Risk assessment system, method and device in complex environment and storage medium
By setting dynamic thresholds and weights in different scenarios, using multiple sensors to collect data in real time and combining it with a dynamic library of environmental benchmark values, the problem of insufficient risk level differentiation in traditional risk assessment methods is solved, and more accurate risk assessment and timely early warning measures are achieved.
Patent Information
- Application Number
- CN202510771240.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-10
- Publication Date
- 2025-09-12
AI Technical Summary
Traditional terrain environment risk assessment methods are unable to distinguish the differences in risk levels in different scenarios. They use fixed thresholds and static weight distribution, resulting in high false alarm rates, delayed responses, and unclear decision-making basis, causing economic losses.
By setting dynamic thresholds and weights in different scenarios, using 3D lidar, binocular cameras, MEMS inertial sensors and other equipment to collect terrain and environmental parameters in real time, and combining them with a dynamic library of environmental benchmark values for comprehensive scoring, future environmental evolution trends are predicted and alerts are generated.
It achieves more accurate risk assessment in complex environments, reduces false alarm rates, improves response speed, provides intuitive risk factor feedback, and supports effective early warning and response measures.
Smart Images
Figure CN120634259A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of environmental safety monitoring and risk assessment, and in particular to a risk assessment system, method, device and storage medium in a complex environment. Background Art
[0002] In the fields of natural disaster warning and engineering safety monitoring, traditional terrain environmental risk assessment methods have significant flaws: they use fixed thresholds (such as a warning for a slope >30°), which cannot distinguish between risk levels in different scenarios; static weight allocation: each parameter uses a fixed weight coefficient; different parameters are judged independently, and there is a lack of a comprehensive risk assessment mechanism.
[0003] This can easily lead to high false alarm rates, delayed responses, and unclear decision-making basis, ultimately leading to misjudgment of environmental risks and causing economic losses. Summary of the Invention
[0004] The present invention provides a risk assessment system, method, device, and storage medium in a complex environment to solve at least one problem in the related art. The technical solution is as follows:
[0005] In a first aspect, embodiments of the present application provide a risk assessment method in a complex environment, comprising:
[0006] According to different scenarios, the thresholds of the terrain environment, flatness and environmental parameters of the corresponding scenarios are preset, and the benchmark parameter intervals corresponding to the terrain environment, flatness and environmental parameters are generated at the same time. The scoring intervals are generated based on the benchmark parameters and thresholds generated by the scenarios, and a dynamic library of environmental benchmark values is established;
[0007] The data of terrain environment, flatness and environmental parameters are collected in real time through the data acquisition device and input into the dynamic database of environmental reference values;
[0008] Predict future environmental evolution trends, assign weights to the terrain environment, flatness, and environmental parameter scores in advance, combine the benchmark parameters and thresholds in the dynamic library of environmental benchmark values, output scores, and generate alerts based on the scores.
[0009] In one embodiment,
[0010] When the baseline parameter ranges of the terrain environment, flatness and environmental parameters of the corresponding scenes are preset, the parameters corresponding to different scenes are mapped to standardized scores of 0-1.
[0011] In one embodiment,
[0012] The preset reference parameter ranges of the terrain environment, flatness and environmental parameters of the corresponding scene include:
[0013] Set the terrain environment, flatness and environmental parameters as the main dimensions, set the secondary parameters corresponding to each main dimension in different scenarios, and set the thresholds corresponding to the secondary parameters;
[0014] Generating different environmental reference parameter thresholds according to different scenarios includes: constructing a parameter association model according to historical data, and adjusting the secondary parameter thresholds in real time.
[0015] In one embodiment,
[0016] The prediction of future environmental evolution trends and the allocation of weights of terrain environment, flatness and environmental parameters in advance include:
[0017] According to the average value of the humidity change parameter detected in the past, the threshold of each secondary parameter is adjusted, and the weight of each secondary parameter is adjusted;
[0018] Predict the environmental evolution trend in the next 2 hours and the growth rate of environmental parameters. Adjust the thresholds and weights of each secondary parameter based on the predicted growth rate of environmental parameters.
[0019] In one embodiment,
[0020] The secondary parameters corresponding to each main dimension in different scenarios include:
[0021] Terrain environment includes: slope, obstacle density, and surface stability;
[0022] Flatness includes: ground undulation, crack density and abnormal vibration frequency;
[0023] Environmental parameters include: air humidity, soil moisture content and rainfall intensity.
