Method and device for determining the state of a clamping arm assembly of a clamping vehicle, computer device

Abstract

The application relates to a method and device for determining the state of a clamping arm assembly of a clamping vehicle, a computer device, a storage medium and a computer program product. The method comprises: obtaining point cloud data obtained by laser irradiation on a reflective column arranged on the clamping arm assembly. Cluster processing is performed based on the point cloud data to obtain a clustering result of the clamping arm assembly corresponding to the reflective column. The to-be-verified state of the clamping arm assembly is determined according to the clustering result, and a task verification code corresponding to the point cloud data is obtained in the case that the to-be-verified state is a normal state; wherein the task verification code represents a preset state of the clamping arm assembly. The to-be-verified state is verified based on the task verification code, and the target state of the clamping arm assembly is determined based on the to-be-verified state in the case that the verification is passed. In this way, the state of the clamping arm assembly is automatically determined.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a method and device for determining the state of a clamping arm assembly of a clamping vehicle, a computer device, a storage medium, and a computer program product. BACKGROUND

[0002] With the development of clamping vehicle technology, the clamping vehicle is manually operated to realize the carrying and transportation of goods. When a large amount of goods needs to be processed, manual operation and on-site scheduling are required, which poses a safety problem.

[0003] In the prior art, AGV (Automated Guided Vehicle) technology is used to realize unmanned clamping vehicle technology. When a clamping operation is required, the clamping arms of the unmanned clamping vehicle are used to clamp the goods. However, the unmanned clamping vehicle cannot obtain the state of the clamping arm assembly in real time. Therefore, during the carrying of a large amount of goods, the state of the clamping arm assembly needs to be determined manually, and the clamping vehicle cannot work efficiently. SUMMARY

[0004] Therefore, it is necessary to provide a method and device for determining the state of a clamping arm assembly of a clamping vehicle, a computer device, a computer readable storage medium, and a computer program product to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for determining the state of a clamping arm assembly of a clamping vehicle. The method comprises:

[0006] obtaining point cloud data obtained by laser irradiation on a reflective column disposed on the clamping arm assembly;

[0007] performing clustering processing based on the point cloud data to obtain a clustering result of the clamping arm assembly corresponding to the reflective column;

[0008] determining a to-be-verified state of the clamping arm assembly according to the clustering result, and obtaining a task verification code corresponding to the point cloud data in a case where the to-be-verified state is a normal state; wherein the task verification code represents a preset state of the clamping arm assembly;

[0009] verifying the to-be-verified state based on the task verification code, and determining a target state of the clamping arm assembly based on the to-be-verified state in a case where the verification is passed.

[0010] In a second aspect, the present application also provides a device for determining the state of a clamping arm assembly of a clamping vehicle. The device comprises:

[0011] obtain point cloud data obtained by laser irradiation on a light-reflecting column arranged on a clamp arm assembly;

[0012] The clustering module is configured to perform clustering processing based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the light-reflecting column.

[0013] The obtaining module is further configured to determine a to-be-verified state of the clamp arm assembly according to the clustering result, and obtain a task verification code corresponding to the point cloud data in a case where the to-be-verified state is a normal state, where the task verification code represents a preset state of the clamp arm assembly.

[0014] The determining module is configured to verify the to-be-verified state based on the task verification code, and determine a target state of the clamp arm assembly based on the to-be-verified state in a case where the verification is passed.

[0015] In a third aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0016] obtain point cloud data obtained by laser irradiation on a light-reflecting column arranged on a clamp arm assembly;

[0017] perform clustering processing based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the light-reflecting column.

[0018] determine a to-be-verified state of the clamp arm assembly according to the clustering result, and obtain a task verification code corresponding to the point cloud data in a case where the to-be-verified state is a normal state, where the task verification code represents a preset state of the clamp arm assembly.

[0019] verify the to-be-verified state based on the task verification code, and determine a target state of the clamp arm assembly based on the to-be-verified state in a case where the verification is passed.

[0020] In a fourth aspect, the present application further provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0021] obtain point cloud data obtained by laser irradiation on a light-reflecting column arranged on a clamp arm assembly;

[0022] perform clustering processing based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the light-reflecting column.

[0023] determine a to-be-checked state of the clamping arm assembly according to the clustering result, and in a case where the to-be-checked state is a normal state, acquire a task check code corresponding to the point cloud data; wherein the task check code represents a preset state of the clamping arm assembly;

[0024] check the to-be-checked state based on the task check code, and in a case where the checking passes, determine a target state of the clamping arm assembly based on the to-be-checked state.

[0025] In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the following steps:

[0026] acquire point cloud data obtained by laser irradiation on a reflective column arranged on a clamping arm assembly;

[0027] perform clustering processing based on the point cloud data to obtain a clustering result of the clamping arm assembly corresponding to the reflective column;

[0028] determine a to-be-checked state of the clamping arm assembly according to the clustering result, and in a case where the to-be-checked state is a normal state, acquire a task check code corresponding to the point cloud data; wherein the task check code represents a preset state of the clamping arm assembly;

[0029] check the to-be-checked state based on the task check code, and in a case where the checking passes, determine a target state of the clamping arm assembly based on the to-be-checked state.

[0030] The above method, device, computer equipment, storage medium and computer program product for determining the state of the clamping arm assembly of the clamping vehicle avoid large-scale laser irradiation on the clamping arm assembly connecting two reflective columns, and simplify the process of acquiring point cloud data. In this way, by performing clustering processing on the obtained point cloud data, the clustering result of the clamping arm assembly corresponding to the reflective column can be directly and accurately obtained, so that the to-be-checked state of the clamping arm assembly of the clamping vehicle in the actual environment is efficiently determined. In a case where the to-be-checked state is a normal state, the to-be-checked state is checked by the task check code representing the preset state, so that after the initial screening of whether the to-be-checked state is a normal state is completed, the re-screening is performed, that is, the effectiveness of the state determination process of the clamping arm assembly is ensured through twice screening. In a case where the checking passes, the target state of the clamping arm assembly is accurately determined according to the to-be-checked state matched with the task check code, the state of the clamping arm assembly is automatically determined, manual determination on the clamping arm assembly is avoided, and thus the working efficiency of the clamping vehicle can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 An application environment diagram of the method for determining the state of the clamping arm assembly of the clamping vehicle in an embodiment;

[0032] Figure 2 A flowchart of the method for determining the state of the clamping arm assembly of the clamping vehicle in an embodiment;

