Vehicle health state determination method and unmanned vehicle

Abstract

The invention provides a vehicle health state determination method and an unmanned vehicle, which can be applied to the technical fields of vehicle control, intelligent auxiliary driving, intelligent mines, vehicle maintenance and the like. The method comprises the steps that operation information, road surface slippery information and current health information of a target vehicle are obtained, the target vehicle comprises a vehicle executing operation in an open-air place, the current health information represents the health state of vehicle parts of the target vehicle, and the operation information is collected when the target vehicle executes operation; determining a loss increment parameter matched with the pavement slippery information; and correcting reference loss information determined based on the operation information by using the loss increment parameter, and determining the health state of the vehicle part according to the difference between the corrected reference loss information and the current health information.

Description

Technical Field

[0001] This invention relates to the technical fields of vehicle control, intelligent assisted driving, smart mining and vehicle maintenance, and more specifically, to a method for determining the health status of a vehicle and an unmanned vehicle. Background Technology

[0002] With the rapid development of artificial intelligence technology, unmanned vehicles can be used to perform tasks in open-pit mines and other environments to improve work efficiency. However, the mining environment is harsh, often involving alternating high and low temperatures, high dust concentrations, and complex conditions such as rugged terrain. When unmanned vehicles operate in this environment for extended periods, components such as engines, transmission systems, and braking systems are prone to wear, aging, and malfunctions. Therefore, accurate health assessments and maintenance of unmanned vehicles are crucial.

[0003] In realizing the concept of this invention, the inventors discovered that the related technologies have at least the following problems: the related technologies assess the health of vehicles by using fixed prediction models or thresholds, ignoring the accelerated wear caused by working environment factors, resulting in low accuracy of health status assessment. Summary of the Invention

[0004] In view of this, the present invention provides a method for determining the health status of a vehicle and an unmanned vehicle.

[0005] One aspect of the present invention provides a method for determining the health status of a vehicle, comprising: acquiring operating information, road surface slippage information, and current health information of a target vehicle, wherein the target vehicle includes vehicles performing operations in an open-air location, the current health information characterizes the health status of vehicle components of the target vehicle, and the operating information is collected when the target vehicle performs operations; determining a loss increment parameter that matches the road surface slippage information; correcting a baseline loss information determined based on the operating information using the loss increment parameter, and determining the health status of the vehicle components based on the difference between the corrected baseline loss information and the current health information.

[0006] According to an embodiment of the present invention, the loss increment parameter includes a first sub-parameter corresponding to the road surface condition; determining the loss increment parameter matching the road surface slippery information includes: determining road surface condition information matching the road surface slippery information; determining the first sub-parameter based on the relative difference between the road surface condition information and the reference road surface condition information, wherein the reference road surface condition information includes the road surface condition of an open-air location under the reference road surface slippery information.

[0007] According to an embodiment of the present invention, the loss increment parameter includes a second sub-parameter corresponding to the braking performance of the target vehicle; determining the loss increment parameter matching the road surface slippage information includes: if the road surface slippage information meets predetermined conditions, determining the reference braking information of the target vehicle when running in the operating state indicated by the reference road surface slippage information and the operating information; and determining the second sub-parameter according to the degree of deviation between the braking information in the operating information and the reference braking information.

[0008] Preferably, the operating information includes at least one of the following: road surface type, road surface slope, load status, and braking information.

[0009] According to an embodiment of the present invention, the braking information includes the number of braking operations and braking performance parameters; determining a second sub-parameter based on the degree of deviation between the braking information in the operating information and the reference braking information includes: determining the second sub-parameter based on a first degree of deviation between the number of braking operations and the reference number of braking operations, and a second degree of deviation between the braking performance parameters and the reference braking performance parameters; preferably, the second sub-parameter is obtained by weighted summation of the first degree of deviation and the second degree of deviation, and the weights of the first degree of deviation and the second degree of deviation are determined based on the historical operating information of the target vehicle.

[0010] According to an embodiment of the present invention, the loss increment parameter includes a first sub-parameter and a second sub-parameter. Determining the loss increment parameter that matches the road surface slipperiness information includes: fusing the first sub-parameter and the second sub-parameter to obtain the loss increment parameter.

[0011] Preferably, the first sub-parameter has a greater impact on the loss increment parameter than the second sub-parameter.

