Sanitation vehicle test information processing method, device, equipment and readable medium

Abstract

The embodiment of the invention discloses a sanitation vehicle test information processing method and device, electronic equipment and a readable medium. A specific embodiment of the method comprises the steps of obtaining a sanitation vehicle test case sequence of a target sanitation vehicle; based on the environmental sanitation vehicle test case sequence, the following environmental sanitation test steps are executed: obtaining case information corresponding to a first environmental sanitation vehicle test case in the environmental sanitation vehicle test case sequence; testing the target environmental sanitation vehicle according to the use case information to generate a test behavior time group, and marking each test behavior time in the test behavior time group according to a behavior label group included in the use case information to generate test behavior information to obtain a test behavior information group; determining a fault prediction reason according to the test fault information; and sending the fault prediction reason and the test behavior information group to a target terminal for display. According to the implementation mode, the driving safety of the sanitation vehicle is improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, equipment, and readable medium for processing test information of a sanitation vehicle. Background Art

[0002] With the continuous development of autonomous driving technology, autonomous driving technology can gradually be applied to the sanitation industry. However, autonomous driving technology is still imperfect, and the autonomous driving system of sanitation vehicles needs to be tested to ensure the safe driving of sanitation vehicles. When testing the autonomous driving system of sanitation vehicles, how to determine the cause of the failure has become an important research topic. At present, when determining the cause of the failure, the usual method is to record the time point of the failure when the failure occurs, take a picture of the scene where the failure occurs, and determine the cause of the failure through the image recognition model.

[0003] However, when the above method is used to determine the cause of the failure of the automatic driving system in the sanitation vehicle, the following technical problems often occur:

[0004] First, when photographing the fault scene, the captured image may be blurred due to environmental factors (for example, light intensity). The image recognition model cannot determine the cause of the fault through the blurred image of the fault scene, resulting in the inability to repair the fault, which in turn leads to lower driving safety of the sanitation vehicle.

[0005] Second, when determining the cause of a fault, there are multiple possible causes of the fault, and the probability of occurrence of different causes of the fault cannot be determined, resulting in a long time required to eliminate each possible cause of the fault one by one.

[0006] Third, when analyzing the cause of a fault, it is impossible to completely restore the fault scene by photographing the scene where the fault occurred, resulting in a long time required to determine the cause of the fault.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0008] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0009] Some embodiments of the present disclosure propose a method, device, electronic device and computer-readable medium for processing test information of sanitation vehicles to solve one or more of the technical problems mentioned in the above background technology section.

[0010] In a first aspect, some embodiments of the present disclosure provide a method for processing sanitation vehicle test information, the method comprising: obtaining a sanitation vehicle test case sequence of a target sanitation vehicle; in response to the current time being a preset test time, based on the above sanitation vehicle test case sequence, performing the following sanitation test steps: obtaining the use case information corresponding to the first sanitation vehicle test case in the above sanitation vehicle test case sequence, wherein the above use case information includes a behavior label group; testing the above target sanitation vehicle according to the above use case information to generate a test behavior time group, and marking each test behavior time in the above test behavior time group according to the behavior label group included in the above use case information to generate test behavior information and obtain a test behavior information group; in response to receiving the test fault information sent by the sanitation vehicle terminal, determining the fault prediction cause according to the above test fault information; sending the above fault prediction cause and the above test behavior information group to the target terminal for display.

[0011] In a second aspect, some embodiments of the present disclosure provide a sanitation vehicle test information processing device, the device comprising: an acquisition unit, configured to acquire a sanitation vehicle test case sequence of a target sanitation vehicle; a testing unit, configured to, in response to the current time being a preset test time, perform the following sanitation test steps based on the above-mentioned sanitation vehicle test case sequence: acquire the use case information corresponding to the first sanitation vehicle test case in the above-mentioned sanitation vehicle test case sequence, wherein the above-mentioned use case information includes a behavior label group; according to the above-mentioned use case information, test the above-mentioned target sanitation vehicle to generate a test behavior time group, and according to the behavior label group included in the above-mentioned use case information, mark each test behavior time in the above-mentioned test behavior time group to generate test behavior information and obtain a test behavior information group; in response to receiving the test fault information sent by the sanitation vehicle terminal, determine the fault prediction cause according to the above-mentioned test fault information; send the above-mentioned fault prediction cause and the above-mentioned test behavior information group to the target terminal for display.

