Intelligent bridge inspection vehicle laser radar abnormal state monitoring method and device

Abstract

The invention relates to the technical field of intelligent control, in particular to an intelligent bridge inspection vehicle laser radar abnormal state monitoring method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining point cloud data of a target bridge during operation of target equipment; based on processing of the point cloud data, acquiring environment feature information in a detection area of the target equipment; according to the environment characteristic information and the motion state information of the target equipment, carrying out abnormal state discrimination; and if the environment feature information and the motion state information meet a preset abnormal state constraint condition, generating and outputting corresponding abnormal state early warning information. By adopting the method, the running state of the intelligent bridge inspection vehicle can be detected, and the safety of bridge inspection work is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular to a method, device, computer equipment, storage medium and computer program product for monitoring abnormal status of a laser radar of an intelligent bridge inspection vehicle. Background Art

[0002] A bridge inspection vehicle is a special vehicle used to inspect and maintain engineering structures such as bridges and tunnels. Bridge inspection vehicles are usually equipped with various testing equipment to evaluate and test the structure, materials and status of bridges.

[0003] In the related technology, traditional bridge inspection often requires inspectors to control the bridge inspection vehicle and approach the bridge structure for observation and detection. However, the current bridge inspection method relies on the normal use of the bridge inspection vehicle, so the stable control of the bridge inspection vehicle needs to be further guaranteed. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for monitoring abnormal status of the intelligent bridge inspection vehicle laser radar, which can detect the operating status of the intelligent bridge inspection vehicle and improve the safety of bridge inspection work in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle. The method comprises:

[0006] When the target equipment is running, the point cloud data of the target bridge is obtained;

[0007] Based on the processing of the point cloud data, obtaining environmental feature information in the detection area of ​​the target device;

[0008] Perform abnormal state determination based on the environmental feature information and the motion state information of the target device;

[0009] If the environmental characteristic information and the motion state information meet the preset abnormal state constraint conditions, corresponding abnormal state warning information is generated and output.

[0010] In one embodiment, if the environmental feature information and the motion state information meet the preset abnormal state constraint condition, generating and outputting corresponding abnormal state warning information includes:

[0011] The abnormal state of the target device is determined based on the environmental feature information and the motion state information, where the abnormal state includes radar posture abnormality, radar blocking abnormality, and point cloud missing abnormality.

[0012] In one embodiment, determining the abnormal state of the target device based on the environmental feature information and the motion state information includes:

[0013] Determining a target point cloud area for calculating a yaw angle of the target device based on processing the point cloud data;

[0014] Determine a maximum probability yaw angle according to the feature point cloud data in the target point cloud area;

[0015] If the difference between the maximum probability yaw angles of two adjacent frames exceeds a preset deviation threshold, it is determined that the radar posture of the target device is abnormal.

[0016] In one embodiment, determining the abnormal state of the target device based on the environmental feature information and the motion state information includes:

[0017] Obtaining the number of point clouds within a preset occlusion distance around the target device;

[0018] If the number of point clouds exceeds a preset upper threshold of the occlusion limit, it is determined that the target device has a radar occlusion anomaly.

[0019] In one of the embodiments, if the number of point clouds exceeds a preset upper threshold of the shielding, determining that the target device has a radar shielding anomaly includes:

[0020] Performing cluster analysis on the point cloud data within the occlusion distance based on a preset clustering algorithm to obtain a point cloud clustering target;

[0021] If the size of the point cloud cluster target exceeds a preset occlusion size threshold, and the number of point clouds exceeds a preset occlusion upper limit threshold, it is determined that the target device has a radar occlusion anomaly.

[0022] In one embodiment, determining the abnormal state of the target device based on the environmental feature information and the motion state information includes:

[0023] Based on the point cloud data, determining the number of valid point clouds and the number of ideal point clouds in operation of the target device;

[0024] Determine the number of lost point clouds based on the number of valid point clouds and the number of ideal point clouds;

[0025] If the number of lost point clouds exceeds a preset missing threshold, it is determined that the target device has a point cloud missing anomaly.

