Driving safety risk processing method and system based on natural disaster early warning

Abstract

The invention discloses a driving safety risk processing method and system based on natural disaster early warning, and the method comprises the steps: obtaining real-time natural disaster early warning information, carrying out the risk assessment based on a preset rule and a preset algorithm according to the natural disaster early warning information, and dividing the disaster grade according to the preset rule; intelligent decision making is carried out according to different disaster levels corresponding to evaluation results, and different early warning notifications are adopted; the driving state of the current vehicle is detected, corresponding different intelligent decisions are made according to the different driving states of the current vehicle in combination with the driving behaviors and the position information, and a corresponding mapping relation is established between the output result of the intelligent decisions and a preset rule. According to the method, disaster-causing risk subareas of multiple natural disasters are formed through an automatic assessment analysis technology, classification and grading are carried out, the accuracy and reliability of risk assessment are improved, disaster risk levels of different areas can be identified and defined more accurately, and a scientific basis is provided for disaster management and emergency response.

Description

Technical Field

[0001] The present invention relates to a driving safety risk processing method and system, and in particular to a driving safety risk processing method and system based on natural disaster early warning. Background Art

[0002] Natural disaster warning mainly relies on data cooperation between specific departments. Due to the data storage in various departments and the data security and confidentiality restrictions, it takes a long time for departments to communicate and discuss to exchange data, and the data structure of each data source platform is inconsistent, resulting in high warning costs and poor timeliness. Especially during holidays or golden weeks, in order to avoid congestion, many people choose to travel at night, and the poor vision leads to the inability to emergency brake at the accident section, resulting in traffic accidents, resulting in life safety and economic losses, and indirectly causing a series of problems such as environmental pollution. It can no longer meet people's requirements and urgently needs to be improved. Summary of the invention

[0003] The purpose of the present invention is to provide a driving safety risk management method and system based on natural disaster warning, to provide vehicle drivers with natural disaster warnings in advance, to reduce the driving risks of vehicle owners, and to solve the shortcomings of the prior art.

[0004] The present invention provides the following scheme:

[0005] A driving safety risk processing method based on natural disaster early warning is applied to a driving safety risk processing system based on natural disaster early warning, comprising:

[0006] Acquire real-time natural disaster warning information, perform risk assessment based on preset rules and preset algorithms according to the natural disaster warning information, and generate assessment results, wherein the preset rules include probability areas and hit areas, and classify disaster levels according to the preset rules;

[0007] Make intelligent decisions based on the disaster levels corresponding to the assessment results, and use different notification methods to provide early warning notifications on the vehicle side and / or cloud side and / or mobile side;

[0008] Detect the current driving status of the vehicle, and make corresponding intelligent decisions based on the current driving status of the vehicle, combined with driving behavior and location information;

[0009] The output result of the intelligent decision is matched with the preset rules, the matching status of the output result and the preset rules is detected, and a corresponding mapping relationship is established between the output result of the intelligent decision and the preset rules.

[0010] Furthermore, the probability regions are specifically different regions divided according to the probability of natural disasters, and the risk level is determined based on the probability of quantified disasters;

[0011] The hit area is specifically determined based on the result of the probability area assessment to determine the severity level of the impact of the disaster on a specific area.

[0012] Furthermore, the risk level is determined based on the probability of quantified disaster occurrence, specifically: the risk level is divided into: a low probability area with a disaster probability of 0-20%, a second low probability area with a disaster probability of 20%-50%, a medium probability area with a disaster probability of 50%-75%, and a high probability area with a disaster probability greater than 75%.

[0013] Furthermore, according to the risk level of the probability area, the severity level of the hit area is determined, specifically: general severity level, important severity level, urgent severity level and critical severity level.

[0014] Furthermore, the intelligent decision-making according to the different disaster levels corresponding to the evaluation results and the use of different notification methods to provide early warning notifications on the vehicle side and / or the cloud side and / or the mobile side further include:

[0015] Obtain natural disaster investigation and assessment data and disaster-prone body investigation and assessment data, pre-process the acquired data, and generate structured data;

[0016] Gridding the structured data to obtain dual discrete data in the time domain and the space domain;

[0017] The double discrete data are calculated using spatial analysis and clustering algorithms to obtain a disaster-prone body risk area, and the disaster-prone body risk area corresponds to the driving route of the vehicle.