[0012] According to an embodiment of the present invention, a vehicle component includes a braking device; the process of correcting reference loss information determined based on operating information using loss increment parameters, and determining the health status of the vehicle component based on the difference between the corrected reference loss information and the current health information, includes: correcting reference loss information for a single braking operation using loss increment parameters to obtain corrected reference loss information for a single braking operation; and determining the health status of the braking device based on the difference between the corrected reference loss information for a single braking operation and the current health information.

[0013] According to an embodiment of the present invention, the reference loss information for a single braking operation includes: the difference in hardware loss information before and after the braking device performs a single braking operation when the target vehicle is in the operating state indicated by the operating information; and / or the reference loss information includes: the heat loss information caused by the braking device performing a single braking operation when the target vehicle is in the operating state indicated by the operating information.

[0014] According to an embodiment of the present invention, the method further includes: when the road surface slippage information is road surface slippage information at a future time after the current time, correcting the baseline loss information determined based on the estimated operating information using the loss increment parameter, and determining the health status of the vehicle component based on the difference between the corrected baseline loss information and the current health information, wherein the estimated operating information is determined based on the operation to be performed by the target vehicle at a future time.

[0015] According to an embodiment of the present invention, the method further includes: generating warning information for the target vehicle when the health status meets the warning conditions.

[0016] Preferably, when the road surface slippery information is the road surface slippery information at a future time and the health status meets the warning conditions, a warning information is generated so that the target vehicle can be repaired based on the warning information before the operation to be performed.

[0017] One aspect of the present invention provides an unmanned vehicle, including: a processor configured to perform the above-described vehicle health status determination method.

[0018] In addition to considering the target vehicle's own operational and current health information, embodiments of this invention also take into account road surface slippage information, which characterizes environmental factors. By determining a loss increment parameter that matches the road surface slippage information, and using this loss increment parameter to correct the baseline loss information determined based on operational information, the invention can adapt in real time to the equipment loss patterns under different slippery operating conditions. This makes the corrected baseline loss information more closely reflect actual operating conditions, solving the problem that traditional fixed lifespan models and thresholds cannot cope with real-time operating condition fluctuations. Subsequently, the health status is determined by the difference between the current health information, which characterizes the real-time state of vehicle components, and the corrected baseline loss information. This improves the dynamism and accuracy of health status assessment, reduces safety issues such as missed alarms, false alarms, and sudden failures, and ultimately lowers vehicle maintenance costs based on health status. Attached Figure Description

[0019] The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0020] Figure 1 An exemplary system architecture for determining vehicle health status according to embodiments of the present invention is shown.

[0021] Figure 2 A flowchart of a method for determining vehicle health status according to an embodiment of the present invention is shown.

[0022] Figure 3A flowchart of a method for correcting reference loss information and determining health status based on loss increment parameters according to an embodiment of the present invention is shown.

[0023] Figure 4 The illustration shows a scenario diagram of determining health status according to an embodiment of the present invention.

[0024] Figure 5 A schematic diagram of the vehicle health status determination device according to an embodiment of the present invention is shown.

[0025] Figure 6 A block diagram of an autonomous vehicle suitable for implementing the method described above, according to an embodiment of the present invention, is shown. Detailed Implementation

[0026] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0030] In the embodiments of this invention, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken to prevent unauthorized access to user personal information data and to maintain the security of user personal information, network security, and other security.

[0031] In the embodiments of the present invention, the user's authorization or consent is obtained before acquiring or collecting the user's personal information.

[0032] In related technologies, the health status assessment and maintenance of autonomous vehicles mainly rely on the autonomous vehicle's own operating parameters or historical data, such as engine operating time, mileage, vibration frequency, etc., and are judged by establishing a fixed life model or threshold.

[0033] However, these traditional methods have significant shortcomings: they completely ignore the unique and variable environmental factors in open-air operations, especially the nonlinear accelerated wear caused by severe weather conditions such as rain and snow, and slippery roads after watering. For example, in rainy or snowy weather, or after watering operations, slippery roads and a decreased coefficient of adhesion lead to a sharp increase in the frequency and intensity of braking by mining trucks, frequent slippage of the drive system, and increased load on the steering system. This causes the wear rate of critical braking components to far exceed the model predictions under ideal operating conditions. Lifetime models or threshold determinations in related technologies cannot perceive or quantify this additional accelerated wear caused by environmental factors, resulting in a significant deviation between the health status assessment results and the actual equipment condition. Consequently, predictive maintenance strategies targeting this health status fail (either sudden failures or over-maintenance), increasing operating costs and safety risks.