[0024] In one embodiment,
[0025] The real-time acquisition of data on terrain environment, flatness and environmental parameters by the data acquisition device includes:
[0026] By integrating 3D laser radar and binocular cameras, terrain point cloud data is generated in real time and slope and obstacle density are calculated;
[0027] The ground vibration spectrum is collected through MEMS inertial sensors (accelerometer + gyroscope), and ground cracks / depressions are analyzed by combining image recognition;
[0028] Deploy temperature and humidity sensors, barometers, and optical rain gauges to dynamically monitor meteorological conditions.
[0029] In one embodiment, it further includes:
[0030] When different secondary parameters exceed thresholds, alerts are generated.
[0031] In a second aspect, an embodiment of the present application provides a risk assessment system, comprising:
[0032] The environmental benchmark value dynamic library stores the terrain environment, flatness and benchmark parameter ranges of environmental parameters in different scenarios, as well as thresholds of different environmental benchmark parameters.
[0033] In a third aspect, an embodiment of the present application provides a risk assessment device, comprising:
[0034] Terrain environment module, used to collect terrain environment data;
[0035] Flatness detection module, used to collect flatness data;
[0036] Environmental parameter module, used to collect environmental parameter data.
[0037] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method in any one of the above-mentioned embodiments.
[0038] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, it implements the method in any one of the above-mentioned embodiments.
[0039] The beneficial effects of the above technical solution include at least:
[0040] By setting thresholds for terrain environment, flatness and environmental parameters in different scenarios, and judging the benchmark parameters corresponding to the stage reached by the terrain environment, flatness and environmental parameters according to the set thresholds, and adding the corresponding benchmark parameters according to the weight of the corresponding scenario, a comprehensive score of the terrain environment, flatness and environmental parameters in the scenario is obtained, and corresponding measures are taken according to the score, which more intuitively reflects the risk factors in different scenarios, so as to facilitate better early warning and response measures.
[0041] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in this application and should not be construed as limiting the scope of this application.
[0043] Figure 1 This is a schematic diagram of the steps of the risk assessment method according to an embodiment of the present application;
[0044] Figure 2 This is a structural block diagram of a risk assessment device according to an embodiment of the present application;
[0045] Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0046] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.
[0047] Reference Figure 1 , showing a flow chart of a risk assessment method according to an embodiment of the present application, the risk assessment method may include at least steps S100-S300:
[0048] S100: Preset thresholds for terrain, smoothness, and environmental parameters for different scenarios, generate benchmark parameter intervals corresponding to the terrain, smoothness, and environmental parameters, generate scoring intervals based on the benchmark parameters and thresholds generated for the scenarios, and establish a dynamic database of environmental benchmark values;
[0049] S200, collecting data on terrain environment, flatness and environmental parameters in real time through a data collection device, and inputting the data into a dynamic database of environmental reference values;
[0050] S300: Predict future environmental evolution trends, assign weights to the terrain environment, flatness, and environmental parameter scores in advance, combine the benchmark parameters and thresholds in the environmental benchmark value dynamic library, output the scores, and generate alerts based on the scores.
[0051] The risk assessment method of the embodiment of the present application can be executed by an electronic control unit, controller, processor, etc. of a terminal such as a computer, mobile phone, tablet, or vehicle-mounted terminal, or can be executed by a cloud server.
[0052] The technical solution of the embodiment of the present application is to set the reference parameter intervals for the terrain environment, flatness and environmental parameters in various scenes (such as mountains, swamps and urban construction sites), and set thresholds;
[0053] When the terrain environment, flatness, and environmental parameters are lower than the threshold in the corresponding scenario, they correspond to the safety benchmark parameters;
[0054] When the terrain environment, flatness, and environmental parameters are at high thresholds in the corresponding scenario, they correspond to warning or high-risk benchmark parameters. Based on different benchmark parameter values, the parameters corresponding to the terrain environment, flatness, and environmental parameters are output. The output parameters are added according to the weights in the scenario to obtain a comprehensive score range. Based on the range of the comprehensive score range, the risk level of the complex environment is determined, and corresponding measures are taken according to the determined risk level.