[0018] Furthermore, the double discrete data is calculated using spatial analysis and clustering algorithms to obtain a disaster-prone body risk area, and the disaster-prone body risk area corresponds to the vehicle's driving route, further comprising:

[0019] The obtaining of natural disaster investigation and assessment data and disaster-prone body investigation and assessment data, preprocessing the obtained data, and generating structured data, specifically, obtaining the original data of the natural disaster investigation and assessment data and disaster-prone body investigation and assessment data through road-side sensors, satellite remote sensing, geographic information systems or on-site investigations;

[0020] The risk area of ​​the disaster-prone body corresponds to the driving route of the vehicle, and further includes: performing cluster analysis on the double discrete data based on the K-means algorithm to generate a risk index and form a risk feature clustering result corresponding to the risk index;

[0021] According to the standard deviation and Euclidean distance of the risk feature clustering results, the clustering effect and the risk area of ​​the disaster-prone body are dynamically adjusted.

[0022] A driving safety risk processing system based on natural disaster warning is used to implement the driving safety risk processing method based on natural disaster warning, comprising:

[0023] A probability region / hit region generation module is used to obtain real-time natural disaster warning information, and to perform risk assessment based on preset rules and preset algorithms according to the natural disaster warning information to generate assessment results. The preset rules include probability regions and hit regions, and the disaster levels are divided according to the preset rules.

[0024] Intelligent decision-making module based on disaster level, making intelligent decisions according to different disaster levels corresponding to the assessment results, and using different notification methods to provide early warning notifications on the vehicle side and / or cloud side and / or mobile side;

[0025] The intelligent decision-making module based on driving behavior and location information detects the current driving status of the vehicle and makes corresponding intelligent decisions based on the driving behavior and location information according to the current driving status of the vehicle;

[0026] The module for establishing a mapping relationship between the output result and the preset rule matches the output result of the intelligent decision with the preset rule, detects the matching status of the output result and the preset rule, and establishes a corresponding mapping relationship between the output result of the intelligent decision and the preset rule.

[0027] A smart cockpit is provided with the driving safety risk processing system based on natural disaster warning, and executes the driving safety risk processing method based on natural disaster warning.

[0028] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method.

[0029] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] The present invention adopts automated data collection and preprocessing steps to improve the data quality and availability of natural disaster risk assessment, and realizes efficient management of large-scale spatial data through grid processing through unified spatial resolution and parallel computing technology, thereby shortening data processing time and improving processing capabilities.

[0032] The present invention forms disaster risk zones for multiple types of natural disasters through automated assessment and analysis technology, and classifies and grades them, thereby improving the accuracy and reliability of risk assessment, being able to more accurately identify and define disaster risk levels in different regions, and providing a scientific basis for disaster management and emergency response.

[0033] The present invention uses spatial analysis and clustering algorithms to divide specific risk areas of hazard-prone bodies, providing more accurate risk assessment results of natural disaster-prone bodies. This method helps to identify and assess the risks of key infrastructure and densely populated areas, thereby more effectively allocating resources and implementing disaster prevention measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 It is a flow chart of the driving safety risk management method based on natural disaster warning.

[0036] Figure 1A It is a flow chart of the optimization technical solution of step S2.

[0037] Figure 1B It is a flow chart of the optimization technical solution of step S23.

[0038] Figure 2 This is the architecture diagram of the driving safety risk management system based on natural disaster warning.

[0039] Figure 3 It is an architectural diagram of an implementation method of an embodiment of the present invention in a specific application scenario.

[0040] Figure 4 It is a flow chart of an implementation method of an embodiment of the present invention in a specific application scenario.

[0041] Figure 5 It is a structural diagram of an electronic device. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 The driving safety risk processing method based on natural disaster warning is applied to the driving safety risk processing system based on natural disaster warning, including:

[0044] Step S1, obtain real-time natural disaster warning information, and perform risk assessment based on preset rules and preset algorithms according to the natural disaster warning information to generate assessment results. The preset rules include probability areas and hit areas, and the disaster levels are divided according to the preset rules. In this step, obtaining real-time natural disaster warning information can be achieved through public data provided by the Natural Disaster Warning Center.