[0034] To this end, embodiments of the present invention provide a method for determining the health status of a vehicle, comprising: acquiring operating information, road surface slipperiness information, and current health information of a target vehicle, wherein the target vehicle includes vehicles performing operations in open-air locations, the current health information characterizes the health status of vehicle components of the target vehicle, and the operating information is collected when the target vehicle performs operations; determining a loss increment parameter that matches the road surface slipperiness information; correcting the baseline loss information determined based on the operating information using the loss increment parameter, and determining the health status of the vehicle components based on the difference between the corrected baseline loss information and the current health information.

[0035] Therefore, in addition to considering the target vehicle's own operating information and current health information, embodiments of the present invention also take into account road surface slippage information, which characterizes environmental factors. By determining a loss increment parameter that matches the road surface slippage information, and using the loss increment parameter to correct the baseline loss information determined based on operating information, the invention can adapt to the equipment loss patterns under different slippery conditions in real time. This makes the corrected baseline loss information more consistent with actual operating conditions, solving the problem that traditional fixed lifespan models and thresholds cannot cope with real-time operating condition fluctuations. Subsequently, the health status is determined by the difference between the current health information, which characterizes the real-time state of vehicle components, and the corrected baseline loss information. This improves the dynamism and accuracy of health status assessment, reduces safety issues such as missed reports, false alarms, and sudden failures, and ultimately lowers vehicle maintenance costs based on health status.

[0036] Figure 1 An exemplary system architecture for applying the vehicle health status determination method and apparatus according to embodiments of the present invention is shown. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to embodiments of the present invention, in order to help those skilled in the art understand the technical content of the present invention, but do not mean that embodiments of the present invention cannot be used in other devices, systems, environments or scenarios.

[0037] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a vehicle 101, a network 102, and a server 103. The network 102 serves as a medium for providing a communication link between the vehicle 101 and the server 103. The network 102 may include various connection types, such as wired and / or wireless communication links, etc.

[0038] Users can use vehicle 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on vehicle 101, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (for example only).

[0039] Vehicle 101 can be a vehicle capable of operating in open-air locations, such as a water sprinkler truck, snow melting truck, earthmoving truck, earthmoving truck, etc. These vehicles can also be driverless vehicles. The embodiments of the present invention do not limit the specific type of vehicle 101, as long as it is a vehicle capable of operating in open-air locations.

[0040] Server 103 can be a server that provides various services, such as a backend management server that supports websites browsed by the user using vehicle 101 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0041] It should be noted that the vehicle health status determination method provided in the embodiments of the present invention can generally be executed by server 103. Correspondingly, the vehicle health status determination device provided in the embodiments of the present invention can generally be located in server 103. The vehicle health status determination method provided in the embodiments of the present invention can also be executed by a server or server cluster that is different from server 103 and capable of communicating with vehicle 101 and / or server 103. Correspondingly, the vehicle health status determination device provided in the embodiments of the present invention can also be located in a server or server cluster that is different from server 103 and capable of communicating with vehicle 101 and / or server 103. Alternatively, the vehicle health status determination method provided in the embodiments of the present invention can also be executed by vehicle 101, or by other vehicles different from vehicle 101. Correspondingly, the vehicle health status determination device provided in the embodiments of the present invention can also be located in vehicle 101, or in other vehicles different from vehicle 101.

[0042] It should be understood that Figure 1 The number of vehicles, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0043] Figure 2 A flowchart of a vehicle health status determination method according to an embodiment of the present invention is shown. Figure 2 As shown, the method for determining the health status of a vehicle includes operations S210 to S230.

[0044] During operation of S210, obtain the target vehicle's operating information, road surface slippage information, and current health information.

[0045] In operation S220, the loss increment parameter is determined to match the road surface slipperiness information.

[0046] In operation S230, the baseline loss information determined based on the operating information is corrected using the loss increment parameter, and the health status of the vehicle components is determined based on the difference between the corrected baseline loss information and the current health information.

[0047] Target vehicles include vehicles operating in open-air locations. Target vehicles refer to vehicles undergoing health assessments, such as water trucks, snow melting trucks, earthmoving vehicles, and earthmoving vehicles. It is understandable that the operation of target vehicles will accelerate the aging / wear of vehicle components, and the degree of aging / wear will vary depending on the operating conditions of the vehicles performing the same task.

