Equipment function anomaly detection method and device, equipment, medium and product

Abstract

The embodiment of the invention provides an equipment function anomaly detection method and device, equipment, a medium and a product. The method comprises the following steps: detecting structured data of a target function to obtain a first detection result indicating whether the structured data is abnormal or not; detecting the traffic element data, and generating a second detection result indicating whether the traffic element data is abnormal or not; performing data extraction processing on each piece of chart data of the target function to obtain to-be-detected data; generating a third detection result for indicating whether the chart data is abnormal or not according to the to-be-detected data; and finally, according to the first detection result, the second detection result and the third detection result, determining an equipment function detection result indicating whether the target function is abnormal or not. According to the scheme, the structured data, the traffic element data and the chart data are processed to determine whether the target function is abnormal, so that the detection accuracy is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, equipment, medium and product for detecting abnormal device function. Background Art

[0002] With the development of technology, vehicles have more and more functions, which require various IoT devices to provide data to realize vehicle functions. IoT devices have various functions to output different data, and then different vehicle functions can be realized according to different data. In order to ensure the normal operation of the equipment, it is necessary to test the equipment function.

[0003] In the prior art, the function of a device is usually detected by obtaining structured data of the device, including structured data collected by the device and structured data generated by the operation of the device, and then judging whether each type of structured data exceeds a threshold or falls within a threshold range, and determining whether the structured data is abnormal. If the structured data is abnormal, it is determined that the device function is abnormal.

[0004] In summary, the prior art can only determine whether a device function is abnormal by detecting structured data, resulting in low detection accuracy. Summary of the invention

[0005] The embodiments of the present application provide a method, apparatus, device, medium and product for detecting abnormal device functions, so as to solve the problem in the prior art that whether a device function is abnormal can only be determined by detecting structured data, resulting in low detection accuracy.

[0006] In a first aspect, an embodiment of the present application provides a method for detecting device function abnormality, comprising:

[0007] According to a preset structured data detection rule, the structured data of the target function is detected to obtain a first detection result indicating whether the structured data is abnormal;

[0008] According to the first preset reference data and the element type of the traffic element data of the target function, the traffic element data is detected to generate a second detection result indicating whether the traffic element data is abnormal;

[0009] Performing data extraction processing on each chart data of the target function to obtain the data to be detected corresponding to each chart data;

[0010] generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data and the second preset reference data corresponding to each of the chart data;

[0011] A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, and the third detection result.

[0012] In a possible implementation manner, the element types of the traffic element data of the target function include map element types, signal light element types, and dynamic element types; the first preset reference data includes element reference data corresponding to each element type;

[0013] The detecting of the traffic element data according to the first preset reference data and the element type of the traffic element data of the target function to generate a second detection result indicating whether the traffic element data is abnormal includes:

[0014] generating a first traffic element image according to the traffic element data whose element type is a map element type;

[0015] generating, based on the first traffic element image and the element reference data corresponding to the map element type, a fourth detection result indicating whether the map element has an abnormality;

[0016] generating a second traffic element image according to the traffic element data whose element type is a signal light element type;

[0017] generating, based on the second traffic element image and the element reference data corresponding to the type of the signal light element, a fifth detection result indicating whether the signal light element has an abnormality;

[0018] Generate a trajectory point image according to the traffic element data whose element type is a dynamic element type;

[0019] generating, according to the trajectory point image and the element reference data corresponding to the dynamic element type, a sixth detection result indicating whether the dynamic element has an abnormality;

[0020] The second detection result is determined according to the fourth detection result, the fifth detection result, and the sixth detection result.

[0021] In a possible implementation manner, the element reference data corresponding to the map element type includes types and locations of a first preset number of first reference targets;

[0022] The step of generating a fourth detection result indicating whether a map element is abnormal based on the first traffic element image and the element reference data corresponding to the map element type includes:

[0023] Performing target detection on the first traffic element image to obtain first target detection data, wherein the first target detection data includes a type and a position of each first detection target;

[0024] If the first target detection data and the element reference data corresponding to the map element type satisfy the first condition, the second condition and the third condition, generating a fourth detection result indicating that there is no abnormality in the map element;

[0025] If the first target detection data and the element reference data corresponding to the map element type do not satisfy at least one of the first condition, the second condition and the third condition, a fourth detection result indicating that an abnormality exists in the map element is generated.

[0026] In a possible implementation manner, the first condition is: the number of first detection targets in the first target detection data is equal to the first preset number;

[0027] The second condition is that: for each of the first reference targets, there exists a first detection target in the first target detection data whose position is the same as that of the first reference target;

[0028] The third condition is that, for each of the first reference targets, the type of the first detection target having the same position as the first reference target is the same as the type of the first reference target.

[0029] In a possible implementation manner, the element reference data corresponding to the signal light element type includes light colors and positions of a second preset number of second reference targets;

[0030] The step of generating a fifth detection result indicating whether the traffic light element is abnormal according to the second traffic element image and the element reference data corresponding to the type of the traffic light element includes:

[0031] Performing target detection on the second traffic element image to obtain second target detection data, wherein the second target detection data includes a light color and a position of each second detection target;

[0032] If the second target detection data and the element reference data corresponding to the signal light element type satisfy the fourth condition, the fifth condition and the sixth condition, a fifth detection result indicating that there is no abnormality in the signal light element is generated;

[0033] If the second target detection data and the element reference data corresponding to the signal light element type do not satisfy at least one of the fourth condition, the fifth condition and the sixth condition, a fifth detection result indicating that an abnormality exists in the signal light element is generated.

[0034] In a possible implementation manner, the fourth condition is: the number of second detection targets in the second target detection data is equal to the second preset number;

[0035] The fifth condition is: for each second reference target, there is a second detection target in the second target detection data whose position is the same as that of the second reference target;

[0036] The sixth condition is that, for each of the second reference targets, the light color of the second detection target at the same position as the second reference target is the same as the light color of the second reference target.

[0037] In a possible implementation manner, generating a sixth detection result indicating whether the dynamic element is abnormal according to the trajectory point image and the element reference data corresponding to the dynamic element type includes:

[0038] Determining the number of track points in each preset grid according to the position of each track point in the track point image;

[0039] Determining the level of each of the preset grids according to the number of trajectory points in each of the preset grids, wherein the level of the preset gateway is proportional to the number of trajectory points in the preset grid;

[0040] The lowest-level grid among all the preset grids is used as the target grid;

[0041] If the number of trajectory points in the target grid falls within a preset number range, generating a sixth detection result indicating that there is no abnormality in the dynamic element;

[0042] If the number of trajectory points in the target grid does not fall within a preset number range, a sixth detection result indicating that an abnormality exists in the dynamic element is generated.

[0043] In a possible implementation manner, the data to be detected corresponding to each of the chart data includes text to be detected and icons to be detected; the second preset reference data corresponding to each of the chart data includes preset reference text and preset icon quantity;

[0044] The step of generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data corresponding to each of the chart data and the second preset reference data comprises:

[0045] If the text to be detected of each chart data is the same as the preset reference text of the chart data, and the number of icons to be detected of each chart data is the same as the preset number of icons of the chart data, then a third detection result indicating that the chart data is free of abnormality is generated;

[0046] If there is at least one chart data whose text to be detected is different from the preset reference text of the chart data, or there is at least one chart data whose number of icons to be detected is different from the preset number of icons of the chart data, a third detection result indicating that there is an abnormality in the chart data is generated.