[0026] In a second aspect, the present application also provides a device for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle. The device comprises:

[0027] Point cloud data module, used to obtain point cloud data of the target bridge when the target equipment is running;

[0028] An environmental feature module, used for acquiring environmental feature information in a detection area of ​​the target device based on processing the point cloud data;

[0029] An abnormality identification module, used for identifying an abnormal state according to the environmental feature information and the motion state information of the target device;

[0030] The abnormality warning module is used to generate and output corresponding abnormality warning information if the environmental feature information and the motion state information meet the preset abnormality constraint conditions.

[0031] In one embodiment, the abnormal warning module includes:

[0032] The abnormal situation module is used to determine the abnormal state of the target device based on the environmental feature information and the motion state information, and the abnormal state includes radar posture abnormality, radar occlusion abnormality and point cloud missing abnormality.

[0033] In one embodiment, the abnormal situation module includes:

[0034] A target point cloud region module, used for determining a target point cloud region for calculating a yaw angle of the target device based on processing the point cloud data;

[0035] A yaw angle module, used to determine the maximum probability yaw angle according to the feature point cloud data in the target point cloud area;

[0036] The deviation determination module is used to determine that the radar posture of the target device is abnormal if the difference between the maximum probability yaw angles of two adjacent frames exceeds a preset deviation threshold.

[0037] In one embodiment, the abnormal situation module includes:

[0038] A point cloud quantity module is used to obtain the number of point clouds within a preset occlusion distance around the target device;

[0039] The occlusion determination module is used to determine that the target device has a radar occlusion anomaly if the number of point clouds exceeds a preset occlusion upper limit threshold.

[0040] In one embodiment, the occlusion determination module includes:

[0041] A clustering analysis module, used to perform clustering analysis on the point cloud data within the occlusion distance based on a preset clustering algorithm to obtain a point cloud clustering target;

[0042] The size determination module is used to determine that the target device has a radar occlusion anomaly if the size of the point cloud clustering target exceeds a preset occlusion size threshold and the number of point clouds exceeds a preset occlusion upper limit threshold.

[0043] In one embodiment, the abnormal situation module includes:

[0044] An effective point cloud module, used to determine the number of effective point clouds and the number of ideal point clouds in the operation of the target device based on the point cloud data;

[0045] A lost point cloud module, used to determine the number of lost point clouds based on the number of valid point clouds and the number of ideal point clouds;

[0046] The loss determination module is used to determine that a point cloud missing anomaly exists in the target device if the number of the lost point clouds exceeds a preset missing threshold.

[0047] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the abnormal state monitoring method of a laser radar for an intelligent bridge inspection vehicle as described in any embodiment of the first aspect when executing the computer program.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle as described in any one of the embodiments in the first aspect.

[0049] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle as described in any one of the embodiments in the first aspect are implemented.

[0050] The above-mentioned intelligent bridge inspection vehicle laser radar abnormal state monitoring method, device, computer equipment, storage medium and computer program product can achieve the following beneficial effects corresponding to the technical problems in the background technology by deducing the technical features in the claims:

[0051] The present application provides a method for monitoring abnormal state of a laser radar for intelligent bridge detection, the steps mainly include obtaining point cloud data of a target bridge during the operation of the target device; obtaining environmental characteristic information in the detection area of ​​the target device based on the processing of the point cloud data; performing abnormal state judgment according to the environmental characteristic information and the motion state information of the target device; if the environmental characteristic information and the motion state information meet the preset abnormal state constraint conditions, then generating and outputting corresponding abnormal state warning information. In implementation, the point cloud data provided by the radar on the intelligent bridge inspection vehicle in the detection is processed to obtain the environmental characteristic information of the operation of the intelligent bridge inspection vehicle, so that the environmental characteristic information and the motion state information of the intelligent bridge inspection vehicle can be used to judge the abnormal state. In the judgment, according to the preset abnormal state constraint conditions, if the corresponding parameters meet the parameter characteristics corresponding to a certain abnormal state, it can be judged that the intelligent bridge inspection vehicle is in the corresponding abnormal state, which helps to improve the judgment efficiency of the abnormal state and improve the safety and stability of the operation of the intelligent bridge inspection vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is an application environment diagram of an abnormal state monitoring method of a laser radar for an intelligent bridge inspection vehicle in one embodiment;