[0048] Operational information is collected when the target vehicle performs its tasks. To accurately determine the health status of the target vehicle based on its real-time operational status, baseline wear information is determined based on the operational information collected during the task's execution. For example, operational information may include: engine operating time, mileage, vibration frequency, braking information, etc. The tasks performed by the target vehicle may include watering, de-icing agent spraying, earth and rock excavation, and earth and rock transportation.

[0049] Road surface slipperiness information refers to information that characterizes the degree of road surface slipperiness. For example, it can be climate information and / or operational information that affects the degree of road surface slipperiness. For instance, it can be climate information that affects the degree of road surface slipperiness, such as rain and snow, which can include weather type, rainfall / snow level, rainfall / snow amount, temperature, etc.; it can also be operational information that affects the degree of road surface slipperiness, such as watering and de-icing agent spraying, which can include spraying volume, spraying direction, etc.

[0050] In one specific embodiment, the target vehicle can obtain information about road surface slipperiness by communicating in real time with monitoring equipment. For example, it can communicate in real time with a weather station deployed in the mine to obtain climate information, and communicate with monitoring sensors or an onboard IoT terminal (used to manage and store monitoring sensor data) to obtain operational information in real time. Similarly, the target vehicle can also obtain the above-mentioned operational information and current health information by communicating with monitoring sensors or an onboard IoT terminal, or by communicating with a server that stores the above information.

[0051] The loss increment parameter refers to the additional wear and tear on vehicle components when driving on roads corresponding to road surface slipperiness information. Different degrees of road surface slipperiness cause different additional wear and tear on vehicle components. Therefore, multiple road surface slipperiness information representing multiple degrees of road surface slipperiness are matched with multiple loss increment parameters. For example, the additional wear and tear on vehicle components differs when driving on dry and wet roads, and also differs when driving on roads with different degrees of wetness.

[0052] In one embodiment, the matching relationship between the loss increment parameter and the road surface slippage information can be predetermined so that the loss increment information matching it can be determined directly based on the current road surface slippage when the target vehicle is performing the current operation.

[0053] As stated above, operational information can determine baseline loss information. The loss increment parameter is the incremental loss caused by additional road surface slippage information. Therefore, the loss increment parameter can be mathematically fused with the baseline loss information to obtain the corrected baseline loss information. Subsequently, the health status is determined by the difference between the current health information and the corrected baseline loss information (e.g., the health status updated after executing the current task).

[0054] Current health information characterizes the health status of vehicle components of the target vehicle. This health status may be the same as or different from the health status obtained after performing the current operation. Vehicle components whose health status needs to be determined may be those affected by whether the road surface is wet or not, such as braking equipment.

[0055] For example, health status can be categorized as: minor wear, severe wear, no wear, awaiting repair, awaiting replacement, etc. Alternatively, health status can also be expressed numerically, with specific values ​​indicating the corresponding status.

[0056] In addition to considering the target vehicle's own operational and current health information, embodiments of this invention also take into account road surface slippage information, which characterizes environmental factors. By determining a loss increment parameter that matches the road surface slippage information, and using this parameter to correct the baseline loss information determined based on operational information, the invention can adapt in real time to the equipment loss patterns under different slippery conditions. This makes the corrected baseline loss information more closely reflect actual operating conditions, solving the problem that traditional fixed lifespan models and thresholds cannot cope with real-time fluctuations in operating conditions. Subsequently, the health status is determined by the difference between the current health information, which characterizes the real-time state of vehicle components, and the corrected baseline loss information. This improves the dynamism and accuracy of health status assessment, reduces safety issues such as missed alarms, false alarms, and sudden failures, and ultimately lowers vehicle maintenance costs based on health status.

[0057] According to an embodiment of the present invention, the loss increment parameter includes a first sub-parameter corresponding to the road surface condition; determining the loss increment parameter matching the road surface slippery information includes: determining road surface condition information matching the road surface slippery information; determining the first sub-parameter based on the relative difference between the road surface condition information and the reference road surface condition information, wherein the reference road surface condition information includes the road surface condition of an open-air location under the reference road surface slippery information.

[0058] In this embodiment, the loss increment parameter can be a first sub-parameter, or a first sub-parameter and its coefficients, etc., and the coefficients can be predetermined.

[0059] Road surface condition information is used to characterize the road surface condition when the target vehicle performs operations. For example, road surface condition information may include dry, wet, waterlogged, snow-covered, and icy. In some embodiments, road surface condition information may be a road surface adhesion coefficient that indicates the above-mentioned road surface condition. This road surface adhesion coefficient quantifies the frictional adhesion between the road surface and the vehicle tires, thereby indicating the corresponding road surface condition (such as dry, wet, etc. mentioned above).