[0047] In a possible implementation, the detecting the structured data of the target function according to the preset structured data detection rule to obtain a first detection result indicating whether the structured data is abnormal includes:

[0048] Determine whether the structured data is empty;

[0049] If the structured data is empty, generating a first detection result indicating that the structured data is abnormal;

[0050] If the structured data is not empty, the data whose field names in the structured data belong to the first preset field name range is used as the first data, and the data whose field names in the structured data belong to the second preset field name range is used as the second data;

[0051] Determine whether each of the first data satisfies the seventh condition, whether each of the second data satisfies the eighth condition, and whether each of the structured data has a field value;

[0052] If each of the first data satisfies the seventh condition, each of the second data satisfies the eighth condition, and each of the data in the structured data has a field value, then a first detection result indicating that the structured data does not have an abnormality is generated;

[0053] If there is at least one of the first data that does not satisfy the seventh condition, there is at least one of the second data that does not satisfy the eighth condition, or there is at least one data in the structured data that does not have a field value, a first detection result indicating that there is an abnormality in the structured data is generated.

[0054] In a possible implementation manner, the seventh condition is: the field value of the first data belongs to a preset range corresponding to the field name of the first data;

[0055] The eighth condition is that the field value of the second data is the same as the preset value corresponding to the field name of the second data.

[0056] In a possible implementation, before determining, according to the first detection result, the second detection result, and the third detection result, a device function detection result indicating whether the target function is abnormal, the method further includes:

[0057] If the target function has a data interface, obtain a data request status code;

[0058] Generate a delay requirement result indicating whether the data interface has a corresponding reference delay length according to the corresponding relationship between the interface identifier and the delay length and the interface identifier of the data interface;

[0059] generating, according to the delay requirement result and the data request status code, a seventh detection result indicating whether a data interface is abnormal;

[0060] The determining, according to the first detection result, the second detection result, and the third detection result, a device function detection result indicating whether the target function is abnormal includes:

[0061] A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, the third detection result, and the seventh detection result.

[0062] In a possible implementation manner, generating, according to the latency requirement result and the data request status code, a seventh detection result indicating whether a data interface is abnormal includes:

[0063] If the delay requirement result indicates that the data interface has a corresponding reference delay length, then obtaining the actual delay length of the data interface;

[0064] Determine whether the reference delay duration is the same as the actual delay duration, and whether the data request status code is the same as a preset status code;

[0065] If the reference delay time length is the same as the actual delay time length, and the data request status code is the same as the preset status code, generating a seventh detection result indicating that there is no abnormality in the data interface;

[0066] If the reference delay time length is different from the actual delay time length, or the data request status code is different from the preset status code, a seventh detection result indicating that an abnormality exists in the data interface is generated.

[0067] In a possible implementation, the method further includes:

[0068] If the delay requirement result indicates that the data interface does not have a corresponding reference delay length, determining whether the data request status code is the same as a preset status code;

[0069] If the data request status code is the same as the preset status code, generating a seventh detection result indicating that there is no abnormality in the data interface;

[0070] If the data request status code is different from the preset status code, a seventh detection result indicating that an abnormality exists in the data interface is generated.

[0071] In a second aspect, an embodiment of the present application provides a device for detecting abnormal function of a device, comprising:

[0072] Detection module for:

[0073] According to a preset structured data detection rule, the structured data of the target function is detected to obtain a first detection result indicating whether the structured data is abnormal;

[0074] According to the first preset reference data and the element type of the traffic element data of the target function, the traffic element data is detected to generate a second detection result indicating whether the traffic element data is abnormal;

[0075] A processing module, used for performing data extraction processing on each chart data of the target function to obtain the data to be detected corresponding to each chart data;

[0076] The detection module is further used for:

[0077] generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data and the second preset reference data corresponding to each of the chart data;

[0078] A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, and the third detection result.

[0079] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0080] Processor, memory, communication interface;

[0081] The memory is used to store executable instructions of the processor;

[0082] Wherein, the processor is configured to execute the device function abnormality detection method described in any one of the first aspects by executing the executable instructions.

[0083] In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the device function abnormality detection method described in any one of the first aspects.

[0084] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the device function abnormality detection method described in any one of the first aspects.

[0085] The device function abnormality detection method, apparatus, device, medium and product provided in the embodiment of the present application detect the structured data of the target function according to the preset structured data detection rule to obtain a first detection result indicating whether the structured data is abnormal; detect the traffic element data according to the first preset reference data and the element type of the traffic element data of the target function to generate a second detection result indicating whether the traffic element data is abnormal; perform data extraction processing on each chart data of the target function to obtain the data to be detected; then generate a third detection result indicating whether the chart data is abnormal according to the data to be detected and the second preset reference data; finally, determine the device function detection result indicating whether the target function is abnormal according to the first detection result, the second detection result and the third detection result. This solution determines whether the target function is abnormal by processing the structured data, the traffic element data and the chart data, thereby effectively improving the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0087] Figure 1 A flowchart of a first embodiment of a method for detecting abnormal device function provided by the present application;

[0088] Figure 2 A flowchart of Embodiment 2 of the device function abnormality detection method provided in this application;

[0089] Figure 3 A flowchart of Embodiment 3 of the device function abnormality detection method provided in this application;

[0090] Figure 4 A schematic diagram of the structure of an embodiment of a device function abnormality detection device provided in the present application;

[0091] Figure 5 A schematic diagram of the structure of an electronic device provided in this application.

[0092] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0093] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0094] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0095] With the development of science and technology, vehicles are becoming more and more intelligent, and they can realize more and more measurement functions, which all rely on data from various IoT devices. IoT devices can be laser radars, cameras, traffic lights, smart cones, etc.

[0096] IoT devices have various functions to output different data, which can then realize different vehicle functions based on different data. In order to ensure the normal operation of the device, the device function needs to be tested.

[0097] In the prior art, the detection of device functions usually involves obtaining structured data of the device, including structured data collected by the device and structured data generated by the device operation, and then determining whether each type of structured data exceeds a threshold or falls within a threshold range, determining whether the structured data is abnormal, and determining that the structured data is abnormal means that the device function is abnormal. Since only detecting structured data can determine whether the device function is abnormal, the problem of low detection accuracy will result.

[0098] In view of the problems existing in the prior art, the inventor discovered in the process of studying the device function abnormality detection method that the data corresponding to the function of the IoT device includes not only structured data, but also traffic element data and chart data. The structured data, traffic element data and chart data are tested respectively to obtain three test results, and then determine whether the device function is abnormal based on the three test results, which effectively improves the detection accuracy. Based on the above invention concept, the device function abnormality detection scheme in this application is designed.

[0099] The executor of the device function abnormality detection method in the present application may be a computer, or a server, a terminal device, a vehicle, etc. The present application does not limit it and the following description will be made using a computer as an example.

[0100] The following is an example of an application scenario of the device function abnormality detection method provided in this application.

[0101] Exemplarily, in this application scenario, IoT devices installed on the roadside collect data, and different functions of IoT devices collect different data. For the target function to be detected, the computer obtains the data of the target function, which includes structured data, traffic element data, and chart data.

[0102] It should be noted that each data in the structured data includes a field name and a field value. The field name can be vehicle location, traffic light location, data collection duration, traffic light color, IoT device identification, temperature, etc.

[0103] The data in traffic element data is the data of traffic elements. Traffic elements can be ground signs, traffic lights, vehicles, pedestrians, cones, signboards, etc. The element types of traffic element data include map element types, traffic light element types and dynamic element types. Elements such as ground signs and signboards whose positions do not change and whose indicated content does not change belong to the map element type, traffic lights belong to the traffic light element type, and elements such as vehicles and pedestrians whose positions will change belong to the dynamic element type.