[0053] Figure 2 It is a schematic diagram of a first process of a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle in one embodiment;

[0054] Figure 3 A second flow chart of a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0055] Figure 4 A third flow chart of a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0056] Figure 5 A fourth flow chart of a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0057] Figure 6 A fifth flow chart of a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0058] Figure 7 A sixth flow chart of a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0059] Figure 8 It is a structural block diagram of an abnormal state monitoring device of a laser radar for an intelligent bridge inspection vehicle in one embodiment;

[0060] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] A bridge inspection vehicle is a special vehicle used to inspect and maintain engineering structures such as bridges and tunnels. Bridge inspection vehicles are usually equipped with various testing equipment to evaluate and test the structure, materials and status of bridges.

[0063] In the related technology, traditional bridge inspection often requires inspectors to control the bridge inspection vehicle and approach the bridge structure for observation and detection. However, the current bridge inspection method relies on the normal use of the bridge inspection vehicle, so the stable control of the bridge inspection vehicle needs to be further guaranteed.

[0064] Based on the above problems, the embodiments of the present application provide a method, apparatus, computer equipment, storage medium and computer program product for monitoring abnormal status of a laser radar of an intelligent bridge inspection vehicle.

[0065] The embodiment of the present application provides a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle, which can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The terminal can be set on an intelligent bridge inspection vehicle for implementing bridge inspection tasks, and the intelligent bridge inspection vehicle can also be provided with. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0066] In one embodiment, Figure 2 As shown, a method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle is provided. Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0067] Step 202: While the target device is running, obtain point cloud data of the target bridge.

[0068] Step 204: Based on the processing of the point cloud data, obtain environmental feature information in the detection area of ​​the target device.

[0069] Step 206: abnormal state determination is performed based on the environmental feature information and the motion state information of the target device.

[0070] Step 208: If the environmental characteristic information and the motion state information meet the preset abnormal state constraint conditions, then corresponding abnormal state warning information is generated and output.

[0071] In the above-mentioned intelligent bridge inspection vehicle laser radar abnormal state monitoring method, reasonable deduction is carried out in combination with the technical features in the embodiment, which can achieve the following beneficial effects of solving the technical problems raised in the background technology:

[0072] The present application provides a method for monitoring abnormal state of a laser radar for intelligent bridge detection, the steps mainly include obtaining point cloud data of a target bridge during the operation of the target device; obtaining environmental characteristic information in the detection area of ​​the target device based on the processing of the point cloud data; performing abnormal state judgment according to the environmental characteristic information and the motion state information of the target device; if the environmental characteristic information and the motion state information meet the preset abnormal state constraint conditions, then generating and outputting corresponding abnormal state warning information. In implementation, the point cloud data provided by the radar on the intelligent bridge inspection vehicle in the detection is processed to obtain the environmental characteristic information of the operation of the intelligent bridge inspection vehicle, so that the environmental characteristic information and the motion state information of the intelligent bridge inspection vehicle can be used to judge the abnormal state. In the judgment, according to the preset abnormal state constraint conditions, if the corresponding parameters meet the parameter characteristics corresponding to a certain abnormal state, it can be judged that the intelligent bridge inspection vehicle is in the corresponding abnormal state, which helps to improve the judgment efficiency of the abnormal state and improve the safety and stability of the operation of the intelligent bridge inspection vehicle.

[0073] In one embodiment, the Figure 3 As shown, step 208 includes:

[0074] Step 302: Determine the abnormal state of the target device based on the environmental feature information and the motion state information, where the abnormal state includes radar posture abnormality, radar occlusion abnormality, and point cloud missing abnormality.

[0075] Among them, the radar posture is abnormal. During the inspection of the bridge inspection vehicle, the body vibrates significantly. Long-term operation may easily cause the laser radar installation to loosen, causing safety hazards. Therefore, it is necessary to detect the changes in the laser radar installation posture; the radar obstruction is abnormal. When the radar is installed in a poor position or blocked by foreign objects, more point clouds are blocked by nearby structures, resulting in a reduction in the laser radar perception range; the point cloud is missing abnormally. The bridge inspection vehicle vibrates violently during the inspection, which may cause serious point cloud data packet loss. At this time, the obstacle perception results of the bridge inspection vehicle are unreliable. All of the above situations require alarm prompts.