[0012] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0013] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0014] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the sanitation vehicle test information processing method of some embodiments of the present disclosure, the transportation safety of sanitation vehicles can be improved. Specifically, the reason for the low transportation safety of sanitation vehicles is that when shooting the fault scene, the captured image may be blurred due to environmental factors (for example, light intensity), and the image recognition model cannot determine the cause of the fault through the blurred fault scene image, resulting in the inability to repair the fault, which in turn leads to low transportation safety of sanitation vehicles. Based on this, the sanitation vehicle test information processing method of some embodiments of the present disclosure, first, obtains the sanitation vehicle test case sequence of the target sanitation vehicle. Thus, the target vehicle can be tested through different test cases. Secondly, in response to the current time being the preset test time, based on the above-mentioned sanitation vehicle test case sequence, the following test steps are performed: First, the use case information corresponding to the first sanitation vehicle test case in the above-mentioned sanitation vehicle test case sequence is obtained. Thus, the test content included in the use case information can be determined. Secondly, according to the above-mentioned use case information, the above-mentioned target sanitation vehicle is tested to generate a test behavior time group, and according to the behavior tag group included in the above-mentioned use case information, each test behavior time in the above-mentioned test behavior time group is marked to generate test behavior information and obtain a test behavior information group. Thus, the target sanitation vehicle can be tested according to the test content, and the test behavior generated by the target sanitation vehicle and the time point corresponding to the test behavior are marked, and then, when the target sanitation vehicle fails, the test behavior information at the time of the failure can be quickly found, which is convenient for determining the cause of the failure. Then, in response to receiving the test failure information sent by the sanitation vehicle end, the predicted cause of the failure is determined according to the above-mentioned test failure information. Thus, the cause of the failure can be determined, so that the cause of the failure can be repaired, so that the sanitation vehicle can be prevented from having such a failure during transportation, thereby improving the transportation safety of the sanitation vehicle. Finally, the above-mentioned predicted cause of the failure and the above-mentioned test behavior information group are sent to the target terminal for display. Thus, the determination of the cause of the failure is completed, and the driving safety of the sanitation vehicle is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0016] Figure 1 is a flow chart of some embodiments of the sanitation vehicle test information processing method according to the present disclosure;

[0017] Figure 2 is a schematic diagram of the structure of some embodiments of the sanitation vehicle test information processing device according to the present disclosure;

[0018] Figure 3 is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure;

[0019] Figure 4 is a test diagram of an interactive display according to some embodiments of the sanitation vehicle test information processing method disclosed herein;

[0020] Figure 5 It is a test scene diagram of a sanitation vehicle according to some embodiments of the sanitation vehicle test information processing method disclosed in the present invention. DETAILED DESCRIPTION

[0021] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0022] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0023] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0024] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0025] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0026] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0027] Figure 1The process 100 of some embodiments of the sanitation vehicle test information processing method according to the present disclosure is shown. The sanitation vehicle test information processing method comprises the following steps:

[0028] Step 101, obtaining a sanitation vehicle test case sequence of a target sanitation vehicle.

[0029] In some embodiments, the execution subject (e.g., a server) of the sanitation vehicle test information processing method may obtain a sequence of sanitation vehicle test cases for a target sanitation vehicle. In practice, the execution subject may obtain a sequence of sanitation vehicle test cases for a target sanitation vehicle through an associated database. The associated database may be a database for storing sanitation vehicle test cases that is connected to the execution subject by wire or wirelessly. The sanitation vehicle test cases in the sanitation vehicle test case sequence may be pre-set test cases for testing sanitation vehicles. The sanitation vehicle test cases in the sanitation vehicle test case sequence may correspond to use case information. The use case information may be configuration information of a sanitation vehicle test case. The use case information may include a behavior tag group. The behavior tags in the behavior tag group may be pre-set tags for characterizing the behavior of a sanitation vehicle.