[0045] Step S2, making intelligent decisions according to different disaster levels corresponding to the assessment results, and using different notification methods to provide early warning notifications on the vehicle side and / or the cloud side and / or the mobile side;

[0046] Step S3, detecting the current driving state of the vehicle, and making corresponding different intelligent decisions according to the different driving states of the current vehicle in combination with driving behavior and location information; illustratively, the intelligent decision includes risk assessment and notification.

[0047] Step S4, matching the output result of the intelligent decision with the preset rule, detecting the matching status of the output result with the preset rule, and establishing a corresponding mapping relationship between the output result of the intelligent decision and the preset rule.

[0048] Steps S1 to S3 provide an intelligent natural disaster warning and driving safety decision-making method, which obtains natural disaster warning information in real time, uses preset rules and algorithms to conduct risk assessment, and adopts corresponding warning notification strategies based on the assessment results. It can also make personalized intelligent decisions based on the vehicle's driving status and driving behavior to improve driving safety.

[0049] The technical solutions provided in steps S1 to S3 can obtain natural disaster information in a timely manner and quickly conduct risk assessment to ensure the timeliness and accuracy of warning information. According to different disaster levels, the system can intelligently select the most appropriate notification method, such as vehicle side, cloud side or mobile side, to ensure that the information can be conveyed to the car owner in a timely and effective manner, realize intelligent warning notification, and combine the real-time driving status and driving behavior of the vehicle to provide personalized risk assessment and warning to enhance driving safety.

[0050] The technical solution provided by steps S1 to S3 can establish a clear decision-making logic by matching the output results of intelligent decision-making with preset rules, improve the response speed and processing capability of the early warning system, and significantly improve the driver's safety protection capabilities in natural disasters through intelligent early warning and decision support, reduce the risk of accidents, achieve effective early warning of natural disasters and real-time protection of driving safety, and improve the efficiency and effectiveness of responding to natural disasters.

[0051] Preferably, the probability areas are specifically different areas divided according to the probability of natural disasters, and the risk level is determined based on the probability of quantified disasters;

[0052] The hit area is specifically determined based on the result of the probability area assessment to determine the severity level of the impact of the disaster on a specific area.

[0053] Exemplarily, the risk level is determined based on the probability of quantifying a disaster, specifically: the risk level is divided into: a low probability area with a disaster probability of 0-20%, a second low probability area with a disaster probability of 20%-50%, a medium probability area with a disaster probability of 50%-75%, and a high probability area with a disaster probability greater than 75%.

[0054] Exemplarily, according to the risk level of the probability area, the severity level of the hit area is determined, specifically: general severity level, important severity level, urgent severity level and critical severity level.

[0055] By determining the risk level based on the quantified probability of disaster occurrence and the severity level of the disaster's impact on a specific area, the probability of disaster occurrence is divided into different areas (probability areas), and the severity level of the impact on a specific area (hit area) is determined based on the results of the assessment of these areas. This achieves a structured and quantitative means to assess and manage the risks brought by natural disasters, and can more accurately assess the risk levels of different areas, thereby providing a scientific basis for decision-making.

[0056] Dividing risk levels into low probability areas, second-low probability areas, medium probability areas and high probability areas will help clarify the risk conditions in different areas, provide guidance for resource allocation and preventive measures, and formulate more targeted disaster management strategies and emergency response plans based on the risk levels of different areas, thereby improving the efficiency and effectiveness of disaster response.

[0057] like Figure 1A As shown, in the optimization technical solution of step S2, the intelligent decision-making is performed according to the different disaster levels corresponding to the evaluation results, and different notification methods are used to perform early warning notifications on the vehicle side and / or the cloud side and / or the mobile side, further including:

[0058] Step S21, obtaining natural disaster investigation and assessment data and disaster-affected body investigation and assessment data, preprocessing the obtained data, and generating structured data;

[0059] Step S22, gridding the structured data to obtain dual discrete data in the time domain and the space domain;

[0060] Step S23, using spatial analysis and clustering algorithms to calculate the double discrete data to obtain a disaster-prone body risk area, wherein the disaster-prone body risk area corresponds to the vehicle's driving route.

