the non-woven fabric flat data set and constructing non-woven fabric flat upper and lower tracks; based on the non-woven fabric flat upper and lower tracks, real-time marking of an abnormal data interval in the current rewinding process is performed, and abnormality determination is performed in cooperation with an image detection mechanism; after the current rewinding process is completed, the non-woven fabric flat upper and lower tracks are updated and optimized. The application can dynamically adapt to roll diameter changes, accurately mark abnormal intervals and accurately determine abnormal working conditions in cooperation with an image detection mechanism, thereby effectively improving the quality control level of spunlace non-woven fabric rewinding production.

This application relates to the field of vehicle control, specifically to a method for controlling a sunroof and a vehicle. This application, upon responding to a sunroof opening operation, compares the actual opening degree of the sunshade with the maximum driven opening degree to clarify the relationship between the two, thus mitigating the risk of premature sunroof opening from the control source. Secondly, based on the comparison results, a targeted motion strategy is formulated. If the actual opening degree of the sunshade already meets the maximum driven opening degree, the sunroof can be directly controlled to open normally. If not, the strategy is adjusted to ensure that the sunroof movement does not exceed the sunshade's adaptation range, avoiding an imbalance in the two opening degrees. Finally, this control logic does not require modification of the existing independent mechanical structure. Through linked judgment and strategy adjustment, it achieves dynamic adaptation between the sunroof opening and the sunshade opening degree, completely solving the problem of sunshade damage due to airflow impact and friction during driving caused by mismatched opening degrees, ensuring component lifespan and driving safety.

The invention discloses an optimal scheduling method for a virtual power plant, and relates to the technical field of power regulation, and the method comprises the steps: obtaining multi-modal data of a detection region, calculating a regional load fluctuation index and a resource mobility index, and generating a dynamic feature data set; performing real-time clustering division on the power grid nodes through a clustering algorithm based on the dynamic feature data set to generate a dynamic sub-region set; if the sub-region feature fluctuation exceeds a boundary stability threshold value, triggering boundary redivision; according to the dynamic sub-region set, comparing a building power consumption predicted value with the available load of the charging pile in real time to calculate a demand difference, and generating an elastic scheduling instruction through multi-objective optimization; uncertain factors are monitored in real time, a risk index is calculated, and the priority and the resource allocation strategy of the elastic scheduling instruction are dynamically adjusted based on the risk index; and generating a risk avoidance scheduling scheme through a robust optimization algorithm.

The present application relates to a method and system for analyzing the development forms of low-altitude economy based on high-density cities, the method comprising obtaining the mission address information and mission type information of each aircraft, and performing path planning within a preset airspace according to the mission address information and mission type information, and obtaining the flight path information of each aircraft; obtaining the real-time traffic flow and airspace status information within the preset airspace, and optimizing and adjusting the flight path information in real time according to the real-time traffic flow and airspace status information; real-time monitoring of aircraft activities within the airspace, obtaining the flight information of each aircraft, and judging whether the corresponding aircraft has any flight violations according to the flight information; real-time tracking and feedback of the flight mission status, and monitoring the aircraft status and equipment operation status within the airspace, triggering equipment alarms in the event of an abnormality and taking preset emergency treatment measures. The present application has the effect of improving the efficiency of low-altitude aircraft control.

The application relates to the technical field of intelligent operation and maintenance of pump equipment, in particular to an intelligent control system and a predictive maintenance method for pump equipment, which comprises the following steps: collecting multi-dimensional operation data of a pump body and associated pipelines to construct an original operation state data set, and obtaining a standardized working condition characteristic vector through working condition identification and feature vector extraction. By improving the golden section optimization algorithm, the optimization interval is dynamically adjusted according to the working condition characteristic vector, the global optimal efficiency operating point is solved by combining a pre-stored system model, and the adaptive adjustment of pump operation parameters is completed. Time series analysis and pattern recognition are carried out on the original data set to generate a time series feature sequence of the equipment health state, the pre-trained long short-term memory network model is input, the fault risk probability of the key components and the residual service life prediction value are output, and maintenance decision instructions are generated accordingly. The method can realize adaptive optimization control of the working condition of the pump equipment, complete the fault risk assessment and life prediction of the equipment, and realize intelligent operation and maintenance control.

