Remote order confirmation method and device, electronic equipment and storage medium

Abstract

The invention discloses a remote order confirmation method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a remote order of a target user for a target vehicle; determining a plurality of to-be-diagnosed items of the target vehicle based on the remote order; determining a priority corresponding to each to-be-diagnosed item in the plurality of to-be-diagnosed items to obtain a plurality of priorities; determining a target priority order based on the plurality of priorities; obtaining a diagnosis step set corresponding to each to-be-diagnosed item in the plurality of to-be-diagnosed items to obtain a plurality of diagnosis step sets; based on the target priority sequence and the multiple diagnosis step sets, performing diagnosis operation on each to-be-diagnosed item to obtain multiple diagnosis results; determining a target diagnosis result list based on the plurality of to-be-diagnosed items, the plurality of diagnosis step sets and the plurality of diagnosis results; and sending confirmation information to the target user based on the target diagnosis result list. According to the embodiment of the invention, it is ensured that the user can obtain the detailed completion condition of each item in the remote order.

Description

Technical Field

[0001] The present invention relates to the field of vehicle diagnosis technology, and in particular to a remote order confirmation method, device, electronic equipment and storage medium. Background Art

[0002] With the development of economy, the application of automobiles in daily life is becoming more and more extensive, and with the continuous advancement of science and technology, the diagnosis of vehicles is becoming more and more common. Remote diagnosis technology enables the service provider of vehicle diagnosis services to remotely diagnose the vehicle of the demander who issues the vehicle diagnosis request.

[0003] Currently, when conducting remote diagnosis of a vehicle, a user uses a remote diagnosis device to issue a remote order. Although the vehicle can be remotely diagnosed, the user cannot obtain the detailed completion status of each item in the remote order. Summary of the invention

[0004] The embodiments of the present application provide a remote order confirmation method, device, electronic device and storage medium, which sends a set of diagnostic steps and diagnostic results corresponding to each item to be diagnosed among multiple items to be diagnosed to the customer for confirmation, thereby ensuring that the user can obtain the detailed completion status of each item in the remote order of the target vehicle.

[0005] In a first aspect, an embodiment of the present application provides a remote order confirmation method, which is applied to a server and includes:

[0006] Obtaining remote orders from target users for target vehicles;

[0007] Determine the items to be diagnosed of the target vehicle based on the remote order, and obtain a plurality of items to be diagnosed;

[0008] Determine the priority corresponding to each of the multiple items to be diagnosed, and obtain multiple priorities;

[0009] Determine the order of priority of goals based on multiple priorities;

[0010] Obtain a diagnostic step set corresponding to each of the multiple diagnostic items to be diagnosed, and obtain multiple diagnostic step sets; one diagnostic step set corresponds to one diagnostic item;

[0011] Based on the target priority order and the multiple diagnostic step sets, a diagnostic operation is performed on each of the multiple diagnostic items to be diagnosed, and multiple diagnostic results are obtained; one diagnostic result corresponds to one diagnostic item;

[0012] Determine a target diagnostic result list based on multiple items to be diagnosed, multiple diagnostic step sets, and multiple diagnostic results; the target diagnostic result list is used to characterize the correspondence between the items to be diagnosed and the diagnostic step sets and diagnostic results;

[0013] A confirmation message is sent to the target user based on the target diagnosis result list; the confirmation message is used by the target user to view multiple diagnosis step sets and multiple diagnosis results corresponding to the remote order.

[0014] In a second aspect, an embodiment of the present application provides a remote order confirmation device, which is applied to a server, and the device includes: an acquisition unit and a processing unit;

[0015] An acquisition unit, used for acquiring a remote order of a target user for a target vehicle;

[0016] A processing unit, used for determining the items to be diagnosed of the target vehicle based on the remote order, and obtaining a plurality of items to be diagnosed;

[0017] Determine the priority corresponding to each of the multiple items to be diagnosed, and obtain multiple priorities;

[0018] Determine the order of priority of goals based on multiple priorities;

[0019] An acquisition unit is used to acquire a diagnostic step set corresponding to each of the multiple diagnostic items to be diagnosed, so as to obtain multiple diagnostic step sets; one diagnostic step set corresponds to one diagnostic item to be diagnosed;

[0020] A processing unit, configured to perform a diagnostic operation on each of the multiple items to be diagnosed based on a target priority order and multiple diagnostic step sets to obtain multiple diagnostic results; one diagnostic result corresponds to one item to be diagnosed;

[0021] Determine a target diagnostic result list based on multiple items to be diagnosed, multiple diagnostic step sets, and multiple diagnostic results; the target diagnostic result list is used to characterize the correspondence between the items to be diagnosed and the diagnostic step sets and diagnostic results;

[0022] A confirmation message is sent to the target user based on the target diagnosis result list; the confirmation message is used by the target user to view multiple diagnosis step sets and multiple diagnosis results corresponding to the remote order.

[0023] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device executes the method of the first aspect.

[0024] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0025] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0026] The implementation of the present invention has the following beneficial effects:

[0027] It can be seen that the remote order confirmation method described in the embodiment of the present invention is applied to the server, firstly, the remote order of the target user for the target vehicle is obtained, and the to-be-diagnosed items of the target vehicle are determined based on the remote order to obtain multiple to-be-diagnosed items, and then the priority corresponding to each of the multiple to-be-diagnosed items is determined to obtain multiple priorities, and then the target priority order is determined based on the multiple priorities, and the diagnostic step set corresponding to each of the multiple to-be-diagnosed items is obtained to obtain multiple diagnostic step sets, wherein one diagnostic step set corresponds to one to-be-diagnosed item, and then a diagnostic operation is performed on each of the multiple to-be-diagnosed items based on the target priority order and the multiple diagnostic step sets to obtain multiple diagnostic results, wherein one diagnostic result corresponds to one to-be-diagnosed item, and then a target diagnostic result list is determined based on the multiple to-be-diagnosed items, the multiple diagnostic step sets, and the multiple diagnostic results, wherein the target diagnostic result list is used to characterize the corresponding relationship between the to-be-diagnosed items and the diagnostic step sets and the diagnostic results, and finally a confirmation message is sent to the target user based on the target diagnostic result list, wherein the confirmation message is used for the target user to view the multiple diagnostic step sets and the multiple diagnostic results corresponding to the remote order, thereby ensuring that the user can obtain the detailed completion status of each item in the remote order of the target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the implementation methods of the present application or the background technology, the drawings required for use in the implementation methods of the present application or the background technology will be described below.