[0104] The chart data includes bar charts, line charts, heat maps, radar charts, etc., as well as various charts related to traffic data, such as intersection vehicle turning statistics, intersection ground sign diagrams, road vehicle location distribution maps, etc.

[0105] The embodiments of the present application do not limit structured data, traffic element data, and chart data, which can be determined first according to actual conditions.

[0106] The computer detects the structured data of the target function according to a preset structured data detection rule, and obtains a first detection result indicating whether there is an abnormality in the structured data.

[0107] The traffic element data is detected according to the first preset reference data and the element type of the traffic element data of the target function, and a second detection result indicating whether the traffic element data is abnormal is generated.

[0108] Data extraction is performed on each chart data of the target function to obtain the data to be detected corresponding to each chart data.

[0109] A third detection result indicating whether the chart data has an abnormality is generated according to the to-be-detected data corresponding to each chart data and the second preset reference data.

[0110] According to the first test result, the second test result, and the third test result, a device function test result indicating whether there is an abnormality in the target function is determined.

[0111] When the equipment function test result indicates that there is an abnormality in the target function, the computer can display the equipment function test result so that the staff can inspect and repair the target function.

[0112] It should be noted that the above scenario is only an example of an application scenario provided by an embodiment of the present application. The embodiment of the present application does not limit the actual form of the various devices included in the scenario, nor does it limit the interaction method between the devices. In the specific application of the solution, it can be set according to actual needs.

[0113] The technical solution of the present application is described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0114] Figure 1 This is a flow chart of the first embodiment of the device function abnormality detection method provided by the present application. The present embodiment of the present application describes the situation in which a computer processes the structured data, traffic element data and chart data of the target function to detect the target function. The method in this embodiment can be implemented by software, hardware or a combination of software and hardware. Figure 1 As shown, the device function abnormality detection method specifically includes the following steps:

[0115] S101: Detecting structured data of a target function according to a preset structured data detection rule to obtain a first detection result indicating whether the structured data has an abnormality.

[0116] In this step, in order to detect the target function of the IoT device, the computer obtains the structured data of the target function, and then detects the structured data of the target function according to the preset structured data detection rules to obtain a first detection result indicating whether there is an abnormality in the structured data.

[0117] Specifically, first determine whether the structured data is empty.

[0118] If the structured data is empty, it means that the target function does not output structured data with content, and a first detection result indicating that an abnormality exists in the structured data is generated.

[0119] If the structured data is not empty, it means that there is data in the structured data. In order to further determine whether there is an abnormality in the structured data, the data whose field names in the structured data belong to the first preset field name range are taken as the first data, and the data whose field names in the structured data belong to the second preset field name range are taken as the second data.

[0120] Exemplarily, the first preset field name range can be {vehicle position, traffic light position, data collection duration}, and the second preset field name range can be {traffic light color, IoT device identification, temperature}. The embodiment of the present application does not limit the first preset field name range and the second preset field name range, and can be determined according to actual conditions.

[0121] It is determined whether each first data satisfies the seventh condition, whether each second data satisfies the eighth condition, and whether each data in the structured data has a field value.

[0122] The seventh condition is that the field value of the first data belongs to a preset range corresponding to the field name of the first data.

[0123] The eighth condition is: the field value of the second data is the same as the preset value corresponding to the field name of the second data.

[0124] If each first data satisfies the seventh condition, each second data satisfies the eighth condition, and each data in the structured data has a field value, indicating that the data of the structured data are all normal data, then a first detection result indicating that there is no abnormality in the structured data is generated.

[0125] If there is at least one first data that does not satisfy the seventh condition, or there is at least one second data that does not satisfy the eighth condition, or there is at least one data in the structured data that does not have a field value, indicating that there is abnormal data in the structured data, a first detection result indicating that there is an abnormality in the structured data is generated.

[0126] The first data satisfies the seventh condition, indicating that the first data is accurate. For example, the field name of the first data is vehicle position, the preset range corresponding to the vehicle position represents the road range, and the field value of the vehicle position belongs to the preset range corresponding to the vehicle position, indicating that the vehicle is on the road and the first data is accurate.

[0127] The second data satisfies the eighth condition, indicating that the second data is correct. For example, the field name of the second data is traffic light color, the preset value corresponding to the traffic light color represents the real color of the traffic light, and the field value of the traffic light color is the same as the preset value corresponding to the field name of the second data, indicating that the traffic light color output by the target function is the same as the real color, and the second data is correct.

[0128] Each data in structured data has a field value, which means that the target function can output complete data.

[0129] It should be noted that the preset range corresponding to the field name can be a geographical range, a time range, a weather range, etc.; the preset value corresponding to the field name can be a color, a device identification, a temperature value, etc. This application does not limit the preset range corresponding to the field name and the preset value corresponding to the field name, and can be determined according to actual conditions.

[0130] It should be noted that the seventh condition, the eighth condition, and each data in the structured data has a field value, which can constitute an assertion in the assertion library.

[0131] S102: Detecting the traffic element data according to the first preset reference data and the element type of the traffic element data of the target function, and generating a second detection result indicating whether the traffic element data is abnormal.

[0132] In this step, in order to detect the target function of the Internet of Things device, the computer obtains the traffic element data of the target function, and then detects the traffic element data based on the first preset reference data and the element type of the traffic element data of the target function, and generates a second detection result indicating whether there is an abnormality in the traffic element data.

[0133] There are multiple types of traffic element data. Traffic element data of each type can be processed and then compared with the first preset reference data to generate a second detection result indicating whether there is an abnormality in the traffic element data.

[0134] S103: Perform data extraction processing on each chart data of the target function to obtain the data to be detected corresponding to each chart data.

[0135] In this step, in order to detect the target function of the IoT device, the computer obtains the chart data of the target function, and then performs data extraction processing on each chart data of the target function to obtain the data to be detected corresponding to each chart data.

[0136] Since the chart data includes text and icons, optical character recognition (OCR) technology can be used to recognize the chart data to obtain the data to be detected. The data to be detected corresponding to each chart data includes text to be detected and icons to be detected.

[0137] S104: generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data corresponding to each chart data and the second preset reference data.

[0138] In this step, after obtaining the data to be detected, the computer generates a third detection result indicating whether the chart data has an abnormality according to the data to be detected and the second preset reference data corresponding to each chart data.

[0139] Specifically, the second preset reference data corresponding to each chart data includes preset reference text and preset icon quantity. It can be determined whether the text to be detected of each chart data is the same as the preset reference text of the chart data, and whether the number of icons to be detected of each chart data is the same as the preset icon quantity of the chart data.

[0140] If the text to be detected of each chart data is the same as the preset reference text of the chart data, and the number of icons to be detected of each chart data is the same as the preset number of icons of the chart data, it means that the chart data output by the target function is normal, then a third detection result indicating that there is no abnormality in the chart data is generated;

[0141] If there is at least one chart data whose text to be detected is different from the preset reference text of the chart data, or there is at least one chart data whose number of icons to be detected is different from the preset number of icons of the chart data, it means that there is abnormal data in the chart data output by the target function, and a third detection result indicating that there is an abnormality in the chart data is generated.

[0142] It should be noted that the preset reference text may be: the number of vehicles, the number of traffic lights, the number of track points, etc. The number of preset icons may be 1, 3, 7, 10, 20, etc. The embodiment of the present application does not limit the number of preset reference texts and preset icons, which may be determined according to actual conditions.

