94 results about "Hazard index" patented technology

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 discloses an offshore oil spill emergency decision-making method based on satellite remote sensing and big data analysis, and relates to the field of offshore oil spill management, and the main scheme is that the method comprises the steps: calculating an oil spill confidence index through oil spill authenticity data, and judging whether the region is real oil spill or not based on the oil spill confidence index; analyzing the oil spill hazard data, and calculating an oil spill comprehensive hazard index; performing comprehensive analysis on the dynamic diffusion data to obtain a dynamic diffusion risk entropy; performing comprehensive analysis on the dynamic diffusion data to obtain an emergency response efficiency index; the oil spill comprehensive hazard index, the dynamic diffusion risk entropy and the emergency response efficiency index are compared with thresholds in a comprehensive evaluation threshold set, the severity level of the oil spill event is judged, and a corresponding emergency decision strategy is selected according to the severity level of the oil spill event; the technical problems of inaccurate oil spill authenticity judgment, incomplete oil spill harmfulness evaluation and insufficient dynamic diffusion data utilization are solved.

The invention provides an abnormity early warning method and early warning system of a mobile energy storage charging pile, and relates to the technical field of abnormity early warning of the mobile energy storage charging pile, and the method comprises the steps: collecting the current, voltage and charging state values of the charging pile in the historical charging process, and calculating the health state value of the charging pile; secondly, performing state marking on historical monitoring data by utilizing an expert scoring method, and constructing an anomaly detection model based on a random forest algorithm; and acquiring current, voltage, charging state value and health state value data at the current moment, and acquiring a current state mark through the model. And if the state is marked as an abnormal state, calculating a danger index at the current moment by combining the temperature and humidity data, and comparing the danger index with a set threshold value to judge whether an early warning needs to be sent out or not. The method can effectively identify the abnormal condition of the charging pile, and gives out early warning in advance, thereby reducing the potential safety hazard and improving the user experience.

The invention discloses a risk assessment method and system for failure operation of an urban gas pipeline and a storage medium, and belongs to the technical field of gas pipeline operation detection, and the method comprises the steps: carrying out the index scoring of a pipeline operation safety index, determining the index weight, and carrying out the calculation to obtain a risk possibility score before correction; performing index scoring and index weight determination on the pipeline safety emergency index, calculating to obtain a safety management risk score, and determining a safety management risk correction coefficient; carrying out coefficient correction on the obtained risk possibility score before correction, obtaining a corrected risk possibility score, and determining a risk possibility evaluation grade; performing index scoring on the pipeline leakage hazard index and determining a corresponding index weight, performing calculation to obtain a risk consequence score, and determining a risk consequence grade of the urban gas pipeline; and performing comprehensive judgment according to a preset risk matrix to determine the safety risk level of the urban gas pipeline and a corresponding management and control strategy scheme.

The invention discloses a coal mine underground safety detection method and system based on face image recognition, and relates to the technical field of coal mine safety detection. The method comprises the following specific implementation steps: through a video target recognition technology and a self-adaptive face recognition method, recognizing the identity of a miner and the wearing condition of safety equipment in each working area, and obtaining a miner number and a first danger index; secondly, according to the motion trail matching and behavior analysis technology, analyzing the danger degree of various behaviors of the miner to obtain a second danger index; then, according to the environmental parameters, evaluating the danger level of the miner working environment, and calculating a third danger index; finally, in combination with the danger index of the miner and the danger degree of the working area, the working safety condition of the miner is comprehensively evaluated, the comprehensive danger index is obtained, danger grading is carried out, early warning information can be sent out in time, the safety risk of underground operation of the coal mine can be effectively reduced, the safety of operators is ensured, and safety accidents are reduced.