[0029] Figure 1 is a schematic diagram of a remote order confirmation system provided by an embodiment of the present application;

[0030] Figure 2 is a flow chart of a remote order confirmation method provided by an embodiment of the present invention;

[0031] Figure 3 It is a structural schematic diagram of a remote order confirmation device provided in an embodiment of the present application;

[0032] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the implementation mode of the present application. Obviously, the described implementation mode is only a part of the implementation mode of the present application, not all the implementation modes. Based on the implementation mode in the present application, all other implementation modes obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0034] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof 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 limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0035] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] See also Figure 1 , Figure 1 1 is a schematic diagram of a remote order confirmation system provided in an embodiment of the present application. The remote order confirmation system 10 includes a target vehicle 101 and a server 102 .

[0037] In this embodiment, the remote order of the target user for the target vehicle 101 is obtained through the server 102, and the items to be diagnosed of the target vehicle 101 are determined based on the remote order to obtain multiple items to be diagnosed, and then the priority corresponding to each item to be diagnosed in the multiple items to be diagnosed is determined to obtain multiple priorities, and then the target priority order is determined based on the multiple priorities, and the diagnostic step set corresponding to each item to be diagnosed in the multiple items to be diagnosed is obtained to obtain multiple diagnostic step sets, wherein one diagnostic step set corresponds to one item to be diagnosed, and then a diagnostic operation is performed on each item to be diagnosed in the multiple items to be diagnosed based on the target priority order and the multiple diagnostic step sets to obtain multiple diagnostic results, wherein one diagnostic result corresponds to one item to be diagnosed, and then a target diagnostic result list is determined based on the multiple items to be diagnosed, the multiple diagnostic step sets, and the multiple diagnostic results, wherein the target diagnostic result list is used to characterize the corresponding relationship between the items to be diagnosed and the diagnostic step sets and the diagnostic results, and finally a confirmation message is sent to the target user based on the target diagnostic result list, wherein the confirmation message is used for the target user to view the multiple diagnostic step sets and the multiple diagnostic results corresponding to the remote order, thereby ensuring that the user can obtain the detailed completion status of each item in the remote order of the target vehicle.

[0038] See also Figure 2 , Figure 2 is a flowchart of a remote order confirmation method provided by an embodiment of the present invention. The method is applied to Figure 1 The server shown includes but is not limited to the following steps:

[0039] S201: Obtain a remote order from a target user for a target vehicle.

[0040] In this embodiment, the target user can send a remote order for the target vehicle through the remote diagnostic device, wherein the remote order information may include the basic information of the target user: such as name, contact information, address, etc., and may also include detailed information of the target vehicle: including vehicle brand, model, frame number, production year, mileage, etc., and may also include the usage of the vehicle: such as daily usage frequency, driving conditions (urban roads, highways, mountain roads, etc.), whether it has been modified, etc., and may also include a vehicle fault description: a detailed description of the problem or abnormal symptoms of the vehicle by the target user, such as difficulty in starting the engine, abnormal noise in the brake system, instrument panel fault light on, etc., and may also include items that the target user wants to diagnose, including: programming, coding, flashing hiding, maintenance light reset, anti-theft light reset, electronic system fault diagnosis, exhaust emission detection and adjustment, tire pressure monitoring system calibration, air conditioning system diagnosis and maintenance, electric power steering system inspection, battery health detection, adaptive cruise control system debugging, automatic parking system calibration, etc.

[0041] S202: Determine the items to be diagnosed of the target vehicle based on the remote order, and obtain a plurality of items to be diagnosed.

[0042] In this embodiment, a part of the items to be diagnosed is determined from the target user's detailed description of the problems or abnormal symptoms occurring in the vehicle, another part of the items to be diagnosed is determined from the items that the target user wants to diagnose, and the items to be diagnosed of the target vehicle are determined based on the two parts of the items to be diagnosed, thereby obtaining a plurality of items to be diagnosed.

[0043] S203: Determine the priority corresponding to each of the multiple items to be diagnosed, and obtain multiple priorities.

[0044] In this embodiment, exemplarily, historical diagnostic data corresponding to each of a plurality of items to be diagnosed within a first preset time period is obtained to obtain target historical diagnostic data, and then, based on the target historical diagnostic data, a stable value corresponding to each of the plurality of items to be diagnosed is predicted to obtain a plurality of target stable values. Specifically, the target stable value corresponding to the first item to be diagnosed is first determined, wherein the first item to be diagnosed is any item to be diagnosed among the plurality of items to be diagnosed.

[0045] Exemplarily, the output power data of the first vehicle system corresponding to the first item to be diagnosed is obtained based on the target historical diagnostic data, wherein each item to be diagnosed corresponds to a vehicle system, and if the first item to be diagnosed is an electronic control system fault diagnosis, the first vehicle system corresponding to the first item to be diagnosed is an electronic control system. If the first vehicle system is an engine system, the output power data of the engine system is the effective power actually output by the engine under different working conditions; if the first vehicle system is a transmission system, the output power data of the transmission system can be the power transmitted to the wheels by transmission components such as a transmission; if the first vehicle system is a braking system, the output power data of the braking system can be the consumed power; if the first vehicle system is an electronic control system, the output power data of the electronic control system can be the control signal power of other systems or the power consumption of related electronic equipment; if the first vehicle system is an electrical system, the output power data of the electrical system can be the electrical power output by the generator; if the first vehicle system is an air conditioning system, the output power data of the air conditioning system can be the operating power of the air conditioning compressor; if the first vehicle system is an emission control system, the output power data of the emission control system can be the power consumed when controlling pollutant emissions.