[0033] Figure 3 A schematic diagram of the clamping vehicle in an embodiment;

[0034] Figure 4 A schematic diagram of the clamping vehicle in another embodiment;

[0035] Figure 5 A flowchart of the method for determining the state of the clamping arm assembly of the clamping vehicle in another embodiment;

[0036] Figure 6 A structural block diagram of the device for determining the state of the clamping arm assembly of the clamping vehicle in an embodiment;

[0037] Figure 7 An internal structural diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0039] The method for determining the state of the clamping arm assembly of the clamping vehicle provided by the embodiments of the present application can be applied to, for example, Figure 1The application environment shown. Among them, the clamp car 102 communicates with the computer device 104 through the network. The data storage system can store the data required by the computer device 104 to process. The data storage system can be integrated on the computer device 104, or placed on the cloud or other network servers. The computer device 104 obtains the point cloud data obtained by laser irradiation on the reflective column deployed on the clamp arm assembly of the clamp car 102. The computer device 104 performs clustering processing based on the point cloud data to obtain the clustering result of the clamp arm assembly corresponding to the reflective column. The computer device 104 determines the to-be-verified state of the clamp arm assembly according to the clustering result, and in the case that the to-be-verified state is a normal state, obtains a task verification code corresponding to the point cloud data; wherein the task verification code represents a preset state of the clamp arm assembly. The computer device 104 verifies the to-be-verified state based on the task verification code, and in the case that the verification is passed, determines the target state of the clamp arm assembly based on the to-be-verified state. Among them, the clamp car 102 can be a manned clamp car or an unmanned clamp car, and the clamp arm assembly is deployed in the clamp car 102. Among them, the computer device 104 can be a server or a terminal. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and the like. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0040] In one embodiment, as shown in Figure 2 , a method for determining the state of the clamp arm assembly of the clamp car is provided. The method is applied to the computer device in Figure 1 for example, including the following steps:

[0041] Step S202, obtaining point cloud data obtained by laser irradiation on the reflective column deployed on the clamp arm assembly.

[0042] Among them, as shown in Figure 3As shown, the clamp truck 102 includes a vehicle body 50, a turntable 40, and a clamp arm assembly 20 for clamping a cylindrical cargo c, the clamp arm assembly 20 is rotatably connected to the vehicle body 50 through the turntable 40, and the clamp arm assembly 20 can rotate around a first axis a through the turntable 40 to adjust the placement angle of the cylindrical cargo c. The clamp truck 102 further includes a sensor 10 disposed on the vehicle body 50, the sensor 10 is used to perform laser scanning on the reflective column 30 arranged on the clamp arm assembly 20 to obtain point cloud data. Wherein, the clamp arm assembly 20 includes two clamp arms 202, the two clamp arms 202 are oppositely arranged on the turntable 40, and at least one clamp arm 202 can rotate around a second axis b perpendicular to the first axis a relative to the turntable 40 (i.e. there are two solutions. Solution one: one of the clamp arms 202 can rotate around the second axis b relative to the turntable 40, and the other clamp arm 202 is fixed on the turntable 40; Solution two: both of the clamp arms 202 can rotate around the second axis b relative to the turntable 40), so as to realize the closing or opening between the two clamp arms 202, thereby realizing the clamping or releasing of the cylindrical cargo c by the clamp arm assembly 20. The clamp arm assembly 20 further includes a connecting piece 201 for connecting the two clamp arms 202 and the turntable 40.

[0043] Wherein, one reflective column 30 is arranged on each clamp arm 202, and the two reflective columns 30 have the same size, and the larger the diameter of the reflective column 30 is, the better, as long as the welding space of the clamp arm 202 is sufficient, so as to ensure that the area of laser scanning is as large as possible to obtain sufficient point cloud data. The height of the reflective column 30 should be greater than the height difference between the installation height of the sensor 10 and the installation height of the bottom of the reflective column 30, so as to facilitate the sensor 10 to perform laser scanning on the whole reflective column 30. Ensure that the two reflective columns 30 and the sensor 10 are in the same plane in the default upright state of the clamp arm 202, and are separated from the clamp arm sensor. Wherein, the clamp arm sensor is located in the middle below the left and right clamp arm roots (not shown, the clamp arm root is one end of the clamp arm 202 connected to the vehicle body 50). Figure 3 It should be noted that, Figure 3 It is only a schematic diagram of the clamp truck, and the specific structure or shape of the clamp truck is not limited. In one embodiment, the structure of the clamp truck is as shown in Figure 4 .

[0044] Specifically, the sensor in the clamp truck performs laser scanning on the reflective column arranged on the clamp arm assembly to obtain point cloud data. The clamp truck sends the point cloud data to a computer device. Wherein, one reflective column is arranged on each clamp arm, and the reflective column is arranged on the end of the clamp arm away from the vehicle body.

[0045] For example, when the main thread in the computer device is executing the work of the clamp car, the main thread of the computer device calls the detection thread newly added by the clamp arm positioning library for determining the state of the clamp arm assembly of the clamp car. The detection thread is a thread independent of the main thread, and the detection thread is in a loop execution state. The sensor performs laser scanning on the reflective column deployed on the clamp arm assembly in response to the newly added detection thread triggered by the computer device to obtain point cloud data at the current time. The sensor sends the point cloud data to the detection thread of the computer device.

[0046] It should be noted that if the laser directly irradiates the clamp arm assembly, not only the irradiation range is increased, but also the area of the clamp arm assembly where the reflective column is not arranged is easily mis-irradiated, that is, effective point cloud data is difficult to obtain. Among them, one reflective column is arranged on each of the two clamp arms, and the reflective column is arranged at the end of the clamp arm away from the vehicle body. The reflective column has a smaller space volume than the clamp arm assembly and is located on the clamp arm assembly, and is easily irradiated. Therefore, by irradiating the reflective column to obtain point cloud data for determining the state of the clamp arm assembly, the effectiveness of the point cloud data is greatly improved. The laser irradiation can be regular irradiation (i.e., laser scanning) or random irradiation, which is not limited.

[0047] In step S204, clustering processing is performed based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column.

[0048] The clustering processing can be a K-means clustering algorithm, a density-based clustering algorithm, or the like, which is not limited. The clustering result includes the number of categories and the number of points in each category.

