219results about How to "Increase initiative" patented technology

The invention relates to the technical field of pipeline data processing and health state evaluation, and discloses a pipeline interior detection method and system based on multi-sensor fusion. The method comprises the following steps: performing health index calculation on pipeline state evaluation data to obtain a pipeline health state index, and performing risk prediction on the pipeline health state index to obtain a risk level; performing pipeline defect feature recognition to obtain defect feature data, and performing multi-category classification processing on the defect feature data to obtain defect type data; performing accurate position analysis on the defect type data to obtain defect space distribution data, performing structural integrity evaluation to obtain a defect severity quantitative data set, performing multi-criterion decision analysis on the defect severity quantitative data set and risk levels to obtain defect repair priority ranking data, and performing defect repair priority ranking on the defect repair priority ranking data; and performing intelligent repair scheme generation on the defect repair priority ranking data to obtain repair suggestion data. According to the invention, the state of the pipeline can be evaluated more comprehensively and accurately.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention discloses a data quality monitoring method and system based on a knowledge graph. According to the method, data tables and fields in multi-source heterogeneous data are constructed into graph nodes, graph edges are constructed through main and foreign key constraint relationships and field value overlapping analysis, and a data blood relationship knowledge graph is formed; business rules are coded into the atlas to execute data constraint check, and an abnormal data influence range is traversed and tracked through a directed graph; and dynamic monitoring is realized based on the time change of the abnormal score sequence. The invention further provides system implementation including data acquisition and preprocessing, knowledge graph construction, quality monitoring, problem diagnosis and quality evaluation. The method solves the problems that a traditional method is difficult to track the influence range of abnormal data and lacks systematic evaluation, and improves the automation level and accuracy of data quality management.

The invention discloses an intelligent water quality monitoring system and method based on a turbidity sensor, and relates to the technical field of water quality monitoring. Data acquisition nodes are arranged in a water area to be monitored, data are acquired by using the turbidity sensor and an intelligent water quality monitoring equipment group, an environment data set S is constructed, and preprocessing is performed; acquiring a turbidity change coefficient # imgabs0 # according to a turbidity data set in the preprocessed environment data set S, performing primary evaluation, marking abnormal data acquisition nodes, acquiring a particulate matter dynamic index KL and a pollution index WR according to a particulate matter data set and an organic pollutant data set in the environment data set S, and performing secondary evaluation according to the turbidity change coefficient # imgabs1 # and the turbidity change coefficient # imgabs0 # in combination with the particulate matter dynamic index KL and the pollution index WR. Acquiring a comprehensive water quality score Q, performing secondary evaluation, and constructing a water quality prediction model to predict the water quality change and regulate and control the water quality for the data acquisition nodes which are normally evaluated for the second time; the intelligent level of water quality monitoring is improved, the accuracy of water quality monitoring is enhanced, and the safety of water quality is ensured.

The invention relates to the technical field of remote medical monitoring, in particular to a medical patient remote monitoring system which comprises a health monitoring module, a disease course prediction module, a physiological regulation module, a medicine management module, a behavior analysis module, a therapy customization module, a dose adjustment module and a system comprehensive management module. According to the invention, accurate physiological monitoring is realized through a data fusion algorithm and a noise filtering technology, the accuracy and reliability of data are ensured, the recurrent neural network model can provide more accurate disease development trend analysis, early diagnosis and timely intervention are facilitated, and the accuracy and reliability of the system are improved. A self-adaptive control algorithm and a biological feedback technology enable a patient to more effectively adjust the physiological state by themselves, the treatment initiative is improved, and the safety and effectiveness of drug treatment are improved through a mode recognition algorithm and drug interaction analysis of drug management. Through deep data mining and personalized treatment strategies of the behavior analysis module and the therapy customization module, a more comprehensive and customized treatment scheme is provided for the patient.