The invention relates to the field of industrial safety monitoring, provides a real-time risk identification and active safety control method and system for industrial operating personnel in order to improve the accuracy and efficiency of a safety monitoring technology, and realizes real-time acquisition and edge preprocessing of human-machine-environment data by integrating a physiological / environmental / equipment multi-mode sensor. An FPN network is utilized to fuse spatial-temporal characteristics, Temporal-PC causal inference and a GBDT integration algorithm are combined, an interpretable risk propagation model is constructed, risk indexes are calculated, three-level early warning is divided, and hierarchical intervention is executed for different risk levels, including local early warning, environment regulation and control, emergency evacuation and multi-department emergency linkage. Through LSTM trend prediction and reinforcement learning dynamic parameter adjustment, closed-loop optimization of an intervention strategy is realized, the problems of information splitting, response lag and the like of a traditional monitoring system are solved, the risk identification precision and emergency disposal efficiency of a chemical high-risk scene are remarkably improved, and the method is suitable for operation safety control in high-temperature, high-pressure, toxic and harmful environments.

The invention provides an automatic gas station filling system, and relates to the technical field of gas station automatic filling. The method comprises the following steps: firstly, acquiring key time sequence data in a filling process, including filling pressure, filling speed and filling temperature; and then, constructing a filling prediction model by utilizing a linear regression model, and predicting the filling speed of the next timestamp by taking the pressure, the temperature and the speed at the current moment as input so as to realize prospective control on the filling process. In the real-time operation process, current filling pressure, speed and temperature data are collected and input into the model, after the predicted filling speed is obtained, dynamic adjustment is conducted in combination with a PID controller, and the smoothness and stability of the filling process are ensured. Besides, a set data threshold value is used for monitoring the filling safety in real time, the filling danger index is calculated and compared with the preset threshold value, and if the filling danger index exceeds the safety standard, the system will automatically give out danger early warning and respond to potential risks in time.

An event chain-oriented offshore oil spill dynamic risk assessment method comprises a data preparation stage, an event chain modeling stage and a dynamic risk assessment stage, and the data preparation stage comprises the steps of A, determining a risk assessment area and a risk assessment unit; the event chain modeling stage comprises the steps of B, oil spill event chain construction and node analysis; the dynamic risk assessment stage comprises the steps that C, from the perspective of quantitative risk assessment, the risk index of a current oil spill event, the vulnerability index of a disaster-bearing body and the risk index of a native event are calculated respectively, and oil spill native event risk assessment is executed; step D, oil spill secondary / derivative event risk assessment; step E, oil spill event chain risk assessment; and step F, carrying out oil spill event chain risk early warning grade division. The evaluation method analyzes and evaluates the influence object of the oil spill event and the risk of secondary / derivative events caused by further disaster due to disaster elements contained in the influence object or the disaster event from the perspective of event evolution.

The invention discloses a lightning multi-parameter space-time analysis method based on clustering tracking and a thunderstorm risk early warning method and system, and relates to the technical field of severe convective weather early warning. The method comprises the following steps: collecting and analyzing lightning data; data preprocessing and space-time collaborative clustering: clustering lightning belonging to the same thunderstorm monomer in the current time window by using optimized clustering parameters; cross-period target tracking and association: constructing a lightning cluster tracking chain of a continuous time window; performing multi-factor dynamic feature extraction, namely capturing real-time dynamic feature parameters on the associated cluster sequence; and constructing a hazard index model and carrying out risk early warning. According to the method, dynamic tracking, strength evaluation and danger grading of thunderstorm activities can be realized by utilizing lightweight and easily acquired lightning positioning data, the method has the characteristics of light calculation load and high real-time performance, and objective and quantitative technical support can be provided for refined short-term and temporary early warning decision making.