[0061] Steps S21 to S23 provide an integrated natural disaster risk assessment process, which obtains and pre-processes natural disaster investigation and assessment data and disaster-prone body investigation and assessment data, and then grids these data, and finally uses spatial analysis and clustering algorithms to calculate the disaster-prone body risk area corresponding to the vehicle's driving route. The processing flow from step S21 to step S23 realizes an accurate assessment of the risk of a specific area under the influence of natural disasters, so as to provide decision support for vehicle driving safety.

[0062] The optimization technical solution provided in steps S21 to S23 generates structured data through preprocessing, which improves the readability and usability of the data, lays a solid foundation for subsequent analysis and processing, and performs dual discrete processing of the data in the time domain and the space domain, so that the risk assessment can consider both time and space factors, improves the accuracy and detail of the assessment, and makes full use of spatial analysis and clustering algorithms to accurately identify the risk areas of the disaster-prone bodies corresponding to the vehicle's travel routes, which helps to formulate targeted driving safety strategies.

[0063] In some embodiments, the optimization technical solution provided in steps S21 to S23 provides driving safety decision support. By identifying risk areas, real-time driving safety suggestions can be provided to the vehicle, such as route adjustment, speed control, etc., thereby reducing driving risks, improving driving safety, and improving disaster response efficiency. It provides technical support for rapid response when a disaster occurs, enabling relevant departments to quickly identify affected areas and take rescue and evacuation measures in a timely manner.

[0064] like Figure 1B As shown, illustratively, in step S23, the dual discrete data is calculated using spatial analysis and clustering algorithms to obtain a disaster-prone body risk area, and the disaster-prone body risk area corresponds to the vehicle's driving route, further comprising:

[0065] Step S231, obtaining natural disaster investigation and assessment data and disaster-prone body investigation and assessment data, preprocessing the obtained data, and generating structured data, specifically: obtaining the original data of the natural disaster investigation and assessment data and the disaster-prone body investigation and assessment data through roadside sensors, satellite remote sensing, geographic information system (GIS) or on-site investigation;

[0066] Step S232, the risk area of ​​the disaster-prone body corresponds to the driving route of the vehicle, further comprising: performing cluster analysis on the double discrete data based on a K-means algorithm to generate a risk index, and forming a risk feature clustering result corresponding to the risk index;

[0067] Step S233, dynamically adjust the clustering effect and the risk area of ​​the disaster-prone body according to the standard deviation and Euclidean distance of the risk feature clustering results.

[0068] The optimization technical solution provided in step S231 to step S233 describes the process of evaluating and managing natural disaster risks by comprehensively using spatial analysis and clustering algorithms, which involves obtaining and preprocessing natural disaster and disaster-prone body data, generating structured data through double discretization, and using K-means algorithm for clustering analysis to identify risk areas. In addition, the accuracy and real-time performance of risk assessment are ensured by dynamically adjusting the clustering effect, so that the risk area of ​​the disaster-prone body matches the vehicle driving route, providing decision support for vehicle driving safety.

[0069] The optimization technical solution provided in steps S231 to S233 integrates the original data through a variety of data acquisition methods (exemplarily, such as sensors, remote sensing, GIS, and field surveys), and performs preprocessing to generate structured data, providing a high-quality data foundation for subsequent analysis, and performing dual discretization processing on the data in the time domain and the spatial domain, thereby improving the accuracy and efficiency of data processing and making risk assessment more detailed and specific.

[0070] The optimization technical solution provided in steps S231 to S233 uses the K-means algorithm to perform cluster analysis on dual discrete data, generates risk index and risk feature clustering results, effectively identifies and distinguishes areas with different risk levels, dynamically adjusts the clustering effect according to the standard deviation and Euclidean distance of the clustering results, optimizes the division of risk areas, improves the accuracy and adaptability of risk assessment, and corresponds the risk area of ​​the disaster-prone body to the vehicle's driving route, providing customized risk assessment and warning for the vehicle, enhancing driving safety, and ultimately achieving decision support, providing real-time risk assessment and warning information for vehicle driving, and helping drivers and relevant management departments make more scientific and safe decisions.

