Safety protection method, device and equipment for bridge inspection vehicle and storage medium

Abstract

The invention provides a safety protection method, device and equipment for a bridge inspection vehicle and a storage medium, and relates to the technical field of bridge detection.The safety protection method for the bridge inspection vehicle comprises the steps that environment point cloud data and environment parameter data around the bridge inspection vehicle are obtained in real time, and time and space synchronization processing is carried out; the environment point cloud data and the environment parameter data after time-space synchronization processing are preprocessed, the environment point cloud data are analyzed through a clustering algorithm, obstacle feature information on the advancing path of the bridge inspection vehicle is recognized, the environment parameter data are analyzed, and environment condition information of the bridge inspection vehicle on the advancing path is determined; and combining the detected obstacle feature information with the environmental condition analysis information, carrying out comprehensive risk assessment, and adjusting the inspection path and operation parameters of the bridge inspection vehicle in real time according to a comprehensive assessment result. The safety and inspection efficiency of the bridge inspection vehicle in a complex environment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge detection, and in particular to a safety protection method, device, equipment and storage medium for a bridge inspection vehicle. Background Art

[0002] With the continuous increase in bridge infrastructure, regular safety inspections of bridges have become a key link in maintaining public safety. Traditional bridge inspection methods mainly rely on manual operations, which are inefficient, high-risk, and costly. In recent years, the application of automated bridge inspection vehicles has gradually become popular. For example, real-time monitoring of the disease conditions of bridge structures is achieved through automation technology. However, when operating in a complex bridge environment, inspection vehicles often face various unforeseen obstacles, such as temporary obstacles such as falling objects on the bridge deck and moving organisms. In addition, on high-altitude bridges, the influence of harsh conditions such as strong winds, high temperatures, and severe vibrations can also seriously threaten the stability and safety of inspection vehicles. Therefore, how to improve the obstacle avoidance ability and abnormal environment adaptability of bridge inspection vehicles during the inspection process has become a difficult problem that needs to be solved urgently.

[0003] In the existing technology, some automatic inspection vehicles are equipped with basic obstacle avoidance functions, such as using simple sensors to sense obstacles ahead, but these technologies usually only have the ability to sense in a single direction, and the accuracy of obstacle recognition in complex environments is low. In addition, the protection measures against the impact of harsh environments have not been fully considered, which often results in the inspection vehicle being unable to work normally in harsh environmental conditions, or even causing danger. Summary of the invention

[0004] The problem solved by the present invention is how to improve the safety and inspection efficiency of bridge inspection vehicles in complex environments.

[0005] In a first aspect, the present invention provides a safety protection method for a bridge inspection vehicle, comprising:

[0006] Acquire environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time and space synchronization processing on the environmental point cloud data and the environmental parameter data;

[0007] Preprocessing the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyzing the preprocessed environmental point cloud data by a clustering algorithm to identify obstacle feature information on the path of the bridge inspection vehicle, and analyzing the preprocessed environmental parameter data to determine environmental condition information on the path of the bridge inspection vehicle;

[0008] The detected obstacle feature information and the environmental condition analysis information are combined to perform a comprehensive risk assessment, and the inspection path and operating parameters of the bridge inspection vehicle are adjusted in real time according to the comprehensive assessment results.

[0009] Optionally, the environmental point cloud data includes three-dimensional spatial coordinates, reflection intensity and a first timestamp, the environmental parameter data includes wind speed, wind direction, temperature and humidity, bridge vibration data and a second timestamp, and the time-space synchronization processing of the environmental point cloud data and the environmental parameter data includes:

[0010] Based on a multi-sensor fusion algorithm, the environmental point cloud data and the environmental parameter data are subjected to time synchronization and spatial alignment processing to obtain target fusion data.

[0011] Optionally, the performing time synchronization and spatial alignment processing on the environmental point cloud data and the environmental parameter data based on a multi-sensor fusion algorithm includes:

[0012] Performing timestamp alignment processing on the environmental point cloud data and the environmental parameter data based on the first timestamp and the second timestamp, and generating a time synchronization data set, wherein the time synchronization data set includes the environmental point cloud data and the environmental parameter data collected at the same time;

[0013] Performing coordinate synchronization processing on the environmental point cloud data and the environmental parameter data in each of the time data sets using a coordinate transformation matrix, and generating a time coordinate synchronization data set, wherein the time coordinate synchronization data set includes the environmental point cloud data and the environmental parameter data collected at the same time and in the same global coordinate system;

[0014] The spatial coordinate synchronization data set is spatially aligned using an iterative closest point algorithm to generate a time-space synchronization data set, and the time-space synchronization data set is fused using a Kalman filter fusion algorithm to obtain target fused data.

