391results about How to "Improve reaction speed" patented technology

The invention discloses a multi-sensor fusion positioning mapping and thermal imaging device and a computer readable medium. The equipment integrates positioning mapping sensors such as a thermal imaging camera, a visible light camera, a laser radar and an inertial navigation system, and is specially designed for high-risk and complex fire emergency environments. Through an end-cloud integrated cluster cooperation system and a multi-link stable transmission technology and in combination with intelligent decision support, high-precision real-time positioning and dynamic 3D temperature measurement mapping can be realized in an environment in which satellite navigation signals are weak or unavailable. The multi-mode portability and environmental adaptability design of the system ensures the high efficiency and safety of an operator / unmanned system under extreme conditions, and the advanced data processing and analysis technology of the system significantly improves the accuracy and response speed of rescue operation. Due to the innovative characteristics, the method has remarkable practical application value and market prospect in the field of fire-fighting emergency rescue.

The invention relates to the technical field of hydraulic engineering management, and discloses a hydraulic engineering seepage intelligent monitoring system. Comprising a multi-sensor sensing module, a data acquisition module, a data fusion and processing module, a machine learning prediction module, an intelligent decision module, an adaptive monitoring scheduling module, an automatic inspection and response module, a digital twinning and simulation analysis module and a communication module. The multi-sensor sensing module is responsible for collecting multi-dimensional data related to seepage in real time. A machine learning algorithm is adopted to intelligently predict the seepage trend, and risk assessment is performed in combination with real-time data, so that compared with a traditional monitoring method based on a preset threshold value, the method can automatically identify a complex seepage mode penguin through learning historical data and real-time data to predict the future risk in advance, and the risk assessment efficiency is improved. The prospective management of the seepage risk of the water conservancy project is realized, and the prediction precision and the response speed of the system are remarkably improved.

The invention relates to the technical field of water quality and water level monitoring, and discloses a water quality and water level monitoring method for a water conservancy project, which comprises the following steps: S1, arranging a multi-parameter water quality and water level monitoring sensor at a key position of the water conservancy project; s2, performing real-time processing on the data acquired by the multiple sensors based on a data fusion algorithm, wherein the data fusion algorithm is based on a Kalman filtering model; s3, acquiring external data through an environment sensor, and dynamically adjusting a water level and a water quality monitoring threshold value based on environment change; s4, predicting the water level and the water quality by using a deep learning algorithm based on LSTM (long short-term memory network); and S5, if the monitoring data exceed the dynamic threshold value, automatically triggering an early warning mechanism. By arranging the multi-parameter water quality sensor and the water level sensor at the key positions of the water conservancy project, the water quality and the water level can be comprehensively monitored, the coverage range and the precision of the monitoring system are greatly improved, and the complex monitoring requirements of multi-source pollution can be effectively met.

The invention relates to a sensor control method of an air purification system. The pollutant concentration gradient is monitored in real time by combining initial distribution of the sensors, and a pollutant diffusion path is simulated by utilizing a fluid dynamic model; a pollutant source is reversely deduced through sensor data, and the acquisition range of the sensor is dynamically changed; the method comprises the following steps: acquiring multi-dimensional data through physical sensors including particulate matters, temperature and humidity and chemical sensors including VOC and harmful gas, and extracting pollutant characteristics by utilizing PCA (principal component analysis); constructing hazard indexes of pollutants including AQI grading standards, and dynamically evaluating the priority of the pollutants in combination with real-time monitoring data of a sensor; for pollutants with different priorities, resources of the air purification system are distributed, and the air volume of a filter, the adsorption strength of activated carbon and the turn-on time of an ultraviolet germicidal lamp are adjusted; a self-adaptive calibration algorithm is introduced, and sensor output including interference correction of temperature and humidity on the particulate matter sensor is adjusted in real time according to the environment reference value; and dynamically adjusting the maintenance and replacement period of the sensor.

The invention provides a 5G network intelligent switching management method based on artificial intelligence, and the method comprises the following steps: collecting and processing network related data, and obtaining a network data set; constructing a switching demand prediction model used for predicting network switching time and a target and a switching effect prediction model used for predicting network performance after switching; optimizing the initial switching strategy in combination with reinforcement learning to obtain an optimal switching strategy; deploying a network switching engine model on the edge computing node, and adjusting an optimal switching strategy in real time according to the network environment change; a virtual simulation digital environment is constructed for network simulation, an optimal switching strategy is evaluated according to the simulated virtual network environment, and then a network switching engine model is optimized in real time according to an evaluation result. According to the invention, the intelligent switching decision is realized, the precision and efficiency of the switching decision can be improved, the network adaptability and diversified requirements are enhanced, and the user experience is improved.