The invention relates to the technical field of vehicle control, and discloses a vehicle starting control method and device, a vehicle and a medium, and the method comprises the following steps: in response to starting operation of the vehicle in an automatic parking state, adjusting parking release control parameters according to current road surface information and vehicle parameters; and releasing the automatic parking state according to the adjusted parking release control parameter. The parking release control parameters are dynamically adjusted according to real-time road surface information and vehicle parameters through a dynamic and self-adaptive parameter adjusting mechanism, the limitation of traditional fixed calibration is eliminated, different road conditions and vehicle states can be adapted, the vehicle starting quality in the automatic parking state is remarkably improved, and the vehicle starting speed is increased. And fixed parameters under the automatic parking function can be continuously optimized, so that the method adapts to individual manufacturing differences of different vehicles, starting abnormities such as slope sliding, dragging and vehicle channeling of the vehicles can be effectively avoided, the safety and smoothness of vehicle starting are improved, and meanwhile the user driving experience and the vehicle intelligent control level are greatly improved.

The application discloses a building cross-subsystem data sharing and dynamic permission allocation method based on Internet of Things, and relates to the technical field of building cross-subsystem data processing. Specifically, the analysis of the analysis timeliness and analysis processing capacity of building cross-subsystem data in the sharing process is carried out, the analysis result is used as a benchmark to quantize the adaptation of permission allocation, the analysis capacity is adapted to dynamic load and permission decision, the data quality and communication state are matched, the timeliness and resource utilization efficiency of permission allocation in the building scene are balanced, and therefore, the accuracy of the permission allocation result is improved.

The application discloses an intelligent lifting type culture net cage integrated with environment sensing and adaptive depth adjustment, and belongs to the technical field of marine culture devices. The net cage main body comprises main columns and a net cover. The main columns are provided in plurality. A connecting rod is arranged between the side portions of two adjacent main columns. The periphery of the net cover is supported and tensioned by the main columns to form a closed culture space. A buoyancy adjusting mechanism is connected with the bottom of the main column and is used for adjusting the overall buoyancy of the net cage main body to vertically lift. A mooring positioning mechanism is connected with the buoyancy adjusting mechanism and is used for anchoring the net cage main body in a predetermined sea area and adapting the vertical lifting movement of the net cage main body. An environment sensing module is installed on the net cage main body. An intelligent control module is electrically connected with the environment sensing module and the buoyancy adjusting mechanism. The intelligent control module is internally provided with a decision algorithm, is used for receiving the data collected by the environment sensing module, and outputs a control instruction to the buoyancy adjusting mechanism according to a preset strategy to adaptively adjust the water depth where the net cage main body is located.

The present invention provides a single crystal furnace thermal field insulation effect detection system based on thermal image analysis. Compared with the prior art, the present invention also includes a first thermal imager for monitoring the thermal distribution of the furnace side wall and generating thermal images, a second thermal imager for monitoring the thermal distribution of the furnace bottom and generating thermal images, a third thermal imager for monitoring the thermal distribution of the furnace top and generating thermal images, a cooling module for at least high temperature protection of the second thermal imager, a processing module for processing the image of the thermal distribution monitoring and imaging of the thermal imager, an evaluation module for evaluating the thermal field insulation effect of the single crystal furnace based on the image processed by the processing module, and an alarm module for triggering an alarm when the evaluation module determines that the thermal insulation effect of the single crystal furnace is lower than the expected thermal insulation effect. The present invention adopts multi-parameter comprehensive analysis and dynamic adaptive adjustment to greatly improve the accuracy, flexibility and industrial application value of monitoring the thermal field insulation performance of the single crystal furnace.

The present disclosure relates to the technical field of communication, in particular to an abnormal call record processing method, device, equipment, medium and product, the method comprising: obtaining a plurality of initial abnormal call records; extracting a first abnormal feature in each of the initial abnormal call records, and constructing an abnormal rule based on the first abnormal feature; wherein the first abnormal feature comprises a call record type, an error code and at least one charging identifier field; analyzing a to-be-audited abnormal call record through at least one preset target rule to obtain an analysis result; in the case that the analysis result and the abnormal rule fail to match, extracting a second abnormal feature in the to-be-audited abnormal call record, and updating the abnormal rule based on the second abnormal feature; wherein the second abnormal feature comprises the first abnormal feature, a call record quantity and a call record date. Through the target rule, it is judged whether the to-be-audited abnormal call record is an unknown abnormal mode, and dynamic adaptation of the abnormal rule is realized.