[0030] In some optional implementations of some embodiments, the use case information further includes use case description information, operation steps and expected results. The use case description information may be pre-entered information for summarizing the use case. The operation steps may be pre-set operation steps for testing the target vehicle. The expected results may be pre-entered test results of testing the target vehicle.

[0031] Step 102, in response to the current time being the preset test time, based on the sanitation vehicle test case sequence, execute the following test steps:

[0032] Step 1021, obtaining the use case information corresponding to the first sanitation vehicle test case in the sanitation vehicle test case sequence.

[0033] In some embodiments, the execution subject may obtain the use case information corresponding to the first sanitation vehicle test case in the sanitation vehicle test case sequence from a database storing use case information through a wired connection or a wireless connection. The use case information includes a behavior tag group.

[0034] Optionally, before step 1022, a test data processing request sent by the target terminal is received.

[0035] In some embodiments, the execution subject may receive a test data processing request sent by the target terminal, wherein the test data processing request may represent processing of the test data.

[0036] Optionally, the test data processing request is analyzed to generate a test data processing type.

[0037] In some embodiments, the execution subject may analyze the test data processing request to generate a test data processing type. The analysis may be to analyze the test data processing request to determine the test data processing type to be performed. The test data processing type may be a certain type of processing of the test data. For example, the test data processing type may include but is not limited to: recording a bag, moving a test log, and closing a bag.

[0038] Optionally, before step 1022, a target data processing script is obtained according to the above test data processing type.

[0039] In some embodiments, the execution subject may obtain a target data processing script according to the test data processing type. In practice, the execution subject may obtain a data processing script corresponding to the test data processing type from a database connected by wire or wireless connection as the target data processing script. The database may be a database for storing data processing scripts. The data processing script may be a pre-stored script for performing test data processing type processing on data.

[0040] Optionally, before step 1022, data processing is performed on the test data according to the above-mentioned target data processing script to generate a data storage file.

[0041] In some embodiments, the execution entity may perform data processing on the test data according to the target data processing script to generate a data storage file.

[0042] As an example, first, in response to the test data processing type being a mobile test log, the execution subject may use the target data processing script to obtain the test log. Second, a log file corresponding to the test log may be generated as a data storage file. Third, the generated data storage file may be stored in a pre-set directory folder.

[0043] Step 1022, based on the use case information, the target sanitation vehicle is tested to generate a test behavior time group, and based on the behavior label group included in the use case information, each test behavior time in the test behavior time group is marked to generate test behavior information to obtain a test behavior information group.

[0044] In some embodiments, the execution subject may test the target sanitation vehicle according to the use case information to generate a test behavior time group, and mark each test behavior time in the test behavior time group according to the behavior tag group included in the use case information to generate test behavior information and obtain a test behavior information group. In practice, for the test behavior time in the test behavior time group, the execution subject may select a behavior tag corresponding to the test behavior time from the behavior tag group to mark the behavior time and obtain the test behavior information.

[0045] Step 1023, in response to receiving the test fault information sent by the sanitation vehicle end, determine the fault prediction cause according to the test fault information.

[0046] In some embodiments, the above-mentioned execution entity can determine the predicted cause of the fault based on the test fault information received from the sanitation vehicle end.

[0047] In the process of adopting technical solutions to solve the above technical problems, the following technical problems often occur: when determining the cause of a fault, there are multiple possible causes of the fault, and the probability of occurrence of different causes of the fault cannot be determined, resulting in a long time to eliminate each possible cause of the fault one by one. In response to the above technical problems, the conventional solution is generally to test each possible cause of the fault one by one. However, the above conventional solution still has the following problem: it takes a long time to test each possible cause of the fault one by one, and the efficiency of fault determination is low.