The invention relates to the technical field of radar detection, in particular to an intelligent collision detection method and system, and the method comprises the following steps: setting an initial emission angle of a radar, monitoring a reflection signal during the movement of an object, recording the return time, calculating a reflection distance according to the light velocity and the time, adjusting the emission angle, optimizing the coverage range, and obtaining angle correction data. According to the method, the position of the object can be more accurately determined by recording the return time of the reflection signal in real time and calculating the reflection distance by using the light velocity and the time difference, and potential collision risks can be more accurately predicted and identified by iteratively optimizing the scanning path, enhancing the adaptability to the dynamic environment and updating the target trajectory information. Intelligent judgment of risk levels and adjustment of emergency response measures are carried out, the response efficiency and accuracy of an early warning system are improved, the function of a collision detection system is enhanced, traffic safety is remarkably improved, and potential accident risks are reduced.

The invention relates to the technical field of financial services, in particular to a testing method and system based on financial environment stability. The method comprises the following steps: carrying out real-time state monitoring on a financial transaction system, and generating system state basic data; constructing a virtual test environment according to the system state basic data, and performing test scene adaptive configuration to generate test environment parameter data; performing multi-dimensional pressure test analysis on the test environment parameter data, calculating a system stability index, and generating stability reference data; performing dynamic load gradient processing according to the stability reference data to obtain system pressure gradient data; and performing risk level mapping on the system pressure gradient data to generate risk assessment mapping data. According to the invention, the virtual test environment is constructed and self-adaptive configuration is carried out, so that a real transaction scene can be simulated, the correlation and effectiveness of the test are improved, and the stable operation of the system under different pressure conditions is ensured.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

The invention relates to the technical field of obstacle avoidance early warning, in particular to an obstacle avoidance early warning method and system for a robot. The method comprises the following steps: acquiring motion data and moving target point data of a robot; robot motion mode classification is conducted on the robot motion data, and a robot motion mode and a robot static mode are generated; performing environment detection on the moving target point data by using a built-in laser radar of the robot according to the static mode to obtain standard static environment detection data; and planning the path according to the standard static environment detection data, and generating initial running path planning data of the robot. According to the invention, the obstacle avoidance early warning precision and real-time performance of the robot in a complex environment are improved by accurately distinguishing static and dynamic objects, reconstructing the motion trail and planning and adjusting the path.

The invention relates to the technical field of wastewater treatment, in particular to a generation method for a chemical production wastewater treatment system control model. The method comprises the following steps: collecting chemical wastewater data by using an online monitoring instrument, wherein the chemical wastewater data comprises tin-containing wastewater data and etching liquid waste liquid data; determining a wastewater recovery control stage according to the chemical wastewater data; constructing a wastewater control model by utilizing chemical engineering simulation software based on the wastewater recovery control stage; inputting the chemical wastewater data into a wastewater control model, simulating a wastewater recovery control process by using simulation software to obtain wastewater recovery control data, and performing control abnormality judgment on the wastewater recovery control data to obtain abnormal wastewater data; the high-definition industrial camera is used for collecting images of the waste water sedimentation tank, and a centrifugal pump image is recognized and obtained from the images of the waste water sedimentation tank. According to the method, wastewater recovery control is optimized based on a wastewater treatment technology, and the wastewater recovery efficiency and the anomaly detection rate are remarkably improved.

The invention relates to the technical field of archive management, and discloses an RFID technology-based intelligent archive cabinet management method, which comprises the following steps of S1, inputting label information and archive information in a system, performing encryption storage through a block chain technology, arranging a sensor in an archive cabinet, acquiring environment data, and associating the environment data with RFID label information in real time; s2, an RFID reader-writer, a sensor and a camera are installed, the working states of the RFID reader-writer, the sensor and the camera are debugged to collect data in various environments, a self-adaptive signal enhancement mechanism is introduced, and the power and the frequency of the RFID reader-writer are adjusted according to the real-time environment. By combining the block chain technology and the digital signature technology, the system ensures that file data cannot be tampered and the operation safety is ensured, the introduction of the block chain provides a highly transparent and safe operation platform for file management, and the overall safety of the system is significantly improved. An intelligent fault prediction and label failure detection mechanism can identify potential problems in advance and prevent faults.