[0046] Specifically, the output power data of the first vehicle system corresponding to the first item to be diagnosed can be determined by the following methods: data screening method, screening out specific parameters or data fields related to the output power from the historical diagnostic data of the first target, which may directly or indirectly reflect the output power; data analysis and modeling method, using statistical analysis or machine learning algorithms to analyze and model the historical diagnostic data of the first target, find features and patterns that are strongly correlated with the output power, and thus infer the output power data; comparison and reference method, comparing the historical diagnostic data of the first target with standard data or reference data of known output power, and estimating the output power of the first vehicle system through similarity or difference analysis; trend analysis method, observing the changing trend of power-related parameters in the historical diagnostic data of the first target over time, and inferring the possible value of the output power based on the trend.

[0047] Exemplarily, fitting is performed based on the output power data to obtain a target output power straight line. Specifically, after obtaining the output power data, a sufficient number of output power data points are determined, and then a suitable fitting method is selected, such as the least squares method, to calculate the straight line parameters that minimize the sum of the squares of the distances from these output power data points to the straight line, and then the calculated parameters are substituted into the straight line equation to obtain the target output power straight line.

[0048] It can be seen that by fitting the target output power straight line, the changing trend of the output power can be intuitively presented and analyzed, which helps to more clearly understand the changing pattern of the system output power over time.

[0049] Exemplarily, the slope corresponding to the target output power line is determined to obtain a first slope. Specifically, the first slope can be obtained by selecting two clear data points on the target output power line and using the ratio of the change in the ordinate to the change in the abscissa between the two points to calculate the slope corresponding to the target output power line.

[0050] Exemplarily, the target fault probability corresponding to the first item to be diagnosed is determined based on the first slope. Specifically, when the first slope is less than or equal to the preset slope, the reference fault probability corresponding to the first slope is first determined. The mapping relationship between the preset slope and the fault probability may be pre-stored, and the reference fault probability corresponding to the first slope is determined based on the mapping relationship. Exemplarily, the target usage time of the first vehicle system is obtained, and the target optimization factor corresponding to the target usage time is determined. Specifically, the mapping relationship between the preset usage time and the optimization factor may be pre-stored, and the target optimization factor corresponding to the target usage time is determined based on the mapping relationship. Exemplarily, the reference fault probability is optimized according to the target optimization factor to obtain the target fault probability corresponding to the first item to be diagnosed. Specifically, the reference fault probability can be adjusted according to the target optimization factor. The specific calculation formula is as follows:

[0051] Target failure probability = reference failure probability × (1 + target optimization factor);

[0052] According to the above formula, the target failure probability corresponding to the first item to be diagnosed can be obtained.

[0053] It can be seen that comprehensively considering the influence of slope and vehicle system usage time to determine the failure probability can more comprehensively and accurately evaluate the possibility of failure and avoid evaluation bias caused by a single factor. The mapping relationship between slope and failure probability, and between usage time and optimization factor established based on a large amount of data makes the determination of failure probability more accurate.

[0054] It should be explained that if the first slope is greater than the preset slope, n output power values ​​in the output power data are obtained, where n is an integer greater than 1. Exemplarily, the maximum output power value and the minimum output power value among the n output power values ​​are determined, and the upper and lower boundaries of the output power value are clarified, which helps to fully understand the change range of the output power. Exemplarily, the output power difference between the minimum output power value and the maximum output power value is determined to obtain the target output power difference, and this difference reflects the overall fluctuation range of the output power. Exemplarily, based on the minimum output power value and the maximum output power value, m interval ranges corresponding to the target output power difference are determined, where m is an integer greater than 1, and then the n output power values ​​fall into the m interval ranges, and the number of output power values ​​in the m interval ranges is counted to obtain the number of m output power values, which can observe the distribution of the output power in more detail. By observing the distribution of the number of output power values ​​in each interval range, it is easier to find the intervals where abnormal values ​​are concentrated, so as to quickly locate the power range where there may be problems. Exemplarily, the weight corresponding to each interval range in the m interval ranges is obtained to obtain m weights, wherein the sum of the m weights is 1, the closer the interval range is to the minimum output power value, the smaller the weight is, the lower the output power may have a relatively small impact on the system performance and the risk of failure, and the higher output power or the power fluctuation near the maximum value is more likely to be related to the key performance problems or potential failures of the system. In order to more accurately reflect the power range that has an important impact on the probability of failure, the weights are allocated more to the power intervals that can better indicate the critical state of the system, that is, the intervals far away from the minimum value and closer to the normal or higher power level. Exemplarily, the number proportion corresponding to each interval range is determined according to the number of m output power values ​​to obtain m number proportions, and a weighted operation is performed according to the m number proportions and the m weights to obtain a target deviation parameter, and a target adjustment parameter corresponding to the target deviation parameter is determined. Specifically, a mapping relationship between a preset deviation parameter and an adjustment parameter can be pre-stored, and the target adjustment parameter corresponding to the target deviation parameter is determined based on the mapping relationship. Exemplarily, the first slope is optimized according to the target adjustment parameter to obtain the second slope. Specifically, the second slope can be adjusted according to the target adjustment parameter. The specific calculation formula is as follows: second slope = first slope × (1 + target adjustment parameter); the second slope can be obtained according to the above formula. After the second slope is obtained, the target failure probability corresponding to the second slope is determined in the same way as the target failure probability corresponding to the first item to be diagnosed is determined based on the first slope.