[0048] In practice, the above execution entity can determine the cause of the fault prediction through the following steps:

[0049] The first step is to determine the fault time point according to the test fault information. In practice, the time point when the sanitation vehicle sends the test fault information can be determined as the fault time point.

[0050] The second step is to obtain the fault test data set of the target sanitation vehicle according to the fault time point. In practice, the fault test data set corresponding to the fault time point is obtained from the target database. The fault test data in the fault test data set may be the driving data of the target vehicle determined by various sensors installed on the target vehicle during the test of the target sanitation vehicle. The fault test data includes but is not limited to: driving speed, vehicle driving angular velocity and steering wheel rotation angle.

[0051] The third step is to input the above fault test data set into a pre-trained fault prediction model to obtain a fault prediction information sequence. The above fault prediction model can be a pre-trained neural network model that takes the fault test data set as input and the fault prediction information sequence as output. The above fault prediction model is used to predict the cause of the target vehicle failure. The above fault test data corresponds to a fault data range.

[0052] As an example, the above-mentioned fault prediction model may include a correspondence table between a fault test data set and fault prediction information. Among them, the above-mentioned correspondence table may be a correspondence table based on the correspondence between a large number of fault test data sets and fault prediction information by a technician in this field. In this way, each fault test data included in the fault test data set is compared with the fault data range corresponding to each fault test data in the correspondence table in turn to determine whether the fault test data is in the corresponding fault data range. Therefore, for each fault prediction information included in the above-mentioned correspondence table, the ratio of the number of fault test data in the corresponding fault data range to the number of fault test data included in the fault test data set can be determined as the fault prediction probability value. The various fault prediction information included in the above-mentioned correspondence table are sorted in descending order according to the corresponding fault prediction probability values ​​to generate a fault prediction information sequence.

[0053] As another example, the above-mentioned fault prediction model can be obtained by training an untrained deep learning model or an untrained deep learning model, and each layer of the initial deep learning model can be set with initial parameters, and the parameters can be continuously adjusted during the training process of the deep learning model. The initial deep learning model can be various types of untrained or untrained artificial neural networks or a model obtained by combining multiple untrained or untrained artificial neural networks. For example, the initial deep learning model can be an untrained convolutional neural network, or an untrained recurrent neural network, or a model obtained by combining an untrained convolutional neural network, an untrained recurrent neural network, and an untrained fully connected layer. In this way, the fault test data set can be input from the input side of the deep learning model, processed by the parameters of each layer in the deep learning model in sequence, and output from the output side of the deep learning model. The information output by the output side is the fault prediction information sequence.

[0054] The fourth step is to determine the cause of the fault prediction based on the above fault prediction information sequence. In practice, the cause of the fault prediction can be determined in various ways. As an example, the first fault prediction information in the above fault prediction information sequence can be determined as the cause of the fault prediction. As another example, each fault prediction information in the above fault prediction information sequence can be excluded in turn to determine the cause of the fault prediction.

[0055] The relevant contents in the first step to the fourth step above serve as an inventive point of the present disclosure, which solves the above technical problem "When determining the cause of the fault, there are multiple possible causes of the fault, and the probability of occurrence of different causes of the fault cannot be determined, resulting in a long time to eliminate each possible cause of the fault one by one". The factors that cause it to take a long time to eliminate each possible cause of the fault one by one are often as follows: When determining the cause of the fault, there are multiple possible causes of the fault, and the probability of occurrence of different causes of the fault cannot be determined, resulting in a long time to eliminate each possible cause of the fault one by one. If the above factors are solved, the effect of reducing the time for eliminating each possible cause of the fault can be achieved. In order to achieve this effect, first, the fault time point is determined based on the above test fault information. Thus, the time when the fault occurred can be determined. Second, based on the above fault time point, the fault test data set of the above target vehicle is obtained. Thus, the vehicle data of the target vehicle when the fault occurs can be obtained. Third, the above fault test data set is input into a pre-trained fault prediction model to obtain a fault prediction information sequence. Thus, the pre-trained fault prediction model can be used to sort possible faults in descending order of probability, so that the faults with higher probability can be eliminated first, thus reducing the time for eliminating each possible fault cause. Fourth, the fault prediction cause is determined based on the above fault prediction information sequence. Thus, the cause of the fault is determined, reducing the time for eliminating each possible fault cause.