The invention discloses an intelligent management system based on a metal structure and oil monitoring, and particularly relates to the technical field of oil monitoring, a state detection division module is used for accurately identifying the start-stop state of target equipment, and self-correlation values of the equipment at different delay moments k are calculated according to the start-stop time sequence of the equipment; whether the start-stop state of the target equipment is a high-frequency start-stop state or not is judged according to the self-correlation values at different delay moments k, sudden anomalies of the equipment in the high-frequency start-stop process are accurately captured, and according to a stress fatigue accumulation coefficient, an oil film fracture abnormal coefficient and a start-stop vibration overload coefficient, the high-frequency start-stop state of the equipment is determined. According to the method, the high-frequency start-stop potential fault hidden danger model is constructed, the high-frequency start-stop potential fault hidden danger index is generated, the potential fault hidden danger of the equipment in the high-frequency start-stop process is effectively evaluated, equipment management personnel are helped to formulate scientific maintenance and management strategies, early warning of the fault hidden danger is achieved, and therefore the occurrence frequency of equipment faults is reduced, and the working efficiency is improved. And the service life of equipment is prolonged.

The invention relates to the technical field of environment monitoring, in particular to a building decoration environment harmful gas dynamic monitoring method based on deep learning, which comprises the following steps: acquiring temperature, humidity, volatile organic compound concentration, carbon dioxide concentration, energy consumption and equipment attitude in a building model coordinate system, and eliminating redundant channels according to spatial topology to form a simplified data stream; dense features are extracted from the simplified data flow through a space-time auto-encoder, query key values are generated through quantification, and adjacent historical features are retrieved and spliced into a fusion feature vector; inputting the fused feature vector and a sensing node coordinate into a physical neural network of an embedded gas diffusion and heat conduction equation, and predicting a temperature gradient and regional carbon equivalent emission; calculating a hazard index field according to prediction, heat flow simulation, construction progress and a material release rate, iteratively optimizing a sensing node set and writing the sensing node set into an environment mirror image body; and reconstructing a coupling field in the environment mirror image body, comparing a measured value to obtain a residual field, and updating a hazard index field and a sensing node set when the environment mirror image body is abnormal.

The invention relates to the technical field of network security, and discloses a network security vulnerability scanning method and a priority evaluation method thereof, and the network security vulnerability scanning method comprises the steps: obtaining internal and external information of a target system, generating a preliminary vulnerability list through matching, screening a target vulnerability according to a preset rule, and verifying vulnerability existence. According to the priority evaluation method for network security vulnerability scanning, the hazard index and the system state quantized value of the vulnerability are comprehensively evaluated, the final priority score is generated, the vulnerability needing to be processed preferentially is determined according to the priority ranking list, and the repair work is guided. By adopting the method and the device, efficient and accurate vulnerability scanning and priority evaluation are realized, the scanning efficiency and the detection precision are remarkably improved, the false alarm rate and the repair cost are effectively reduced, meanwhile, the dynamic adaptability is relatively high, the strategy can be optimized in real time according to the system load and the network environment change, and a comprehensive guarantee is provided for the network security.

The invention discloses a radar sounding-based hail grading early warning method and system for a convective monomer hazard index and jump increase, and relates to the technical field of severe convective weather early warning. According to the method, monomer segmentation is carried out and a severe convection monomer hazard model is constructed based on regional multi-weather radar data and air sounding data, a discrimination threshold is determined based on local air sounding data, and segmented convection monomers are screened for three times to obtain classification types of the convection monomers; and on the basis, tracking a hazard index track of a convective monomer core area in real time, and when a monomer hazard index shows a remarkable jump characteristic in a set time window, dynamically judging the occurrence probability and grade of hail, and pushing a digital alarm. The classification, digitization and progressive algorithm of the invention fuses multi-source data collaborative analysis, dynamic threshold determination and real-time evolution tracking, and significantly improves the timeliness and accuracy of the turning point of the heavy rainfall hail severe convection weather process and hail classification early warning identification.