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

The invention relates to the technical field of ethanol production, and discloses an ethanol production concentration online monitoring method, device and system, and the method comprises the steps: collecting historical data in an ethanol production process, and carrying out the preprocessing to form a feature data set; based on the data set, an ethanol concentration prediction model is constructed, and a deep learning algorithm is utilized to predict a future concentration change trend. And obtaining production state data and environmental condition data in real time, and inputting the data into the prediction model to obtain a predicted concentration value. Detecting the concentration value in real time by combining a sensor on the ethanol production line, comparing and analyzing the concentration values, judging whether deviation exists or not according to a preset concentration deviation threshold value, and recording a deviation point. According to the number of the deviation points, the deviation value and a preset evaluation rule, the concentration control state is evaluated, if an abnormity is found, an alarm mechanism is triggered, and an evaluation result and alarm information are sent to related personnel in time. The intelligent level of concentration monitoring in the ethanol production process is improved, and potential problems can be found and prevented in advance.

The invention discloses an intelligent auxiliary system and method for regular inspection of a pressure vessel, and aims to improve the intelligence, efficiency and accuracy of inspection of the pressure vessel. The system comprises a data acquisition module, a data processing module, a risk assessment module, an inspection plan generation module, a report generation module, a remote monitoring module and a historical data management module. Operation parameters of the pressure vessel are collected in real time through the sensor, the data processing module carries out elimination and trend analysis on abnormal data, and the risk assessment module assesses the operation state of the pressure vessel through machine learning and a fuzzy logic algorithm. The system dynamically generates an inspection plan according to an evaluation result and adjusts an inspection period through Bayesian updating, and a generated inspection report has a digital signature to ensure safety. The historical data management module stores and analyzes long-term data and supports trend prediction of equipment health conditions, and the remote monitoring module provides real-time state checking and early warning.

The invention discloses a network security situation awareness and early warning system and method based on artificial intelligence, relates to the technical field of network security situation awareness and early warning, and solves the technical problem that the early warning precision is not high due to the fact that the influence of changes of an encryption traffic proportion and encryption strength on network security monitoring is not considered in the prior art. The method comprises the following steps: acquiring network data in a network in a preset time period; calculating a network security evaluation coefficient based on the network security data; determining whether to send early warning information based on the network security evaluation coefficient and a preset network security threshold; training an artificial intelligence model based on the historical network security data and the corresponding network security assessment coefficient to obtain a network security assessment coefficient prediction model; predicting the network security data based on the network security evaluation coefficient prediction model; whether early warning is carried out or not is judged based on the prediction result and a preset network security threshold value, the network is processed based on the early warning information, and the technical problem is solved.

The invention discloses an orthopedic patient data management and remote monitoring platform, and relates to the technical field of monitoring management, the orthopedic patient data management and remote monitoring platform comprises a monitoring management platform, the monitoring management platform is in communication connection with a data acquisition module, a data processing module, an abnormity monitoring module, an abnormity early warning module and a remote guidance module, all the modules are connected through electric signals. According to the orthopedic patient data management and remote monitoring platform, various monitoring devices and wearable devices are integrated, physiological parameters and motion parameters of a patient are collected, a comprehensive patient health condition view is provided for medical staff, and the medical staff can find abnormal physiological and motion characteristics of the patient in time through real-time monitoring, so that the medical staff can monitor the abnormal physiological and motion characteristics of the patient in time. In addition, the abnormal trend in the patient data is identified, targeted diagnosis and treatment reminding is provided for medical staff through setting of the abnormal early warning coefficient and the abnormal early warning level, and the risk of missed diagnosis and misdiagnosis is remarkably reduced.

The invention relates to a deep learning method for fire risk monitoring and early warning, and belongs to the technical field of fire safety. Through integration of various sensors (such as an infrared imager, a smoke detector and a temperature sensor), the method realizes omnibearing perception of a monitoring area, and significantly improves the accuracy and reliability of fire monitoring. A convolutional neural network (CNN) and a long and short term memory network (LSTM) are adopted, so that the system can effectively reduce false alarm and missing alarm, monitors key parts in real time, collects multiple parameters such as temperature and voltage, and dynamically evaluates the fire risk through intelligent analysis. In addition, an efficient communication link between the early warning system and other safety systems is established, and it is ensured that emergency measures can be taken quickly when the fire risk is detected. The system also has the functions of target tracking and behavior recognition so as to judge the authenticity and severity of the fire. Through modular design and environmental adaptability optimization, the system still operates stably in various complex environments.