[0056] Step 1024, sending the fault prediction cause and test behavior information group to the target terminal for display.

[0057] In some embodiments, the execution subject may send the fault prediction reason and the test behavior information group to a target terminal for display, wherein the target terminal may be a terminal connected to the execution subject by wire or wirelessly and having permission to receive the fault prediction reason and the test behavior information group.

[0058] Optionally, after step 1024, in response to the sanitation vehicle test case sequence being empty after deleting the first sanitation vehicle test case, the test of the target sanitation vehicle is stopped.

[0059] In some embodiments, the execution entity may stop testing the target sanitation vehicle in response to the sanitation vehicle test case sequence being empty after deleting the first sanitation vehicle test case.

[0060] Optionally, in response to the sanitation vehicle test case sequence from which the first sanitation vehicle test case is deleted being not empty, the sanitation vehicle test case sequence from which the first sanitation vehicle test case is deleted is used as the sanitation vehicle test case sequence, and the above test steps are performed again.

[0061] In some embodiments, in response to the sanitation vehicle test case sequence that deletes the first sanitation vehicle test case being not empty, the above-mentioned execution entity may use the sanitation vehicle test case sequence that deletes the first sanitation vehicle test case as the sanitation vehicle test case sequence and execute the above-mentioned test steps again.

[0062] In the process of adopting technical solutions to solve the above technical problems, the following technical problems often occur: when analyzing the cause of the fault, it is impossible to completely restore the fault scene by shooting the scene where the fault occurred, resulting in a long time to determine the cause of the fault.

[0063] Optionally, the above execution subject may also perform the following restoration steps:

[0064] The first step is to determine the sanitation vehicle test case corresponding to the test failure information as the target test case according to the test failure information. In practice, the sanitation vehicle test case of the target sanitation vehicle test can be determined as the target test case when the test failure information is generated.

[0065] The second step is to obtain a fault test data set corresponding to the target test case as the target test data set. In practice, a test data set generated in the process of testing the target test case can be obtained as the target test data set. The fault test data set in the fault test data set can be a driving data set of the vehicle at each moment in the process of testing the target test case.

[0066] The third step is to generate a three-dimensional test case model based on the use case information corresponding to the target test case. In practice, the three-dimensional test case model can be generated by various methods. As an example, the three-dimensional test case model can be generated by semantically analyzing the use case information and inputting the analysis results into a three-dimensional modeling software. For example, the three-dimensional modeling software can be SoftImage. As another example, a three-dimensional scanner can be controlled to scan the test case scene to generate a three-dimensional test case model.

[0067] The fourth step is to map each obstacle corresponding to the target test case to the three-dimensional test case model to generate a mapped test case model.

[0068] The fifth step is to map the target vehicle to the vehicle starting position included in the mapped test case model, and restore the vehicle test process according to the target test data set.

[0069] The relevant contents in the first step to the fifth step above serve as an inventive point of the present disclosure, and solve the technical problem three mentioned in the background technology, "When analyzing the cause of the fault, the scene of the fault cannot be completely restored by photographing the scene of the fault, resulting in a long time to determine the cause of the fault". The factors that cause it to take a long time to determine the cause of the fault are often as follows: When analyzing the cause of the fault, the scene of the fault cannot be completely restored by photographing the scene of the fault, resulting in a long time to determine the cause of the fault. If the above factors are solved, the effect of reducing the time to determine the cause of the fault can be achieved. In order to achieve this effect, first, according to the above test fault information, the vehicle test case corresponding to the above test fault information is determined as the target test case. Thus, it can be determined in which test case the fault occurs. Second, the test data set corresponding to the above target test case is obtained as the target test data set. Thus, the test data of the target vehicle of the test case in which the fault occurs can be obtained. Third, according to the use case information corresponding to the above target test case, a three-dimensional test case model is generated. Thus, the test scene of the test case can be represented by a three-dimensional model. Fourth, each obstacle corresponding to the above target test case is mapped to the above three-dimensional test case model to generate a mapped test case model. Thus, each obstacle in the test scene can be mapped to the three-dimensional model. Fifth, the target vehicle is mapped to the vehicle starting position included in the mapped test case model, and the vehicle test process is restored according to the target test data set. Thus, the process of the failure can be restored in the three-dimensional model through the measured test data, so that the cause of the failure can be easily determined, reducing the time to determine the cause of the failure.