The invention belongs to the technical field of geotechnical engineering rock mass strength parameter testing, discloses a parameter calibration method of a Hoek-Brown empirical strength criterion, and solves the problems that an existing method depends on a single data source, is low in fitting precision and does not consider rock mass dynamic disturbance. The method comprises the following steps: acquiring indoor test and field actual measurement multi-source data, and carrying out abnormal value elimination and normalization preprocessing; constructing a rock mass disturbance coefficient dynamic correction model to correct basic parameters; an improved cuckoo-least square fusion algorithm is adopted to fit the core parameters; and the parameter precision is verified in combination with reserved data. The method is comprehensive in data source, parameters can dynamically adapt to working conditions, fitting precision is high, and the method can be widely applied to rock mass strength evaluation of geotechnical engineering such as tunnels and side slopes.

The invention relates to the technical field of information processing, in particular to a non-contact code read-write system and device for fire alarm, and the system comprises an RFID tag end and a code read-write device. The RFID tag end is made of a metal-resistant material and a ceramic packaging material with strong severe environment tolerance. The early warning module is used for acquiring environment data and judging whether active alarm is triggered or not according to change characteristics of the environment data, the storage module is used for recording key logs, and the analysis module is used for analyzing stored key log data and actively feeding back equipment states through Bluetooth beacons. And the coding read-write device comprises an RFID reading module for realizing information interaction with the RFID tag end, an RFID write-in module and a signal receiving module based on Bluetooth. The problems that in the prior art, an encoding read-write system cannot actively early warn and give an alarm and feed back the condition of fire alarm equipment, and RFID radio frequency signals are prone to being affected by severe environment factors such as high temperature, dense smoke and water mist and the like and are attenuated are solved.

The present application belongs to the field of big data processing, and specifically relates to a multi-supplier cooperative store digitized management system and method. Multi-supplier historical delivery time sequence data and store warehouse space time-varying occupation data are collected; a dynamic space-time graph is constructed with supplier nodes and store physical storage location nodes as vertices and logistics time delay and supply-demand dependency as edges; a time sequence attention mechanism is embedded into a graph convolution network to extract space-time coupling features in a concurrent performance state, representing the dynamic mapping correlation between supplier delivery time sequence and store storage location release time sequence; a storage location allocation and delivery time sequence joint optimization model is constructed based on the space-time coupling features, with physical storage location capacity constraints and logistics distribution time sequence constraints as boundary conditions. Dynamic adaptation of cross-supplier logistics time sequence and store physical space is achieved, physical warehouse space occupation and blockage defects caused by concurrent warehousing are overcome, and inventory accumulation and shortage phenomena caused by supply-demand space-time mismatch are eliminated.

The application discloses a laboratory motion line safety early warning method and device, which realizes personnel space-time trajectory reconstruction and accurate identification of abnormal motion line segments by real-time collection of relevant personnel positioning and behavior monitoring data streams, formation of motion line basic data with space-time stamps and identity tags, and combination with standard templates, and provides reliable support for risk assessment. By combining personnel characteristics and basic risk coefficients corresponding to the identity, the personal risk contribution value is calculated and the personal risk field is generated, realizing accurate quantification and differentiated assessment of personnel risk. Superimposing the personal risk field generates a dynamic group risk heat map, which can intuitively present the global risk distribution; building a risk coupling network can predict the conduction effect of high-risk areas, dynamically determine the early warning threshold according to the regional personnel and activity types, effectively improve the early warning accuracy, and reduce false positives and false negatives. In summary, the application realizes accurate control and risk early disposal of the whole process of laboratory motion line safety, and significantly improves the fine and intelligent level of safety management.