[0015] Optionally, the environmental point cloud data and the environmental parameter data after the time-space synchronization processing are preprocessed, the preprocessed environmental point cloud data are analyzed by a clustering algorithm, obstacle feature information on the path of the bridge inspection vehicle is identified, and the preprocessed environmental parameter data are analyzed to determine the environmental condition information on the path of the bridge inspection vehicle, including:

[0016] Preprocessing the environmental point cloud data and the environmental parameter data after time-space synchronization processing, wherein the preprocessing includes denoising, downsampling and normal vector processing;

[0017] Clustering the preprocessed environmental point cloud data using a spatial clustering algorithm to obtain obstacle feature information, wherein the obstacle feature information includes obstacle size information, obstacle shape information, and obstacle position information;

[0018] The pre-processed environmental parameter data and the travel path of the bridge inspection vehicle are spatially correlated to extract environmental influencing factors and potential risk areas at different locations and time periods to obtain environmental condition information.

[0019] Optionally, the pre-processed environmental parameter data is spatially associated with the travel path of the bridge inspection vehicle, and environmental influencing factors and potential risk areas in different travel path segments and time periods are extracted to obtain environmental condition information including:

[0020] Associating the preprocessed environmental parameter data with the time nodes on the travel path to generate environmental influencing factors corresponding to the travel path segment and the time period;

[0021] The environmental impact parameters are analyzed, potential risk areas on the travel path segment are identified and marked, and risk assessment is performed on the potential risk areas to obtain environmental condition information.

[0022] Optionally, the environmental condition information includes environmental influencing factors, the potential risk area and the risk level.

[0023] Optionally, the step of combining the detected obstacle feature information with the environmental condition analysis information to conduct a comprehensive risk assessment, and adjusting the inspection path and operating parameters of the bridge inspection vehicle in real time according to the comprehensive assessment result includes:

[0024] The obstacle feature information and the environmental condition analysis information are integrated to obtain risk integration information, and the risk integration information is calculated using a weighted evaluation algorithm to obtain a comprehensive risk index on each travel path segment;

[0025] The comprehensive risk index is judged to obtain a comprehensive evaluation result, wherein the comprehensive evaluation result includes risk level classification information of the travel path segment;

[0026] The inspection path and operating parameters of the bridge inspection vehicle are adjusted in real time based on the comprehensive evaluation results.

[0027] In a second aspect, the present invention provides a safety protection device for a bridge inspection vehicle, comprising:

[0028] A data acquisition module is used to acquire the environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time and space synchronization processing on the environmental point cloud data and the environmental parameter data;

[0029] A data analysis module, used to pre-process the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyze the pre-processed environmental point cloud data through a clustering algorithm, identify the characteristic information of obstacles on the path of the bridge inspection vehicle, and analyze the pre-processed environmental parameter data to determine the environmental condition information on the path of the bridge inspection vehicle;

[0030] The control module is used to combine the detected obstacle feature information with the environmental condition analysis information to perform a comprehensive risk assessment, and adjust the inspection path and operating parameters of the bridge inspection vehicle in real time according to the comprehensive assessment results.

[0031] In a third aspect, the present invention provides an electronic device, including a memory and a processor;

[0032] The memory is used to store computer programs;

[0033] The processor is used to implement the safety protection method for the bridge inspection vehicle as described in the first aspect when executing the computer program.