[0049] Specifically, the computer device can perform clustering processing on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. Alternatively, the computer device can perform data preprocessing on the point cloud data to obtain target point cloud data, and perform clustering processing on the target point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. The data preprocessing is a filtering of the point cloud data, such as filtering the point cloud data according to an intensity threshold, or filtering the point cloud data according to a coordinate condition, which is not limited.

[0050] For example, the computer device performs clustering processing on the point cloud data by using the Euclidean clustering algorithm (i.e., K-means clustering algorithm) in the PCL library (Point Cloud Library) to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. Alternatively, the computer device performs data preprocessing on the point cloud data to obtain target point cloud data, and performs clustering processing on the target point cloud data by using the Euclidean distance algorithm in the PCL library to obtain a clustering result of the clamp arm assembly corresponding to the reflective column.

[0051] It should be noted that the clustering result is determined based on the point cloud data of the reflector column, that is, the clustering result represents the class of the reflector column (i.e., the category described below). Since the reflector column is correspondingly arranged at the end of the clamping arm away from the vehicle body, the clustering result is a result corresponding to the clamping arm assembly.

[0052] In step S206, the to-be-verified state of the clamping arm assembly is determined according to the clustering result, and in the case that the to-be-verified state is a normal state, a task verification code corresponding to the point cloud data is obtained; wherein the task verification code represents a preset state of the clamping arm assembly.

[0053] Wherein, the to-be-verified state represents the state of the clamping arm assembly determined based on the point cloud data, which can be a normal state or an abnormal state. As shown in Figure 3 When the clamping arm assembly as a whole rotates around the second axis, the states involved include the upright state, the horizontal state and the rotating state. Among them, the upright state and the horizontal state are normal states, and the rotating state is an abnormal state. The task verification code is obtained by pre-judging the clamping arm assembly at the current time, representing the preset state of the clamping arm assembly.

[0054] Specifically, the computer device determines the number of categories in the clustering result and the number of point cloud data contained in each category. The computer device determines the to-be-verified state of the clamping arm assembly according to at least one of the number of categories or the number of points contained in each category. In the case that the to-be-verified state is a normal state, a task verification code corresponding to the point cloud data is obtained. The computer device performs a calculation corresponding to the clustering result based on the point cloud data to obtain a calculation result.

[0055] Wherein, the calculation corresponding to the clustering result can be the distance between the two target point cloud data with the farthest distance, or the target points of each category can be determined respectively, and the distance between any two target points can be calculated by the method of mean value calculation. Wherein, the category of clustering can be regarded as a set, that is, the point cloud data with the same attribute or satisfying the same condition is regarded as a set.

[0056] In step S208, the to-be-verified state is verified based on the task verification code, and in the case that the verification is passed, the target state of the clamping arm assembly is determined based on the to-be-verified state.

[0057] Specifically, according to the task verification code, the to-be-verified state is verified, and in the case that the verification is passed, the computer device takes the to-be-verified state as the target state of the clamping arm assembly, generates a normal state code corresponding to the to-be-verified state, and takes the calculation result as the distance between the positions where the two clamping arms of the clamping arm assembly respectively set the reflector column.

[0058] The normal state code is a legal state code, and the state code is an enumerated variable with a variable name (for example, the variable name of the legal state code 1 is LegalResultCylinder, and the variable name of the legal state code 2 is LegalResultLine) and a numerical value (for example, 0x07...).

[0059] It should be noted that the state to be verified of the clamping arm assembly is determined according to the clustering result, and therefore, the calculation corresponding to the clustering result can be regarded as the calculation corresponding to the state to be verified, that is, according to the state of the clamping arm assembly, the distance between the positions where the two clamping arms of the clamping arm assembly respectively set the reflective columns can be accurately and effectively determined through the corresponding calculation, which is helpful for the main thread work of the subsequent clamping vehicle.

[0060] In the method for determining the state of the clamping arm assembly of the clamping vehicle, the reflective column arranged on the clamping arm assembly is irradiated by laser, thereby avoiding large-scale laser irradiation on the clamping arm connecting the two reflective columns and simplifying the process of obtaining point cloud data. In this way, the clustering result of the clamping arm assembly corresponding to the reflective column can be directly and accurately obtained by clustering the obtained point cloud data, and therefore, the state to be verified of the clamping arm assembly in the actual environment is efficiently determined. In the case where the state to be verified is a normal state, the state to be verified is verified by the task verification code representing the preset state, so that after the initial screening of whether the state to be verified is a normal state is completed, the re-screening is performed, that is, the effectiveness of the state determination process of the clamping arm assembly is ensured through twice screening. In the case where the verification is passed, the target state of the clamping arm assembly is accurately determined according to the state to be verified matching the task verification code, the state of the clamping arm assembly is automatically determined, manual determination of the state of the clamping arm assembly is avoided, and therefore, the working efficiency of the clamping vehicle can be improved.

[0061] In one embodiment, the clustering processing based on the point cloud data to obtain the clustering result of the clamping arm assembly corresponding to the reflective column includes: obtaining the reflectivity corresponding to each point cloud data respectively, and screening each point cloud data based on the reflectivity to obtain target point cloud data. The target point cloud data is clustered to obtain the clustering result of the clamping arm assembly corresponding to the reflective column.

[0062] The reflectivity is the ability of an object to reflect light. Each point cloud data corresponds to a reflectivity. The reflectivity of 0 represents the darkest, and the reflectivity of 255 represents the brightest.

[0063] Specifically, the computer device obtains the reflectivity corresponding to each point cloud data sent by the sensor. The computer device compares the reflectivity corresponding to each point cloud data with the reflectivity threshold respectively, and takes the point cloud data greater than or equal to the reflectivity threshold as the target point cloud data. The computer device determines the target clustering algorithm from the plurality of clustering algorithms, and performs clustering processing on the target point cloud data based on the target clustering algorithm to obtain the clustering result of the clamp assembly corresponding to the reflection column. Wherein, the point cloud data is screened by reflectivity, which can also be used as a data preprocessing method.

[0064] For example, the computer device filters the point cloud data with reflectivity less than or equal to 240, and takes the point cloud data with reflectivity greater than 240 as the target point cloud data. The computer device performs clustering processing on the target point cloud data by the Euclidean distance algorithm in the PCL library to obtain the clustering result of the clamp assembly corresponding to the reflection column.

[0065] It should be noted that the point cloud data with reflectivity less than or equal to the reflectivity threshold is the surrounding environment point cloud data, not the reflection column point cloud data. Therefore, by screening the point cloud data by reflectivity, the interference of the environment point cloud data is avoided, and the accuracy and effectiveness of the clustering result are ensured.