[0060] In one embodiment, the road surface slippery information includes climate information. Road surface condition information under various climate influences can be determined based on expert experience to obtain a matching relationship between the two. Thus, the corresponding road surface condition information is determined based on the above matching relationship for the road surface slippery information obtained during the operation.

[0061] For example, road surface slipperiness information includes weather type and rainfall / snow level. If the weather type is rain and the rainfall / snow level is low, the road surface condition information is wet or the corresponding road surface adhesion coefficient. Further, if the rainfall / snow level is medium or high, the road surface condition information is water accumulation or the corresponding road surface adhesion coefficient. If the weather type is snow and the rainfall / snow level is low, the road surface condition information is wet or the corresponding road surface adhesion coefficient. Further, if the rainfall / snow level is medium or high, the road surface condition information is snow cover or the corresponding road surface adhesion coefficient. Alternatively, if the rainfall / snow level is medium or high and the temperature is below 0°C, the road surface condition information can be determined as icy or the corresponding road surface adhesion coefficient. If the weather type is sunny, the road surface condition information is dry or the corresponding road surface adhesion coefficient.

[0062] In another embodiment, the road surface slippery information includes operation information. The road surface condition under the influence of multiple operation information can be determined based on expert experience to obtain the matching relationship between the two. Then, based on the above matching relationship, the road surface condition information that matches the road surface slippery information obtained by performing the operation is determined.

[0063] For example, road surface slipperiness information includes the amount of water sprayed. If the sprayed amount is greater than a certain threshold, the road surface condition information is water accumulation or the corresponding road adhesion coefficient; conversely, if it is less than a certain threshold, the road surface condition information is wet or the corresponding road adhesion coefficient. If the water sprayed amount is zero, the road surface condition information is dry or the corresponding road adhesion coefficient. Alternatively, road surface slipperiness information includes the amount of de-icing agent sprayed. If the sprayed amount is greater than a certain threshold, the road surface condition information is wet / icy or the corresponding road adhesion coefficient; conversely, if it is less than a certain threshold, the road surface condition information is snow cover or the corresponding road adhesion coefficient.

[0064] In another embodiment, road surface slippage information can include both operational and weather information. For example, if the water spraying volume exceeds a certain threshold when the weather is sunny, the road surface condition information is water accumulation or the corresponding road surface adhesion coefficient. If the weather is sunny and no operations have occurred, the road surface condition information is dry or the corresponding road surface adhesion coefficient.

[0065] The baseline road surface condition information includes the road surface condition of an open-air location under the baseline road surface slipperiness information. That is, the baseline road surface condition information can be the road surface condition that causes the least additional damage to the target vehicle, such as dry or the corresponding road surface adhesion coefficient. For example, the baseline road surface slipperiness information could be that the weather type is sunny and / or no work is being carried out.

[0066] Considering that it is difficult to accurately quantify the degree of slippage of different road surfaces based solely on road surface condition information, the relative difference between the road surface condition information and the reference road surface condition information can be defined as the first sub-parameter. For example, this first sub-parameter could be the difference or ratio between the road surface condition information and the reference road surface condition information.

[0067] For example, the first sub-parameter can be determined by referring to the following formula (1):

[0068] (1).

[0069] in, and These represent the baseline pavement condition information (such as the adhesion coefficient of a dry pavement) and the current pavement condition information (such as the adhesion coefficient of the current pavement condition), respectively. This represents the first sub-parameter, also known as the theoretical wear amplification factor.

[0070] In this embodiment, the different degrees of slipperiness of the same road surface under the same road surface condition can be represented by the specific value of the road surface adhesion coefficient, so as to further quantify the different degrees of slipperiness of the road surface based on the quantification of the road surface condition.

[0071] For example, when the road surface condition is "snow-covered," the corresponding road surface adhesion coefficient is 0.25 (which can be determined based on historical data and the real-time road surface slippage under the current road surface condition). The baseline road surface condition information is a road surface adhesion coefficient of 0.8 in a dry state. Then the first sub-parameter K theoretical = 0.8 / 0.25 = 3.2. Alternatively, if the road surface is "snow-covered" and more slippery than it is currently, the corresponding road surface adhesion coefficient can be 0.20.