[0034] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the safety protection method for the bridge inspection vehicle as described in the first aspect is implemented.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: by real-time acquisition and time-space synchronous processing of environmental point cloud data and environmental parameter data around the bridge inspection vehicle, the temporal and spatial consistency of the data can be ensured, and the perception accuracy of the bridge inspection vehicle to the surrounding environment can be enhanced; by using a clustering algorithm to analyze the pre-processed environmental point cloud data, the characteristic information of obstacles on the path of the bridge inspection vehicle can be effectively identified; by analyzing the environmental parameter data, the environmental condition information on the path of the inspection vehicle can be determined in real time, so that the inspection vehicle can dynamically adapt to different environmental changes and ensure the safety of driving; finally, the obstacle characteristic information is combined with the environmental condition analysis information to conduct a comprehensive risk assessment, so that the bridge inspection vehicle can make intelligent decisions in a complex environment, and adjust the inspection path and operating parameters in real time according to the comprehensive evaluation results, thereby improving the safety and efficiency of the inspection task. The present invention can improve the safety and inspection efficiency of bridge inspection vehicles in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of a safety protection method for a bridge inspection vehicle provided by an embodiment of the present invention;

[0037] Figure 2A schematic flow chart of step S12 of a safety protection method for a bridge inspection vehicle provided in one embodiment of the present invention;

[0038] Figure 3 A schematic structural diagram of a safety protection device for a bridge inspection vehicle provided by one embodiment of the present invention;

[0039] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0041] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0042] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0043] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0044] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0045] Reference Figure 1The present invention provides a safety protection method for a bridge inspection vehicle, comprising:

[0046] S11. Acquire environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time-space synchronization processing on the environmental point cloud data and the environmental parameter data.

[0047] Specifically, during the driving process of the bridge inspection vehicle, the environmental point cloud data and environmental parameter data around the bridge inspection vehicle can be obtained in real time through various sensors installed on the inspection vehicle (such as laser radar, shooting components, wind speed sensors, temperature and humidity sensors, etc.). The environmental point cloud data may include the three-dimensional spatial coordinate information and reflection intensity information of the bridge structure, and the environmental parameter data includes wind speed, wind direction, temperature, humidity and other information related to the bridge environment. In order to ensure the consistency and accuracy of the environmental point cloud data and environmental parameter data, the acquired environmental point cloud data and environmental parameter data are processed synchronously in time and space, so that the environmental point cloud data and environmental parameter data can be analyzed and processed under the same time and space reference.

[0048] S12. Preprocess the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyze the preprocessed environmental point cloud data through a clustering algorithm, identify the characteristic information of obstacles on the path of the bridge inspection vehicle, and analyze the preprocessed environmental parameter data to determine the environmental condition information of the bridge inspection vehicle on the path of travel.

[0049] After completing the time-space synchronization processing, the synchronized environmental point cloud data and environmental parameter data are preprocessed. Preprocessing includes denoising, data filtering, downsampling and other operations. Then, a clustering algorithm is used to analyze the preprocessed environmental point cloud data to identify the characteristic information of obstacles on the inspection vehicle's path, such as the location, size, shape, etc. At the same time, the synchronized environmental parameter data is analyzed to determine the environmental condition information on the inspection vehicle's path. The environmental condition information includes environmental factors such as wind speed, wind direction, temperature and humidity, and bridge vibration data that may affect the operation of the inspection vehicle.

[0050] S13. Combining the detected obstacle feature information with the environmental condition analysis information to conduct a comprehensive risk assessment, and adjusting the inspection path and operating parameters of the bridge inspection vehicle in real time according to the comprehensive assessment results.

[0051] Specifically, the detected obstacle feature information and environmental condition analysis information are combined, and then a comprehensive risk assessment is performed. The comprehensive risk assessment calculates the risk index of the inspection vehicle on the current path by comprehensively considering the obstacle characteristics and environmental conditions (such as strong wind areas, abnormal temperature and humidity, vibration, etc.); finally, according to the level of the risk index, the inspection path and operating parameters of the inspection vehicle are adjusted in real time, such as changing the driving speed, adjusting the path to avoid high-risk areas, or optimizing the working mode of the sensor, to ensure that the inspection vehicle performs inspection tasks safely and efficiently in complex environments.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: by real-time acquisition and time-space synchronous processing of environmental point cloud data and environmental parameter data around the bridge inspection vehicle, the temporal and spatial consistency of the data can be ensured, and the perception accuracy of the bridge inspection vehicle to the surrounding environment can be enhanced; by using a clustering algorithm to analyze the pre-processed environmental point cloud data, the characteristic information of obstacles on the path of the bridge inspection vehicle can be effectively identified; by analyzing the environmental parameter data, the environmental condition information on the path of the inspection vehicle can be determined in real time, so that the inspection vehicle can dynamically adapt to different environmental changes and ensure the safety of driving; finally, the obstacle characteristic information is combined with the environmental condition analysis information to perform a comprehensive risk assessment, so that the bridge inspection vehicle can make intelligent decisions in a complex environment, and adjust the inspection path and operating parameters in real time according to the comprehensive evaluation results, thereby improving the safety and efficiency of the inspection task. The present invention can improve the safety and inspection efficiency of bridge inspection vehicles in complex environments.