The invention discloses a regional heavy pollution discriminating and forecasting system and method based on big data, and relates to the technical field of environmental pollution monitoring. Comprising a multi-source data acquisition module, an adaptive weight distribution module, a feature extraction module, a heavy pollution discrimination module, a pollution diffusion path simulation module, an intelligent early warning and protection suggestion module, an event-driven dynamic monitoring module and a multi-scale meteorological prediction optimization module. And the pollution diffusion path simulation module performs prediction analysis on a pollution diffusion path and an influence range by using a diffusion path simulation algorithm. By designing the pollution diffusion path simulation module and the intelligent early warning and protection suggestion module, the pollutant diffusion path prediction and early warning functions are realized, and the problem that early warning lags due to the fact that the pollution diffusion path simulation and early warning module is separated and cannot realize timely linkage of prediction results and protection suggestions is solved; and a more timely and effective prevention and control strategy is provided, and the capability of coping with heavy pollution events is enhanced.

The invention relates to the technical field of intelligent spraying control, in particular to a vamp spraying control method and system for an intelligent shoemaking production line, and the method comprises the following steps: collecting spraying speed, spraying quantity, pressure and coating quality information, analyzing the influence of parameters on coating uniformity, coverage range and drying speed according to the current spraying effect, and generating a parameter influence data set. According to the method, the influence of each parameter on the coating quality is calculated, the priorities of the parameters in the control process are sequentially determined, and then historical records and current monitoring data are compared, so that abnormal fluctuation can be identified, potential abnormal conditions and positions of future key objects can be predicted according to the data, and fault modes can be identified. And the predictability and the reaction speed of the system are improved. And in combination with an image processing technology, the coating thickness of the vamp spraying area is further analyzed through image comparison, so that the thin coating area, the thick coating area and the spraying missing area can be more accurately detected, and the spraying process is more stable and efficient.

The invention provides an inventory management method for e-commerce, which integrates links of multi-source data analysis and prediction, intelligent replenishment, dynamic inventory tracking and return management, constructs a demand prediction model by using historical sales records, user behaviors and market dynamic information, sets a safe inventory level based on a prediction result, and provides an inventory management method for e-commerce. An automatic purchase request and a quick response replenishment mechanism are implemented, real-time inventory synchronization is achieved through an API, inventory changes are monitored through the Internet of Things technology, a commodity processing decision is guided for a returned commodity development prediction model, unsalable commodities are regularly examined, products close to the expiration date are reasonably handled, and a KPI is set and comprises the inventory turnover rate and the order accuracy rate. And closed-loop control of inventory management is realized.

The invention discloses a dynamic deviation rectifying method and system for sliding and lifting of a concrete sliding formwork of an ultra-large special-shaped vertical shaft. The method comprises the following steps that an ultra-large special-shaped vertical shaft model is established; configuring a deflection monitoring instrument; measuring the deflection amount of the sliding mode and generating measurement data; analyzing and processing the measurement data and generating a deviation correction control signal; deviation rectifying equipment performs slip form deviation rectifying; verifying a deviation correction result; and performing real-time dynamic analysis on the measured slip form deflection data, and generating a real-time dynamic deviation correction control signal to control deviation correction equipment to complete dynamic deviation correction of the slip form. The position, the posture and the motion state of the sliding mode can be monitored in real time, then measurement data are processed, and finally a deviation rectification control signal is generated to control deviation rectification equipment, so that the dynamic real-time deviation rectification work of the deflection of the sliding mode is realized. Therefore, high-precision, high-efficiency and high-adaptability dynamic deviation correction is realized, dependence on manual operation is reduced, high construction cost is reduced, adaptability and flexibility are enhanced, and safety and reliability of construction are improved at the same time.

The invention discloses an unmanned warehouse shelf-oriented automatic cargo checking method and system, and the method is characterized in that the method comprises the following steps: employing a target detection algorithm to extract a cargo boundary frame from captured continuous cargo images; for each cargo bounding box, calculating shape features, color features, texture features, size features and local features of the cargo in the cargo bounding box, and splicing feature vectors of the cargo according to the features; pre-defining cargo templates of different types of cargos, matching the cargo templates with the feature vectors of the cargos so as to obtain a category label of each cargo bounding box, and iteratively optimizing the target detection algorithm through a loss function; and tracking the goods extracted by the target detection algorithm by using a Kalman filtering target tracking algorithm, and obtaining the position and quantity information of the goods from the continuous goods images.