[0072] Furthermore, besides accurately quantifying the characteristics of different road surface slipperiness, considering the diverse road surface types in open areas (such as gravel roads, slag roads, temporary paved roads, etc.), and the inherent differences in the baseline road surface condition information for various road surface types (e.g., the inherent adhesion capabilities of dry gravel roads and dry asphalt roads are different), the road surface condition that causes the least additional damage to the target vehicle under a specific road surface type can be used as the baseline road surface condition information to further accurately quantify the characteristics of different road surface slipperiness. For example, in a mining operation scenario, the baseline road surface condition information could be the road adhesion coefficient of a dry compacted soil road surface (such as 0.8 mentioned above).

[0073] In embodiments of the present invention, the first sub-parameter corresponding to the road surface condition is determined as the incremental loss parameter. This allows for precise correction of the baseline loss information from the dimension of the additional nonlinear loss caused by the road surface condition, thereby improving the accuracy of the subsequently determined health status information. Furthermore, in this embodiment, the first sub-parameter is determined using the relative difference between the current road surface condition information and the baseline road surface condition information. This accurately reflects the degree of influence of the current working condition on the road surface adhesion capability, and thus determines the degree of influence of the road surface adhesion capability on the subsequent baseline loss information, thereby improving the dynamism and accuracy of the health status assessment.

[0074] According to an embodiment of the present invention, the loss increment parameter includes a second sub-parameter corresponding to the braking performance of the target vehicle; determining the loss increment parameter matching the road surface slippage information includes: if the road surface slippage information meets predetermined conditions, determining the reference braking information of the target vehicle when running in the operating state indicated by the reference road surface slippage information and the operating information; and determining the second sub-parameter according to the degree of deviation between the braking information in the operating information and the reference braking information.

[0075] It is understandable that slippery road surfaces cause significant additional wear and tear on vehicle components related to the braking system in the target vehicle. Therefore, the wear increment parameter can be a second sub-parameter, or a second sub-parameter and its coefficient, etc., and the coefficient can be predetermined.

[0076] In this embodiment, when the road surface is dry and / or slightly slippery, the wear on vehicle components related to the braking equipment is normal wear or negligible minor additional wear. To balance accuracy and computing power, the calculation process of the second sub-parameter can be triggered when the road surface slipperiness information meets predetermined conditions.

[0077] For example, the predetermined conditions may include at least one of the following: the climate type in the climate information (if the road surface slippery information includes climate information) is a specific climate type (e.g., rain and / or snow) and / or the rainfall / snow level is greater than a threshold; the spraying volume in the operation information (if the road surface slippery information includes operation information) meets the operation volume requirements.

[0078] In practical applications, vehicle operating information affects braking performance. For example, different road surface types, different road surface slopes, and different load conditions have different effects on braking performance. In order to accurately quantify the additional losses caused by the current vehicle, the reference braking information required for the vehicle to run under various road surface slipperiness conditions and various operating conditions can be determined in advance to obtain a reference for quantifying and determining the second sub-parameter.

[0079] Preferably, the operational information includes at least one of the following: road surface type, road surface slope, load status, and braking information. Road surface type, road surface slope, and load status are used to indicate the road surface type, road surface slope, and load level where the target vehicle is currently performing the operation, respectively. Braking information refers to information related to braking operations.

[0080] For example, a test vehicle (the target vehicle or a vehicle of the same type as the target vehicle) can be controlled to operate in advance under the conditions indicated by baseline road surface slipperiness information and operating information to obtain braking information under various conditions. When assessing the health status of the target vehicle, baseline braking information that matches the current operating state and baseline road surface slipperiness information is selected. For instance, the vehicle can be controlled to operate in advance under the aforementioned baseline road surface slipperiness information conditions, obtaining braking information under different road surface types, uphill and downhill slopes, and various road surface gradients and load conditions (fully loaded / heavily loaded / unloaded).

[0081] In some embodiments, the difference between the braking information and the reference braking information can be determined as the aforementioned deviation degree; alternatively, the ratio of the difference between the braking information and the reference braking information to the reference braking information can be used as the deviation degree. This deviation degree can then be used as the second sub-parameter.

[0082] In this embodiment, the second sub-parameter corresponding to braking performance is determined as the incremental loss parameter. This allows for precise correction of the baseline loss information from the perspective of braking performance (causing additional nonlinear losses), thereby improving the accuracy of the subsequently determined health status information. Furthermore, in this embodiment, using baseline braking information that is completely consistent with the vehicle's current operating conditions to determine the second sub-parameter accurately reflects the degree of impact of the current operating conditions on braking performance. This not only adapts to the varied road and operational scenarios in mines but also improves the dynamism and accuracy of health status assessment.