[0076] In this embodiment, a variety of abnormal situations that have a significant impact on the intelligent bridge inspection vehicle are pre-set, which helps to specifically identify and warn of abnormal conditions with a significant impact, and helps to improve the operational safety of the intelligent bridge inspection vehicle.

[0077] In one embodiment, the Figure 4 As shown, step 302 includes:

[0078] Step 402: Based on the processing of the point cloud data, determine a target point cloud area for calculating the yaw angle of the target device.

[0079] Step 404: Determine the maximum probability yaw angle according to the feature point cloud data in the target point cloud area.

[0080] Step 406: If the difference between the maximum probability yaw angles of two adjacent frames exceeds a preset deviation threshold, it is determined that the radar posture of the target device is abnormal.

[0081] For example, the specific process is as follows:

[0082] Obtain bridge point cloud data and extract the point cloud area used to calculate the yaw angle.

[0083] Calculate the coordinates of the center point of the point cloud collection, and recalculate the coordinate values ​​using this coordinate as the origin of the point cloud collection.

[0084] Calculate the heading angle of each point in the point cloud collection to Divide the angle interval into sectors.

[0085] Calculate the point cloud distance value in each sector area and select the point with the largest distance value as the edge point.

[0086] According to step 4, a set of edge contour points is obtained, the slope of two adjacent edge points is calculated, and the inverse tangent value is calculated as the yaw angle.

[0087] Collect the yaw angles calculated in step 5 to The interval is calculated by histogram, and the bin with the largest proportion is taken as the maximum probability yaw angle.

[0088] Considering the stability of the estimated angle, according to the formula Calculate the final yaw angle, where The number of point clouds in the bin representing the maximum probability yaw angle; Indicates the number of point clouds in the angle bin before the maximum probability yaw angle; Indicates the number of point clouds in an angle bin after the maximum probability yaw angle; Represents the sum of the number of three bin point clouds; represents the maximum probability yaw angle; The previous angle representing the maximum probability yaw angle; The latter angle representing the maximum probability yaw angle.

[0089] Repeat steps 1-7 to calculate the difference between the yaw angles of two adjacent frames, and determine whether the radar attitude is abnormal based on the set deviation angle threshold θt. If the angle difference exceeds the threshold, an abnormal attitude alarm is issued.

[0090] In this embodiment, in the identification of abnormal radar posture, the analysis scope is narrowed to the target point cloud area through further processing of the point cloud data, and then the maximum probability yaw angle is calculated based on the feature point cloud data in the target point cloud area. The yaw state of the target device is determined based on the maximum probability yaw angle, which helps to improve the efficiency and accuracy of yaw state calculation and identification.

[0091] In one embodiment, the Figure 5 As shown, step 302 includes:

[0092] Step 502: Obtain the number of point clouds within a preset occlusion distance around the target device.

[0093] Step 504: If the number of point clouds exceeds a preset upper threshold of the occlusion limit, it is determined that the target device has a radar occlusion anomaly.

[0094] For example, the laser radar is installed in multiple locations on the bridge inspection vehicle. If the installation location is not good, most of the point clouds will scan nearby structures. In addition, if the workpiece inside the bridge inspection vehicle is loose and blocks the radar, the sensing area will be limited, reducing the sensitivity of sensing obstacles. Therefore, the laser radar needs to detect the point cloud occlusion in real time.

[0095] The specific process is as follows:

[0096] 1. The laser radar obtains the surrounding environment point cloud and sets the upper limit threshold T1 of the occlusion point cloud and the occlusion detection distance d.

[0097] 2. When installing the radar, calculate the number of point clouds within the obstruction detection distance and compare it with the upper limit threshold of the obstruction point cloud. If it exceeds the threshold, an obstruction alarm will be issued.

[0098] In this embodiment, by performing quantitative analysis on the point cloud data within the shielding distance, it is determined whether there is a target that may cause shielding, which helps to judge whether there is an abnormal situation of radar shielding.

[0099] In one embodiment, the Figure 6 As shown, step 504 includes:

[0100] Step 602: performing cluster analysis on the point cloud data within the occlusion distance based on a preset clustering algorithm to obtain a point cloud clustering target.