The invention discloses a fault prediction analysis method based on driving behaviors and a related device. The method comprises the following steps: acquiring current driving behavior data of a target driver for a target vehicle; determining a driving habit type of the target driver according to the current driving behavior data; when the driving habit type is the preset driving habit type, obtaining driver state data of the target driver; determining a target danger index according to the driving habit type and the driver state data; acquiring historical driving behavior data and historical fault data of the target vehicle, and training a preset model based on the historical driving behavior data and the historical fault data to obtain a correlation analysis model; and when the target danger index is greater than a preset danger index threshold, inputting the current driving behavior data into the correlation analysis model to obtain a target fault probability. By adopting the method and the device, the fault is accurately predicted based on the driving behavior so as to avoid the occurrence of the fault.

The invention discloses a building construction site safety management informatization system and method, and relates to the technical field of safety management. The method comprises the following steps: reading and recording hazard index readings of different construction points on a construction site on line in real time; obtaining data logs of reading change of different hazard index items of different construction points of the construction site according to the hazard index reading which is read and recorded in real time; unsafe construction sections in the construction site are obtained according to the data logs of the reading changes of the different hazard index items of the different construction points of the construction site; obtaining the unsafe coefficient of each unsafe construction section according to the data logs of the reading changes of different hazard index items of different construction points in the unsafe construction section; and sequentially arranging the unsafe construction sections from high to low according to the unsafe coefficients of the unsafe construction sections to obtain a field detection sequence of the unsafe construction sections. According to the invention, timely and effective safety management of the building construction site is realized.

The invention relates to an occupational hazard comprehensive risk assessment and early warning method based on multi-source heterogeneous data, and belongs to the field of occupational health and occupational disease control. The method specifically comprises the following steps: constructing a pneumoconiosis risk index system and an early warning model, a radioactive risk index system and an evaluation model, and a noise hazard index system and an evaluation model of an operator; then, in combination with other physical factor index systems and evaluation models, a multi-source heterogeneous comprehensive evaluation or early warning model is established; occupational environment monitoring data, occupational health monitoring data, occupational disease data and occupational health supervision data are collected or collected; dividing the resource data into dust, radioactivity, industrial noise and other physical factors, and constructing a multi-source heterogeneous data occupational hazard prevention and control decision support big data center; and then a multi-dimensional indexing technology for occupational hazard data is designed by using a data distributed acquisition and storage technology, and an extensible occupational hazard early warning support platform with high performance and high fault tolerance is constructed.

The invention discloses a hail suppression operation method and system based on a convective monomer hazard index and hail identification, and relates to the technical field of meteorological monitoring and artificial influence weather command operation. According to the method, a severe convection monomer hazard model and a hail identification model are established based on multi-source observation such as weather radar and air sounding, the type of each severe convection monomer is identified in the hail identification model by combining two sections of score thresholds, and the development stage corresponding to the severe convection monomer is determined. A sectional type operation strategy is constructed according to the identification type and stage of the severe convection monomers, and a task distribution mechanism of multiple convection monomers and multiple operation points and an element method for calculating cannonball launching parameters are established. According to the invention, a precise operation process is provided for hail suppression command, human intervention in hail suppression operation is reduced, and the efficiency and accuracy of decision execution are improved.

The invention is applicable to the technical field of data processing, and provides an air adaptive purification control method and device, terminal equipment and a storage medium, and the method realizes dynamic adaptive power adjustment by constructing a pollution gas coupling kinetic model. A historical concentration mean value, a nonlinear response mechanism and periodic change characteristic parameters are introduced into the model, low power can be kept in low pollution, the purification efficiency can be rapidly improved in high pollution, the pollution trend can be pre-judged, the power can be adjusted in advance, and energy waste is avoided. The multi-pollutant synergistic effect is considered, interaction of different gases is treated in a cross term mode, the purification priority is distributed according to hazard index differentiation, and energy consumption generated by excessive treatment of low-hazard pollutants is reduced. According to the method, dual-mode switching is achieved in combination with hazard index comparison, dynamic balance of energy consumption and the purification effect is achieved while the purification effect is guaranteed, compared with a traditional linear control and single pollutant treatment mode, energy consumption control is more intelligent and efficient, and energy consumption of the air purification system can be effectively reduced.