The invention discloses a mouse dynamic adjustment method, device, system and equipment and a medium, and the method comprises the steps: obtaining a game picture through intelligent glasses, and obtaining a target key element in the game picture; obtaining a mouse operation process corresponding to the target key element, and determining a mouse parameter adjustment strategy based on the mouse operation process; and performing mouse parameter adjustment according to the mouse parameter adjustment strategy so as to enable the mouse parameters to adapt to the mouse operation process. Through data interaction between the intelligent glasses and the electronic equipment, a cooperative working mechanism of the intelligent glasses and the mouse peripheral is constructed, the advantage of the multi-mode perception capability of the intelligent glasses is fully played, the mouse parameter adjustment is assisted through the intelligent glasses, the manual adjustment frequency of a player is reduced, and the user experience is improved. In this way, on one hand, the adaptation degree of the mouse parameters and the game scene can be improved, and on the other hand, the game immersion of the game player is improved, and the game player is helped to improve the competitive performance in the game.

The application provides a three-dimensional Gaussian representation optimization method and system for sparse view angle new view angle synthesis, and belongs to the field of computer vision and three-dimensional reconstruction. A sparse view angle image set and camera parameters are acquired, and a three-dimensional Gaussian primitive is initialized; binocular reprojection geometric constraints are constructed, and monocular depth prior is acquired; pixel-level gating weights are generated according to at least binocular reprojection error, monocular depth gradient and Gaussian visibility; the weights are used to adaptively weight binocular consistency loss and monocular depth consistency loss, and Gaussian primitive parameters are jointly optimized in combination with color reconstruction loss; the pixel-level gating weights are aggregated into Gaussian-level reliability weights, and the splitting densification and pruning processes of the Gaussian primitive are synchronously regulated; and the optimized three-dimensional Gaussian scene representation is output for new view angle rendering.

This invention belongs to the field of smart city information technology, specifically relating to an adaptive operation method for industrial robots in elevator installation and maintenance. It covers the entire process of industrial robots, from elevator scene perception and operation planning to safe execution. Employing core algorithms such as multi-dimensional feature modeling of the elevator car / shaft, scenario-based adaptation of installation and maintenance operation trajectories, and dynamic early warning of human-machine-equipment safety interaction, it overcomes the technical bottlenecks of traditional elevator operation robots, which suffer from poor scene adaptability, low operation accuracy, and insufficient safety control. This achieves accurate perception and modeling of elevator scenes, dynamic optimization and adaptation of operation trajectories, and real-time early warning and control of safety risks, significantly improving the efficiency, accuracy, and reliability of elevator installation and maintenance operations. It is suitable for the full-scenario operation needs of various types of elevators, including residential, commercial, and high-speed elevators, for installation, commissioning, inspection, and maintenance.

The invention discloses a DDoS attack recognition detection method and system based on a self-training word segmentation device, and the method comprises the steps: obtaining a data packet embedding vector through the mapping of a packet encoder, and forming a network flow packet-level embedding sequence; enabling the network flow packet-level embedding sequence to pass through a flow aggregator to obtain a flow-level embedding vector; calculating a risk score of the network flow level embedded vector through a lightweight discrimination head; inputting the risk score into a trigger, and identifying a high-risk network flow; generating a fusion embedded representation of the high-risk network flow: constructing the flow part into a flow text sequence, encoding through a self-training word segmentation device to obtain an embedded representation, and fusing the embedded representation of the natural language part and the embedded representation of the flow part into a fusion embedded representation; and inputting the fusion embedded representation of the high-risk network flow into an attack identification model to predict whether a DDoS attack exists or not. The invention relates to the field of information security networks, can solve the problems of insufficient flow representation capability, poor adaptation to attack mode change, strong dependence on annotated data and the like in the prior art, and realizes accurate detection of DDoS attacks.

The application relates to the technical field of computers and discloses a wireless micro-current tactile feedback game control device interaction method and system, which comprises the following steps: constructing an interaction feature matrix of user operation data and game scene data; generating a wireless micro-current control signal of a game control device through the interaction feature matrix and determining a body surface stimulation partition of the game control device; setting a stimulation combination mode of the wireless micro-current control signal and the body surface stimulation partition according to a game scene type and user operation preferences; extracting a response delay parameter and a stimulation coverage index in the stimulation combination mode, calculating a stimulation deviation value of the wireless micro-current control signal; calculating a tactile feedback threshold value corresponding to the game control device by using the stimulation deviation value; and generating a micro-current tactile feedback scheme of the game control device by combining the stimulation combination mode and the tactile feedback threshold value. The application can significantly improve the game immersion of users.