[0070] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the sanitation vehicle test information processing method of some embodiments of the present disclosure, the transportation safety of sanitation vehicles can be improved. Specifically, the reason for the low transportation safety of sanitation vehicles is that when shooting the fault scene, the captured image may be blurred due to environmental factors (for example, light intensity), and the image recognition model cannot determine the cause of the fault through the blurred fault scene image, resulting in the inability to repair the fault, which in turn leads to low transportation safety of sanitation vehicles. Based on this, the sanitation vehicle test information processing method of some embodiments of the present disclosure, first, obtains the sanitation vehicle test case sequence of the target sanitation vehicle. Thus, the target vehicle can be tested through different test cases. Secondly, in response to the current time being the preset test time, based on the above-mentioned sanitation vehicle test case sequence, the following test steps are performed: First, the use case information corresponding to the first sanitation vehicle test case in the above-mentioned sanitation vehicle test case sequence is obtained. Thus, the test content included in the use case information can be determined. Secondly, according to the above-mentioned use case information, the above-mentioned target sanitation vehicle is tested to generate a test behavior time group, and according to the behavior tag group included in the above-mentioned use case information, each test behavior time in the above-mentioned test behavior time group is marked to generate test behavior information and obtain a test behavior information group. Thus, the target sanitation vehicle can be tested according to the test content, and the test behavior generated by the target sanitation vehicle and the time point corresponding to the test behavior are marked, and then, when the target sanitation vehicle fails, the test behavior information at the time of the failure can be quickly found, which is convenient for determining the cause of the failure. Then, in response to receiving the test failure information sent by the sanitation vehicle end, the predicted cause of the failure is determined according to the above-mentioned test failure information. Thus, the cause of the failure can be determined, so that the cause of the failure can be repaired, so that the sanitation vehicle can be prevented from having such a failure during transportation, thereby improving the transportation safety of the sanitation vehicle. Finally, the above-mentioned predicted cause of the failure and the above-mentioned test behavior information group are sent to the target terminal for display. Thus, the determination of the cause of the failure is completed, and the driving safety of the sanitation vehicle is improved.

[0071] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a sanitation vehicle test information processing device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the sanitation vehicle test information processing device can be specifically applied to various electronic devices.

[0072] like Figure 2As shown, the sanitation vehicle test information processing device 200 of some embodiments includes: an acquisition unit 201 and a test unit 202. The acquisition unit 201 is configured to acquire a sanitation vehicle test case sequence of a target sanitation vehicle; the test unit 202 is configured to respond to the current time being a preset test time, and based on the above sanitation vehicle test case sequence, perform the following sanitation test steps: acquire the use case information corresponding to the first sanitation vehicle test case in the above sanitation vehicle test case sequence, wherein the above use case information includes a behavior tag group; according to the above use case information, test the above target sanitation vehicle to generate a test behavior time group, and according to the behavior tag group included in the above use case information, mark each test behavior time in the above test behavior time group to generate test behavior information, and obtain a test behavior information group; in response to receiving the test fault information sent by the sanitation vehicle terminal, determine the fault prediction cause according to the above test fault information; send the above fault prediction cause and the above test behavior information group to the target terminal for display.

[0073] It is understandable that the units recorded in the sanitation vehicle test information processing device 200 are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the sanitation vehicle test information processing device 200 and the units contained therein, and will not be described in detail here.