[0053] In one embodiment, the environmental point cloud data includes three-dimensional spatial coordinates, reflection intensity and a first timestamp, the environmental parameter data includes wind speed, wind direction, temperature and humidity, bridge vibration data and a second timestamp, and the time-space synchronization processing of the environmental point cloud data and the environmental parameter data includes:

[0054] Based on a multi-sensor fusion algorithm, the environmental point cloud data and the environmental parameter data are subjected to time synchronization and spatial alignment processing to obtain target fusion data.

[0055] It should be explained that during the driving process, the bridge inspection vehicle can collect environmental point cloud data and environmental parameter data in real time through various sensors installed on the bridge inspection vehicle. The environmental point cloud data is mainly obtained by laser radar, which contains the three-dimensional spatial coordinate information and reflection intensity information of the bridge structure and the surrounding environment, and is accompanied by the first timestamp of the collection. The environmental parameter data is obtained through equipment such as wind speed sensors, wind direction sensors, temperature and humidity sensors and bridge vibration sensors, which record the wind speed, wind direction, temperature and humidity and bridge vibration conditions, and are accompanied by the second timestamp of the collection.

[0056] This embodiment performs time and space synchronous processing of environmental point cloud data and environmental parameter data, which not only ensures the accuracy and consistency of the data, but also provides bridge inspection vehicles with more reliable environmental perception and decision-making support capabilities in complex bridge environments, thereby improving the safety and operational efficiency of inspection vehicles in actual inspection tasks.

[0057] Furthermore, the time synchronization and spatial alignment processing of the environmental point cloud data and the environmental parameter data based on the multi-sensor fusion algorithm includes:

[0058] The environmental point cloud data and the environmental parameter data are timestamp aligned based on the first timestamp and the second timestamp, and a time synchronization data set is generated. The time synchronization data set includes the environmental point cloud data and the environmental parameter data collected at the same time.

[0059] Specifically, a multi-sensor fusion algorithm can be used to match and align the collected environmental point cloud data and environmental parameter data by timestamp, and adjust the data collected at different times to a unified time base; and generate a time-synchronized data set, which contains the environmental point cloud data and environmental parameter data collected at the same time point, thereby ensuring the time consistency of subsequent analysis and processing.

[0060] The environmental point cloud data and the environmental parameter data in each of the time data sets are subjected to coordinate synchronization processing using a coordinate transformation matrix, and a time coordinate synchronization data set is generated, wherein the time coordinate synchronization data set includes the environmental point cloud data and the environmental parameter data collected at the same time and in the same global coordinate system.

[0061] Furthermore, since various sensors are installed in different positions on the inspection vehicle, the collected environmental parameter data may be in different local coordinate systems. Through the coordinate transformation matrix, these data are uniformly converted to the same global coordinate system to ensure the spatial consistency of all data. Then, a time coordinate synchronization data set is generated, which contains the environmental point cloud data and environmental parameter data collected at the same time point and in the same global coordinate system.

[0062] The spatial coordinate synchronization data set is spatially aligned using an iterative closest point algorithm to generate a time-space synchronization data set, and the time-space synchronization data set is fused using a Kalman filter fusion algorithm to obtain target fused data.

[0063] Specifically, Kalman filtering is a recursive data fusion method that can reduce data noise and enhance data stability and consistency by filtering and fusing different data sources in a time-space synchronized data set. This embodiment obtains target fusion data through Kalman filtering fusion, so that the target fusion data simultaneously includes time-synchronized and space-aligned environmental point cloud data and environmental parameter data, providing a data basis for environmental perception, obstacle recognition and path planning of the inspection vehicle.

[0064] This embodiment realizes the time synchronization and spatial alignment processing of environmental point cloud data and environmental parameter data, ensuring the temporal and spatial consistency and fusion effect of environmental point cloud data and environmental parameter data, and the target fusion data provides strong support for the efficient and safe operation of bridge inspection vehicles in complex environments, which can improve the overall performance and task execution efficiency of the inspection vehicles.