[0083] According to a specific embodiment of the present invention, the braking information includes the number of braking operations and braking performance parameters; determining a second sub-parameter based on the degree of deviation between the braking information in the operation information and the reference braking information includes: determining the second sub-parameter based on a first degree of deviation between the number of braking operations and the reference number of braking operations, and a second degree of deviation between the braking performance parameters and the reference braking performance parameters.

[0084] The number of braking operations and braking performance parameters can be the average of the number of braking operations and braking performance parameters for the target vehicle on a predetermined road segment / length. Braking performance parameters can be the control quantities required for the target vehicle to perform braking operations and / or the quantities acting on vehicle components within the braking system.

[0085] For example, the ratio of the difference between the braking information and the reference braking information to the reference braking information can be used as the degree of deviation. The first degree of deviation and the second degree of deviation are obtained when the braking information consists of the number of braking operations and braking performance parameters. The second sub-parameter is obtained by summing the first degree of deviation and the second degree of deviation.

[0086] Preferably, the second sub-parameter can be obtained by weighted summation of the first and second deviation degrees. The weights of the first and second deviation degrees are determined based on the historical operating information of the target vehicle. For example, statistical, deep learning, and big data analysis methods can be used to analyze the historical operating information to determine the incremental impact of braking frequency and braking performance parameters on additional losses, thereby obtaining the weights of the first and second deviation degrees.

[0087] For example, the second sub-parameter can be determined according to the following formula (2):

[0088] (2).

[0089] in, This is the second sub-parameter, also known as the correction factor. and These are the reference braking count and reference braking performance parameters from the reference braking information. and These are the number of braking cycles and braking performance parameters, respectively. , , , These are the first degree of deviation, the second degree of deviation, and their corresponding weights.

[0090] In one specific embodiment, and These are the average number of braking actions per kilometer (or per 100 kilometers) and the average braking pressure, respectively. and These are the average number of braking operations per kilometer (or per 100 kilometers) and the average braking pressure, respectively, during the current operation.

[0091] In embodiments of the present invention, by considering both the number of braking events and braking performance parameters in the braking information, and the additional nonlinear losses caused by the braking frequency and braking intensity, the baseline loss information can be accurately corrected to improve the accuracy of the subsequently determined health status information.

[0092] According to another specific embodiment of the present invention, the loss increment parameter includes a first sub-parameter and a second sub-parameter. Determining the loss increment parameter that matches the road surface slipperiness information includes: fusing the first sub-parameter and the second sub-parameter to obtain the loss increment parameter.

[0093] For example, the loss increment parameter can be obtained by summing or weighted summing the first and second sub-parameters. Alternatively, the product of the first and second sub-parameters can also be used as the loss increment parameter.

[0094] Preferably, the first sub-parameter has a greater impact on the loss increment parameter than the second sub-parameter.

[0095] For example, when weighting the first sub-parameter and the second sub-parameter, the weight of the first sub-parameter is greater than the weight of the second sub-parameter, so that the first sub-parameter has a greater impact on the loss increment parameter than the second sub-parameter has.

[0096] Alternatively, the first sub-parameter can be used as a coefficient to amplify the sum of the second sub-parameter and the reference value, so that the influence of the first sub-parameter on the loss increment parameter is greater than that of the second sub-parameter on the loss increment parameter.

[0097] In one example, the loss increment parameter It can be determined according to the following formula (3):

[0098] (3).

[0099] Where "1" represents the baseline value. This is the first sub-parameter corresponding to the road surface condition. Typically a value greater than 1, it is used to amplify the loss increment parameter by means of a higher effect than the second sub-parameter, in order to highlight the more sensitive additional losses caused by road conditions to vehicle components.

[0100] If the target vehicle is currently running based on the baseline road surface slipperiness information and operating information, the loss increment parameter is a predetermined value of 1, that is... =1. For example, determined based on current actual road surface slipperiness information and operational information. If the value is 3.5, then the wear rate of the current braking equipment is a predetermined value. 3.5 times that.

[0101] In embodiments of the present invention, a hybrid model architecture combining a first sub-parameter determined by a theoretical model of road surface condition and a second sub-parameter determined by a correction model of deviation from current real-time operating information enables precise correction of loss information and physically interpretable quantification, thereby enabling precise measurement of health status.

[0102] Figure 3 A flowchart illustrating a method for correcting reference loss information and determining health status based on loss increment parameters according to an embodiment of the present invention is shown. Figure 3 As shown, the method includes operations S331 to S332.