[0066] In this embodiment, the target point cloud data corresponding to the reflection column is obtained by screening each point cloud data by reflectivity, avoiding the interference of the environment point cloud data. In this way, by performing clustering processing on the target point cloud data, the effectiveness and accuracy of the clustering result are ensured. In addition, by effectively screening the point cloud data, the data calculation amount can be greatly reduced, and the efficiency of data processing is realized.

[0067] In one embodiment, the normal state includes an upright state and a horizontal state, and the clustering result includes the number of categories and the number of points contained in the category. The method for determining the to-be-checked state of the clamp assembly according to the clustering result includes: in the case that the clustering result meets a first clustering condition, determining that the to-be-checked state represents the state of the clamp assembly as the upright state; wherein the first clustering condition is that the number of categories is a first number of categories, and the number of points contained in the category is greater than or equal to a first number of points. In the case that the clustering result meets a second clustering condition, determining that the to-be-checked state represents the state of the clamp assembly as the horizontal state; wherein the second clustering condition is that the number of categories is a second number of categories, and the number of points contained in the category is greater than or equal to a second number of points; wherein the first number of categories is greater than the second number of categories, and the first number of points is less than the second number of points.

[0068] Specifically, the computer device determines the number of classes in the clustering result and the number of points contained in the classes. In a case where the number of classes is a first number of classes and the number of points contained in the classes is greater than or equal to a first number of points, the computer device determines that the clustering result satisfies a first clustering condition. In a case where the clustering result satisfies the first clustering condition, the computer device determines that the state to be checked represents a state of the clamp arm assembly as an upright state. In a case where the number of classes is a second number of classes and the number of points contained in the classes is greater than or equal to a second number of points, the computer device determines that the state to be checked represents a state of the clamp arm assembly as a horizontal state. In a case where the clustering result does not satisfy the first clustering condition and does not satisfy the second clustering condition, the computer device determines that the state to be checked represents a state of the clamp arm assembly as an abnormal state. The first number of classes is greater than the second number of classes, and the first number of points is less than the second number of points.

[0069] It should be noted that the classes can be classes obtained by directly clustering all point cloud data, or classes obtained by filtering the point cloud data to obtain target point cloud data and then clustering the target point cloud data. Therefore, the number of points contained in the classes can represent the number of point cloud data contained in the classes, or the number of target point cloud data contained in the classes, and is not limited in particular.

[0070] It should be noted that, in the present embodiment, the computer device determines the number of classes in the clustering result and the number of points contained in the classes. Figure 3 As shown in FIG. 6, the distance between the positions where the two clamp arms of the clamp arm assembly are respectively provided with the reflective columns can determine the angle at which the two clamp arms are spread apart from the end of the vehicle body. When the reflective columns are perpendicular to the ground, the state of the clamp arm assembly is an upright state. Obviously, in the upright state, the number of classes is two, that is, there are two classes. When the reflective columns are parallel to the ground, the state of the clamp arm assembly is a horizontal state. Obviously, in the horizontal state, the number of classes is one, and at this time, the reflective columns present a strip shape. It should be noted that in the computer device, the first number of points and the second number of points are both a kind of preset external parameters, which are used to indicate the main thread work of the clamp vehicle.

[0071] For example, in a case where the number of classes is two and the number of points contained in each class is greater than or equal to the first number of points, the computer device determines that the state to be checked represents a state of the clamp arm assembly as an upright state. In a case where the number of classes is one and the number of points contained in the class is greater than or equal to the second number of points, the computer device determines that the state to be checked represents a state of the clamp arm assembly as a horizontal state. The first number of points is less than the second number of points. The first number of points can be 6, and in other embodiments, the first number of points can be less than 6, for example, 5, or greater than 10, and the second number of points can be 25.

[0072] In the embodiment, in a case where the clustering result satisfies a first clustering condition, it is determined that the to-be-verified state represents a state of the clamp arm assembly as an upright state; the first clustering condition is that the number of categories is a first category number, and the number of points included in the category is greater than or equal to a first point number. In a case where the clustering result satisfies a second clustering condition, it is determined that the to-be-verified state represents a state of the clamp arm assembly as a horizontal state; the second clustering condition is that the number of categories is a second category number, and the number of points included in the category is greater than or equal to a second point number. In this way, the to-be-verified state is determined by the number of categories and the number of points included in the category, which can avoid misjudgment of the to-be-verified state, thereby ensuring the accuracy of the to-be-verified state.

[0073] In one embodiment, in a case where the state of the clamp arm assembly is determined as the upright state, the computer device performs mean value processing on the coordinate information of the target point cloud data in each category to obtain a mean value point corresponding to each category. The computer device calculates the distance between any two mean value points based on the coordinate information of the mean value points corresponding to each category to obtain a calculation result corresponding to the upright state. In a case where the state of the clamp arm assembly is determined as the horizontal state, the computer device takes the distance between the two target point cloud data farthest away as the calculation result corresponding to the horizontal state.

[0074] The coordinate information represents an x-axis coordinate and a y-axis coordinate.

[0075] For example, in a case where the state of the clamp arm assembly is determined as the upright state, there are two categories, which are category 1 and category 2. Category 1 includes 7 target point cloud data, and category 2 includes 10 target point cloud data. The computer device takes the mean value of the horizontal coordinates and the mean value of the vertical coordinates of the 7 target point cloud data in category 1 as the horizontal coordinates and the vertical coordinates of the mean value point M, respectively. The mean value point M represents each target point data included in category 1. Similarly, the computer device calculates the mean value of the horizontal coordinates and the mean value of the vertical coordinates of the 10 target point cloud data in category 2 as the horizontal coordinates and the vertical coordinates of the mean value point N, respectively. The mean value point N represents each target point data included in category 2. The calculation result corresponding to the upright state is determined based on the mean value points M and N. In a case where the state of the clamp arm assembly is determined as the horizontal state, there is one category, which includes 27 target point cloud data. The distance between the two target point cloud data farthest away is directly taken as the calculation result corresponding to the horizontal state, which can be regarded as the height of the light column. The computer device determines the angle at which the two clamp arms are opened away from the vehicle body based on the calculation result.

[0076] In the embodiment, in the case that the to-be-verified state is determined to be the normal state according to the clustering result, the calculation corresponding to the to-be-verified state is performed according to the target point cloud data, so that the calculation result corresponding to the current state can be accurately and timely determined, and the angle of the two clamping arms away from the one end of the vehicle body can be effectively determined.