[0143] It should be noted that the execution order of step S101, step S102, and step S103-step S104 can be: first execute step S101, then execute step S102, and then execute step S103-step S104; it can also be: first execute step S102, then execute step S101, and then execute step S103-step S104; it can also be: first execute step S103-step S104, then execute step S102-step S101, and then execute; it can also be: step S101, step S102, step S103-step S104 are executed simultaneously. The embodiment of the present application does not limit the execution order of step S101, step S102, and step S103-step S104, which can be determined according to actual conditions.

[0144] S105: Determine, based on the first detection result, the second detection result, and the third detection result, a device function detection result indicating whether there is an abnormality in the target function.

[0145] In this step, after the computer obtains the first test result, the second test result, and the third test result, it determines the device function test result indicating whether there is an abnormality in the target function based on the first test result, the second test result, and the third test result.

[0146] If the first detection result indicates that there is no abnormality in the structured data, the second detection result indicates that there is no abnormality in the traffic element data, and the third detection result indicates that there is no abnormality in the chart data, a device function detection result indicating that there is no abnormality in the target function is generated.

[0147] If the first detection result indicates that there is an abnormality in the structured data, or the second detection result indicates that there is an abnormality in the traffic element data, or the third detection result indicates that there is an abnormality in the chart data, then a device function detection result indicating that the target function is no longer abnormal is generated.

[0148] It should be noted that the computer can also generate a test report, and add the first test result, the second test result, the third test result and the device function test result to the test report.

[0149] The device function abnormality detection method provided in this embodiment detects the structured data of the target function according to the preset structured data detection rules to obtain a first detection result indicating whether the structured data is abnormal; detects the traffic element data according to the first preset reference data and the element type of the traffic element data of the target function to generate a second detection result indicating whether the traffic element data is abnormal; performs data extraction processing on each chart data of the target function to obtain the data to be detected; then generates a third detection result indicating whether the chart data is abnormal according to the data to be detected and the second preset reference data; finally, determines the device function detection result indicating whether the target function is abnormal according to the first detection result, the second detection result and the third detection result. Compared with the prior art that only detects the structured data, this solution determines whether the target function is abnormal by processing the structured data, the traffic element data and the chart data, thereby effectively improving the detection accuracy.

[0150] Figure 2 This is a flow chart of the second embodiment of the device function abnormality detection method provided by the present application. Based on the above embodiment, the present embodiment of the present application describes the situation where the computer generates the second detection result according to the first preset reference data and the element type of the traffic element data of the target function. Figure 2 As shown, the device function abnormality detection method specifically includes the following steps:

[0151] S201: Generate a first traffic element image according to traffic element data whose element type is a map element type.

[0152] After the computer obtains the traffic element data of the target function, it can detect the traffic element data according to the first preset reference data and the element type of the traffic element data of the target function, and generate a second detection result indicating whether there is an abnormality in the traffic element data.

[0153] The element types of the traffic element data of the target function include map element types, traffic light element types and dynamic element types; the first preset reference data includes element reference data corresponding to each element type.

[0154] In this step, for traffic element data whose element type is a map element type, a first traffic element image is generated based on the data.

[0155] Each data in the traffic element data whose element type is the map element type is data corresponding to the map element, including the real geographical location and type of the map element, so the map element can be generated in a first preset map without map elements to obtain a first traffic element image.

[0156] S202: Generate a fourth detection result indicating whether there is an abnormality in the map element according to the first traffic element image and the element reference data corresponding to the map element type.

[0157] In this step, after the computer generates the first traffic element image, it generates a fourth detection result indicating whether there is an abnormality in the map element according to the first traffic element image and the element reference data corresponding to the map element type.

[0158] The element reference data corresponding to the map element type includes types and locations of a first preset number of first reference targets.

[0159] It should be noted that the position of the first reference target is the position in the first preset map. The type of the first reference target can be a ground sign, a signboard, a cone barrel, etc. The embodiment of the present application does not limit the type of the first reference target and can be determined according to actual conditions.

[0160] Specifically, target detection is performed on the first traffic element image to obtain first target detection data, where the first target detection data includes the type and position of each first detection target.

[0161] It should be noted that the position of the first detection target is the position in the first preset map. The type of the first detection target can be a ground indicator, a sign, a cone, etc. The embodiment of the application does not limit the type of the first detection target, which can be determined according to actual conditions.

[0162] Then, it is determined whether the first target detection data and the element reference data corresponding to the map element type satisfy the first condition, the second condition and the third condition.

[0163] The first condition is that the number of first detection targets in the first target detection data is equal to a first preset number.

[0164] The second condition is that for each first reference target, there exists a first detection target in the first target detection data whose position is the same as that of the first reference target.

[0165] The third condition is that, for each first reference target, the type of the first detection target having the same position as that of the first reference target is the same as the type of the first reference target.

[0166] If the first target detection data and the element reference data corresponding to the map element type satisfy the first condition, the second condition and the third condition, indicating that the quantity, position and type of the traffic element data of the map element type output by the target function are normal, then a fourth detection result indicating that there is no abnormality in the map element is generated;

[0167] If the first target detection data and the element reference data corresponding to the map element type do not satisfy at least one of the first condition, the second condition and the third condition, it means that the quantity, position or type of traffic element data of the map element type output by the target function is abnormal, and a fourth detection result indicating that there is an abnormality in the map element is generated.

[0168] It should be noted that the first preset number is the number of all map elements within the geographical range of the first preset map. The first preset number can be 5, 10, 20, etc. The embodiment of the present application does not limit the first preset number, and it can be determined according to actual conditions.

[0169] S203: Generate a second traffic element image according to the traffic element data whose element type is a traffic light element type.

[0170] In this step, for traffic element data whose element type is a signal light element type, a second traffic element image is generated based on these data.

[0171] Each data in the traffic element data whose element type is the traffic light element type is data corresponding to the traffic light, including the real geographical location and light color of the traffic light, so the traffic light can be generated in a second preset map without a traffic light. The generated traffic light needs to display the light color to obtain a second traffic element image.

[0172] S204: Generate a fifth detection result indicating whether there is an abnormality in the traffic light element according to the second traffic element image and the element reference data corresponding to the type of the traffic light element.

[0173] In this step, after the computer generates the second traffic element image, a fifth detection result indicating whether there is an abnormality in the traffic light element is generated based on the second traffic element image and the element reference data corresponding to the type of traffic light element.

[0174] The element reference data corresponding to the signal light element type includes light colors and positions of a second preset number of second reference targets.

[0175] It should be noted that the position of the second reference target is the position in the second preset map, which indicates the real position of the second reference target, and the second reference target is a traffic light.

[0176] Specifically, target detection is performed on the second traffic element image to obtain second target detection data, where the second target detection data includes the light color and position of each second detection target.

[0177] Then, it is determined whether the second target detection data and the element reference data corresponding to the signal light element type satisfy the fourth condition, the fifth condition and the sixth condition.

[0178] The fourth condition is that the number of second detection targets in the second target detection data is equal to the second preset number.

[0179] The fifth condition is that for each second reference target, there exists a second detection target in the second target detection data whose position is the same as that of the second reference target.

[0180] The sixth condition is that, for each second reference target, the light color of the second detection target at the same position as the second reference target is the same as the light color of the second reference target.