[0055] It can be seen that through the detailed analysis of the output power value and multiple rounds of calculation and adjustment, the fault probability related to the output power can be evaluated more accurately, and the misjudgment and missed judgment can be reduced. The maximum value, minimum value, difference, interval range, weight and other factors of the output power are integrated, and various possible situations of system performance are fully considered, making the determination of the fault probability more comprehensive and reliable. When the first slope does not meet the requirements, a more accurate second slope and the corresponding fault probability are obtained through a series of operations, avoiding the need to re-collect a large amount of data for analysis and improving work efficiency.

[0056] Exemplarily, the target stability value corresponding to the first item to be diagnosed is determined based on the target fault probability. Specifically, the reference stability value corresponding to the target fault probability is obtained, which can be a mapping relationship between a preset fault probability and a stability value, and the reference stability value corresponding to the target fault probability is determined based on the mapping relationship. Exemplarily, the historical stability value within the second preset time period is obtained to obtain k historical stability values, wherein k is a positive integer, each historical stability value corresponds to a stability value detection time, and the start time of the second preset time period is earlier than the start time of the first preset time period. Exemplarily, fitting is performed according to the k historical stability values ​​and the corresponding stability value detection time to obtain a target fitting straight line, wherein the horizontal axis of the target fitting straight line is time and the vertical axis is the historical stability value, and the fitting methods that can be selected include the least squares method, the orthogonal distance regression method, the piecewise straight line fitting method, and the like. Exemplarily, the slope corresponding to the target fitting straight line is obtained to obtain a third slope, and the third slope can be obtained by selecting two clear data points on the target fitting straight line and calculating the slope corresponding to the target output power straight line by using the ratio of the change amount of the ordinate between the two points to the change amount of the abscissa. Exemplarily, the target fine-tuning parameter corresponding to the third slope is determined. Specifically, a mapping relationship between a preset slope and a fine-tuning parameter may be pre-stored, and the target fine-tuning parameter corresponding to the third slope is determined based on the mapping relationship. Exemplarily, the reference stability value is fine-tuned based on the target fine-tuning parameter to obtain the target stability value. Specifically, the reference stability value may be fine-tuned according to the target fine-tuning parameter. The specific calculation formula is as follows: target stability value = reference stability value × (1 + target fine-tuning parameter). The target stability value may be obtained according to the above formula.

[0057] It can be seen that by obtaining historical stability values ​​and fitting them, we can make full use of past data information, so that the determined target stability value does not only rely on the current target failure probability, but combines long-term trends and laws, thereby improving the reliability and accuracy of the stability value. The fine-tuning parameters are determined according to the slope of the fitting straight line, and the reference stability value is adjusted. This can timely reflect the changing trend of system performance, and integrates the influence of failure probability and historical data, making the determination of the target stability value more scientific and precise, which helps to more accurately evaluate the status of the vehicle system.

[0058] It should be explained that if the target stability value corresponding to the first item to be diagnosed is less than the preset stability value, an alarm message corresponding to the first item to be diagnosed is generated, and multiple diagnostic results are determined based on the items to be diagnosed other than the first item to be diagnosed among the multiple items to be diagnosed, wherein the alarm message is used to prompt that the first item to be diagnosed has a fault, and the alarm message can be a text message or a voice broadcast message, which is not limited here. It can be seen that when the target stability value corresponding to the first item to be diagnosed is less than the preset stability value, an alarm can be quickly issued when the stability value of the first item to be diagnosed is lower than the preset value, excluding the first item to be diagnosed that has been determined to have a fault, and determining the diagnostic result based on other normal items to be diagnosed, which can reduce the interference of the faulty item on the overall diagnosis, thereby improving the accuracy and reliability of the diagnostic result.

[0059] S204: Determine a target priority order based on multiple priorities.

[0060] In this embodiment, the target priority order is determined by sorting according to the priority level, where the larger the priority level is, the higher the order is.

[0061] S205: Obtain a diagnosis step set corresponding to each of the multiple items to be diagnosed, to obtain multiple diagnosis step sets.

[0062] In this embodiment, one diagnostic step set corresponds to one item to be diagnosed. Exemplarily, the vehicle identification number of the target vehicle is obtained. The vehicle identification number is a unique code consisting of 17 characters, which is used to identify the manufacturer, model, year, production plant and other important features of the vehicle. Exemplarily, the target diagnostic table of the target vehicle is determined based on the vehicle identification number, wherein the target diagnostic table is used to characterize the correspondence between different diagnostic items and diagnostic step sets. The function of this target diagnostic table is to clearly display each item that may need to be diagnosed (such as engine performance, brake system status, etc.) and a series of detailed diagnostic steps corresponding thereto (which may include using specific tools for detection, reading specific data parameters, performing specific test operations, etc.), and then determine the diagnostic step set corresponding to each item to be diagnosed in the target diagnostic table among multiple items to be diagnosed, and obtain multiple diagnostic step sets. Specifically, for multiple specific items that need to be diagnosed at present, the set of diagnostic steps corresponding to each item to be diagnosed is searched and clarified in the acquired target diagnostic table, so that a corresponding, complete and accurate set of diagnostic steps is obtained for each item to be diagnosed.

[0063] It can be seen that obtaining the corresponding diagnostic table based on the vehicle's unique vehicle identification number can ensure that the diagnostic steps are fully matched with the configuration and characteristics of the specific vehicle, improve the accuracy of the diagnosis, avoid misdiagnosis or missed diagnosis that may result from the use of general steps, and accurately obtain the corresponding set of diagnostic steps for each item to be diagnosed, making the diagnostic process more targeted and organized, reducing unnecessary operations and time waste, and thus improving the overall efficiency of the diagnostic work.

[0064] S206: Perform a diagnosis operation on each of the multiple items to be diagnosed based on the target priority order and the multiple diagnosis step sets to obtain multiple diagnosis results.