[0055] By setting thresholds for terrain environment, flatness, and environmental parameters in different scenarios, and judging the corresponding benchmark parameters of the stage reached by the terrain environment, flatness, and environmental parameters based on the set thresholds, and adding the corresponding benchmark parameters according to the weight of the corresponding scenario, a comprehensive score of the terrain environment, flatness, and environmental parameters in the scenario is obtained, and corresponding measures are taken according to the score, which more intuitively reflects the risk factors in different scenarios, so as to facilitate better early warning and response measures;
[0056] It should be noted that the weights of the benchmark parameters output by the terrain environment, flatness, and environmental parameters can be adjusted according to factors such as humidity and rainfall in the scene;
[0057] Specifically, when presetting the baseline parameter ranges for the terrain environment, flatness, and environmental parameters of the corresponding scenarios, the parameters corresponding to different scenarios are mapped to a standardized score of 0-1. The specific comprehensive score ranges are shown in the following table.
[0058] 0-0.3 Low-risk routine monitoring, data updated every hour
[0059] 0.3-0.7 Medium risk: Initiate drone re-inspection, and refresh data every 10 minutes. Comprehensive score interval risk level response measures example
[0060] 0.7-1.0 High risk triggers sound and light alarms, automatically sending evacuation instructions to the terminal device
[0061] Dynamic weight allocation algorithm: The entropy weight method is used to automatically calculate the weight of each parameter (for example, in heavy rain weather, the humidity weight is increased to 0.4, and the terrain weight is reduced to 0.3).
[0062] Calculation of comprehensive risk index:
[0063]
[0064] where w iis the weight of each parameter, S i For single item scoring, C j is the coupling risk factor (such as high humidity + steep slope triggering landslide coefficient k j =1.2).
[0065] In one embodiment, step S100 includes steps S110-S120:
[0066] S110: Setting the terrain environment, flatness, and environmental parameters as primary dimensions, setting secondary parameters corresponding to each primary dimension in different scenarios, and setting thresholds corresponding to the secondary parameters.
[0067] S120: Build a parameter association model based on historical data and adjust the secondary parameter thresholds in real time.
[0068] Different secondary parameters correspond to different thresholds in different scenarios. The parameters are output according to the threshold of the corresponding secondary parameter, and the primary parameter is obtained through the weight of the secondary parameter. The weight of the primary parameter in the terrain is then added to output the risk coefficient. The threshold of the secondary parameter changes according to different scenarios and temperature, rainfall, etc.
[0069] Historical data: A parameter association model is constructed through a BP neural network. For example, when the slope is monitored to be greater than 30° and the rainfall for three consecutive days is greater than 50 mm, the soil moisture threshold is automatically lowered from 18% to 15%.
[0070] Real-time prediction compensation: The ARIMA time series model is used to predict the environmental evolution trend in the next two hours. If the predicted humidity growth rate exceeds 5% / h, the current humidity benchmark range is tightened in advance (for example, the warning threshold is reduced from 25% to 22%).
[0071] In order to avoid false positives caused by mechanical threshold determination, a membership function is used to implement progressive risk scoring:
[0072] Example: Slope scoring function:
[0073]
[0074] When the actual slope θ = 28°, the score S = 0.53 (linear interpolation)
[0075] If the superimposed rainfall is greater than 40 mm, the score is increased to 0.64 by a correction factor of λ = 1.2
[0076] In one embodiment, the secondary parameters corresponding to each primary dimension in different scenarios include:
[0077] Terrain environment includes: slope, obstacle density, and surface stability;
[0078] Flatness includes: ground undulation, crack density and abnormal vibration frequency;
[0079] Environmental parameters include: air humidity, soil moisture content and rainfall intensity.
[0080] Terrain environment module: Integrates 3D lidar and binocular cameras to generate terrain point cloud data in real time and calculate slope and obstacle density.
[0081] Flatness detection module: Uses MEMS inertial sensors (accelerometer + gyroscope) to collect ground vibration spectrum and combines image recognition to analyze ground cracks / depressions.
[0082] Environmental parameter module: Deploy temperature and humidity sensors, barometers, and optical rain gauges to dynamically monitor meteorological conditions.