[0181] If the second target detection data and the element reference data corresponding to the traffic light element type meet the fourth, fifth and sixth conditions, indicating that the quantity, position and light color of the traffic element data of the traffic light element type output by the target function are normal, then the fifth detection result indicating that there is no abnormality in the traffic light element is generated.

[0182] If the second target detection data and the element reference data corresponding to the traffic light element type do not satisfy at least one of the fourth, fifth and sixth conditions, it means that the quantity of traffic element data of the traffic light element type output by the target function is abnormal, or the position is abnormal, or the light color is abnormal, then a fifth detection result indicating that there is an abnormality in the traffic light element is generated.

[0183] It should be noted that the second preset number is the number of all traffic lights within the geographical range of the second preset map. The second preset number can be 5, 10, 20, etc. The embodiment of the present application does not limit the second preset number, and it can be determined according to actual conditions.

[0184] S205: Generate a track point image according to the traffic element data whose element type is a dynamic element type.

[0185] In this step, for traffic element data of dynamic element type, which is data of elements such as vehicles and pedestrians whose positions may change, this data includes the positions of the trajectory points of the elements, so a trajectory point image can be generated.

[0186] Track points may be generated in a third preset map having a preset grid, and a track point image may be obtained.

[0187] S206: Generate a sixth detection result indicating whether there is an abnormality in the dynamic element according to the trajectory point image and the element reference data corresponding to the dynamic element type.

[0188] In this step, after the computer generates the trajectory point image, it generates a sixth detection result indicating whether there is an abnormality in the dynamic element based on the trajectory point image and the element reference data corresponding to the dynamic element type.

[0189] Specifically, according to the position of each track point in the track point image, the number of track points in each preset grid is determined. For each preset grid, the track points whose positions belong to the preset grid are determined, and then the number of these track points is determined to obtain the number of track points in the preset grid.

[0190] The level of each preset grid is determined according to the number of track points in each preset grid, and the level of the preset gateway is proportional to the number of track points in the preset grid.

[0191] The grid with the least number of track points among all preset grids is taken as the first grid; the grid with the most track points among all preset grids is taken as the second grid; the number of track points in the first grid is taken as the first number, and the number of track points in the second grid is taken as the second number. The preset level number is divided into number ranges according to the first number and the second number, and the range difference of each number range is equal. Each number range corresponds to a level, and the larger the number range, the larger the level. Then, the level of each preset grid can be determined according to the number range corresponding to the level and the number of track points in each preset grid.

[0192] It should be noted that the number of preset levels can be 5, 10, 50, etc. The embodiment of the present application is not limited to the number of preset levels and can be determined according to actual conditions.

[0193] Then, the lowest-level grid among all preset grids is used as the target grid. It is determined whether the number of track points in the target grid falls within the preset number range.

[0194] If the number of trajectory points in the target grid falls within a preset number range, indicating that the number of traffic element data of the dynamic element type output by the target function is normal, a sixth detection result indicating that there is no abnormality in the dynamic element is generated;

[0195] If the number of trajectory points in the target grid does not fall within a preset number range, indicating that the number of traffic element data of the dynamic element type output by the target function is abnormal, a sixth detection result indicating that an abnormality exists in the dynamic element is generated.

[0196] It should be noted that if the number of target grids is greater than 1, the number of track points in the target grids is within the preset number range, which means that the number of track points in each target grid is within the preset number range. The number of track points in the target grid does not belong to the preset number range, which means that the number of track points in at least one target grid does not belong to the preset number range.

[0197] It should be noted that the preset number range can be 0-10, 0-20, 0-100, etc. The embodiment of the present application does not limit the preset number range, which can be determined according to actual conditions.

[0198] It should be noted that the execution order of step S201-step S202, step S203-step S204, and step S205-step S206 can be: first execute step S201-step S202, then execute step S203-step S204, and then execute step S205-step S206; it can also be: first execute step S205-step S206, then execute step S203-step S204, and then execute step S201-step S202; it can also be: first execute step S203-step S204, then execute step S201-step S202, and then execute step S205-step S206; it can also be: step S201-step S202, step S203-step S204, and step S205-step S206 are executed simultaneously. The embodiment of the present application does not limit the execution order of step S201-step S202, step S203-step S204, and step S205-step S206, which can be determined according to actual conditions.

[0199] S207: Determine the second detection result according to the fourth detection result, the fifth detection result, and the sixth detection result.

[0200] In this step, after obtaining the fourth detection result, the fifth detection result, and the sixth detection result, the computer determines the second detection result according to the fourth detection result, the fifth detection result, and the sixth detection result.

[0201] If the fourth detection result indicates that there is no abnormality in the map element, the fifth detection result indicates that there is no abnormality in the traffic light element, and the sixth detection result indicates that there is no abnormality in the dynamic element, a second detection result indicating that there is no abnormality in the traffic element data is generated.

[0202] If the fourth detection result indicates that there is an abnormality in the map element, or the fifth detection result indicates that there is an abnormality in the traffic light element, or the sixth detection result indicates that there is an abnormality in the dynamic element, a second detection result indicating that there is an abnormality in the traffic element data is generated.

[0203] It should be noted that the computer can also display the first traffic element image, the second traffic element image and the track point image so that the staff can check the relevant abnormalities.

[0204] The device function abnormality detection method provided in this embodiment generates a first traffic element image for the traffic element data of the map element type, and then performs target detection and compares it with the element reference data to obtain a fourth detection result. The second traffic element image is generated for the traffic element data of the signal light element type, and then performs target detection and compares it with the element reference data to obtain a fifth detection result. This can realize the processing of unstructured data of these two types of elements, and also improve the accuracy of the detection results. The track point image is generated for the traffic element data of the dynamic element type, and then the sixth detection result is generated according to the number of track points in the preset grid, which realizes the processing of unstructured data of the dynamic element type and also improves the accuracy of the detection results.

[0205] Figure 3 This is a flow chart of the third embodiment of the device function abnormality detection method provided by the present application. Based on the above embodiment, the present embodiment of the present application describes the situation where the computer detects the data interface of the target function to obtain the seventh detection result and then generates the device function detection result. Figure 3 As shown, the device function abnormality detection method specifically includes the following steps:

[0206] S301: If the target function has a data interface, obtain a data request status code.

[0207] After the computer generates the first test result, the second test result and the third test result, since the function of the Internet of Things device has a data interface, if you want to determine whether there is any abnormality in the function, you also need to test the data interface.

[0208] In this step, if the target function has a data interface, the data request status code is obtained.

[0209] The computer sends a status code acquisition request to the IoT device to obtain the status code of the data interface of the target function. The IoT device sends the data request status code to the computer through the data interface of the target function, and the computer can obtain the data request status code.

[0210] S302: Generate a delay requirement result indicating whether the data interface has a corresponding reference delay length according to the corresponding relationship between the interface identifier and the delay length and the interface identifier of the data interface.

[0211] In this step, after the computer obtains the data request status code, since some data interfaces may have delay requirements, that is, the data interfaces have corresponding reference delay lengths, the time from the target function collecting data to the receiver receiving the data sent through the data interface is less than or equal to the reference delay length. Therefore, it is necessary to generate a delay requirement result indicating whether the data interface has a corresponding reference delay length based on the correspondence between the interface identifier and the delay length, as well as the interface identifier of the data interface.

[0212] The computer stores the correspondence between interface identifiers and delay durations. The data interfaces corresponding to these interface identifiers are all interfaces with delay requirements. Therefore, if the data interface of the target function is determined to be in the correspondence between the interface identifier and the delay duration, a delay requirement result indicating that the data interface has a corresponding reference delay duration is generated, and the reference delay duration corresponding to the data interface can also be determined.