[0065] In this embodiment, one diagnostic result corresponds to one item to be diagnosed. Specifically, first, according to the target priority order, the diagnostic operation is started from the item to be diagnosed with the highest priority. For each item to be diagnosed, the operation is performed in sequence according to its corresponding diagnostic step set. When executing each step, the acquired data and observed phenomena are carefully recorded. After completing all diagnostic steps of an item to be diagnosed, the collected information is comprehensively analyzed to obtain the diagnostic result of the item. Then, the above process is repeated for the next item to be diagnosed in sequence according to the priority order until the diagnostic operations of all items to be diagnosed are completed, thereby obtaining multiple diagnostic results.

[0066] S207: Determine a target diagnosis result list based on the multiple items to be diagnosed, the multiple diagnosis step sets, and the multiple diagnosis results.

[0067] In this embodiment, the target diagnostic result list is used to characterize the correspondence between the items to be diagnosed, the diagnostic step set, and the diagnostic result. Specifically, first, create a table whose columns can be "items to be diagnosed", "diagnostic step set", and "diagnostic result", and then fill each item to be diagnosed into the "items to be diagnosed" column in turn. For each item to be diagnosed, extract its corresponding diagnostic step set from the existing data and fill it into the "diagnostic step set" column. Then, fill the diagnostic result obtained from the actual diagnostic operation into the "diagnostic result" column to obtain the target diagnostic result list.

[0068] S208: Send confirmation information to the target user based on the target diagnosis result list.

[0069] In this embodiment, the confirmation information is used for the target user to view the multiple diagnostic step sets and multiple diagnostic results corresponding to the remote order. Specifically, after the confirmation information is sent to the target user, the target user can view the multiple diagnostic step sets and multiple diagnostic results corresponding to the remote order in the confirmation information. If the diagnostic steps or diagnostic results therein are incorrect, the target user can modify the remote order information and then re-perform the diagnostic operation.

[0070] In summary, the implementation of the present invention has the following beneficial effects:

[0071] It can be seen that the remote order confirmation method described in the embodiment of the present invention is applied to the server, firstly, the remote order of the target user for the target vehicle is obtained, and the to-be-diagnosed items of the target vehicle are determined based on the remote order to obtain multiple to-be-diagnosed items, and then the priority corresponding to each of the multiple to-be-diagnosed items is determined to obtain multiple priorities, and then the target priority order is determined based on the multiple priorities, and the diagnostic step set corresponding to each of the multiple to-be-diagnosed items is obtained to obtain multiple diagnostic step sets, wherein one diagnostic step set corresponds to one to-be-diagnosed item, and then a diagnostic operation is performed on each of the multiple to-be-diagnosed items based on the target priority order and the multiple diagnostic step sets to obtain multiple diagnostic results, wherein one diagnostic result corresponds to one to-be-diagnosed item, and then a target diagnostic result list is determined based on the multiple to-be-diagnosed items, the multiple diagnostic step sets, and the multiple diagnostic results, wherein the target diagnostic result list is used to characterize the corresponding relationship between the to-be-diagnosed items and the diagnostic step sets and the diagnostic results, and finally a confirmation message is sent to the target user based on the target diagnostic result list, wherein the confirmation message is used for the target user to view the multiple diagnostic step sets and the multiple diagnostic results corresponding to the remote order, thereby ensuring that the user can obtain the detailed completion status of each item in the remote order of the target vehicle.

[0072] See also Figure 3 , Figure 3 It is a structural diagram of a remote order confirmation device provided in an embodiment of the present application, which is applied to a server. The remote order confirmation device 300 includes: an acquisition unit 301 and a processing unit 302;

[0073] An acquisition unit 301 is used to acquire a remote order of a target user for a target vehicle;

[0074] The processing unit 302 is used to determine the items to be diagnosed of the target vehicle based on the remote order, and obtain a plurality of items to be diagnosed;

[0075] Determine the priority corresponding to each of the multiple items to be diagnosed, and obtain multiple priorities;

[0076] Determine the order of priority of goals based on multiple priorities;

[0077] An acquisition unit 301 is used to acquire a diagnosis step set corresponding to each of the multiple items to be diagnosed, and obtain multiple diagnosis step sets; one diagnosis step set corresponds to one item to be diagnosed;

[0078] The processing unit 302 is used to perform a diagnostic operation on each of the multiple items to be diagnosed based on the target priority order and the multiple diagnostic step sets to obtain multiple diagnostic results; one diagnostic result corresponds to one item to be diagnosed;

[0079] Determine a target diagnostic result list based on multiple items to be diagnosed, multiple diagnostic step sets, and multiple diagnostic results; the target diagnostic result list is used to characterize the correspondence between the items to be diagnosed and the diagnostic step sets and diagnostic results;

[0080] A confirmation message is sent to the target user based on the target diagnosis result list; the confirmation message is used by the target user to view multiple diagnosis step sets and multiple diagnosis results corresponding to the remote order.

[0081] In some possible implementations, in determining the priority corresponding to each of the multiple items to be diagnosed and obtaining the multiple priorities, the processing unit 302 is specifically configured to:

[0082] Acquire historical diagnostic data corresponding to each of the multiple items to be diagnosed within a first preset time period to obtain target historical diagnostic data;

[0083] According to the target historical diagnostic data, the stability value corresponding to each of the multiple items to be diagnosed is predicted according to the prediction method in the following steps S1-S5 to obtain multiple target stability values, which is specifically the following steps:

[0084] S1. Acquire output power data of a first vehicle system corresponding to a first item to be diagnosed based on target historical diagnostic data; each item to be diagnosed corresponds to a vehicle system, and the first item to be diagnosed is any item to be diagnosed among multiple items to be diagnosed;

[0085] S2. Fitting is performed according to the output power data to obtain a target output power straight line;

[0086] S3, determining the slope corresponding to the target output power straight line to obtain a first slope;

[0087] S4, determining a target failure probability corresponding to the first item to be diagnosed based on the first slope;

[0088] S5. Determine a target stability value corresponding to the first item to be diagnosed based on the target failure probability;

[0089] The priority of each of the multiple items to be diagnosed is determined based on the multiple target stability values ​​to obtain multiple priorities; the smaller the stability value of the item to be diagnosed, the higher the priority of the item to be diagnosed.