[0071] The optimization technical solution provided in steps S231 to S233 involves spatial analysis and dual discrete data, wherein:

[0072] Spatial analysis is a core technology in geographic information systems (GIS) that involves the analysis of geospatial data to reveal relationships, patterns, and trends among the data. Spatial analysis can include the following types:

[0073] 1. Descriptive analysis: describes the spatial distribution characteristics of data, such as central tendency, degree of dispersion, etc.

[0074] 2. Spatial relationship analysis: study the relationship between different geographic elements, such as proximity, connectivity and inclusion.

[0075] 3. Pattern recognition: Identifying recurring patterns in spatial data, such as hotspot analysis or cluster detection.

[0076] 4. Spatial process simulation: simulate and predict the development process of spatial phenomena, such as flood spread, disease transmission, etc.

[0077] 5. Spatial statistical analysis: Apply statistical methods to analyze spatial data and evaluate the relationship between spatial variables.

[0078] Spatial analysis is used to gain a deep understanding and interpretation of geospatial data to support decision making and problem solving.

[0079] Doubly discrete data:

[0080] Double discrete data refers to data that is discretized in both time and space. In geographic information systems and related fields, the following can be achieved:

[0081] 1. Time Discretization: The continuous time series is divided into a series of time intervals or time periods, each of which represents an independent unit of analysis.

[0082] 2. Spatial discretization: The continuous geographic space is divided into a series of grids or cells, each grid represents a specific geographic area and can contain data within that area.

[0083] Double discretization allows for separate analysis and processing of data within each temporal and spatial unit, which is particularly useful when dealing with large-scale or complex spatiotemporal data. For example, in the fields of environmental monitoring, traffic management, disaster warning, etc., double discretization data can provide a more detailed and specific understanding of the phenomenon.

[0084] For the method steps disclosed in the above embodiments, the method steps are described as a series of action combinations for the purpose of simple description, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0085] Any process or method description described in a flowchart or other manner may be understood as representing a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations, in which the order shown or discussed may not be followed, including executing and implementing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved, or executing computer instructions and implementing the corresponding functions according to program structures such as loops and branches, which can be naturally understood by those skilled in the art when implementing the embodiments of the present invention.

[0086] like Figure 2 The driving safety risk processing system based on natural disaster warning is used to implement the driving safety risk processing method based on natural disaster warning, including:

[0087] A probability region / hit region generation module is used to obtain real-time natural disaster warning information, and to perform risk assessment based on preset rules and preset algorithms according to the natural disaster warning information to generate assessment results. The preset rules include probability regions and hit regions, and the disaster levels are divided according to the preset rules.

[0088] Intelligent decision-making module based on disaster level, making intelligent decisions according to different disaster levels corresponding to the assessment results, and using different notification methods to provide early warning notifications on the vehicle side and / or cloud side and / or mobile side;

[0089] The intelligent decision-making module based on driving behavior and location information detects the current driving status of the vehicle and makes corresponding intelligent decisions based on the driving behavior and location information according to the current driving status of the vehicle;

[0090] The module for establishing a mapping relationship between the output result and the preset rule matches the output result of the intelligent decision with the preset rule, detects the matching status of the output result and the preset rule, and establishes a corresponding mapping relationship between the output result of the intelligent decision and the preset rule.

[0091] The implementation methods of the system described above are merely illustrative, for example, the various functional modules, units or subsystems in the system may or may not be physically separated, or may or may not be physical units, that is, they may be located in the same place, or they may be distributed to multiple different systems and their subsystems or modules. Those skilled in the art may select some or all of the functional modules, units or subsystems according to actual needs to achieve the purpose of the embodiments of the present invention, and those of ordinary skill in the art may understand and implement the above-mentioned situations without creative work.

[0092] like Figure 3 The embodiment of the present invention shown in the figure is implemented in a specific application scenario. This embodiment discloses a method flow of a disaster warning system, which can issue a warning to the car owner in time to prevent or reduce the impact that may be caused by natural disasters. The method flow includes:

[0093] 1. Acquisition of early warning information: First, obtain real-time natural disaster early warning information through the natural disaster early warning center. The natural disaster information includes but is not limited to predictions and warnings of natural disasters such as earthquakes, floods, and typhoons.

[0094] 2. Warning rule matching: Once the warning information is obtained, it is analyzed according to the preset warning rules to determine when and how to issue a warning to the car owner.