The invention relates to the technical field of remote monitoring, in particular to a building foundation pit remote monitoring method and system, and the method comprises the following steps: collecting geological, meteorological and construction activity data, regularly reading the surface rainfall and underground water level variation of a building foundation pit, calculating the indexes of water level change and rainfall, and generating an environment change data set. According to the invention, through regular collection and analysis of changes of surface precipitation and underground water level and calculation of influence indexes of the changes, monitoring data is no longer an isolated numerical value, but is converted into environmental data having substantial influence on stability of the building foundation pit, response speed and accuracy of the monitoring system are improved, and through real-time image and risk level calculation, the stability of the building foundation pit is improved. According to the technical scheme, potential dangerous areas are identified, the risk assessment process is optimized, dynamic management of risks and synchronous updating of a risk level table of priority ranking and sound-light alarm are achieved, an early warning strategy is more effective, and the initiative and preventability of safety monitoring are improved.

The invention relates to the technical field of distribution management, in particular to an optimization method formed through secondary feedback of a rider, and the method comprises the steps: S1, building a networking platform, and collecting various information of the rider in real time; s2, a feedback mechanism is established, and a rider feeds back important information encountered in the distribution process in real time; s3, collecting the work data of the rider, and generating a personalized work capability evaluation model; s4, the networked map software collects road information, and establishes a route prediction mechanism in combination with feedback data and historical data; s5, monitoring distribution information of the rider in real time, and providing a processing scheme in case of an emergency; and S6, obtaining distribution information, and learning and optimizing an evaluation model and route planning of the rider. The method is beneficial for greatly improving the flexibility and response speed of the platform to cope with emergencies, ensuring dynamic optimization of the distribution plan of the rider, improving the consumer experience, enhancing the participation sense of the rider, and improving the working enthusiasm and initiative of the rider.

The invention provides an early warning method in construction temporary warehouse electric power material supply, which comprises the following steps: establishing a three-dimensional electromagnetic field distribution model based on actual environment influence according to a relative position relationship between a highway construction temporary warehouse and a high-voltage transmission line, and obtaining electromagnetic environment theoretical distribution of each warehouse; in the temporary warehouse, electromagnetic sensors are arranged according to materials, highly sensitive to electromagnetic interference, of electronic components, and large-dynamic-range electromagnetic sensors are arranged according to materials, high in electromagnetic field induction, of metal materials; monitoring data collected by various electromagnetic sensors of the warehouse are transmitted back to a power grid dispatching center in real time through the low-power-consumption wide-area Internet of Things and are associated with warehouse material structure parameters, and a database is constructed; historical data of a power grid load and an electromagnetic environment are mined, an association rule of an electromagnetic interference characteristic and a power transmission line operation condition is extracted, and a collaborative prediction model of a multi-warehouse electromagnetic safety situation is established.

The present invention discloses a network security protection method and system, which relates to the field of network security technology, including obtaining user behavior data of a target user and determining the user's current network security level; automatically adjusting weight parameters of an activity index, a login anomaly index, a running index and an operation anomaly index according to the real-time status and historical data of the network environment; establishing a hybrid state model using the collected user behavior data to identify and predict normal and abnormal behavior patterns; calculating the comprehensive security parameters of the target user based on the dynamically adjusted weight parameters, and then determining the user's overall security level; dynamically adjusting the network access control strategy according to the user's security level. The present invention forms a set of advanced network security protection mechanisms by integrating user behavior analysis, environmental adaptability weight adjustment, hybrid state model prediction, comprehensive security parameter calculation and dynamic access control strategy.