[0213] If it is determined that the data interface of the target function is not in the corresponding relationship between the interface identifier and the delay duration, a delay requirement result indicating that the data interface does not have a corresponding reference delay duration is generated.

[0214] S303: Generate a seventh detection result indicating whether there is an abnormality in the data interface according to the delay requirement result and the data request status code.

[0215] In this step, after the computer generates the delay requirement result, it can generate a seventh detection result indicating whether there is an abnormality in the data interface according to the delay requirement result and the data request status code.

[0216] Specifically, if the delay requirement result indicates that a corresponding reference delay length exists for the data interface, then the actual delay length of the data interface is acquired.

[0217] The method to obtain the actual delay duration is: the computer sends a data acquisition request to the IoT device, the IoT device collects data and marks the timestamp, and then the IoT device sends the timestamp-marked data to the computer through the data interface of the target function. The computer uses the time between the time the data is received and the timestamp as the actual delay duration.

[0218] Then determine whether the reference delay length is the same as the actual delay length, and whether the data request status code is the same as the preset status code.

[0219] If the reference delay length is the same as the actual delay length, and the data request status code is the same as the preset status code, indicating that there is no problem with the data interface of the target function, a seventh detection result indicating that there is no abnormality in the data interface is generated.

[0220] If the reference delay length is different from the actual delay length, or the data request status code is different from the preset status code, it means that there is a problem with the data interface of the target function, and a seventh detection result indicating that there is an abnormality in the data interface is generated.

[0221] If the delay requirement result indicates that the data interface does not have a corresponding reference delay length, it is determined whether the data request status code is the same as the preset status code.

[0222] If the data request status code is the same as the preset status code, it means that there is no problem with the data interface of the target function, and a seventh detection result indicating that there is no abnormality in the data interface is generated.

[0223] If the data request status code is different from the preset status code, it means that there is a problem with the data interface of the target function, and a seventh detection result indicating that there is an abnormality in the data interface is generated.

[0224] It should be noted that the reference delay length can be 200 milliseconds, 500 milliseconds, 1 second, etc., and the preset status code can be 200, 202, 204, etc. The embodiment of the present application does not limit the reference delay length and the preset status code, and can be determined according to actual conditions.

[0225] S304: Determine, based on the first detection result, the second detection result, the third detection result, and the seventh detection result, a device function detection result indicating whether there is an abnormality in the target function.

[0226] In this step, after the computer obtains the first test result, the second test result, the third test result and the seventh test result, it determines the device function test result indicating whether there is an abnormality in the target function based on the first test result, the second test result, the third test result and the seventh test result.

[0227] If the first test result indicates that there is no abnormality in the structured data, the second test result indicates that there is no abnormality in the traffic element data, the third test result indicates that there is no abnormality in the chart data, and the seventh test result indicates that there is no abnormality in the data interface, then a device function test result indicating that there is no abnormality in the target function is generated.

[0228] If the first test result indicates that there is an abnormality in the structured data, or the second test result indicates that there is an abnormality in the traffic element data, or the third test result indicates that there is an abnormality in the chart data, or the seventh test result indicates that there is an abnormality in the data interface, then a device function test result indicating that the target function is no longer abnormal is generated.

[0229] The device function abnormality detection method provided in this embodiment determines the device function detection result by obtaining the first detection result, the second detection result, and the third detection result, and then combining the seventh detection result obtained by detecting the data interface, thereby further improving the detection accuracy.

[0230] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0231] Figure 4 This is a schematic diagram of the structure of an embodiment of the device function abnormality detection device provided in this application; Figure 4 As shown, the device function abnormality detection device 40 includes:

[0232] The detection module 41 is used for:

[0233] According to a preset structured data detection rule, the structured data of the target function is detected to obtain a first detection result indicating whether the structured data is abnormal;

[0234] According to the first preset reference data and the element type of the traffic element data of the target function, the traffic element data is detected to generate a second detection result indicating whether the traffic element data is abnormal;

[0235] A processing module 42 is used to perform data extraction processing on each chart data of the target function to obtain the data to be detected corresponding to each chart data;

[0236] The detection module 41 is further used for:

[0237] generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data and the second preset reference data corresponding to each of the chart data;

[0238] A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, and the third detection result.

[0239] Furthermore, the element types of the traffic element data of the target function include map element types, signal light element types and dynamic element types; the first preset reference data includes element reference data corresponding to each element type;

[0240] The detection module 41 is specifically used for:

[0241] generating a first traffic element image according to the traffic element data whose element type is a map element type;

[0242] generating, based on the first traffic element image and the element reference data corresponding to the map element type, a fourth detection result indicating whether the map element has an abnormality;

[0243] generating a second traffic element image according to the traffic element data whose element type is a signal light element type;

[0244] generating, based on the second traffic element image and the element reference data corresponding to the type of the signal light element, a fifth detection result indicating whether the signal light element has an abnormality;

[0245] Generate a trajectory point image according to the traffic element data whose element type is a dynamic element type;

[0246] generating, according to the trajectory point image and the element reference data corresponding to the dynamic element type, a sixth detection result indicating whether the dynamic element has an abnormality;

[0247] The second detection result is determined according to the fourth detection result, the fifth detection result, and the sixth detection result.

[0248] Furthermore, the element reference data corresponding to the map element type includes types and locations of a first preset number of first reference targets;

[0249] The detection module 41 is further specifically used for:

[0250] Performing target detection on the first traffic element image to obtain first target detection data, wherein the first target detection data includes a type and a position of each first detection target;

[0251] If the first target detection data and the element reference data corresponding to the map element type satisfy the first condition, the second condition and the third condition, generating a fourth detection result indicating that there is no abnormality in the map element;

[0252] If the first target detection data and the element reference data corresponding to the map element type do not satisfy at least one of the first condition, the second condition and the third condition, a fourth detection result indicating that an abnormality exists in the map element is generated.

[0253] Further, the first condition is: the number of first detection targets in the first target detection data is equal to the first preset number;

[0254] The second condition is that: for each of the first reference targets, there exists a first detection target in the first target detection data whose position is the same as that of the first reference target;

[0255] The third condition is that, for each of the first reference targets, the type of the first detection target having the same position as the first reference target is the same as the type of the first reference target.

[0256] Further, the element reference data corresponding to the signal light element type includes light colors and positions of a second preset number of second reference targets;

[0257] The detection module 41 is further specifically used for:

[0258] Performing target detection on the second traffic element image to obtain second target detection data, wherein the second target detection data includes a light color and a position of each second detection target;

[0259] If the second target detection data and the element reference data corresponding to the signal light element type satisfy the fourth condition, the fifth condition and the sixth condition, a fifth detection result indicating that there is no abnormality in the signal light element is generated;

[0260] If the second target detection data and the element reference data corresponding to the signal light element type do not satisfy at least one of the fourth condition, the fifth condition and the sixth condition, a fifth detection result indicating that an abnormality exists in the signal light element is generated.

[0261] Further, the fourth condition is: the number of second detection targets in the second target detection data is equal to the second preset number;

[0262] The fifth condition is: for each second reference target, there is a second detection target in the second target detection data whose position is the same as that of the second reference target;

[0263] The sixth condition is that, for each of the second reference targets, the light color of the second detection target at the same position as the second reference target is the same as the light color of the second reference target.