[0101] Step 604: If the size of the point cloud clustering target exceeds a preset occlusion size threshold, and the number of point clouds exceeds a preset occlusion upper limit threshold, it is determined that the target device has a radar occlusion anomaly.

[0102] Exemplarily, based on step 504, further optimization is performed by clustering the point cloud within the occlusion detection distance, calculating the cluster target size, and setting an occlusion size threshold. If the size is greater than the threshold, occlusion is determined to have occurred.

[0103] In this embodiment, further analysis and verification of the radar obstruction situation from the dimension of the size of the obstructing target is helpful to improve the accuracy of radar obstruction analysis and improve the accuracy of abnormal situation judgment of the intelligent bridge inspection vehicle.

[0104] In one embodiment, the Figure 7 As shown, step 302 includes:

[0105] Step 702: Based on the point cloud data, determine the number of valid point clouds and the number of ideal point clouds in operation of the target device.

[0106] Step 704: Determine the number of lost point clouds based on the number of valid point clouds and the number of ideal point clouds.

[0107] Step 706: If the number of missing point clouds exceeds a preset missing threshold, it is determined that a point cloud missing anomaly exists in the target device.

[0108] For example, when the bridge inspection vehicle is driving, the laser radar has obvious vibration. Although the laser radar has passed the earthquake resistance test, there is a moving mechanical structure inside it. After long-term operation, the laser radar may have point cloud data missing problems, thus affecting the accuracy of environmental perception.

[0109] The specific process is as follows:

[0110] 1. The laser radar obtains the surrounding environment point cloud and sets the missing point cloud ratio threshold T2.

[0111] 2. After the lidar is installed, ensure that the working environment is normal, and calculate the average of the percentage of lost point clouds for n consecutive frames as T2.

[0112] 3. When the vehicle is running, the percentage of the number of valid point clouds and the number of ideal point clouds is calculated, and the proportion of lost point clouds is inferred. When it is greater than the threshold T2, an abnormal point cloud missing alarm is issued.

[0113] In this embodiment, calculating the number of lost point clouds helps to verify the functional performance of the laser radar and helps to improve the accuracy of abnormal situation identification.

[0114] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0115] Based on the same inventive concept, the embodiment of the present application also provides a device for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle for realizing the above-mentioned method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle provided below can refer to the limitations of the method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle above, and will not be repeated here.

[0116] In one embodiment, Figure 8 As shown, a device for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle is provided, comprising: a point cloud data module, an environmental feature module, an abnormality identification module and an abnormality warning module, wherein:

[0117] Point cloud data module, used to obtain point cloud data of the target bridge when the target equipment is running;

[0118] An environmental feature module, used for acquiring environmental feature information in a detection area of ​​the target device based on processing the point cloud data;

[0119] An abnormality identification module, used for identifying an abnormal state according to the environmental feature information and the motion state information of the target device;

[0120] The abnormality warning module is used to generate and output corresponding abnormality warning information if the environmental feature information and the motion state information meet the preset abnormality constraint conditions.

[0121] In one embodiment, the abnormal warning module includes:

[0122] The abnormal situation module is used to determine the abnormal state of the target device based on the environmental feature information and the motion state information, and the abnormal state includes radar posture abnormality, radar occlusion abnormality and point cloud missing abnormality.

[0123] In one embodiment, the abnormal situation module includes:

[0124] A target point cloud area module, used to determine a target point cloud area for calculating the yaw angle of the target device based on processing the point cloud data;

[0125] A yaw angle module, used to determine the maximum probability yaw angle according to the feature point cloud data in the target point cloud area;

[0126] The deviation determination module is used to determine that the radar posture of the target device is abnormal if the difference between the maximum probability yaw angles of two adjacent frames exceeds a preset deviation threshold.

[0127] In one embodiment, the abnormal situation module includes:

[0128] A point cloud quantity module is used to obtain the number of point clouds within a preset occlusion distance around the target device;

[0129] The occlusion determination module is used to determine that the target device has a radar occlusion anomaly if the number of point clouds exceeds a preset occlusion upper limit threshold.