The invention discloses a water conservancy scenic spot disaster early warning flood prevention platform and method based on digital twinning, and relates to the technical field of disaster early warning flood prevention. The main scheme is as follows: a sensor is utilized to collect various data of a water conservancy scenic spot, and then water level, water velocity, rainfall, geology and facility availability indexes are calculated respectively; a digital twinborn model is constructed based on three-dimensional modeling software through the indexes, comprehensive environment data indexes are simulated and calculated, data indexes in a future period of time can be input to simulate future conditions, finally, a disaster early warning threshold value is set, the future comprehensive environment data indexes are compared with the disaster early warning threshold value, and whether early warning is triggered or not is judged. And if so, generating and issuing early warning information.

The invention is suitable for the field of electric energy dynamic balance and distributed energy storage control, and provides a method and a device for distributed energy storage control of a photovoltaic system. Predicting respective output data of a flexible electrical load, a photovoltaic system and an energy storage system in the DC micro-grid; matching an operation regulation and control strategy of the current DC micro-grid in a preset strategy library according to the output data, and enabling the DC micro-grid to maintain electric energy dynamic balance; based on a big data technology, mining key composition equipment of the DC micro-grid, and obtaining market update data of all the key composition equipment to make a sample set; according to the method, the output of the DC micro-grid can be predicted according to the acquired related data, and whether the equipment with low updating cost is subjected to overclocking use or not is judged through the preset economical efficiency analysis model, namely the equipment updating evaluation model, so that the power supplement of the AC power distribution network is reduced.

The invention relates to the technical field of geological monitoring, in particular to a geological monitoring early warning method and system for geological investigation, and the method comprises the following steps: deploying an acoustic sensor in a geological investigation area, recording sound wave signals generated by earth crust movement and fault activity, capturing sound waves in a target frequency range in real time, and transmitting the sound wave signals to the geological investigation area; meanwhile, underground water level, ground temperature and terrain deformation data are obtained through satellite scanning of the earth surface and are integrated with acoustic data, and a geological activity preliminary data set is generated; according to the method, through extraction of the key sound wave data and calculation of the correlation between the acoustic data and the earth surface change, the accuracy of geological anomaly mode recognition is remarkably improved, nonlinear time series analysis is introduced to further analyze the trend and seasonal fluctuation in the data, and stack-up analysis is performed based on the analysis result, so that the accuracy of geological anomaly mode recognition is improved. The scientificity of predicting the future geological change direction is enhanced, and the initiative and timeliness of disaster prevention are greatly improved through automatic comparison between real-time risk assessment and a preset threshold value and triggering of an alarm mechanism.

The invention discloses a network security situation prediction method and system, and relates to the technical field of network security situation assessment. The network security situation prediction method comprises the following steps: continuously obtaining network equipment operation data of a plurality of network operation time periods when network equipment operates, and carrying out preprocessing analysis to obtain a network internal stability index and a network external threat index of each network operation time period when the network equipment operates; according to the method and the device, the data of the plurality of network operation time periods during the operation of the network equipment is preprocessed and analyzed, the internal stability and external threat factors of the network are comprehensively evaluated, so that the network security index is generated, and the network security index of each network operation time period can accurately reflect the security state of the current network; according to the method, the change rate of the network security index is analyzed, the security index of the next time period is predicted, the monitoring accuracy of the network security situation can be effectively improved, potential network risks are found in time, and corresponding protection measures are taken.

The invention discloses an electric power system vulnerability dynamic risk assessment method and system. The method comprises the steps of obtaining power grid assets, business functions borne by the power grid assets and vulnerability information; calculating asset physical importance based on the real-time data of the power grid; for each vulnerability, respectively calculating a physical risk evaluation value and a business influence evaluation value of the vulnerability; and finally, carrying out weighted fusion on the two to obtain dynamic comprehensive risk scores and sorting. By fusing vulnerability static attributes, asset dynamic physical importance and business criticality, a scene-adaptive multi-dimensional risk assessment model is constructed, and the transformation of the vulnerability risk of the power system from'static pervasive assessment 'to'dynamic accurate portrait' is realized. According to the method, the integrating degree and decision guidance value of the risk assessment result and the actual security situation of the power grid are improved, the problem that the assessment result is one-sided and rigid due to separation from a business scene and a physical operation state in the prior art is solved, and the initiative and accuracy of network security protection of the power dispatching system are remarkably improved.