[0077] In one embodiment, the task verification code carries preset state information of the clamping arm assembly, and the verification of the to-be-verified state based on the task verification code includes: comparing the preset state information in the task verification code with the to-be-verified state. In the case that the preset state information is consistent with the to-be-verified state, it is determined that the verification is passed. In the case that the preset state information is inconsistent with the to-be-verified state, it is determined that the verification is failed.

[0078] Specifically, the computer device compares the preset state information in the task verification code with the to-be-verified state. In the case that the preset state information is consistent with the to-be-verified state, it is determined that the verification is passed, the calculation result corresponding to the verification is obtained, and the normal state code corresponding to the to-be-verified state is generated. The computer device directly updates the calculation result as the distance between the parts where the two clamping arms of the clamping arm assembly are respectively provided with the reflective columns. In the case that the preset state information is inconsistent with the to-be-verified state, it is determined that the verification is failed, and the calculation result corresponding to the verification is deleted. The computer device updates any value as the distance between the parts where the two clamping arms of the clamping arm assembly are respectively provided with the reflective columns. The computer device determines the opening angle of the one end of the two clamping arms away from the vehicle body according to the distance between the parts where the two clamping arms of the clamping arm assembly are respectively provided with the reflective columns.

[0079] For example, the to-be-verified state corresponding to the verification passed is the upright state, and the computer device generates the normal state code corresponding to the upright state, such as the legal state code 1. The to-be-verified state corresponding to the verification passed is the horizontal state, and the computer device generates the normal state code corresponding to the horizontal state, such as the legal state code 2.

[0080] In the embodiment, by comparing the preset state information in the task verification code with the to-be-verified state, the effectiveness of the to-be-verified state is verified, and the accuracy of the state estimation of the clamping arm assembly is ensured, that is, the accuracy of the determination of the state of the clamping arm assembly is greatly improved.

[0081] In one embodiment, the method further includes: in the case that the to-be-verified state is not the normal state, generating an abnormal state code corresponding to the to-be-verified state which is not the normal state, and the abnormal state code is used to prompt not to process the to-be-verified state which is not the normal state.

[0082] The abnormal state code represents the state to be checked, which can be understood as an illegal state code, and the abnormal check code can be in the form of a number, a word, or a character, without limitation.

[0083] Specifically, in the case that the state to be checked is not a normal state, the computer device generates an abnormal state code corresponding to the state to be checked that is not a normal state, triggers the sensor to re-emit laser light to the reflective column to obtain point cloud data, and returns to the step of performing clustering processing based on the point cloud data to obtain the clustering result corresponding to the clamp arm assembly, and the step continues to be executed until the target state of the clamp arm assembly is determined.

[0084] For example, in the case that the state to be checked is a rotating state, the computer device generates an illegal state code, triggers the sensor to re-emit laser light to the reflective column to obtain point cloud data, and returns to the step of performing clustering processing based on the point cloud data to obtain the clustering result corresponding to the clamp arm assembly, and the step continues to be executed until the target state of the clamp arm assembly is determined.

[0085] In this embodiment, in the case that the state to be checked is not a normal state, an abnormal state code corresponding to the state to be checked that is not a normal state is generated, which can prompt the computer device not to process in time and avoid useless calculation, greatly simplifying the calculation amount.

[0086] In one embodiment, the method further includes: in the case that the check fails, generating an abnormal state code corresponding to the state to be checked that fails the check, and the abnormal state code is used to prompt not to process the state to be checked that fails the check.

[0087] Specifically, in the case that the check fails, the computer device directly generates an abnormal state code corresponding to the state to be checked that fails the check. The computer device deletes the calculation result corresponding to the check failure, and takes any numerical value as the calculation result corresponding to the state to be checked that is not a normal state. The computer device triggers the sensor to re-emit laser light to the reflective column to obtain point cloud data, and returns to the step of performing clustering processing based on the point cloud data to obtain the clustering result of the clamp arm assembly corresponding to the reflective column, and the step continues to be executed until the target state of the clamp arm assembly is determined.

[0088] In this embodiment, in the case that the check fails, an abnormal state code corresponding to the state to be checked that fails the check is generated, which can prompt the computer device not to process in time and avoid useless calculation, greatly simplifying the calculation amount.

[0089] In order to more clearly understand the technical solutions of the present application, a more detailed embodiment is provided for description. As Figure 5As shown, at least two threads in the computer device are involved in the process of running the unmanned clamp car, wherein the main thread (which can also be regarded as the AGV main program) for executing the work of the unmanned clamp car and the detection thread for determining the state of the clamp arm assembly of the unmanned clamp car are involved.

[0090] Step 1: Deploy a reflective column on the clamp arm assembly. When the main thread in the computer device executes the main work program of the clamp car, the computer device creates a detection thread assigned to the main thread through a C++ algorithm and calls the clamp arm positioning library.

[0091] Step 2: The sensor performs laser scanning on the reflective column on the clamp arm assembly in response to the detection thread triggered by the computer device to obtain point cloud data at the current time. The sensor sends the point cloud data to the detection thread of the computer device. Two reflective columns are respectively installed on the two clamp arms, and the reflective columns are installed on the ends of the clamp arms away from the car body.

[0092] Step 3: The computer device compares the reflectivity of each point cloud data with the reflectivity threshold respectively, and takes the point cloud data greater than or equal to the reflectivity threshold as the target point cloud data. The computer device determines the target clustering algorithm from the plurality of clustering algorithms, and performs clustering processing on the target point cloud data based on the target clustering algorithm to obtain the clustering result of the clamp arm assembly corresponding to the reflectivity column, the clustering result including the number of categories and the number of points contained in each category. The number of points contained in each category is the number of target point cloud data contained in each category. In the case that the number of categories is a first number of categories and the number of points contained in each category is greater than or equal to a first number of points, it is determined that the clustering result meets a first clustering condition. In the case that the clustering result meets the first clustering condition, the computer device determines that the to-be-verified state represents the state of the clamp arm assembly as an upright state. In the case that the number of categories is a second number of categories and the number of points contained in each category is greater than or equal to a second number of points, the computer device determines that the to-be-verified state represents the state of the clamp arm assembly as a horizontal state. In the case that the clustering result does not meet the first clustering condition and does not meet the second clustering condition, it is determined that the to-be-verified state represents the state of the clamp arm assembly as an abnormal state. In the case that the state of the clamp arm assembly is determined as the upright state, the computer device generates a normal state code corresponding to the upright state, such as a legal state code 1, and performs mean value processing on the coordinate information of the target point cloud data in each category to obtain the mean value point corresponding to each category. The computer device calculates the distance between any two mean value points based on the coordinate information of the mean value points corresponding to each category to obtain a calculation result corresponding to the upright state. In the case that the state of the clamp arm assembly is determined as the horizontal state, the computer device generates a normal state code corresponding to the horizontal state, such as a legal state code 2, and directly takes the distance between the two target point cloud data farthest away as the calculation result corresponding to the horizontal state. In the case that the to-be-verified state is not a normal state, the computer device generates an abnormal state code corresponding to the to-be-verified code that is not a normal state, triggers the sensor to re-emit laser light to the reflectivity column to obtain point cloud data, and returns to the clustering processing based on the point cloud data to obtain the clustering result corresponding to the clamp arm assembly. The detection thread in the computer device returns the normal state code or the abnormal state code to the main thread, and sends the calculation result corresponding to the normal state code to the main thread.