[0090] In some possible implementations, in determining the target failure probability corresponding to the first item to be diagnosed based on the first slope, the processing unit 302 is specifically configured to:

[0091] determining a reference failure probability corresponding to the first slope;

[0092] Obtaining a target usage time of a first vehicle system;

[0093] Determine the target optimization factor corresponding to the target usage time;

[0094] The reference failure probability is optimized according to the target optimization factor to obtain the target failure probability corresponding to the first item to be diagnosed.

[0095] In some possible implementations, the processing unit 302 is further specifically configured to:

[0096] If the first slope is greater than the preset slope, then obtaining n output power values ​​in the output power data; n is an integer greater than 1;

[0097] Determine a maximum output power value and a minimum output power value among the n output power values;

[0098] Determine an output power difference between a minimum output power value and a maximum output power value to obtain a target output power difference value;

[0099] According to the minimum output power and the maximum output power, m interval ranges corresponding to the target output power difference are determined; m is an integer greater than 1;

[0100] The n output power values ​​fall into m intervals;

[0101] Count the number of output power values ​​in the m interval ranges to obtain the number of m output power values;

[0102] Obtain the weight corresponding to each interval range in the m interval ranges to obtain m weight values; the sum of the m weight values ​​is 1, and the weight of the interval range closer to the minimum output power value is smaller;

[0103] Determine the quantity ratio corresponding to each interval range according to the number of m output power values, and obtain m quantity ratios;

[0104] A weighted operation is performed based on the m quantity proportions and the m weights to obtain the target deviation parameter;

[0105] determining a target adjustment parameter corresponding to the target deviation parameter;

[0106] Optimizing the first slope according to the target adjustment parameters to obtain a second slope;

[0107] A target failure probability corresponding to the first item to be diagnosed is determined based on the second slope.

[0108] In some possible implementations, in determining the target stability value corresponding to the first item to be diagnosed based on the target failure probability, the processing unit 302 is specifically configured to:

[0109] Obtain a reference stable value corresponding to the target failure probability;

[0110] Obtaining historical stable values ​​within a second preset time period, obtaining k historical stable values; k is a positive integer, and each historical stable value corresponds to a stable value detection moment; the start moment of the second preset time period is earlier than the start moment of the first preset time period;

[0111] According to k historical stable values ​​and corresponding stable value detection moments, fitting is performed to obtain a target fitting straight line; the horizontal axis of the target fitting straight line is time and the vertical axis is the historical stable value;

[0112] Obtain the slope corresponding to the target fitting straight line to obtain the third slope;

[0113] determining a target fine-tuning parameter corresponding to the third slope;

[0114] The reference stability value is fine-tuned based on the target fine-tuning parameter to obtain the target stability value.

[0115] In some possible implementations, the processing unit 302 is further specifically configured to:

[0116] If the target stability value corresponding to the first item to be diagnosed is less than the preset stability value, an alarm message corresponding to the first item to be diagnosed is generated, and multiple diagnostic results are determined based on multiple items to be diagnosed except the first item to be diagnosed; the alarm message is used to indicate that a fault has occurred in the first item to be diagnosed.

[0117] In some possible implementations, in acquiring a diagnosis step set corresponding to each of the multiple items to be diagnosed to obtain multiple diagnosis step sets, the processing unit 302 is specifically configured to:

[0118] Obtain the vehicle identification number of the target vehicle;

[0119] Determine a target diagnosis table for a target vehicle based on a vehicle identification number; the target diagnosis table is used to characterize the correspondence between different diagnosis items and a set of diagnosis steps;

[0120] A diagnostic step set corresponding to each of the multiple diagnostic items in the target diagnostic table is determined to obtain multiple diagnostic step sets.

[0121] See also Figure 4 , Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4As shown, the electronic device 400 includes a transceiver 401, a processor 402 and a memory 403. They are connected via a bus 404. The memory 403 is used to store computer programs and data, and the transceiver 401 can transmit the data stored in the memory 403 to the processor 402. The above program includes instructions for executing the following steps:

[0122] Obtaining remote orders from target users for target vehicles;

[0123] Determine the items to be diagnosed of the target vehicle based on the remote order, and obtain a plurality of items to be diagnosed;

[0124] Determine the priority corresponding to each of the multiple items to be diagnosed, and obtain multiple priorities;

[0125] Determine the order of priority of goals based on multiple priorities;

[0126] Obtain a diagnostic step set corresponding to each of the multiple diagnostic items to be diagnosed, and obtain multiple diagnostic step sets; one diagnostic step set corresponds to one diagnostic item;

[0127] Based on the target priority order and the multiple diagnostic step sets, a diagnostic operation is performed on each of the multiple diagnostic items to be diagnosed, and multiple diagnostic results are obtained; one diagnostic result corresponds to one diagnostic item;

[0128] Determine a target diagnostic result list based on multiple items to be diagnosed, multiple diagnostic step sets, and multiple diagnostic results; the target diagnostic result list is used to characterize the correspondence between the items to be diagnosed and the diagnostic step sets and diagnostic results;

[0129] A confirmation message is sent to the target user based on the target diagnosis result list; the confirmation message is used by the target user to view multiple diagnosis step sets and multiple diagnosis results corresponding to the remote order.