[0130] In one embodiment, the occlusion determination module includes:

[0131] A clustering analysis module, used to perform clustering analysis on the point cloud data within the occlusion distance based on a preset clustering algorithm to obtain a point cloud clustering target;

[0132] The size determination module is used to determine that the target device has a radar occlusion anomaly if the size of the point cloud clustering target exceeds a preset occlusion size threshold and the number of point clouds exceeds a preset occlusion upper limit threshold.

[0133] In one embodiment, the abnormal situation module includes:

[0134] An effective point cloud module, used to determine the number of effective point clouds and the number of ideal point clouds in the operation of the target device based on the point cloud data;

[0135] A lost point cloud module, used to determine the number of lost point clouds based on the number of valid point clouds and the number of ideal point clouds;

[0136] The loss determination module is used to determine that a point cloud missing anomaly exists in the target device if the number of the lost point clouds exceeds a preset missing threshold.

[0137] Each module in the above-mentioned intelligent bridge inspection vehicle laser radar abnormal state monitoring device can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0138] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for monitoring abnormal state of an intelligent bridge inspection vehicle laser radar is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0139] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0140] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0142] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0144] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0145] The technical features of the above embodiments may be arbitrarily combined. 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.

[0146] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring abnormal state of a laser radar of an intelligent bridge inspection vehicle, characterized in that: The method comprises: When the target equipment is running, the point cloud data of the target bridge is obtained; Based on the processing of the point cloud data, obtaining environmental feature information in the detection area of ​​the target device; Perform abnormal state determination based on the environmental feature information and the motion state information of the target device; If the environmental characteristic information and the motion state information meet the preset abnormal state constraint conditions, corresponding abnormal state warning information is generated and output.

2. The method according to claim 1, characterized in that If the environmental feature information and the motion state information meet the preset abnormal state constraint condition, generating and outputting corresponding abnormal state warning information includes: The abnormal state of the target device is determined based on the environmental feature information and the motion state information, where the abnormal state includes radar posture abnormality, radar blocking abnormality, and point cloud missing abnormality.

3. The method according to claim 2, characterized in that The determining the abnormal state of the target device based on the environmental feature information and the motion state information includes: Determining a target point cloud area for calculating a yaw angle of the target device based on processing the point cloud data; Determine a maximum probability yaw angle according to the feature point cloud data in the target point cloud area; If the difference between the maximum probability yaw angles of two adjacent frames exceeds a preset deviation threshold, it is determined that the radar posture of the target device is abnormal.

4. The method according to claim 2, characterized in that: The determining the abnormal state of the target device based on the environmental feature information and the motion state information includes: Obtaining the number of point clouds within a preset occlusion distance around the target device; If the number of point clouds exceeds a preset upper threshold of the occlusion limit, it is determined that the target device has a radar occlusion anomaly.

5. The method according to claim 2, characterized in that: If the number of point clouds exceeds a preset upper threshold of the shielding, determining that the target device has a radar shielding anomaly includes: Performing cluster analysis on the point cloud data within the occlusion distance based on a preset clustering algorithm to obtain a point cloud clustering target; If the size of the point cloud cluster target exceeds a preset occlusion size threshold, and the number of point clouds exceeds a preset occlusion upper limit threshold, it is determined that the target device has a radar occlusion anomaly.

6. The method according to claim 2, characterized in that The determining the abnormal state of the target device based on the environmental feature information and the motion state information includes: Based on the point cloud data, determining the number of valid point clouds and the number of ideal point clouds in operation of the target device; Determine the number of lost point clouds based on the number of valid point clouds and the number of ideal point clouds; If the number of lost point clouds exceeds a preset missing threshold, it is determined that the target device has a point cloud missing anomaly.

7. An intelligent bridge inspection vehicle laser radar abnormal state monitoring device, characterized in that: The device comprises: Point cloud data module, used to obtain point cloud data of the target bridge when the target equipment is running; An environmental feature module, used for acquiring environmental feature information in a detection area of ​​the target device based on processing the point cloud data; An abnormality identification module, used for identifying an abnormal state according to the environmental feature information and the motion state information of the target device; The abnormality warning module is used to generate and output corresponding abnormality warning information if the environmental feature information and the motion state information meet the preset abnormality constraint conditions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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