[0065] Reference Figure 2 , the environmental point cloud data and the environmental parameter data after the time-space synchronization processing are preprocessed, the preprocessed environmental point cloud data are analyzed by a clustering algorithm, the obstacle feature information on the path of the bridge inspection vehicle is identified, and the preprocessed environmental parameter data are analyzed to determine the environmental condition information on the path of the bridge inspection vehicle, including:

[0066] S121, preprocessing the environmental point cloud data and the environmental parameter data after time-space synchronization processing, wherein the preprocessing includes denoising, downsampling combing and normal vector processing.

[0067] Specifically, denoising refers to a statistical-based filtering algorithm that denoises the environmental point cloud data to remove isolated points and noise data, thereby improving the purity and reliability of the data; downsampling and combing refers to downsampling the environmental point cloud data through a voxel grid filtering method to reduce the amount of data, thereby reducing the computational complexity, while maintaining the geometric details of the bridge structure and ensuring the accuracy and efficiency of subsequent processing; normal vector processing refers to calculating the normal vector of each environmental point cloud data point, thereby enhancing the surface feature description of the data and improving the accuracy of obstacle recognition in the case of complex surfaces and multi-layer structures.

[0068] S122. Clustering the preprocessed environment point cloud data using a spatial clustering algorithm to obtain obstacle feature information, where the obstacle feature information includes obstacle size information, obstacle shape information, and obstacle position information.

[0069] It needs to be explained that by clustering the pre-processed environmental point cloud data through a clustering algorithm, the volume or area of ​​the obstacle can be identified and estimated; by analyzing the point cloud distribution in the clustering results, the obstacle shape information (such as cylindrical, square, complex shape, etc.) can be identified to achieve further classification processing and path planning; the obstacle position information can be calculated through the specific spatial position of each obstacle on the bridge, including the distance and azimuth relative to the bridge inspection vehicle, so as to achieve accurate identification and positioning of the obstacle.

[0070] S123, spatially correlating the preprocessed environmental parameter data with the travel path of the bridge inspection vehicle, extracting environmental influencing factors and potential risk areas at different locations and time periods, and obtaining environmental condition information.

[0071] Specifically, the environmental parameter data (such as wind speed, wind direction, temperature and humidity, bridge vibration frequency, etc.) are spatially associated with the specific path segments of the bridge inspection vehicle. Based on the results of the spatial association analysis, the environmental factors that have a significant impact on the operation of the bridge inspection vehicle are extracted, such as strong wind environment, high temperature environment and strong vibration environment. Then, the environmental condition information is determined based on the extracted environmental influencing factors.

[0072] This embodiment can provide accurate and reliable decision support for path planning and real-time adjustment of bridge inspection vehicles, ensuring that inspection tasks are completed safely and efficiently in various complex environments.

[0073] Furthermore, the pre-processed environmental parameter data is spatially associated with the path of the bridge inspection vehicle, and environmental influencing factors and potential risk areas in different path segments and time periods are extracted to obtain environmental condition information including:

[0074] The pre-processed environmental parameter data is associated with the time nodes on the travel path to generate environmental influencing factors corresponding to the travel path segment and the time period.

[0075] It can be understood that each environmental parameter data point (such as wind speed, wind direction, temperature and humidity, bridge vibration frequency, etc.) is marked with a corresponding timestamp to match the specific time node on the inspection vehicle's driving path, thereby generating an environmental influencing factor data set corresponding to the travel path segment and time period. This data set includes the corresponding environmental conditions when the bridge inspection vehicle passes through a specific path segment, such as the wind speed, temperature, and vibration conditions at a certain moment, which can ensure the temporal and spatial consistency of environmental influencing factors; then, statistical methods and trend analysis algorithms are used to identify key environmental factors that may have an adverse effect on the operation of the inspection vehicle. Environmental influencing factors may include high wind speed, abnormal temperature and humidity, severe vibration of the bridge, and dense obstacles.

[0076] The environmental impact parameters are analyzed, potential risk areas on the travel path segment are identified and marked, and risk assessment is performed on the potential risk areas to obtain environmental condition information.

[0077] By analyzing environmental impact factors, it is possible to identify and mark the travel path segments that may have high environmental risks within a specific time period; then, risk assessment can be performed on the marked potential risk areas. The assessment process can combine parameters such as the intensity and duration of environmental impact factors and the operating status of bridge inspection vehicles to calculate the risk level of each potential risk area; through risk assessment, detailed environmental condition information can be generated.