The invention relates to the technical field of temperature control, in particular to a traditional Chinese medicine raw material extraction process temperature control system and method.The system comprises a real-time environment monitoring module, a temperature prediction adjusting module, an abnormal temperature analysis module and a temperature control optimization response module; collecting a temperature signal of the traditional Chinese medicine raw material extraction equipment, and recording the humidity change of the extraction environment. According to the invention, through real-time acquisition and analysis of temperature and humidity data, the precision and the reaction speed of temperature regulation are further improved, so that even tiny temperature fluctuation can be detected and brought into regulation, through temperature prediction, the system can effectively predict the future temperature change trend, and through the combination of the prediction capability and the instant reaction, the real-time performance of the system is improved. The temperature in the extraction process is maintained in the optimal state, the extraction rate and quality of effective components are greatly optimized, and due to timely recognition and regulation of abnormal temperature points, potential quality problems are prevented, and energy consumption is reduced.

The invention relates to the technical field of network advertisements, in particular to an advertisement data management system based on market trend analysis, which comprises a data acquisition module, an analysis module, a prediction module, a conversion rate calculation module and an adjustment module. The advertisement effect is reflected by acquiring click and purchase data after advertisement putting, the change trend of user behaviors is accurately recognized through the analysis module, and the user change rate is calculated, so that data support is provided for an advertisement putting strategy, and the advertisement putting strategy is more accurate by analyzing historical data, predicting the future purchase trend and comparing with predicted data in real time. The conversion rate change of the advertisement is accurately evaluated, a basis is provided for optimizing the advertisement effect, whether the advertisement putting duration needs to be adjusted or not is judged according to the comprehensive analysis of the user change rate and the conversion rate change rate, efficient utilization of advertisement resources is ensured, the accuracy and flexibility of advertisement putting are remarkably improved, the conversion rate and the return on investment of the advertisement are facilitated, and the user experience is improved. And continuous optimization and improvement of the advertisement effect are realized.

The invention discloses a time series data anomaly detection model construction method and an anomaly detection method and system, and the method comprises the steps: reconstructing time series data through employing a model constructed based on a TCN-LSTM-CMA auto-encoder, and generating reconstructed data; calculating a reconstruction error by comparing the time sequence data and the reconstruction data before and after reconstruction; and according to the calculated reconstruction error, carrying out anomaly detection by adopting an isolated forest algorithm, and identifying abnormal data in the time series data. The invention provides a brand-new TCN-LSTM-CMA auto-encoder model construction method, and the method is combined with an isolated forest method to carry out time series data anomaly detection. According to the method, the defects of an existing anomaly detection method in the aspects of precision, adaptability and robustness are overcome. The method has a wide application prospect in the aspect of time series data anomaly detection in the fields of industrial monitoring, network security and the like.

The invention discloses a blue-green algae growth prediction method and an intelligent regulation and control device, and relates to the field of blue-green algae growth prediction and regulation and control, and the method comprises the steps: carrying out the collection of blue-green algae growth related data, data preprocessing, random forest model construction, SVM model construction, and model weight fusion of a final random forest prediction model and a final SVM prediction model to obtain a fusion model. And dynamically optimizing the fusion model, finally obtaining the dynamically optimized fusion model, obtaining a real-time prediction result according to the dynamically optimized fusion model by using the blue-green algae growth related data collected by the sensor module in real time, and adjusting the blue-green algae growth culture conditions in real time. According to the method, the advantages of the random forest model and the SVM model are combined, a fusion model with dynamic optimization and real-time adjustment functions is developed, and the accuracy and adaptability of blue-green algae growth prediction are remarkably improved. And meanwhile, data acquisition, processing and prediction modules are integrated, so that the accuracy of blue-green algae growth prediction and the automation degree of the system are remarkably improved.

The invention provides an amine low-water-content absorbent for capturing carbon dioxide and application of the amine low-water-content absorbent. The amine low-water absorbent comprises, by mass, 20%-50% of an absorbent main agent, 35%-70% of an auxiliary solvent and 2%-20% of water, and the range of a solvent effect factor delta of the amine low-water absorbent is 0.28-0.32. According to the method, the composition of the water-less absorbent is screened and optimized through a solvent effect factor formula, the amine water-less absorbent which is easy to desorb, low in regeneration energy consumption and low in viscosity characteristic is obtained, and wide application of the amine water-less absorbent in a carbon capture technology is promoted.