The invention discloses a finance and taxation data processing method and system, relates to the technical field of data storage processing, and classifies finance and taxation information resources into structured and unstructured data, so that an enterprise can more clearly know the composition of own data. The classification is beneficial to subsequent storage, query and analysis, data confusion is avoided, and the internal relation between structured and unstructured finance and tax data can be accurately found out through relevance evaluation. Therefore, the enterprise does not ignore data which seems to be independent but actually correlated, the data with remarkable correlation is integrated, the finance and taxation conditions of the enterprise can be reflected more comprehensively, the correlated data are stored in a unified mode, financial evaluation values and insight evaluation values are analyzed, multi-dimensional data factors are considered in the mode, and the enterprise finance and taxation evaluation efficiency is improved. Potential risks are judged according to the comprehensive risk assessment value, early warning display is carried out according to the risk types, and an enterprise can take corresponding measures according to specific risk conditions.

The invention discloses an unmanned store risk identification intelligent management system based on big data analysis, and belongs to the technical field of risk identification management. The method and the device are used for solving the technical problem of poor commodity settlement and display and placement risk identification and management effects during unmanned store operation in the existing scheme. In the supervision processing unmanned store operation process, different users carry out settlement of different articles, supervision processing data of different users are analyzed and combined, supervision statistical data of all settlement commodities are processed and analyzed, and an existing settlement risk prevention scheme is dynamically managed according to an analysis result. According to the method, the falling conditions of all the placed commodities in the unmanned store are monitored and counted, data processing and analysis are carried out on the display abnormity degrees corresponding to all the display racks for placing the commodities in the unmanned store, the display abnormity degrees corresponding to different display racks are determined according to analysis results, and targeted local display rack commodity placement management is implemented.

The invention relates to the technical field of medical services, in particular to an integrated intelligent medical service system, method and equipment. The system comprises a doctor-seeing appointment service module, a hospital guide service module, a post-diagnosis service module and a service rating module. The doctor-seeing appointment service module is used for generating doctor-seeing appointment suggestions according to doctor-seeing demands input by the patient and receiving doctor-seeing appointment information selected by the patient from the doctor-seeing appointment suggestions; the hospital guide service module is used for carrying out hospital guide on the patient based on the treatment appointment information in the treatment process of the patient and recording treatment data in the treatment process of the patient; the after-diagnosis service module is used for generating health suggestions based on the treatment data and recording feedback information of the patient for the health suggestions; and the service rating module is used for generating a rating report of the doctor seeing service for the patient based on the doctor seeing reservation information, the doctor seeing data and the feedback information, and generating an improvement strategy based on the rating report. The service quality and efficiency can be improved.

The invention relates to the technical field of industrial data processing, in particular to an intelligent safety accident early warning method and system based on risk assessment, and the method comprises the steps: obtaining a mining operation event, extracting response situations before and after the operation event, checking the response situations with a reference response template, and judging a disturbance response according to the deviation degree; a ventilation network model is constructed, and when it is detected that a monitoring point is abnormal, management traceability is carried out along a traversal upstream path in combination with an air volume distribution proportion; predicting the propagation trajectory of the abnormal gas along the wind flow path and the time of reaching a downstream node based on the ventilation network model, and obtaining spatial correlation analysis; analyzing the disturbance response and the spatial correlation, calculating a coupling risk increment, and outputting a comprehensive risk value; judging the critical degree grade of the comprehensive risk value time series data; and determining a scheduling level according to the combination of the comprehensive risk value level and the critical degree level, when the critical index indicates to evolve to a dangerous state, improving the scheduling level in advance, and generating a differentiated resource scheduling suggestion in combination with the propagation trajectory.