[0093] Step 4: In the case that the to-be-verified state is a normal state, the main thread in the computer device obtains a task verification code corresponding to the point cloud data, the task verification code carrying preset state information of the clamp arm assembly, and the computer device compares the preset state information in the task verification code with the to-be-verified state (corresponding to Figure 5corresponding to the check pass, and generates a normal state code corresponding to the state to be checked. The computer device directly updates the calculation result as the distance between the parts where the two clamping arms of the clamping arm assembly respectively set the reflective column. At this time, the detection thread obtains the next point cloud data corresponding to the next moment, takes the next point cloud data as the point cloud data corresponding to the current moment, and returns to the step of performing clustering processing based on the point cloud data to obtain the clustering result of the clamping arm assembly corresponding to the reflective column, and the step continues to be executed until the new thread is terminated. In the case where the preset state information is inconsistent with the state to be checked, it is determined that the check fails, and the calculation result corresponding to the check failure is deleted. The computer device takes an arbitrary numerical value as the calculation result corresponding to the state to be checked which is not normal (which can be understood as illegal or abnormal state). Figure 5 corresponding to the reflective column, and the step continues to be executed until the target state of the clamping arm assembly is determined.

[0094] It should be noted that the detection thread is continuously executed in a loop, that is, in the case where the check passes, the detection thread judges the clamping arm assembly at the next moment. In the case where the check fails, the detection thread first determines the state of the clamping arm assembly at the current moment, and then judges the clamping arm assembly at the next moment after obtaining the target state of the clamping arm assembly at the current moment.

[0095] Step 5: The computer device receives a measured distance measured by a person, and the measured distance represents the distance between the parts where the two clamping arms of the clamping arm assembly respectively set the reflective column measured by the person on site, and performs distance verification based on the measured distance and the distance between the parts where the two clamping arms of the clamping arm assembly respectively set the reflective column. In the case where the distance verification passes, it is determined that the distance between the parts where the two clamping arms of the clamping arm assembly respectively set the reflective column is the target distance.

[0096] In the embodiment, by laser irradiation on the reflective column arranged on the clamp arm assembly, large-scale laser irradiation on the clamp arm assembly connecting two reflective columns is avoided, and the process of obtaining point cloud data is simplified. Further, in combination with the reflectivity threshold, the point cloud data is screened to determine effective target point cloud data. In this way, on the basis of ensuring information effectiveness, the data amount is reduced, thereby greatly improving the efficiency of determining the state of the clamp arm assembly. In this way, by clustering the target point cloud data, the clustering result of the clamp arm assembly corresponding to the reflective column can be directly and accurately obtained, and thus the to-be-verified state of the clamp arm assembly in the actual environment is efficiently determined. In the case where the to-be-verified state is a normal state, the to-be-verified state is verified by the task verification code representing the preset state, so that after the initial screening of whether the to-be-verified state is a normal state is completed, the to-be-verified state is screened again, that is, the effectiveness of the state determination process of the clamp arm assembly is ensured through twice screening. In the case where the verification is passed, the target state of the clamp arm assembly is accurately determined according to the to-be-verified state matching the task verification code, the state of the clamp arm assembly is automatically determined, manual determination of the state of the clamp arm assembly is avoided, and thus the working efficiency of the clamp car can be improved.

[0097] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0098] Based on the same inventive concept, the embodiments of the present application also provide a device for determining the state of the clamp arm assembly of the clamp car, which is used to implement the method for determining the state of the clamp arm assembly of the clamp car as described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more device embodiments for determining the state of the clamp arm assembly of the clamp car provided below can be referred to the limitations of the method for determining the state of the clamp arm assembly of the clamp car described above, which will not be repeated here.

[0099] In one embodiment, as shown in Figure 6 a device for determining the state of the clamp arm assembly of the clamp car is provided, comprising: an acquisition module 602, a clustering module 604 and a determination module 606, wherein:

[0100] The acquisition module 602 is configured to acquire point cloud data obtained by laser irradiation on a reflective column disposed on a clamp arm assembly.

[0101] The clustering module 604 is configured to perform clustering processing based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column.

[0102] The acquisition module 602 is further configured to determine a to-be-verified state of the clamp arm assembly according to the clustering result, and acquire a task verification code corresponding to the point cloud data in a case where the to-be-verified state is a normal state; wherein the task verification code represents a preset state of the clamp arm assembly.

[0103] The determination module 606 is configured to verify the to-be-verified state based on the task verification code, and determine a target state of the clamp arm assembly based on the to-be-verified state in a case where the verification is passed.

[0104] In an embodiment, the clustering module 604 is configured to acquire a reflectivity corresponding to each point cloud data respectively, and perform screening on each point cloud data based on the reflectivity to obtain target point cloud data. The clustering module 604 is configured to perform clustering processing on the target point cloud data to obtain the clustering result of the clamp arm assembly corresponding to the reflective column.

[0105] In an embodiment, the acquisition module 602 is configured to determine that the to-be-verified state represents a state of the clamp arm assembly as an upright state in a case where the clustering result satisfies a first clustering condition; wherein the first clustering condition is that a category number is a first category number, and a point number contained in a category is greater than or equal to a first point number. The acquisition module 602 is configured to determine that the to-be-verified state represents a state of the clamp arm assembly as a horizontal state in a case where the clustering result satisfies a second clustering condition; wherein the second clustering condition is that the category number is a second category number, and the point number contained in the category is greater than or equal to a second point number; wherein the first category number is greater than the second category number, and the first point number is less than the second point number.