[0095] 3. Rule judgment: After matching the warning information with rules, a judgment will be made to determine whether it is necessary to issue a warning to the car owner. The content of the warning includes an assessment of factors such as the severity of the disaster, the scope of impact and the possibility.

[0096] 4. Warning notification: If the rule judgment result indicates that a warning is needed, the system will trigger a warning notification through the integrated messaging system. The notification can be made through a variety of channels, including but not limited to phone calls, APP push, and vehicle built-in systems.

[0097] 5. User receives warning: After receiving the warning information through phone, APP or vehicle built-in system, the car owner can take corresponding preventive measures according to the content of the warning, such as changing the driving route, parking to avoid danger, etc.

[0098] like Figure 4 The embodiment of the present invention shown is a reminder notification system based on natural disaster warning, and the system architecture includes the following key components: Natural Disaster Warning Center: responsible for collecting and analyzing natural disaster data and generating warning information.

[0099] Rule Scheduling Processing Center: Contains the warning rule database, which is used to store and manage warning rules.

[0100] Message center: responsible for judging the results according to the rules and sending early warning notifications to car owners through appropriate communication channels.

[0101] The workflow of the above key components is as follows:

[0102] The natural disaster warning center obtains warning messages and makes judgments based on pre-set disaster warning rules. If conditions are met, the message system is called to notify users via phone, APP, and vehicle.

[0103] It also includes: the interaction between the cloud and the vehicle, including the acquisition and return of warning information, triggering notification center warnings, and implementing rule scheduling processing and message notifications.

[0104] The natural disaster warning system of the embodiment of the present invention can provide natural disaster warnings to vehicle owners in a timely and effective manner, thereby improving driving safety and reducing losses that may be caused by natural disasters.

[0105] like Figure 5 As shown, the present invention not only provides a driving safety risk processing method and system based on natural disaster warning, but also provides a corresponding smart cockpit, electronic equipment and storage medium:

[0106] A smart cockpit is provided with the driving safety risk processing system based on natural disaster warning, and executes the driving safety risk processing method based on natural disaster warning.

[0107] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method.

[0108] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method.

[0109] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 5As shown, the device 800 includes a processor 801, a memory 802, a communication interface 803 and a bus 804. The processor 801, the memory 802 and the communication interface 803 communicate through the bus 804, and can also communicate through other means such as wireless transmission. The memory 802 is used to store instructions, and the processor 801 is used to execute the instructions stored in the memory 802. The memory 802 stores program code 8021, and the processor 801 can call the program code 8021 stored in the memory 802 to execute the steps of the driving safety risk processing method based on natural disaster warning.

[0110] It should be understood that in the embodiment of the present application, the processor 801 may be a CPU, and the processor 801 may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) 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.

[0111] The memory 802 may include a read-only memory ROM and a random access memory RAM, and provides instructions and data to the processor 801. The memory 802 may also include a non-volatile random access memory. The memory 802 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (FRAM).

[0112] RAM, SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory

[0113] (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (doubledate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synchlink DRAM, SLDRAM) and direct memory bus random access memory (direct rambus RAM, DR RAM).

[0114] The bus 804 may include, in addition to the data bus, a power bus, a control bus, a status signal bus, etc. However, for the sake of clarity, various buses are labeled as bus 804 in the figure.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid state drive (SSD).

[0116] Case No.: FAW041125-C-XCN1240542 15

[0117] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, any one of the embodiments claimed in the claims can be used in any combination in the embodiments of the present invention.

[0119] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means 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 invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0120] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0121] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any way. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features. Throughout the specification, the same reference numerals indicate the same elements.

[0122] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including corresponding claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise explicitly stated, each feature disclosed in this specification (including corresponding claims, abstracts and drawings) can be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A driving safety risk processing method based on natural disaster early warning, applied to a driving safety risk processing system based on natural disaster early warning, characterized in that: include: Acquire real-time natural disaster warning information, perform risk assessment based on preset rules and preset algorithms according to the natural disaster warning information, and generate assessment results, wherein the preset rules include probability areas and hit areas, and classify disaster levels according to the preset rules; Make intelligent decisions based on the disaster levels corresponding to the assessment results, and use different notification methods to provide early warning notifications on the vehicle side and / or cloud side and / or mobile side; Detect the current driving status of the vehicle, and make corresponding intelligent decisions based on the current driving status of the vehicle, combined with driving behavior and location information; The output result of the intelligent decision is matched with the preset rules, the matching status of the output result and the preset rules is detected, and a corresponding mapping relationship is established between the output result of the intelligent decision and the preset rules.