[0083] The preset benchmark parameter range is divided into three levels of indicators based on the physical characteristics of environmental factors and risk action mechanisms:
[0084]
[0085]
[0086] Dynamically matching benchmark intervals based on scene types uses a two-layer architecture of scene labeling and machine learning correction. As shown in the following table, the thresholds corresponding to mountainous areas, swamps, and urban construction sites are used as examples:
[0087]
[0088] In one embodiment, step S200 includes steps S210-S230:
[0089] S210, by integrating 3D laser radar and binocular camera, generates terrain point cloud data in real time and calculates slope and obstacle density.
[0090] S220: Use MEMS inertial sensors (accelerometer + gyroscope) to collect ground vibration spectrum and combine image recognition to analyze ground cracks / depressions.
[0091] S230, deploy temperature and humidity sensors, barometers, and optical rain gauges to dynamically monitor meteorological conditions.
[0092] In this embodiment, the terrain environment module integrates a 3D laser radar and a binocular camera to generate terrain point cloud data in real time and calculate the slope and obstacle density.
[0093] Flatness detection module: Uses MEMS inertial sensors (accelerometer + gyroscope) to collect ground vibration spectrum and combines image recognition to analyze ground cracks / depressions.
[0094] Environmental parameter module: Deploy temperature and humidity sensors, barometers, and optical rain gauges to dynamically monitor meteorological conditions.
[0095] In one embodiment, step S300 includes steps S310-S320:
[0096] S310 , adjusting the thresholds of the respective secondary parameters and the weights of the respective secondary parameters according to the average values of the humidity change parameters detected in the past.
[0097] S320: Predict the environmental evolution trend in the next two hours and predict the growth rate of environmental parameters. Adjust the threshold and the weight of each secondary parameter based on the predicted growth rate of environmental parameters.
[0098] In this embodiment, based on the average values of past data under different environments, the thresholds of various secondary parameters are adjusted, and the weights of various parameters are adjusted, thereby reducing data differentiation. In addition, based on factors such as humidity and weather changes in the past, the environmental evolution trend in the next 2 hours is predicted in advance, and the thresholds and weights of basic parameters are adjusted in real time, which further improves the accuracy of the system evaluation. Combined with statistics, the future environment is predicted and analyzed, and early prevention is carried out, which further improves the security of risk assessment.
[0099] This embodiment shows a risk assessment system of the present application, which includes:
[0100] The environmental benchmark value dynamic library stores the terrain environment, flatness and benchmark parameter ranges of environmental parameters in different scenarios, as well as thresholds of different environmental benchmark parameters.
[0101] The system is provided in an electronic device.
[0102] Reference Figure 2 , shows a structural block diagram of a risk assessment device 200 according to an embodiment of the present application, which may include:
[0103] Terrain environment module, used to collect terrain environment data;
[0104] Flatness detection module, used to collect flatness data;
[0105] Environmental parameter module, used to collect environmental parameter data.
[0106] In this embodiment, the terrain environment module integrates a 3D laser radar and a binocular camera to generate terrain point cloud data in real time and calculate the slope and obstacle density.
[0107] Flatness detection module: Uses MEMS inertial sensors (accelerometer + gyroscope) to collect ground vibration spectrum and combines image recognition to analyze ground cracks / depressions.
[0108] Environmental parameter module: Deploy temperature and humidity sensors, barometers, and optical rain gauges to dynamically monitor meteorological conditions.
[0109] The functions of each module in the device of the embodiment of the present application can be found in the corresponding description in the above method and will not be repeated here.
[0110] Reference Figure 3 , shows a block diagram of an electronic device according to an embodiment of the present application. The electronic device includes a memory 310 and a processor 320. The memory 310 stores instructions executable on the processor 320, which loads and executes the instructions to implement the risk assessment method in the above embodiment. The number of the memory 310 and the processor 320 can be one or more.
[0111] In one embodiment, the electronic device further includes a communication interface 330 for communicating with external devices and performing data exchange transmission. If the memory 310, the processor 320, and the communication interface 330 are implemented independently, the memory 310, the processor 320, and the communication interface 330 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.
[0112] Optionally, in a specific implementation, if the memory 310, the processor 320 and the communication interface 330 are integrated on a chip, the memory 310, the processor 320 and the communication interface 330 can communicate with each other through an internal interface.
[0113] An embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the risk assessment method provided in the above embodiment.