The invention relates to the technical field of behavior recognition, in particular to a construction site safety behavior recognition method based on deep learning, which comprises the steps of collecting environmental data of a construction site in real time, preprocessing the environmental data to improve data quality and diversity, extracting feature vectors, recognizing specific behaviors and identities of construction site personnel, and predicting personnel behaviors. Potential dangerous behaviors are identified, multi-level risk assessment is carried out according to risk levels, after high-risk behaviors are identified, the system not only triggers an alarm, but also automatically takes intervention measures, and through linkage with construction site mechanical equipment, operation of dangerous equipment is automatically stopped so as to prevent accident expansion. According to the invention, through multi-modal data acquisition and fusion processing, the accuracy of behavior identification is improved, refined multi-level risk management is realized, the system can rapidly trigger alarm and automatically take intervention measures, and the emergency processing efficiency and the overall safety level of a construction site are significantly improved.

The invention discloses an Internet of Things data analysis system based on artificial intelligence, and relates to the technical field of telecommunication, the system analyzes the coupling effect in signal propagation through a path selection and prediction module and an AI algorithm, and outputs an environmental influence function TW; and the dimension reduction feature data set F is combined with the environmental influence function TW to calculate and output a prediction path optimization parameter Pout. The intelligent path selection model not only considers factors such as signal intensity attenuation, noise interference and building reflection, but also combines the influence of terrain and weather, so that the prediction accuracy is greatly improved. And the dynamic path adjustment switching module realizes intelligent adjustment of the communication path based on the quality score comparison of the current path Pcurr and the predicted optimal path Pout and the setting of a threshold value M. And the response speed and the decision quality of the system in the case of network condition change are enhanced.

The invention discloses an outdoor fire hydrant water pressure real-time monitoring system, and relates to the technical field of fire fighting. When the system operates, a data acquisition and preprocessing module performs efficient fusion of multi-source data, and provides accurate water pressure data Px (t); the correlation analysis module evaluates the relationship between adjacent fire hydrants by using the geographic position data and the correlation coefficient, and can discover potential risks in time and generate an early warning identifier; the anomaly detection and evaluation module dynamically analyzes the water pressure data and the early warning identifier to provide an accurate fire hydrant anomaly risk evaluation strategy scheme; the path analysis module accurately judges an abnormal propagation path by analyzing the abnormal index gradient Gxy (t) of the adjacent fire hydrants, and reveals a potential regional problem; according to the method, the effective regional exception handling strategy is generated and executed immediately, the response speed and the decision-making ability are enhanced, and particularly, when regional or systematic problems are handled, early warning can be performed in advance, and optimal countermeasures can be taken.

The invention relates to the technical field of unmanned aerial vehicle dispatching, in particular to a disaster area emergency unmanned aerial vehicle networking method based on reinforcement learning, which comprises the following steps: taking an unmanned aerial vehicle as an intelligent agent, and taking horizontal and vertical coordinates, speed, direction, residual energy consumption and user throughput of the unmanned aerial vehicle as an observation space of the unmanned aerial vehicle; taking the speed scheduling scheme and the bandwidth allocation scheme of the unmanned aerial vehicle as an action space of the unmanned aerial vehicle; introducing a graph attention mechanism to aggregate observation features of adjacent unmanned aerial vehicles; based on the aggregated observation features, bandwidth allocation actions and corresponding rewards of the unmanned aerial vehicle under each speed scheduling are obtained; and introducing a Jain's fair index to measure the fairness of the service quality of the unmanned aerial vehicle in the region, and optimizing the speed scheduling and bandwidth allocation of the unmanned aerial vehicle. Under the condition of limited resources, the communication bandwidth is reasonably allocated, and the service coverage rate of the affected user is maximized.