2. The driving safety risk processing method based on natural disaster warning according to claim 1 is characterized in that: The probability areas are specifically different areas divided according to the probability of natural disasters, and the risk level is determined based on the probability of quantified disasters; The hit area is specifically determined based on the result of the probability area assessment to determine the severity level of the impact of the disaster on a specific area.

3. The driving safety risk processing method based on natural disaster warning according to claim 1 is characterized in that: The risk level is determined based on the probability of quantified disaster occurrence, specifically: the risk level is divided into: a low probability area with a disaster probability of 0-20%, a second low probability area with a disaster probability of 20%-50%, a medium probability area with a disaster probability of 50%-75%, and a high probability area with a disaster probability greater than 75%.

4. The driving safety risk processing method based on natural disaster warning according to claim 3 is characterized in that: According to the risk level of the probability area, the severity level of the hit area is determined, specifically: general severity level, important severity level, urgent severity level and critical severity level.

5. The driving safety risk processing method based on natural disaster warning according to claim 1 is characterized in that: The intelligent decision-making according to the different disaster levels corresponding to the evaluation results and the use of different notification methods to provide early warning notifications on the vehicle side and / or the cloud side and / or the mobile side further include: Obtain natural disaster investigation and assessment data and disaster-prone body investigation and assessment data, pre-process the acquired data, and generate structured data; Gridding the structured data to obtain dual discrete data in the time domain and the space domain; The double discrete data are calculated using spatial analysis and clustering algorithms to obtain a disaster-prone body risk area, and the disaster-prone body risk area corresponds to the driving route of the vehicle.

6. The driving safety risk processing method based on natural disaster warning according to claim 5 is characterized in that: The double discrete data is calculated using spatial analysis and clustering algorithms to obtain a disaster-prone body risk area, wherein the disaster-prone body risk area corresponds to the vehicle's driving route, further comprising: The obtaining of natural disaster investigation and assessment data and disaster-prone body investigation and assessment data, preprocessing the obtained data, and generating structured data, specifically, obtaining the original data of the natural disaster investigation and assessment data and disaster-prone body investigation and assessment data through road-side sensors, satellite remote sensing, geographic information systems or on-site investigations; The risk area of ​​the disaster-prone body corresponds to the driving route of the vehicle, and further includes: performing cluster analysis on the double discrete data based on the K-means algorithm to generate a risk index and form a risk feature clustering result corresponding to the risk index; According to the standard deviation and Euclidean distance of the risk feature clustering results, the clustering effect and the risk area of ​​the disaster-prone body are dynamically adjusted.

7. A driving safety risk processing system based on natural disaster warning, used to implement the driving safety risk processing method based on natural disaster warning according to any one of claims 1 to 6, characterized in that: include: A probability region / hit region generation module is used to obtain real-time natural disaster warning information, and to perform risk assessment based on preset rules and preset algorithms according to the natural disaster warning information to generate assessment results. The preset rules include probability regions and hit regions, and the disaster levels are divided according to the preset rules. Intelligent decision-making module based on disaster level, making intelligent decisions according to different disaster levels corresponding to the assessment results, and using different notification methods to provide early warning notifications on the vehicle side and / or cloud side and / or mobile side; The intelligent decision-making module based on driving behavior and location information detects the current driving status of the vehicle and makes corresponding intelligent decisions based on the driving behavior and location information according to the current driving status of the vehicle; The module for establishing a mapping relationship between the output result and the preset rule matches the output result of the intelligent decision with the preset rule, detects the matching status of the output result and the preset rule, and establishes a corresponding mapping relationship between the output result of the intelligent decision and the preset rule.

8. A smart cockpit, characterized in that: The smart cockpit is provided with a driving safety risk processing system based on natural disaster warning as described in claim 7, and executes a driving safety risk processing method based on natural disaster warning as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: It stores a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the method described in any one of claims 1 to 6.

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