[0264] Furthermore, the detection module 41 is further configured to:

[0265] Determining the number of track points in each preset grid according to the position of each track point in the track point image;

[0266] Determining the level of each of the preset grids according to the number of trajectory points in each of the preset grids, wherein the level of the preset gateway is proportional to the number of trajectory points in the preset grid;

[0267] The lowest-level grid among all the preset grids is used as the target grid;

[0268] If the number of trajectory points in the target grid falls within a preset number range, generating a sixth detection result indicating that there is no abnormality in the dynamic element;

[0269] If the number of trajectory points in the target grid does not fall within a preset number range, a sixth detection result indicating that an abnormality exists in the dynamic element is generated.

[0270] Further, the data to be detected corresponding to each of the chart data includes text to be detected and icons to be detected; the second preset reference data corresponding to each of the chart data includes preset reference text and preset icon quantity;

[0271] The detection module 41 is further specifically used for:

[0272] If the text to be detected of each chart data is the same as the preset reference text of the chart data, and the number of icons to be detected of each chart data is the same as the preset number of icons of the chart data, then a third detection result indicating that the chart data is free of abnormality is generated;

[0273] If there is at least one chart data whose text to be detected is different from the preset reference text of the chart data, or there is at least one chart data whose number of icons to be detected is different from the preset number of icons of the chart data, a third detection result indicating that there is an abnormality in the chart data is generated.

[0274] Furthermore, the detection module 41 is further configured to:

[0275] Determine whether the structured data is empty;

[0276] If the structured data is empty, generating a first detection result indicating that the structured data is abnormal;

[0277] If the structured data is not empty, the data whose field names in the structured data belong to the first preset field name range is used as the first data, and the data whose field names in the structured data belong to the second preset field name range is used as the second data;

[0278] Determine whether each of the first data satisfies the seventh condition, whether each of the second data satisfies the eighth condition, and whether each of the structured data has a field value;

[0279] If each of the first data satisfies the seventh condition, each of the second data satisfies the eighth condition, and each of the data in the structured data has a field value, then a first detection result indicating that the structured data does not have an abnormality is generated;

[0280] If there is at least one of the first data that does not satisfy the seventh condition, there is at least one of the second data that does not satisfy the eighth condition, or there is at least one data in the structured data that does not have a field value, a first detection result indicating that there is an abnormality in the structured data is generated.

[0281] Furthermore, the seventh condition is: the field value of the first data belongs to a preset range corresponding to the field name of the first data;

[0282] The eighth condition is that the field value of the second data is the same as the preset value corresponding to the field name of the second data.

[0283] Furthermore, the processing module 42 is also used for:

[0284] If the target function has a data interface, obtain a data request status code;

[0285] Generate a delay requirement result indicating whether the data interface has a corresponding reference delay length according to the corresponding relationship between the interface identifier and the delay length and the interface identifier of the data interface;

[0286] Furthermore, the detection module 41 is also used for:

[0287] generating, according to the delay requirement result and the data request status code, a seventh detection result indicating whether a data interface is abnormal;

[0288] A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, the third detection result, and the seventh detection result.

[0289] Furthermore, the detection module 41 is further configured to:

[0290] If the delay requirement result indicates that the data interface has a corresponding reference delay length, then obtaining the actual delay length of the data interface;

[0291] Determine whether the reference delay duration is the same as the actual delay duration, and whether the data request status code is the same as a preset status code;

[0292] If the reference delay time length is the same as the actual delay time length, and the data request status code is the same as the preset status code, generating a seventh detection result indicating that there is no abnormality in the data interface;

[0293] If the reference delay time length is different from the actual delay time length, or the data request status code is different from the preset status code, a seventh detection result indicating that an abnormality exists in the data interface is generated.

[0294] Furthermore, the detection module 41 is further configured to:

[0295] If the delay requirement result indicates that the data interface does not have a corresponding reference delay length, determining whether the data request status code is the same as a preset status code;

[0296] If the data request status code is the same as the preset status code, generating a seventh detection result indicating that there is no abnormality in the data interface;

[0297] If the data request status code is different from the preset status code, a seventh detection result indicating that an abnormality exists in the data interface is generated.

[0298] The device function abnormality detection apparatus provided in this embodiment is used to execute the technical solution in any of the aforementioned method embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0299] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 5 As shown, the electronic device 50 includes:

[0300] Processor 51, memory 52, and communication interface 53;

[0301] The memory 52 is used to store executable instructions of the processor 51;

[0302] The processor 51 is configured to execute the technical solution in any of the aforementioned method embodiments by executing the executable instructions.

[0303] Optionally, the memory 52 may be independent or integrated with the processor 51 .

[0304] Optionally, when the memory 52 is a device independent of the processor 51, the electronic device 50 may further include:

[0305] The bus 54 , the memory 52 and the communication interface 53 are connected to the processor 51 via the bus 54 and communicate with each other. The communication interface 53 is used to communicate with other devices.

[0306] Optionally, the communication interface 53 may be implemented by a transceiver. The communication interface is used to implement communication between the database access device and other devices (such as a client, a read-write library, and a read-only library). The memory may include a random access memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0307] The bus 54 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0308] The above-mentioned processor can be a general-purpose processor, including a central processing unit CPU, a network processor (NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0309] The electronic device is used to execute the technical solution in any of the aforementioned method embodiments, and its implementation principle and technical effect are similar and will not be repeated here.

[0310] An embodiment of the present application also provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the technical solution provided by any of the aforementioned method embodiments is implemented.

[0311] An embodiment of the present application also provides a computer program product, including a computer program, which is used to implement the technical solution provided by any of the aforementioned method embodiments when executed by a processor.

[0312] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0313] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting abnormal device function, characterized in that: include: According to a preset structured data detection rule, the structured data of the target function is detected to obtain a first detection result indicating whether the structured data is abnormal; According to the first preset reference data and the element type of the traffic element data of the target function, the traffic element data is detected to generate a second detection result indicating whether the traffic element data is abnormal; Performing data extraction processing on each chart data of the target function to obtain the data to be detected corresponding to each chart data; generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data and the second preset reference data corresponding to each of the chart data; A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, and the third detection result.

2. The method according to claim 1, characterized in that The element types of the traffic element data of the target function include map element types, signal light element types and dynamic element types; the first preset reference data includes element reference data corresponding to each element type; The detecting of the traffic element data according to the first preset reference data and the element type of the traffic element data of the target function to generate a second detection result indicating whether the traffic element data is abnormal includes: generating a first traffic element image according to the traffic element data whose element type is a map element type; generating, based on the first traffic element image and the element reference data corresponding to the map element type, a fourth detection result indicating whether the map element has an abnormality; generating a second traffic element image according to the traffic element data whose element type is a signal light element type; generating, based on the second traffic element image and the element reference data corresponding to the type of the signal light element, a fifth detection result indicating whether the signal light element has an abnormality; Generate a trajectory point image according to the traffic element data whose element type is a dynamic element type; generating, according to the trajectory point image and the element reference data corresponding to the dynamic element type, a sixth detection result indicating whether the dynamic element has an abnormality; The second detection result is determined according to the fourth detection result, the fifth detection result, and the sixth detection result.