The invention discloses a tea production cycle monitoring technology based on remote sensing images, particularly relates to the field of remote sensing image calculation, and comprises a monitoring area division module, a monitoring data acquisition module, a monitoring data analysis module, a comprehensive analysis module and an early warning module. According to the invention, through a tea production period monitoring technology based on remote sensing images, accurate monitoring and management of the whole production process of the tea garden are realized, through monitoring area division, data acquisition, analysis, comprehensive evaluation, early warning and other modules, the accuracy and real-time performance of monitoring data are improved, the scientificity and efficiency of tea garden management are enhanced, and in addition, the tea production period monitoring system is suitable for popularization and application. Potential problems can be found and early warned in time, powerful decision support is provided for tea garden managers, the quality and benefits of tea production are improved, and sustainable development of the tea industry is promoted.

The present invention relates to the technical field of pipeline data processing and health status assessment, and discloses a pipeline internal detection method and system based on multi-sensor fusion. The method includes: calculating the health index of pipeline status assessment data to obtain pipeline health status indicators, performing risk prediction on pipeline health status indicators to obtain risk levels; performing pipeline defect feature identification to obtain defect feature data, performing multi-category classification processing on defect feature data to obtain defect type data; performing precise position analysis on defect type data to obtain defect spatial distribution data and perform structural integrity assessment to obtain defect severity quantification data set; performing multi-criteria decision analysis on defect severity quantification data set and risk level to obtain defect repair priority ranking data, performing intelligent repair plan generation on defect repair priority ranking data to obtain repair suggestion data. The present invention can achieve a more comprehensive and accurate assessment of pipeline status.

The invention relates to the technical field of safety early warning, in particular to a safety protection early warning method based on a neural network, and the method comprises the following steps: collecting the pH value, dissolved oxygen concentration, turbidity, temperature and flow of effluent quality, carrying out data cleaning and abnormal value elimination, carrying out time sequence sorting, and generating water quality time sequence data. According to the method, the neural network model is applied to predict the water quality parameters such as the pH value, the dissolved oxygen concentration, the turbidity, the temperature and the flow, the prediction error is combined with the short-term trend value, and the water quality risk is dynamically evaluated. And meanwhile, the parameter weight is dynamically adjusted to match the real-time importance of each parameter, so that the prediction accuracy is improved. The analysis of the long-period trend and the fluctuation range enables the early warning system to perform effective early warning before the risk reaches a threshold value, and allows timely countermeasures to be taken, thereby enhancing the perspectiveness and initiative of water quality management.

The invention relates to a smart factory-based conference solution pushing method and a medium, and relates to the technical field of online conferences. Based on the audio information and / or text information of the online conference, obtaining first conference participation information and first fault information in real time; the first conference participation information comprises conference participants and conference content; the first fault information comprises a fault object, fault data and a fault solution, and the fault data comprises a fault type and a fault position. Taking the first participation information and the first fault information as input of a trained fault processing result prediction network, and obtaining a predicted fault processing result as a first predicted fault processing result; wherein the fault processing result comprises fault processing duration and fault processing cost. And displaying the first predicted fault processing result. Compared with the prior art, the method can assist participants in discussion and accelerate the problem solving progress, so that the problem solving working efficiency is improved, and the online conference progress is promoted.

The embodiment of the invention discloses a vehicle detection method and device, equipment and a medium. The method comprises the following steps: acquiring identity identification information corresponding to a to-be-detected vehicle, and determining vehicle information corresponding to the to-be-detected vehicle based on the identity identification information; if the vehicle information authentication is passed and the appearance data acquisition of the to-be-detected vehicle is completed, obtaining the storage battery state of the to-be-detected vehicle; electrical safety detection of the to-be-detected vehicle is completed based on the state of the storage battery, and communication connection with a vehicle-mounted automatic diagnosis system of the to-be-detected vehicle is established; and receiving chassis test data of the vehicle reported by the vehicle-mounted automatic diagnosis system through the communication connection, and generating a detection report of the to-be-detected vehicle based on the chassis test data, the electrical safety detection, the appearance data and the identity label. According to the embodiment of the invention, the efficiency and accuracy of vehicle detection are improved, the possibility of human errors is reduced, and a detailed detection report also provides clear information about the health condition of the vehicle for a vehicle owner.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.