This application discloses a dynamic emergency decision-making method, device, equipment, and storage medium for urban flood disasters, relating to the field of emergency management and response scheduling technology. The method includes: first, establishing a differential equation for the safety state transition of an urban area, dividing the area into four safety states: normal, affected, recovering, and paralyzed, and defining their transition relationships; based on this, constructing a stochastic differential game model for flood disaster emergency decision-making; solving for the saddle point strategy using a forward-backward scanning algorithm to obtain the optimal emergency prevention and control strategy; then, conducting multi-dimensional simulation verification using typical flood disaster events as samples; finally, adjusting the strategy based on the simulation results to form a target emergency prevention and control strategy for actual emergency decision-making. This application can take into account both spatiotemporal dynamic characteristics and stochastic uncertainties in urban flood disaster emergency decision-making, achieving dynamic adaptation and continuous, efficient decision-making between the disaster system and the emergency response system.

The invention discloses a routing method, device and equipment for a distributed reasoning task, a medium and a product. The method comprises the following steps: acquiring the distributed reasoning task; according to the distributed reasoning task, determining a task characteristic parameter set, a slice state set of the network slices meeting task requirements and a node state set of the node set; based on the task feature parameter set, the slice state set and the node state set, performing multi-dimensional evaluation and screening on nodes in the node set to obtain an effective candidate node set; and according to the effective candidate node set and the multi-dimensional joint decision-making condition, carrying out elastic routing decision-making to obtain an optimal node. Effective candidate nodes are screened by combining the states of tasks, slices and nodes with a multi-dimensional evaluation mode, and then elastic routing decision is carried out according to a multi-dimensional joint decision condition to obtain an optimal node, so that dynamic adaptation of a distributed reasoning task, an optimal calculation node and slice resources is realized, the delay violation rate is remarkably reduced, and the resource utilization rate is improved.

The invention discloses a flywheel lithium battery hybrid energy storage capacity configuration method and system, and particularly relates to the field of flywheel lithium batteries, and the method comprises the steps: S1, collecting multi-dimensional operation parameters through multi-sensor fusion, and carrying out the data preprocessing through a Kalman filtering algorithm; s3, constructing a reinforcement learning collaborative model, obtaining equipment attenuation efficiency, and optimizing and outputting an optimal power distribution proportion of the flywheel and the lithium battery through model training; s5, constructing an attention mechanism model to predict future comprehensive power fluctuation, and optimizing prediction precision; s7, acquiring real-time actual capacity, setting dual trigger conditions, updating the reinforcement learning model and dynamically adjusting the capacity; through a multi-sensor fusion acquisition technology, a wavelet packet decomposition fluctuation extraction technology and an attention mechanism enhanced LSTM prediction technology, accurate perception and prediction of new energy fluctuation and load demands are realized, and the problem of low configuration accuracy caused by dependence on an empirical formula and a static model in the prior art is solved.

This invention relates to the field of intelligent traffic control technology, specifically a method and system for traffic control at intersections within a wharf. The method includes: acquiring vehicles waiting to pass in the controlled lanes, vehicle information, and initial urgency weights; calculating the urgency of vehicles waiting to pass based on the vehicle information and initial urgency weights; sorting the vehicles according to their urgency to obtain a release queue for the controlled lanes, and grouping the release queues into release groups; calculating the Pareto optimal solution set for the release groups using a multi-objective optimization algorithm based on a pre-set inter-group release order and vehicle urgency; adjusting the initial urgency weights based on the Pareto optimal solution set and iteratively obtaining an optimal travel time allocation set and adjusted release groups; and controlling the intersection traffic based on the inter-group release order, the optimal travel time allocation set, and the adjusted release groups. This method solves the problem of insufficiently scientific and reasonable traffic control at intersections within wharves in existing technologies.