[0078] This embodiment can provide information support for bridge inspection vehicles to conduct safe and efficient inspections in complex bridge environments, help adjust the inspection path and operating parameters of the bridge inspection vehicles in real time, and effectively respond to environmental changes and potential risks during the inspection process.

[0079] Specifically, the environmental condition information includes environmental influencing factors, the potential risk area and the risk level.

[0080] Environmental influencing factors include high wind speed, abnormal temperature and humidity, and severe vibration of bridges. Potential risk areas include high wind speed areas, abnormal temperature and humidity areas, and severe vibration of bridges. Risk levels include low-risk areas, medium-risk areas, and high-risk areas.

[0081] In one embodiment, the step of combining the detected obstacle feature information with the environmental condition analysis information to conduct a comprehensive risk assessment, and adjusting the inspection path and operating parameters of the bridge inspection vehicle in real time according to the comprehensive assessment result includes:

[0082] The obstacle feature information and the environmental condition analysis information are fused to obtain risk fusion information, and the risk fusion information is calculated using a weighted evaluation algorithm to obtain a comprehensive risk index on each travel path segment.

[0083] Specifically, the detected obstacle feature information is fused with the environmental condition analysis information to form a comprehensive risk fusion information data set. The obstacle feature information may include the location, size, shape and dynamic characteristics (such as whether it moves) of the obstacle, while the environmental condition analysis information includes wind speed, wind direction, temperature and humidity, vibration and other environmental factors that may affect the operation of the patrol vehicle; then a weighted evaluation algorithm is used to calculate the risk fusion information generated above. Among them, the weighted evaluation algorithm assigns weights according to the degree of influence of each factor on the safe operation of the patrol vehicle. For example, the location and size of the obstacle can be assigned a higher weight, while the temperature and humidity may be assigned a lower weight. By calculating the risk fusion information of each path segment, a comprehensive risk index is obtained.

[0084] The comprehensive risk index is judged to obtain a comprehensive evaluation result, which includes risk level classification information of the travel path segment.

[0085] Judging the comprehensive risk index includes classifying the risk levels of the route segments, which are usually divided into low risk, medium risk and high risk; low risk means that the comprehensive risk index is within a safe range, and the bridge inspection vehicle can continue to travel along the predetermined path and speed; medium risk means that the comprehensive risk index is high and there is a certain risk. It is recommended that the bridge inspection vehicle slow down or fine-tune the path to avoid potential threats; high risk means that the comprehensive risk index exceeds the safe range, indicating that there is a serious risk, and the bridge inspection vehicle needs to take measures such as detour, deceleration or even stop to ensure safety.

[0086] The inspection path and operating parameters of the bridge inspection vehicle are adjusted in real time based on the comprehensive evaluation results.

[0087] Specifically, for high-risk path sections, the inspection vehicle can be controlled to detour or choose an alternative path with lower risk; in medium-risk areas, the bridge inspection vehicle can slow down to increase reaction time and reduce risk; according to specific environmental conditions, the bridge inspection vehicle can also adjust other operating parameters such as sensor sensitivity and vehicle posture to optimize the operating effect of the bridge inspection vehicle.

[0088] This embodiment fully considers the impact of obstacles and abnormal inspection environment on the bridge inspection vehicle during the inspection process, and makes real-time adjustments to the inspection path and operating parameters based on the comprehensive evaluation results, which can improve the safety of the bridge inspection vehicle and the success rate of task execution.

[0089] Reference Figure 3 The present invention provides a safety protection device 30 for a bridge inspection vehicle, comprising:

[0090] The data acquisition module 31 is used to acquire the environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time and space synchronization processing on the environmental point cloud data and the environmental parameter data;

[0091] The data analysis module 32 is used to pre-process the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyze the pre-processed environmental point cloud data through a clustering algorithm, identify the characteristic information of obstacles on the path of the bridge inspection vehicle, and analyze the pre-processed environmental parameter data to determine the environmental condition information on the path of the bridge inspection vehicle;

[0092] The control module 33 is used to combine the detected obstacle feature information and the environmental condition analysis information to perform a comprehensive risk assessment, and adjust the inspection path and operating parameters of the bridge inspection vehicle in real time according to the comprehensive assessment results.