The invention relates to the technical field of flood prevention management, in particular to a hydraulic engineering system real-time scheduling method based on a neural network technology, and the method comprises the steps: obtaining the real-time data of a hydraulic engineering system at a fixed time interval; performing image recognition on the satellite cloud picture by adopting a CNN model to obtain a rain cloud region map and a rain cloud region edge map; edge roughness coefficients, internal density coefficients and rain cloud thickness coefficients of the rain cloud area map and the rain cloud area edge map are calculated, so that rainfall coefficients are obtained; analyzing the rain cloud area map at continuous time intervals by using an optical flow method, obtaining the moving speed and direction of the rain cloud and the shortest distance between the rain cloud and the water conservancy project area, and obtaining a rain cloud influence coefficient; predicting the future water level of the reservoir by using a deep learning model, and calculating a vulnerability coefficient according to the flood control data; the flood control coefficient is calculated according to the rainfall coefficient, the rain cloud influence coefficient and the vulnerability coefficient, and the flood control regulation and control model is constructed, so that the flexibility of real-time scheduling of the hydraulic engineering system is effectively improved.

The invention relates to the technical field of tunnel environment monitoring, in particular to a tunnel environment real-time monitoring and evaluation system which comprises an environment monitoring module, a video monitoring module, a data transmission module, a data processing module, a ventilation adjusting module, a traffic signal control module, an information board issuing module and a user interaction module. The environment monitoring module collects tunnel environment data in real time, the video monitoring module obtains tunnel video data, the data transmission module transmits the data to the data processing module, and the data processing module processes the received data and evaluates the environment safety condition in the tunnel. The ventilation adjusting module automatically adjusts a ventilation system in the tunnel according to an evaluation result, the traffic signal control module automatically controls traffic lights, the information board issuing module issues early warning information through an electronic information board, and the user interaction module provides a visual interface; according to the invention, comprehensive, real-time and intelligent monitoring and adjustment of the environment in the tunnel can be realized, and the safety management level and the operation efficiency of the tunnel are improved.

The invention discloses an enterprise cross-department data collaborative optimization method based on a distributed architecture, which comprises the following steps: S1, deploying distributed computing nodes in each department of an enterprise, and connecting the distributed computing nodes through a network to form a distributed architecture, S2, establishing a multi-subject game model; s3, executing a multi-stage optimization process through the multi-agent game model; s4, in a local optimization stage, independently optimizing a department strategy of each department based on the enterprise cross-department data updated in real time; s5, in a cross-department cooperation and competition stage, all departments are based on shared data; s6, in the global optimization stage, the distributed system continuously updates data of all departments and feeds back the data to the multi-subject game model in the system; s7, applying a dynamic programming algorithm to dynamically adjust the enterprise overall collaboration strategy based on the real-time data and the historical data; and S8, in the distributed computing nodes, monitoring strategy execution results of all departments. According to the method, the data collaboration capability and decision-making efficiency in an enterprise are remarkably improved.

The invention relates to a TPA-LSTM-based shunting area wet and slippery road surface vehicle type-based dynamic early warning and speed guiding method. The method comprises the following steps: S1, collecting traffic flow operation time sequence data in a highway shunting area wet and slippery road surface scene; s2, predicting speed limit values of different vehicle types for safe passing on the wet and slippery road surface in the shunting area; s3, formulating early warning information release content; s4, determining an early warning information issuing mode; s5, determining an information issuing position of the highway shunting area, wherein the information issuing position is comprehensively determined according to an existing shunting area ETC portal layout principle in combination with a warning information issuing standard; and S6, early warning information is pushed to the vehicle end, and the ETC door frame receives the optimal passing speed information of the wet and slippery road surface pushed by the traffic management background and issues the information to vehicle-mounted OBU equipment with a voice broadcast function of the vehicle. According to the method, the real-time performance, the dynamic adaptability, the personalized early warning and the driver response effect are remarkably improved, so that the occurrence rate of accidents in the diversion area in a wet and slippery road scene in rainy days is greatly reduced.

The invention discloses a dance formation self-organization control method based on a robot group. The method comprises the following steps: S1, obtaining dynamic changes of light frequency, color and intensity through a spectrum camera; s2, judging an effective light source and an invalid light source, and removing the invalid light source to form a target light signal; s3, converting the optical signal into a digital signal through a photoelectric converter and a digital signal processor, and extracting the characteristics of the digital signal to form a characteristic instruction; s4, comparing the feature instruction with a predefined action matching database in real time, and outputting a robot action instruction; s5, analyzing the characteristics of the received digital signal, predicting an action instruction at the next moment, and generating the next action instruction in advance; s6, generating a robot action track by using a path planning algorithm; and S7, adopting a distributed control strategy, dynamically adjusting the motion track, and completing dance arrangement. According to the invention, dance arrangement of the robot group is controlled through light, and diversity of dance of the robot group is realized.