The invention discloses a fire safety early warning method and system based on wireless networking, and relates to the technical field of fire safety early warning. According to the fire safety early warning method based on the wireless networking, electric power and environment monitoring data of a plurality of areas in the wireless networking of a shopping mall are acquired in real time, and an electric fire danger induction index and a fire danger environment disturbance index are analyzed; meanwhile, shopping mall monitoring image angle sequence data of each area is obtained, a visual potential safety hazard index is analyzed based on a pre-trained fire safety identification model, the safety condition of each area is comprehensively evaluated according to an electrical fire danger induction index and a fire danger environment disturbance index, and finally a comprehensive safety perception index of each area is generated. According to the invention, fire safety early warning is carried out on the corresponding area through the comprehensive safety perception index of each area, so that fire hazards in multi-area and complex layout environments such as shopping malls and the like are identified and positioned in real time, the accuracy of fire safety early warning is effectively improved, and the overall fire safety level is improved.

The invention discloses a self-adaptive alarm triggering method and device based on environmental perception, and belongs to the field of forest fire control, and the method comprises the steps: collecting distributed perception data, and obtaining multi-source positioning data; establishing an environment sensing model, performing environment sensing on the distributed sensing data, and determining a fire-fighting danger basic index; performing fusion processing on the multi-source positioning data by using an adaptive Kalman filtering algorithm to obtain target positioning information; correcting the fire-fighting danger basic index to obtain a fire-fighting danger evaluation index; and when the fire hazard index exceeds the preset fire hazard index threshold value, alarm information is sent through a long-distance redundancy communication mechanism. According to the invention, the technical problem of difficult search and rescue communication when a positioning communication signal is unstable and is lost and trapped due to a complex terrain environment in a forest fire-fighting scene in the prior art is solved; the technical effects of realizing high-reliability environment perception, accurate personnel positioning and stable dual-channel redundant communication in a forest fire-fighting complex environment are achieved.

The invention relates to the technical field of data processing, in particular to a threat recognition system and method.The method comprises the steps that a self-adaptive collection module obtains network flow data and operation log output feature vectors; the multi-dimensional rule module generates rule confidence; the statistical modeling module outputs an abnormal probability score; the machine learning module outputs a classification probability in response to the abnormal probability score dynamic selection model combination; the simulation decision module is fused with the multi-source data to execute threat diffusion simulation and output hazard indexes; the grading execution module triggers grading response according to the hazard index; and the feedback optimization module adjusts model parameters in a cross-layer manner. According to the method, noise interference is eliminated through feature optimization, a multi-dimensional model is fused to calibrate an output result, a threat grading mechanism preferentially responds to key threats, and closed-loop feedback is continuously injected into a false alarm data training model, so that the problem of high false alarm rate caused by insufficient training data coverage of a machine learning model in a complex dynamic network environment is solved; security resource allocation is optimized and key threat response delay is shortened.

The invention belongs to the technical field of image data processing, and particularly relates to an electron beam welding quality detection method and system, and the method comprises the steps: obtaining a welding seam image under the irradiation of a light source in a fixed direction; identifying and extracting defect plaques and expanded neighborhoods thereof and reconstructing the defect plaques and the expanded neighborhoods thereof into a local three-dimensional surface; obtaining a shadow depth index representing the depth of the defect plaque by analyzing the shadow feature of the defect plaque under directional illumination; based on the maximum principal curvature and the direction of each point of the reconstructed three-dimensional surface, obtaining a stress concentration ratio for evaluating the sharpness degree and the direction consistency of the micromorphology of the defect plaque; and obtaining a comprehensive hazard index through the shadow depth index and the stress concentration ratio, and carrying out detection and quality evaluation on the defect plaque according to the index. According to the method, the high-risk defect patches and the surface flaws are distinguished by evaluating the depth and the stress concentration ratio of the defect patches, and a reliable basis is provided for welding quality evaluation.