[0074] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0075] like Figure 3 As shown, the electronic device 300 may include a processing device 301 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0076] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0077] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0078] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0079] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0080] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the sanitation vehicle test case sequence of the target sanitation vehicle. In response to the current time being the preset test time, based on the sanitation vehicle test case sequence, the following sanitation test steps are performed: obtain the use case information corresponding to the first sanitation vehicle test case in the sanitation vehicle test case sequence, wherein the use case information includes a behavior tag group; according to the use case information, test the target sanitation vehicle to generate a test behavior time group, and according to the behavior tag group included in the use case information, mark each test behavior time in the test behavior time group to generate test behavior information and obtain a test behavior information group; in response to receiving the test fault information sent by the sanitation vehicle terminal, determine the fault prediction cause according to the test fault information; send the fault prediction cause and the test behavior information group to the target terminal for display.

[0081] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0083] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor includes an acquisition unit and a test unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the acquisition unit may also be described as a "unit for acquiring a sequence of sanitation vehicle test cases for a target sanitation vehicle".

[0084] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0085] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for processing sanitation vehicle test information, comprising: Obtain a sanitation vehicle test case sequence of a target sanitation vehicle; In response to the current time being the preset test time, based on the sanitation vehicle test case sequence, the following sanitation test steps are performed: Obtaining use case information corresponding to the first sanitation vehicle test case in the sanitation vehicle test case sequence, wherein the use case information includes a behavior tag group; According to the use case information, the target sanitation vehicle is tested to generate a test behavior time group, and according to the behavior tag group included in the use case information, each test behavior time in the test behavior time group is marked to generate test behavior information, thereby obtaining a test behavior information group; In response to receiving the test fault information sent by the sanitation vehicle terminal, determining the predicted cause of the fault according to the test fault information; The fault prediction cause and the test behavior information group are sent to a target terminal for display.

2. The method according to claim 1, wherein: The testing steps also include: In response to the sanitation vehicle test case sequence being empty after deleting the first sanitation vehicle test case, testing of the target sanitation vehicle is stopped.

3. The method according to claim 1, wherein: The method further comprises: In response to the sanitation vehicle test case sequence from which the first sanitation vehicle test case is deleted is not empty, the sanitation vehicle test case sequence from which the first sanitation vehicle test case is deleted is used as the sanitation vehicle test case sequence, and the test step is executed again.

4. The method according to claim 1, wherein: Before testing the target sanitation vehicle according to the use case information, the testing step further includes: Receiving a test data processing request sent by the target terminal; Analyzing the test data processing request to generate a test data processing type; According to the test data processing type, obtaining a target data processing script; According to the target data processing script, data processing is performed on the test data to generate a data storage file.

5. The method according to claim 1, wherein: The sanitation vehicle test cases in the sanitation vehicle test case sequence correspond to use case information, and the use case information also includes use case description information, operation steps, test circuits and expected results.

6. A sanitation vehicle test information processing device, comprising: An acquisition unit configured to acquire a sanitation vehicle test case sequence of a target sanitation vehicle; The test unit is configured to, in response to the current time being a preset test time, perform the following sanitation test steps based on the sanitation vehicle test case sequence: obtain the use case information corresponding to the first sanitation vehicle test case in the sanitation vehicle test case sequence, wherein the use case information includes a behavior tag group; test the target sanitation vehicle according to the use case information to generate a test behavior time group, and mark each test behavior time in the test behavior time group according to the behavior tag group included in the use case information to generate test behavior information to obtain a test behavior information group; in response to receiving the test fault information sent by the sanitation vehicle end, determine the fault prediction cause according to the test fault information; The fault prediction cause and the test behavior information group are sent to a target terminal for display.

7. An interactive terminal, comprising: one or more processors; An interactive display, used to display data information of the vehicle drone and sanitation platform information, the data information including: steering wheel rotation angle, rotation speed, vehicle speed, left turn signal, right turn signal, fog light, fault light, brake pedal status and accelerator pedal status; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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