[0093] The beneficial effects of this embodiment are as follows: the data acquisition module 31 is used to acquire and synchronously process the environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time and space, so as to ensure the temporal and spatial consistency of the data and enhance the perception accuracy of the inspection vehicle to the surrounding environment; the data analysis module 32 is used to analyze the pre-processed environmental point cloud data using a clustering algorithm, which can effectively identify the characteristic information of obstacles on the path of the bridge inspection vehicle; by analyzing the environmental parameter data, the environmental condition information on the path of the inspection vehicle can be determined in real time, so that the inspection vehicle can dynamically adapt to different environmental changes and ensure the safety of driving; the control module 33 is used to combine the obstacle characteristic information with the environmental condition analysis information, and conduct a comprehensive risk assessment, so that the bridge inspection vehicle can make intelligent decisions in a complex environment, and adjust the inspection path and operating parameters in real time according to the comprehensive assessment results, thereby improving the safety and efficiency of the inspection task. The present invention can improve the safety and inspection efficiency of bridge inspection vehicles in complex environments.

[0094] Reference Figure 4 , the present invention provides an electronic device 40, including a memory 41 and a processor 42;

[0095] The memory 41 is used to store computer programs;

[0096] The processor 42 is used to implement the above-mentioned safety protection method for a bridge inspection vehicle when executing the computer program.

[0097] In other words, an electronic device 40 includes a memory 41 and a processor 42 coupled to the memory 41; the memory 41 is configured to store a computer program; and the processor 42 is configured to perform the following operations when executing the computer program:

[0098] Acquire environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time and space synchronization processing on the environmental point cloud data and the environmental parameter data;

[0099] Preprocessing the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyzing the preprocessed environmental point cloud data by a clustering algorithm to identify obstacle feature information on the path of the bridge inspection vehicle, and analyzing the preprocessed environmental parameter data to determine environmental condition information on the path of the bridge inspection vehicle;

[0100] The detected obstacle feature information and the environmental condition analysis information are combined to perform a comprehensive risk assessment, and the inspection path and operating parameters of the bridge inspection vehicle are adjusted in real time according to the comprehensive assessment results.

[0101] The present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned safety protection method for a bridge inspection vehicle is implemented.

[0102] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0103] Acquire environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time and space synchronization processing on the environmental point cloud data and the environmental parameter data;

[0104] Preprocessing the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyzing the preprocessed environmental point cloud data by a clustering algorithm to identify obstacle feature information on the path of the bridge inspection vehicle, and analyzing the preprocessed environmental parameter data to determine environmental condition information on the path of the bridge inspection vehicle;

[0105] The detected obstacle feature information and the environmental condition analysis information are combined to perform a comprehensive risk assessment, and the inspection path and operating parameters of the bridge inspection vehicle are adjusted in real time according to the comprehensive assessment results.

[0106] An electronic device 40 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 40 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 40 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] The electronic device 40 includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0108] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0109] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A safety protection method for a bridge inspection vehicle, characterized in that: include: Acquire environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time and space synchronization processing on the environmental point cloud data and the environmental parameter data; Preprocessing the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyzing the preprocessed environmental point cloud data by a clustering algorithm to identify obstacle feature information on the path of the bridge inspection vehicle, and analyzing the preprocessed environmental parameter data to determine environmental condition information on the path of the bridge inspection vehicle; The detected obstacle feature information and the environmental condition analysis information are combined to perform a comprehensive risk assessment, and the inspection path and operating parameters of the bridge inspection vehicle are adjusted in real time according to the comprehensive assessment results.

2. The safety protection method for a bridge inspection vehicle according to claim 1 is characterized in that: The environmental point cloud data includes three-dimensional space coordinates, reflection intensity and a first timestamp, the environmental parameter data includes wind speed, wind direction, temperature and humidity, bridge vibration data and a second timestamp, and the time-space synchronization processing of the environmental point cloud data and the environmental parameter data includes: Based on a multi-sensor fusion algorithm, the environmental point cloud data and the environmental parameter data are subjected to time synchronization and spatial alignment processing to obtain target fusion data.