The present invention relates to a power transmission line galloping criterion and early warning method based on a grey correlation model, comprising the following steps: step 1: constructing a power transmission line galloping monitoring system based on an ultrasonic transducer to achieve more accurate and more relaxed monitoring of the dancing trajectory of the conductor; step 2: establishing a power transmission line galloping online monitoring and early warning criterion based on the monitored dancing trajectory; step 3: establishing an index system according to the main causes of transmission line galloping failures; step 4: using a hierarchical analysis method to assign weights to each index according to the influence of each index; step 5: using a grey correlation model to perform correlation analysis on current data and reference data, thereby calculating the risk index of the current line galloping failure and predicting the possibility of the failure; the present invention has the advantages of establishing an index system, constructing monitoring and early warning criterion, effectively preventing galloping failures and reducing the harm of galloping.

This invention discloses a method for earthquake landslide hazard zoning based on subtractive clustering ANFIS, belonging to the field of earthquake landslide hazard zoning technology. The method includes the following steps: collecting earthquake landslide monitoring data including seismic data, topographic data, meteorological and hydrological data, and image data; extracting features from the image data to obtain feature-extracted data; based on the earthquake landslide monitoring data, using a subtractive clustering algorithm to determine the initial cluster centers of the earthquake landslide area, thereby obtaining initial clustering information for the earthquake landslide; and combining the earthquake landslide hazard levels and their monitoring data within each geographic grid unit to draw an earthquake landslide hazard zoning map. The data acquisition technology, subtractive clustering algorithm application technology, ANFIS model construction technology, and data calibration technology in this method are closely integrated with modern information technology. The hazard calibration coefficient is used to calibrate the hazard index, improving the accuracy of earthquake landslide hazard assessment.

This invention relates to a method for risk assessment of multiple ecological factors in lakes based on DOM (Dissipative Oxygen Species), nutrients, and the coupling of functional bacteria and pathogen resistance. The invention aims to address the technical problems of current lake risk assessment methods, which primarily focus on evaluating single resistance risks, neglect the ecological buffering role of functional bacteria, and fail to reflect the coupled risks of multiple ecological factors in lakes. This invention obtains DOM parameters, nutrient parameters, functional bacteria indicators, pathogenic bacteria indicators, and drug resistance transmission factor indicators; calculates a comprehensive risk value R based on the environmental stress index E, ecological buffering index B, transmission hazard index H, and DOM-mediated transmission triggering coefficient K, and performs risk classification and spatial zoning assessment. This invention incorporates DOM activity, nutrient imbalance, the ecological buffering capacity of functional bacteria, and the drug resistance transmission potential of pathogenic bacteria into a unified evaluation framework, applicable to comprehensive ecological security assessment, risk early warning, and zoning management of lakes such as Poyang Lake.

The invention relates to the technical field of satellite ground equipment and the like, and provides a landslide prediction method and system based on a GNSS receiver, and the receiver synchronously obtains a three-frequency carrier signal, constructs a non-combined double-difference model, and carries out the hierarchical processing of a troposphere dry component and a zenith wet component; and two groups of ultra-wide lane ambiguity are generated based on original observation, cascade rounding is performed according to the length of the wavelength after a noise threshold is met, and a whole cycle result is output. And writing a whole-cycle result into integer linear constraints, and reducing the dimension of the narrow lane ambiguity into one dimension and thoroughly removing an ionosphere item in cooperation with geometric correlation and ionosphere-eliminating equivalent transformation. Baseline displacement, zenith moisture content and residual narrow lane ambiguity are used as states, extended Kalman filtering is implemented by combining an elevation angle weighted noise matrix, a narrow lane floating point solution is obtained, and displacement is updated after a fixed integer is checked through an LAMBDA algorithm and a ratio. And finally, the displacement rate and the hourly rainfall are synthesized into a risk index according to historical weights, a landslide early warning is output when the double indexes exceed the threshold, and high-reliability risk judgment of the landslide is realized.