[0114] An embodiment of the present application also provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the method provided in the embodiment of the present application.
[0115] An embodiment of the present application also provides a chip, including: an input interface, an output interface, a processor and a memory. The input interface, the output interface, the processor and the memory are connected through an internal connection path. The processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided in the embodiment of the application.
[0116] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processing (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor that supports the Advanced RISC Machine (ARM) architecture.
[0117] Furthermore, optionally, the above-mentioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. The volatile memory may include random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DR RAM).
[0118] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.
[0119] In the description of this specification, the reference terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.
[0120] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, features specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.
[0121] Any process or method description in a flow chart or otherwise described herein can be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process. The scope of the preferred embodiments of the present application includes additional implementations in which the functions may be performed in a different order than shown or discussed, including in a substantially simultaneous manner or in a reverse order depending on the functions involved.
[0122] The logic and / or steps represented in the flowchart or otherwise described herein may be considered, for example, as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device).
[0123] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.
[0124] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the aforementioned integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.
[0125] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.
Claims
1. A risk assessment method in a complex environment, characterized by: include: According to different scenarios, the thresholds of the terrain environment, flatness and environmental parameters of the corresponding scenarios are preset, and the benchmark parameter intervals corresponding to the terrain environment, flatness and environmental parameters are generated at the same time. The scoring intervals are generated based on the benchmark parameters and thresholds generated by the scenarios, and a dynamic library of environmental benchmark values is established; The data of terrain environment, flatness and environmental parameters are collected in real time through the data acquisition device and input into the dynamic database of environmental reference values; Predict future environmental evolution trends, assign weights to the terrain environment, flatness, and environmental parameter scores in advance, combine the benchmark parameters and thresholds in the dynamic library of environmental benchmark values, output scores, and generate alerts based on the scores.
2. The risk assessment method according to claim 1, characterized in that: When presetting the baseline parameter ranges of the terrain environment, flatness, and environmental parameters corresponding to the scene, the parameters corresponding to different scenes are mapped to a standardized score of 0-1.
3. The risk assessment method according to claim 1, characterized in that: The preset reference parameter ranges of the terrain environment, flatness and environmental parameters of the corresponding scene include: Set the terrain environment, flatness and environmental parameters as the main dimensions, set the secondary parameters corresponding to each main dimension in different scenarios, and set the thresholds corresponding to the secondary parameters; Generating different environmental reference parameter thresholds according to different scenarios includes: constructing a parameter association model according to historical data, and adjusting the secondary parameter thresholds in real time.
4. The risk assessment method according to claim 3, characterized in that: The prediction of future environmental evolution trends and the allocation of weights of terrain environment, flatness and environmental parameters in advance include: According to the average value of the humidity change parameter detected in the past, the threshold of each secondary parameter is adjusted, and the weight of each secondary parameter is adjusted; Predict the environmental evolution trend in the next 2 hours and the growth rate of environmental parameters. Adjust the thresholds and weights of each secondary parameter based on the predicted growth rate of environmental parameters.
5. The risk assessment method according to claim 3, characterized in that: The secondary parameters corresponding to each main dimension in different scenarios include: Terrain environment includes: slope, obstacle density, and surface stability; Flatness includes: ground undulation, crack density and abnormal vibration frequency; Environmental parameters include: air humidity, soil moisture content and rainfall intensity.
6. The risk assessment method according to claim 5, characterized in that: The real-time acquisition of data on terrain environment, flatness and environmental parameters by the data acquisition device includes: By integrating 3D laser radar and binocular cameras, terrain point cloud data is generated in real time and slope and obstacle density are calculated; The ground vibration spectrum is collected through MEMS inertial sensors (accelerometer + gyroscope), and ground cracks / depressions are analyzed by combining image recognition; Deploy temperature and humidity sensors, barometers, and optical rain gauges to dynamically monitor meteorological conditions.
7. A risk assessment system, characterized in that: include: The environmental benchmark value dynamic library stores the terrain environment, flatness and benchmark parameter ranges of environmental parameters in different scenarios, as well as thresholds of different environmental benchmark parameters.
8. A risk assessment device, characterized in that: include: Terrain environment module, used to collect terrain environment data; Flatness detection module, used to collect flatness data; Environmental parameter module, used to collect environmental parameter data.
9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.