[0130] In some possible implementations, in determining the priority corresponding to each of the multiple items to be diagnosed to obtain multiple priorities, the above program includes instructions for performing the following steps:

[0131] Acquire historical diagnostic data corresponding to each of the multiple items to be diagnosed within a first preset time period to obtain target historical diagnostic data;

[0132] According to the target historical diagnostic data, the stability value corresponding to each of the multiple items to be diagnosed is predicted according to the prediction method in the following steps S1-S5 to obtain multiple target stability values, which is specifically the following steps:

[0133] S1. Acquire output power data of a first vehicle system corresponding to a first item to be diagnosed based on target historical diagnostic data; each item to be diagnosed corresponds to a vehicle system, and the first item to be diagnosed is any item to be diagnosed among multiple items to be diagnosed;

[0134] S2. Fitting is performed according to the output power data to obtain a target output power straight line;

[0135] S3, determining the slope corresponding to the target output power straight line to obtain a first slope;

[0136] S4, determining a target failure probability corresponding to the first item to be diagnosed based on the first slope;

[0137] S5. Determine a target stability value corresponding to the first item to be diagnosed based on the target failure probability;

[0138] The priority of each of the multiple items to be diagnosed is determined based on the multiple target stability values ​​to obtain multiple priorities; the smaller the stability value of the item to be diagnosed, the higher the priority of the item to be diagnosed.

[0139] In some possible implementations, in terms of determining the target failure probability corresponding to the first item to be diagnosed based on the first slope, the program includes instructions for executing the following steps:

[0140] determining a reference failure probability corresponding to the first slope;

[0141] Obtaining a target usage time of a first vehicle system;

[0142] Determine the target optimization factor corresponding to the target usage time;

[0143] The reference failure probability is optimized according to the target optimization factor to obtain the target failure probability corresponding to the first item to be diagnosed.

[0144] In some possible implementations, the above program includes instructions for performing the following steps:

[0145] If the first slope is greater than the preset slope, then obtaining n output power values ​​in the output power data; n is an integer greater than 1;

[0146] Determine a maximum output power value and a minimum output power value among the n output power values;

[0147] Determine an output power difference between a minimum output power value and a maximum output power value to obtain a target output power difference value;

[0148] According to the minimum output power and the maximum output power, m interval ranges corresponding to the target output power difference are determined; m is an integer greater than 1;

[0149] The n output power values ​​fall into m intervals;

[0150] Count the number of output power values ​​in the m interval ranges to obtain the number of m output power values;

[0151] Obtain the weight corresponding to each interval range in the m interval ranges to obtain m weight values; the sum of the m weight values ​​is 1, and the weight of the interval range closer to the minimum output power value is smaller;

[0152] Determine the quantity ratio corresponding to each interval range according to the number of m output power values, and obtain m quantity ratios;

[0153] A weighted operation is performed based on the m quantity proportions and the m weights to obtain the target deviation parameter;

[0154] determining a target adjustment parameter corresponding to the target deviation parameter;

[0155] Optimizing the first slope according to the target adjustment parameters to obtain a second slope;

[0156] A target failure probability corresponding to the first item to be diagnosed is determined based on the second slope.

[0157] In some possible implementations, in terms of determining the target stability value corresponding to the first item to be diagnosed based on the target failure probability, the above program includes instructions for executing the following steps:

[0158] Obtain a reference stable value corresponding to the target failure probability;

[0159] Obtaining historical stable values ​​within a second preset time period, obtaining k historical stable values; k is a positive integer, and each historical stable value corresponds to a stable value detection moment; the start moment of the second preset time period is earlier than the start moment of the first preset time period;

[0160] According to k historical stable values ​​and corresponding stable value detection moments, fitting is performed to obtain a target fitting straight line; the horizontal axis of the target fitting straight line is time and the vertical axis is the historical stable value;

[0161] Obtain the slope corresponding to the target fitting straight line to obtain the third slope;

[0162] determining a target fine-tuning parameter corresponding to the third slope;

[0163] The reference stability value is fine-tuned based on the target fine-tuning parameter to obtain the target stability value.

[0164] In some possible implementations, the above program includes instructions for performing the following steps:

[0165] If the target stability value corresponding to the first item to be diagnosed is less than the preset stability value, an alarm message corresponding to the first item to be diagnosed is generated, and multiple diagnostic results are determined based on multiple items to be diagnosed except the first item to be diagnosed; the alarm message is used to indicate that a fault has occurred in the first item to be diagnosed.

[0166] In some possible implementations, in terms of obtaining a diagnostic step set corresponding to each of a plurality of items to be diagnosed to obtain a plurality of diagnostic step sets, the program includes instructions for executing the following steps:

[0167] Obtain the vehicle identification number of the target vehicle;

[0168] Determine a target diagnosis table for a target vehicle based on a vehicle identification number; the target diagnosis table is used to characterize the correspondence between different diagnosis items and a set of diagnosis steps;

[0169] A diagnostic step set corresponding to each of the multiple diagnostic items in the target diagnostic table is determined to obtain multiple diagnostic step sets.

[0170] It should be understood that the electronic devices in this application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, mobile Internet devices MID (Mobile Internet Devices, MID for short) or wearable devices or servers, edge computing nodes, etc. The above electronic devices are only examples, not exhaustive, and include but are not limited to the above electronic devices.

[0171] The present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any remote order confirmation method described in the above method implementation.

[0172] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any remote order confirmation method recorded in the above method implementation.

[0173] It should be noted that, for the above-mentioned various method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0174] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0177] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0178] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of each implementation method of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0179] A person skilled in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0180] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A remote order confirmation method, characterized in that: Applied to a server, the method includes: Obtaining remote orders from target users for target vehicles; Determine the items to be diagnosed of the target vehicle based on the remote order, and obtain a plurality of items to be diagnosed; Determine the priority corresponding to each of the multiple items to be diagnosed, and obtain multiple priorities; determining a target priority order based on the plurality of priorities; Obtain a diagnostic step set corresponding to each of the multiple items to be diagnosed, to obtain multiple diagnostic step sets; one diagnostic step set corresponds to one item to be diagnosed; Based on the target priority order and the multiple diagnostic step sets, a diagnostic operation is performed on each of the multiple items to be diagnosed to obtain multiple diagnostic results; one diagnostic result corresponds to one item to be diagnosed; Determine a target diagnostic result list based on the multiple items to be diagnosed, the multiple diagnostic step sets, and the multiple diagnostic results; the target diagnostic result list is used to characterize the correspondence between the items to be diagnosed and the diagnostic step sets and the diagnostic results; A confirmation message is sent to the target user based on the target diagnosis result list; the confirmation message is used by the target user to view the multiple diagnosis step sets and the multiple diagnosis results corresponding to the remote order.