The invention relates to the field of gas detection and early warning, in particular to an intelligent gas detection and early warning method and device for a multi-module integrated device. The method comprises the following steps: collecting multi-dimensional gas parameters of a scene, a real-time spectrum monitoring image and real-time positioning information of a user; performing environment temperature and humidity compensation on the multi-dimensional gas parameters to generate environment compensation gas parameters; performing abnormal gas concentration identification and toxic gas detection on the environmental compensation gas parameters to obtain an abnormal gas identification result; performing targeted gas spectrum analysis on the real-time spectrum monitoring image according to an abnormal gas identification result, and performing concentration situation change prediction so as to generate concentration situation prediction data; and performing comprehensive gas risk index evaluation according to the concentration situation prediction data to generate an environmental risk index. The mining area abnormal gas early warning is accurately and efficiently completed, and the safety and efficiency of mining area management are improved.

The invention relates to a method for determining a navigation route (1) of a vehicle, in which further road users (2) are detected by means of a sensor system (3), and sensor data generated by the sensor system (3) are evaluated by a computing device (4) in order to determine the behavior of the respective road user (2) in the road traffic. The method according to the invention is characterized in that the computing device (4) stores a database (5) comprising behavior limit values; -the computing device (4) compares the behavior observed by the road user (2) with the behavior limit values stored in the database (5) and counts the events (E) on the road section on which the road user (2) stays if the respective observed behavior of the road user (2) exceeds the associated behavior limit value; -the computing device (4) computes a hazard index (GI) for at least one road section as a function of the number and value of events counted over the respective road section over the specified time period; and-the navigation unit takes into account the risk index (GI) assigned to the respective route section for route planning, the route section of which the risk index (GI) exceeds a specified risk index threshold value being avoided in order to determine the navigation route (1).

The invention provides a regional soil heavy metal water input type pollution risk assessment method and system, and relates to the technical field of soil pollution risk assessment. Aiming at the problem of insufficient accuracy of existing pollution risk assessment, the method comprises the following steps: acquiring a sub-basin boundary, a cadmium-containing wastewater discharge point, cultivated land distribution data, a grain yield, cultivated land irrigation water consumption and a flood-submerged cultivated land area; calculating a cadmium emission hazard index, an irrigation exposure index, a flood exposure index and a vulnerability index according to the acquired data; after standardization processing is conducted on the key indexes, cadmium pollution risk values of the irrigation approaches and the flood approaches of all the sub-basins are calculated; dividing an irrigation exposure type and a flood exposure type according to a set threshold value, and determining a comprehensive risk type by combining the dual-path risk value; a sensitive analysis model is adopted, the key index is used as an input quantity, the double-path risk value is used as an output quantity, and a sampling and statistical method is set to identify a sensitive factor with the maximum influence of the risk value. According to the method, comprehensive and accurate evaluation of the watershed scale pollution risk is realized.

The invention relates to the technical field of drainage risk prediction, in particular to a transient bright and full flow prediction method for a large rainstorm drainage tunnel system. According to the method, firstly, all changing curves are corrected, then the risk probability of transient clear full flow of each monitoring point is obtained at each monitoring moment, and then all risk sections and corresponding risk intensity in a tunnel are determined; matching the risk segments at different monitoring moments, and obtaining impurity blocking parameters of each risk segment at the current moment according to changes of the matched risk segments; and then, at the current moment, obtaining expansion influence degrees among different risk segments, further obtaining a danger prediction index of each risk segment, and finally determining all transient bright full flow segments. According to the method, the transient bright and full flow risk sections are preliminarily determined according to the corrected acquired information, then the risk index of each risk section is accurately analyzed and predicted in combination with the expansion change and impurity deposition influence of the transient bright and full flow risk sections, and the transient bright and full flow prediction effect of the large rainstorm drainage tunnel system is improved.