[0106] In an embodiment, the determination module 606 is configured to compare preset state information in the task verification code with the to-be-verified state. The determination module 606 is configured to determine that the verification is passed in a case where the preset state information is consistent with the to-be-verified state. The determination module 606 is configured to determine that the verification is not passed in a case where the preset state information is not consistent with the to-be-verified state.

[0107] In an embodiment, the acquisition module 602 is further configured to generate an abnormal state code corresponding to the to-be-verified state that is not the normal state in a case where the to-be-verified state is not the normal state, and the abnormal state code is used to prompt that the to-be-verified state that is not the normal state is not processed.

[0108] In an embodiment, the determining module 606 is further configured to, in the case of a failed check, generate an exception state code corresponding to the to-be-checked state that fails the check, the exception state code being used to prompt that the to-be-checked state that fails the check is not processed.

[0109] The modules in the state determining apparatus of the clamping arm assembly of the clamp car can be implemented by software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so that the processor can call and execute the operations corresponding to the modules.

[0110] In an embodiment, a computer device is provided, which can be a server. An internal structure diagram of the computer device can be as shown in Figure 7 The computer device includes a processor, a memory, an input / output interface, and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured 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, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store the state determining data of the clamping arm assembly of the clamp car. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with terminals outside through a network connection. The computer program is executed by the processor to implement a state determining method of a clamping arm assembly of a clamp car.

[0111] Those skilled in the art can understand that Figure 7 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not limit the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program: obtaining point cloud data obtained by laser irradiation on a reflective column deployed on a clamp arm assembly. Performing clustering processing based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. Determining a to-be-verified state of the clamp arm assembly according to the clustering result, and in a case where the to-be-verified state is a normal state, obtaining a task verification code corresponding to the point cloud data; wherein the task verification code represents a preset state of the clamp arm assembly. Verifying the to-be-verified state based on the task verification code, and in a case where the verification is passed, determining a target state of the clamp arm assembly based on the to-be-verified state.

[0113] In one embodiment, the processor further implements the following steps when executing the computer program: obtaining reflectivity corresponding to each point cloud data, and performing screening on each point cloud data based on the reflectivity to obtain target point cloud data. Performing clustering processing on the target point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column.

[0114] In one embodiment, the processor further implements the following steps when executing the computer program: in a case where the clustering result satisfies a first clustering condition, determining that the to-be-verified state represents a state of the clamp arm assembly as an upright state; wherein the first clustering condition is that the number of categories is a first number of categories, and the number of points contained in a category is greater than or equal to a first number of points. In a case where the clustering result satisfies a second clustering condition, determining that the to-be-verified state represents a state of the clamp arm assembly as a horizontal state; wherein the second clustering condition is that the number of categories is a second number of categories, and the number of points contained in a category is greater than or equal to a second number of points; wherein the first number of categories is greater than the second number of categories, and the first number of points is less than the second number of points. In one embodiment, the processor further implements the following steps when executing the computer program: comparing preset state information in the task verification code with the to-be-verified state. In a case where the preset state information is consistent with the to-be-verified state, determining that the verification is passed. In a case where the preset state information is inconsistent with the to-be-verified state, determining that the verification is not passed.

[0115] In one embodiment, the processor further implements the following steps when executing the computer program: in a case where the to-be-verified state is not a normal state, generating an abnormal state code corresponding to the to-be-verified state that is not a normal state, the abnormal state code being used to prompt not to process the to-be-verified state that is not a normal state.

[0116] In one embodiment, the processor further implements the following steps when executing the computer program: in a case where the verification is not passed, generating an abnormal state code corresponding to the to-be-verified state that is not passed, the abnormal state code being used to prompt not to process the to-be-verified state that is not passed.

[0117] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the following steps: obtaining point cloud data obtained by laser irradiation on a reflective column arranged on a clamp arm assembly. Clustering processing is performed based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. A to-be-verified state of the clamp arm assembly is determined according to the clustering result, and in a case where the to-be-verified state is a normal state, a task verification code corresponding to the point cloud data is obtained; wherein the task verification code represents a preset state of the clamp arm assembly. The to-be-verified state is verified based on the task verification code, and in a case where the verification is passed, a target state of the clamp arm assembly is determined based on the to-be-verified state. In one embodiment, the computer program is executed by the processor to further implement the following steps: obtaining a reflectivity corresponding to each point cloud data, and selecting each point cloud data based on the reflectivity to obtain target point cloud data. The target point cloud data is subjected to clustering processing to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. In one embodiment, the computer program is executed by the processor to further implement the following steps: in a case where the clustering result satisfies a first clustering condition, determining that the to-be-verified state represents a state of the clamp arm assembly as an upright state; wherein the first clustering condition is that the number of categories is a first number of categories, and the number of points included in the category is greater than or equal to a first number of points. In a case where the clustering result satisfies a second clustering condition, it is determined that the to-be-verified state represents a state of the clamp arm assembly as a horizontal state; wherein the second clustering condition is that the number of categories is a second number of categories, and the number of points included in the category is greater than or equal to a second number of points; wherein the first number of categories is greater than the second number of categories, and the first number of points is less than the second number of points. In one embodiment, the computer program is executed by the processor to further implement the following steps: comparing preset state information in the task verification code with the to-be-verified state. In a case where the preset state information is consistent with the to-be-verified state, it is determined that the verification is passed. In a case where the preset state information is inconsistent with the to-be-verified state, it is determined that the verification is not passed.

[0118] In one embodiment, the computer program is executed by the processor to further implement the following steps: in a case where the to-be-verified state is not a normal state, generating an abnormal state code corresponding to the to-be-verified state that is not a normal state, the abnormal state code being used to prompt not to process the to-be-verified state that is not a normal state.

[0119] In one embodiment, the computer program is executed by the processor to further implement the following steps: in a case where the verification is not passed, generating an abnormal state code corresponding to the to-be-verified state that is not passed, the abnormal state code being used to prompt not to process the to-be-verified state that is not passed.