2. The method according to claim 1, characterized in that The determining of the priority corresponding to each of the plurality of items to be diagnosed to obtain a plurality of priorities includes: Acquire historical diagnostic data corresponding to each of the plurality of items to be diagnosed within a first preset time period to obtain target historical diagnostic data; According to the target historical diagnostic data, the stability value corresponding to each of the multiple items to be diagnosed is predicted according to the prediction method in the following steps S1-S5 to obtain multiple target stability values, which is specifically the following steps: S1. Acquire output power data of a first vehicle system corresponding to a first item to be diagnosed based on the target historical diagnostic data; each item to be diagnosed corresponds to a vehicle system, and the first item to be diagnosed is any item to be diagnosed among the multiple items to be diagnosed; S2. Fitting is performed according to the output power data to obtain a target output power straight line; S3, determining the slope corresponding to the target output power straight line to obtain a first slope; S4. determining a target failure probability corresponding to the first item to be diagnosed based on the first slope; S5. determining a target stability value corresponding to the first item to be diagnosed based on the target failure probability; The priority of each of the multiple items to be diagnosed is determined based on the multiple target stability values ​​to obtain the multiple priorities; the smaller the stability value of the item to be diagnosed, the higher the priority of the item to be diagnosed.

3. The method according to claim 2, characterized in that The determining, based on the first slope, a target failure probability corresponding to the first item to be diagnosed includes: determining a reference failure probability corresponding to the first slope; Obtaining a target usage time of the first vehicle system; Determine a target optimization factor corresponding to the target usage time; The reference failure probability is optimized according to the target optimization factor to obtain the target failure probability corresponding to the first item to be diagnosed.

4. The method according to claim 2 or 3, characterized in that The method further comprises: If the first slope is greater than a preset slope, then obtaining n output power values ​​in the output power data; n is an integer greater than 1; Determine a maximum output power value and a minimum output power value among the n output power values; Determine an output power difference between the minimum output power value and the maximum output power value to obtain a target output power difference; According to the minimum output power value and the maximum output power value, m interval ranges corresponding to the target output power difference are determined; m is an integer greater than 1; The n output power values ​​fall into the m interval ranges; Counting the number of output power values ​​in the m interval ranges to obtain m number of output power values; Obtaining a weight corresponding to each of the m interval ranges to obtain m weights; the sum of the m weights is 1, and the weight of the interval range closer to the minimum output power value is smaller; Determine the quantity ratio corresponding to each interval range according to the m output power values, and obtain m quantity ratios; Performing a weighted operation according to the m quantity proportions and the m weights to obtain a target deviation parameter; determining a target adjustment parameter corresponding to the target deviation parameter; Optimizing the first slope according to the target adjustment parameter to obtain a second slope; A target failure probability corresponding to the first item to be diagnosed is determined based on the second slope.

5. The method according to claim 4, characterized in that The determining, based on the target failure probability, a target stability value corresponding to the first item to be diagnosed includes: Obtaining a reference stable value corresponding to the target failure probability; Obtaining historical stable values ​​within a second preset time period to obtain k historical stable values; k is a positive integer, and each historical stable value corresponds to a stable value detection moment; the start moment of the second preset time period is earlier than the start moment of the first preset time period; Fitting is performed according to the k historical stable values ​​and the corresponding stable value detection moments to obtain a target fitting straight line; the horizontal axis of the target fitting straight line is time and the vertical axis is the historical stable value; Obtaining the slope corresponding to the target fitting straight line to obtain a third slope; determining a target fine-tuning parameter corresponding to the third slope; The reference stable value is fine-tuned based on the target fine-tuning parameter to obtain the target stable value.

6. The method according to claim 5, characterized in that The method further comprises: If the target stability value corresponding to the first item to be diagnosed is less than the preset stability value, an alarm message corresponding to the first item to be diagnosed is generated, and the multiple diagnostic results are determined based on the multiple items to be diagnosed except the first item to be diagnosed; the alarm message is used to prompt that the first item to be diagnosed has a fault.

7. The method according to claim 1, characterized in that The step of obtaining a diagnosis step set corresponding to each of the multiple items to be diagnosed to obtain multiple diagnosis step sets includes: Obtaining a vehicle identification number of the target vehicle; Determine a target diagnostic table for the target vehicle based on the vehicle identification number; the target diagnostic table is used to characterize the correspondence between different diagnostic items and diagnostic step sets; A diagnostic step set corresponding to each of the multiple items to be diagnosed in the target diagnostic table is determined to obtain multiple diagnostic step sets.

8. A remote order confirmation device, characterized in that: Applied to a server, the device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire a remote order from a target user for a target vehicle; The processing unit is used to determine the items to be diagnosed of the target vehicle based on the remote order to obtain a plurality of items to be diagnosed; Determine the priority corresponding to each of the multiple items to be diagnosed, and obtain multiple priorities; determining a target priority order based on the plurality of priorities; The acquisition unit is used to acquire a diagnostic step set corresponding to each of the multiple items to be diagnosed, to obtain multiple diagnostic step sets; one diagnostic step set corresponds to one item to be diagnosed; The processing unit is used to perform a diagnostic operation on each of the multiple items to be diagnosed based on the target priority order and the multiple diagnostic step sets to obtain multiple diagnostic results; one diagnostic result corresponds to one item to be diagnosed; Determine a target diagnostic result list based on the multiple items to be diagnosed, the multiple diagnostic step sets, and the multiple diagnostic results; the target diagnostic result list is used to characterize the correspondence between the items to be diagnosed and the diagnostic step sets and the diagnostic results; A confirmation message is sent to the target user based on the target diagnosis result list; the confirmation message is used by the target user to view the multiple diagnosis step sets and the multiple diagnosis results corresponding to the remote order.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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