This application provides a model training method, an image detection method, and a storage medium, relating to the field of computer processing technology. The method includes: iteratively optimizing the initial parameters corresponding to the enhancement algorithms in the target enhancement strategy set based on sample data, a target enhancement strategy set, and an initial model, generating optimal enhancement parameters for each algorithm; processing the sample data based on the optimal enhancement parameters to generate optimal sample data; and training the initial model based on the optimal sample data to obtain a target model. The target enhancement strategy set is a collection of multiple enhancement algorithms determined based on the distribution characteristics of the sample data. Because this application employs an iterative optimization mechanism for enhancement parameters based on feedback from sample data and the initial model, it achieves dynamic adaptation between the enhancement strategies and data distribution characteristics, thus significantly improving parameter optimization efficiency, avoiding model overfitting, and enhancing the generalization ability, robustness, and practical adaptability of the target model.

The utility model discloses peeling treatment equipment for primary processing of agricultural products, which comprises a box body and a moving frame, a plurality of obliquely arranged peeling rollers are rotatably connected between the front inner wall and the rear inner wall of the moving frame, the left side wall of the moving frame is fixedly connected with the telescopic end of an electric push rod, and the electric push rod is fixedly connected to the surface of the outer left side wall of the box body; two supporting cylinders are rotatably connected between the front and rear inner walls of the box body on the right side of the moving frame, the surfaces of the two supporting cylinders are in transmission connection with a friction belt, a cleaning roller is rotatably connected between the front and rear inner walls of the box body on one side of the friction belt, and a discharge port is formed in the lower end of the left side wall of the box body; according to the agricultural product peeling device, due to the fact that the inclined peeling roller is arranged, agricultural products of different sizes can make contact with the friction belt and the peeling roller at the same time after entering the agricultural product peeling device, the agricultural products of different sizes can be peeled through the device, and the practicability of the device is improved.

This invention relates to the field of digital twin technology, and more particularly to a predictive maintenance platform for the entire lifecycle of a heat dissipation system based on digital twins. The platform includes: a data acquisition module; a model building module; a block partitioning module for determining whether to perform direct partitioning based on regional coupling strength or related partitioning based on heat exchange correlation, according to the fault propagation coefficient; a category analysis module for determining block categories based on temperature deviation and determining whether to adjust the block categories of the second-class blocks based on the contaminant deposition impact and fluid pressure anomaly of the second-class blocks, according to the maximum fluctuation cycle decay rate; an update analysis module for determining the update frequency of each updated block based on a comprehensive evaluation value; and an update optimization module for determining whether to perform optimization adjustments based on the block change coefficient. This invention can improve model update efficiency.

The application discloses a waterproof and breathable type brain and spinal cord nerve injury wound protection dressing, which is sequentially stacked and arranged from inside to outside as a wound adhesion inner layer, a functional sustained-release middle layer and a waterproof and breathable outer layer; the three-layer synergistic dressing structure of the wound adhesion inner layer, the functional sustained-release middle layer and the waterproof and breathable outer layer is constructed, waterproof sealing and the dynamic adaptation of the air permeability performance of the brain and spinal cord nerve injury wound are realized, the target anti-scar polypeptide is covalently bonded on the inner layer, the effective inhibition of excessive scar hyperplasia and neuroma formation is realized, the matching regulation of the gradient degradation drug-loaded microsphere system and the air permeation pore size of the middle layer is realized, the accurate release of active factors in the whole cycle of nerve repair is realized, the long-acting waterproof and breathable protection in a dynamic scene is realized through the design of the outer layer of super-hydrophobic nanofiber membrane, the risk of postoperative complications is reduced, and the integration and synergy of the physical protection of the wound and the nerve function repair are realized.

The invention discloses a self-adaptive identification method and system for an abnormal state of an electric energy meter and a medium. The method comprises the following steps: acquiring basic data of the electric energy meter; inputting the basic data into an XGBoost and multi-attention fusion model for training, generating a reality model, and generating a virtual model consistent with the time step of the reality model; calculating a relative deviation rate and a trend difference degree of the electrical characteristic data in the real model and the virtual electrical characteristic data in the virtual model at the same time step, and taking the relative deviation rate as a characteristic difference degree; constructing a three-dimensional evaluation index system, and marking time steps exceeding a threshold value as difference indexes; the difference indexes are input into a bidirectional LSTM model, and the abnormal type of the electric energy meter is judged in combination with a dynamic decision boundary optimized by a Q-learning algorithm; and according to an abnormal type determination result, triggering an XGBoost and multi-attention fusion model to perform parameter updating. According to the invention, the misjudgment rate and the missed judgment probability in a complex scene are reduced.