3. The method according to claim 2, characterized in that The element reference data corresponding to the map element type includes types and locations of a first preset number of first reference targets; The step of generating a fourth detection result indicating whether a map element is abnormal based on the first traffic element image and the element reference data corresponding to the map element type includes: Performing target detection on the first traffic element image to obtain first target detection data, wherein the first target detection data includes a type and a position of each first detection target; If the first target detection data and the element reference data corresponding to the map element type satisfy the first condition, the second condition and the third condition, generating a fourth detection result indicating that there is no abnormality in the map element; If the first target detection data and the element reference data corresponding to the map element type do not satisfy at least one of the first condition, the second condition and the third condition, a fourth detection result indicating that an abnormality exists in the map element is generated.

4. The method according to claim 3, characterized in that The first condition is that the number of first detection targets in the first target detection data is equal to the first preset number; The second condition is that: for each of the first reference targets, there exists a first detection target in the first target detection data whose position is the same as that of the first reference target; The third condition is that, for each of the first reference targets, the type of the first detection target having the same position as the first reference target is the same as the type of the first reference target.

5. The method according to claim 2, characterized in that: The element reference data corresponding to the signal light element type includes light colors and positions of a second preset number of second reference targets; The step of generating a fifth detection result indicating whether the traffic light element is abnormal according to the second traffic element image and the element reference data corresponding to the type of the traffic light element includes: Performing target detection on the second traffic element image to obtain second target detection data, wherein the second target detection data includes a light color and a position of each second detection target; If the second target detection data and the element reference data corresponding to the signal light element type satisfy the fourth condition, the fifth condition and the sixth condition, a fifth detection result indicating that there is no abnormality in the signal light element is generated; If the second target detection data and the element reference data corresponding to the signal light element type do not satisfy at least one of the fourth condition, the fifth condition and the sixth condition, a fifth detection result indicating that an abnormality exists in the signal light element is generated.

6. The method according to claim 5, characterized in that The fourth condition is that the number of second detection targets in the second target detection data is equal to the second preset number; The fifth condition is: for each second reference target, there is a second detection target in the second target detection data whose position is the same as that of the second reference target; The sixth condition is that, for each of the second reference targets, the light color of the second detection target at the same position as the second reference target is the same as the light color of the second reference target.

7. The method according to claim 2, characterized in that The step of generating a sixth detection result indicating whether the dynamic element is abnormal according to the trajectory point image and the element reference data corresponding to the dynamic element type includes: Determining the number of track points in each preset grid according to the position of each track point in the track point image; Determining the level of each of the preset grids according to the number of trajectory points in each of the preset grids, wherein the level of the preset gateway is proportional to the number of trajectory points in the preset grid; The lowest-level grid among all the preset grids is used as the target grid; If the number of trajectory points in the target grid falls within a preset number range, generating a sixth detection result indicating that there is no abnormality in the dynamic element; If the number of trajectory points in the target grid does not fall within a preset number range, a sixth detection result indicating that an abnormality exists in the dynamic element is generated.

8. The method according to claim 1, characterized in that The data to be detected corresponding to each of the chart data includes text to be detected and icons to be detected; the second preset reference data corresponding to each of the chart data includes preset reference text and preset icon quantity; The step of generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data corresponding to each of the chart data and the second preset reference data comprises: If the text to be detected of each chart data is the same as the preset reference text of the chart data, and the number of icons to be detected of each chart data is the same as the preset number of icons of the chart data, then a third detection result indicating that the chart data is free of abnormality is generated; If there is at least one chart data whose text to be detected is different from the preset reference text of the chart data, or there is at least one chart data whose number of icons to be detected is different from the preset number of icons of the chart data, a third detection result indicating that there is an abnormality in the chart data is generated.

9. The method according to claim 1, characterized in that: The detecting of the structured data of the target function according to the preset structured data detection rule to obtain a first detection result indicating whether the structured data is abnormal includes: Determine whether the structured data is empty; If the structured data is empty, generating a first detection result indicating that the structured data is abnormal; If the structured data is not empty, the data whose field names in the structured data belong to the first preset field name range is taken as the first data, and the data whose field names in the structured data belong to the second preset field name range is taken as the second data; Determine whether each of the first data satisfies the seventh condition, whether each of the second data satisfies the eighth condition, and whether each of the structured data has a field value; If each of the first data satisfies the seventh condition, each of the second data satisfies the eighth condition, and each of the data in the structured data has a field value, then a first detection result indicating that the structured data does not have an abnormality is generated; If there is at least one of the first data that does not satisfy the seventh condition, there is at least one of the second data that does not satisfy the eighth condition, or there is at least one data in the structured data that does not have a field value, a first detection result indicating that there is an abnormality in the structured data is generated.

10. The method according to claim 9, characterized in that The seventh condition is: the field value of the first data belongs to the preset range corresponding to the field name of the first data; The eighth condition is that the field value of the second data is the same as the preset value corresponding to the field name of the second data.

11. The method according to any one of claims 1 to 9, characterized in that: Before determining, based on the first detection result, the second detection result, and the third detection result, a device function detection result indicating whether the target function is abnormal, the method further includes: If the target function has a data interface, obtain a data request status code; Generate a delay requirement result indicating whether the data interface has a corresponding reference delay length according to the corresponding relationship between the interface identifier and the delay length and the interface identifier of the data interface; generating, according to the delay requirement result and the data request status code, a seventh detection result indicating whether a data interface is abnormal; The determining, according to the first detection result, the second detection result, and the third detection result, a device function detection result indicating whether the target function is abnormal includes: A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, the third detection result, and the seventh detection result.

12. The method according to claim 11, characterized in that The generating, according to the delay requirement result and the data request status code, a seventh detection result indicating whether a data interface is abnormal includes: If the delay requirement result indicates that the data interface has a corresponding reference delay length, then obtaining the actual delay length of the data interface; Determine whether the reference delay duration is the same as the actual delay duration, and whether the data request status code is the same as a preset status code; If the reference delay time length is the same as the actual delay time length, and the data request status code is the same as the preset status code, generating a seventh detection result indicating that there is no abnormality in the data interface; If the reference delay time length is different from the actual delay time length, or the data request status code is different from the preset status code, a seventh detection result indicating that an abnormality exists in the data interface is generated.

13. The method according to claim 12, characterized in that The method further comprises: If the delay requirement result indicates that the data interface does not have a corresponding reference delay length, determining whether the data request status code is the same as a preset status code; If the data request status code is the same as the preset status code, generating a seventh detection result indicating that there is no abnormality in the data interface; If the data request status code is different from the preset status code, a seventh detection result indicating that an abnormality exists in the data interface is generated.

14. A device for detecting abnormal equipment function, characterized in that: include: Detection module for: According to a preset structured data detection rule, the structured data of the target function is detected to obtain a first detection result indicating whether the structured data is abnormal; According to the first preset reference data and the element type of the traffic element data of the target function, the traffic element data is detected to generate a second detection result indicating whether the traffic element data is abnormal; A processing module, used for performing data extraction processing on each chart data of the target function to obtain the data to be detected corresponding to each chart data; The detection module is further used for: generating a third detection result indicating whether the chart data has an abnormality according to the to-be-detected data and the second preset reference data corresponding to each of the chart data; A device function detection result indicating whether the target function is abnormal is determined according to the first detection result, the second detection result, and the third detection result.

15. An electronic device, characterized in that: include: Processor, memory, communication interface; The memory is used to store executable instructions of the processor; Wherein, the processor is configured to execute the device function abnormality detection method described in any one of claims 1 to 13 by executing the executable instructions.

16. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the device function abnormality detection method according to any one of claims 1 to 13 is implemented.

17. A computer program product, characterized in that It includes a computer program, which is used to implement the device function abnormality detection method according to any one of claims 1 to 13 when executed by a processor.

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