3. The safety protection method for a bridge inspection vehicle according to claim 2 is characterized in that: The time synchronization and spatial alignment processing of the environmental point cloud data and the environmental parameter data based on the multi-sensor fusion algorithm includes: Performing timestamp alignment processing on the environmental point cloud data and the environmental parameter data based on the first timestamp and the second timestamp, and generating a time synchronization data set, wherein the time synchronization data set includes the environmental point cloud data and the environmental parameter data collected at the same time; Performing coordinate synchronization processing on the environmental point cloud data and the environmental parameter data in each of the time data sets using a coordinate transformation matrix, and generating a time coordinate synchronization data set, wherein the time coordinate synchronization data set includes the environmental point cloud data and the environmental parameter data collected at the same time and in the same global coordinate system; The spatial coordinate synchronization data set is spatially aligned using an iterative closest point algorithm to generate a time-space synchronization data set, and the time-space synchronization data set is fused using a Kalman filter fusion algorithm to obtain target fused data.

4. The safety protection method for a bridge inspection vehicle according to claim 1 is characterized in that: The environmental point cloud data and the environmental parameter data after the time-space synchronization processing are preprocessed, and the preprocessed environmental point cloud data are analyzed by a clustering algorithm to identify the characteristic information of obstacles on the path of the bridge inspection vehicle. The pre-processed environmental parameter data is analyzed to determine the environmental condition information of the bridge inspection vehicle on the travel path, including: Preprocessing the environmental point cloud data and the environmental parameter data after time-space synchronization processing, wherein the preprocessing includes denoising, downsampling and normal vector processing; Clustering the preprocessed environmental point cloud data using a spatial clustering algorithm to obtain obstacle feature information, wherein the obstacle feature information includes obstacle size information, obstacle shape information, and obstacle position information; The pre-processed environmental parameter data and the travel path of the bridge inspection vehicle are spatially correlated to extract environmental influencing factors and potential risk areas at different locations and time periods to obtain environmental condition information.

5. The safety protection method for a bridge inspection vehicle according to claim 4 is characterized in that: The pre-processed environmental parameter data is spatially associated with the path of the bridge inspection vehicle, and environmental influencing factors and potential risk areas in different path segments and time periods are extracted to obtain environmental condition information including: Associating the preprocessed environmental parameter data with the time nodes on the travel path to generate environmental influencing factors corresponding to the travel path segment and the time period; The environmental impact parameters are analyzed, potential risk areas on the travel path segment are identified and marked, and risk assessment is performed on the potential risk areas to obtain environmental condition information.

6. The safety protection method for a bridge inspection vehicle according to claim 5 is characterized in that: The environmental condition information includes environmental influencing factors, the potential risk area and the risk level.

7. The safety protection method for a bridge inspection vehicle according to claim 1, characterized in that: The step of combining the detected obstacle feature information with the environmental condition analysis information to conduct a comprehensive risk assessment and adjusting the inspection path and operating parameters of the bridge inspection vehicle in real time according to the comprehensive assessment result includes: The obstacle feature information and the environmental condition analysis information are integrated to obtain risk integration information, and the risk integration information is calculated using a weighted evaluation algorithm to obtain a comprehensive risk index on each travel path segment; The comprehensive risk index is judged to obtain a comprehensive evaluation result, wherein the comprehensive evaluation result includes risk level classification information of the travel path segment; The inspection path and operating parameters of the bridge inspection vehicle are adjusted in real time based on the comprehensive evaluation results.

8. A safety protection device for a bridge inspection vehicle, characterized in that: include: A data acquisition module is used to acquire the environmental point cloud data and environmental parameter data around the bridge inspection vehicle in real time, and perform time and space synchronization processing on the environmental point cloud data and the environmental parameter data; A data analysis module, used to pre-process the environmental point cloud data and the environmental parameter data after time-space synchronization processing, analyze the pre-processed environmental point cloud data through a clustering algorithm, identify the characteristic information of obstacles on the path of the bridge inspection vehicle, and analyze the pre-processed environmental parameter data to determine the environmental condition information on the path of the bridge inspection vehicle; The control module is used to combine the detected obstacle feature information with the environmental condition analysis information to perform a comprehensive risk assessment, and adjust the inspection path and operating parameters of the bridge inspection vehicle in real time according to the comprehensive assessment results.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the safety protection method for the bridge inspection vehicle as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the safety protection method for the bridge inspection vehicle according to any one of claims 1 to 7 is implemented.

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