[0120] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the following steps: obtaining point cloud data obtained by laser irradiation on a reflective column deployed on a clamp arm assembly. Performing clustering processing based on the point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. Determining a to-be-verified state of the clamp arm assembly according to the clustering result, and in the case that the to-be-verified state is a normal state, obtaining a task verification code corresponding to the point cloud data; wherein the task verification code represents a preset state of the clamp arm assembly. Verifying the to-be-verified state based on the task verification code, and in the case that the verification is passed, determining a target state of the clamp arm assembly based on the to-be-verified state. In one embodiment, the computer program, when executed by the processor, further implements the following steps: obtaining a reflectivity corresponding to each point cloud data, and performing screening on each point cloud data based on the reflectivity to obtain target point cloud data. Performing clustering processing on the target point cloud data to obtain a clustering result of the clamp arm assembly corresponding to the reflective column. In one embodiment, the computer program, when executed by the processor, further implements the following steps: in the case that the clustering result satisfies a first clustering condition, determining that the to-be-verified state represents a state of the clamp arm assembly as an upright state; wherein the first clustering condition is that the number of categories is a first number of categories, and the number of points contained in the category is greater than or equal to a first number of points. In the case that the clustering result satisfies a second clustering condition, determining that the to-be-verified state represents a state of the clamp arm assembly as a horizontal state; wherein the second clustering condition is that the number of categories is a second number of categories, and the number of points contained in the category is greater than or equal to a second number of points; wherein the first number of categories is greater than the second number of categories, and the first number of points is less than the second number of points.

[0121] In one embodiment, the computer program, when executed by the processor, further implements the following steps: comparing preset state information in the task verification code with the to-be-verified state. In the case that the preset state information is consistent with the to-be-verified state, determining that the verification is passed. In the case that the preset state information is inconsistent with the to-be-verified state, determining that the verification is not passed.

[0122] In one embodiment, the computer program, when executed by the processor, further implements the following steps: in the case that the to-be-verified state is not a normal state, generating an abnormal state code corresponding to the to-be-verified state that is not a normal state, the abnormal state code being used to prompt not to process the to-be-verified state that is not a normal state.

[0123] In one embodiment, the computer program, when executed by the processor, further implements the following steps: in the case that the verification is not passed, generating an abnormal state code corresponding to the to-be-verified state that is not passed, the abnormal state code being used to prompt not to process the to-be-verified state that is not passed.

[0124] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of the country and region.

[0125] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of each method can be included. In the embodiments provided in the present application, any reference to memory, database or other medium 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 storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric 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 but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0126] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0127] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method of determining the state of a clamping arm assembly of a clamping vehicle, characterized by The method comprises: acquiring point cloud data obtained by laser irradiation on a reflective column arranged on a clamping arm assembly; performing clustering processing based on the point cloud data to obtain a clustering result of the clamping arm assembly corresponding to the reflective column, the clustering result comprising a category number and a point number contained in each category; in a case where the clustering result meets a first clustering condition, determining that a state of the clamping arm assembly represented by the to-be-verified state is an upright state, wherein the first clustering condition is that the category number is a first category number and the point number contained in each category is greater than or equal to a first point number; in a case where the clustering result meets a second clustering condition, determining that a state of the clamping arm assembly represented by the to-be-verified state is a horizontal state, wherein the second clustering condition is that the category number is a second category number and the point number contained in each category is greater than or equal to a second point number, wherein the first category number is greater than the second category number and the first point number is less than the second point number; in a case where the to-be-verified state is a normal state, acquiring a task verification code corresponding to the point cloud data, wherein the task verification code represents a preset state of the clamping arm assembly, and the normal state comprises the upright state and the horizontal state; verifying the to-be-verified state based on the task verification code, and in a case where the verification is passed, determining a target state of the clamping arm assembly based on the to-be-verified state.

2. The method of claim 1, wherein, The clustering processing based on the point cloud data to obtain a clustering result of the clamping arm assembly corresponding to the reflective column comprises: acquiring a reflectivity corresponding to each point cloud data, and performing screening on each point cloud data based on the reflectivity to obtain target point cloud data; performing clustering processing on the target point cloud data to obtain a clustering result of the clamping arm assembly corresponding to the reflective column.

3. The method of claim 1, wherein, The task verification code carries preset state information of the clamping arm assembly, and the verification of the to-be-verified state based on the task verification code comprises: comparing the preset state information in the task verification code with the to-be-verified state; in a case where the preset state information is consistent with the to-be-verified state, determining that the verification is passed; in a case where the preset state information is inconsistent with the to-be-verified state, determining that the verification is not passed.

4. The method of claim 1, wherein, The method further comprises: in a case where the to-be-verified state is not a normal state, generating an abnormal state code corresponding to the to-be-verified state that is not a normal state, the abnormal state code being used to prompt that the to-be-verified state that is not a normal state is not processed.

5. The method of claim 1, wherein, The method further comprises: in a case where the verification is not passed, generating an abnormal state code corresponding to the to-be-verified state that is not passed, the abnormal state code being used to prompt that the to-be-verified state that is not passed is not processed.

6. A device for determining the state of a clamping arm assembly of a clamping vehicle, characterized by The device comprises: an acquisition module configured to acquire point cloud data obtained by laser irradiation on a reflective column arranged on a clamping arm assembly; a clustering module configured to perform clustering processing based on the point cloud data to obtain a clustering result of the clamping arm assembly corresponding to the reflective column, the clustering result comprising a category number and a point number contained in each category; The acquisition module is further configured to determine that the to-be-verified state of the clamping arm assembly represents an upright state of the clamping arm assembly when the clustering result satisfies a first clustering condition, wherein the first clustering condition is that the number of categories is a first number of categories, and the number of points included in each category is greater than or equal to a first number of points; and determine that the to-be-verified state represents a horizontal state of the clamping arm assembly when the clustering result satisfies a second clustering condition, wherein the second clustering condition is that the number of categories is a second number of categories, and the number of points included in each category is greater than or equal to a second number of points; wherein the first number of categories is greater than the second number of categories, and the first number of points is less than the second number of points; and acquire a task verification code corresponding to the point cloud data when the to-be-verified state is a normal state, wherein the task verification code represents a preset state of the clamping arm assembly, and the normal state includes the upright state and the horizontal state. The determination module is configured to verify the to-be-verified state based on the task verification code, and determine a target state of the clamping arm assembly based on the to-be-verified state when the verification is passed.

7. The apparatus of claim 6, wherein, The clustering module is configured to acquire a reflectivity corresponding to each point cloud data, and filter each point cloud data based on the reflectivity to obtain target point cloud data. The target point cloud data is subjected to clustering processing to obtain a clustering result of the clamping arm assembly corresponding to the light column. 8.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-7. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 5.

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