[0103] In operation S331, the reference loss information for a single braking operation is corrected using the loss increment parameter to obtain the corrected reference loss information for a single braking operation.

[0104] In operation S332, the health status of the braking device is determined based on the difference between the corrected reference loss information for a single braking operation and the current health information.

[0105] Vehicle components include braking systems. Braking systems can be devices that directly engage with the wheels / axles to generate braking force, such as brake pads, brake chambers, and brake levers; or devices that provide stable braking pressure, such as air compressors and brake pumps.

[0106] It is understandable that each braking operation causes wear and tear on the braking equipment, thus reducing its health status based on the current health information. The wear and tear caused by a single braking operation is related to road surface slippage information and operational information. Therefore, the baseline wear and tear information for a single braking operation (the wear and tear during operation according to this operational information) can be corrected using the wear and tear increment parameter determined above. The current health information is then subtracted from the corrected baseline wear and tear information for a single braking operation to obtain the health status of the braking equipment, which can also be understood as the remaining health status.

[0107] For example, the health status of the braking device can be determined according to formula (4).

[0108] (4).

[0109] in, This represents the baseline loss information for a single braking operation. The loss increment parameter determined above, Indicates the health status of the braking equipment. This indicates current health information.

[0110] Alternatively, braking information may also include the number of braking actions, and the health status can be determined based on the difference between the current health information and the corrected baseline wear information after the cumulative number of braking actions, such as... N represents the number of braking operations performed by the target vehicle after its current health status is determined, i.e., the number of braking operations.

[0111] It is understood that the baseline loss information is determined based on expert experience or historical operating data without considering road surface slippage. In this embodiment of the invention, the baseline loss information that matches the baseline loss information can be directly obtained from the expert database of the server based on the operating information.

[0112] In this embodiment, by dynamically introducing a loss increment coefficient related to weather such as rain and snow to correct the baseline loss information, the current health information and the above-corrected baseline loss information can be used to obtain a true reflection of the "instantaneous loss" of vehicle components and equipment under harsh working conditions, thereby improving the accuracy of health status assessment.

[0113] Figure 4 A scenario diagram illustrating the determination of health status according to an embodiment of the present invention is shown. For example... Figure 4 As shown, when the target vehicle 401 operates in an open area, it can pass through three areas, such as the first area 402, the second area 403, and the third area 404. Taking earth and rock transportation operations in a mining scenario as an example, before going to the earth and rock area, the target vehicle 401 passes through the first area 402, the second area 403, and the third area 404 in sequence, and its load status can be empty. During the process of moving from the earth and rock area to the loading and unloading area, when passing through the third area 404, the second area 403, and the first area 402 in sequence, its load status can be heavy.

[0114] Zones 402, 403, and 404 belong to different road surface types. Under moderate snow conditions, the road surface conditions in the three zones are icy, covered with snow, and slippery, respectively.

[0115] If braking operations are performed in all three regions, the first sub-parameter of each region can be determined based on road surface slippage information. Then, the corresponding baseline braking information is obtained based on the load state, and the actual operating information after passing through the three regions is determined, followed by the determination of the second sub-parameter. Finally, the first and second sub-parameters of each of the three regions are fused to obtain the loss increment parameter for the corresponding region.

[0116] By combining load status and road surface conditions, six different loss increment parameters, such as K1 to K6, can be determined when the vehicle sequentially passes through the first area 402, the second area 403, the third area 404 (the first passage through these areas is unloaded), the third area 404, the second area 403, and the first area 402 (the second passage through these areas is heavily loaded) during the entire process of "going to the soil and rock area - from the soil and rock area to the loading and unloading area". For the number of braking operations performed by the target vehicle 401 in each area, the baseline loss information for each single braking operation can be corrected using K1 to K6. Based on the current health information and the corrected baseline loss information, the health status of the vehicle components can be obtained. This health status can be the health status of the braking components after the entire soil and rock handling operation is completed.

[0117] According to an embodiment of the present invention, the reference loss information for a single braking operation includes: the difference in hardware loss information before and after the braking device performs a single braking operation when the target vehicle is in the operating state indicated by the operating information; and / or the reference loss information includes: the heat loss information caused by the braking device performing a single braking operation when the target vehicle is in the operating state indicated by the operating information.

[0118] In some embodiments, the braking device generates physical hardware wear information after performing a braking operation. For example, taking brake pads as an example, after performing a braking operation, the thickness of the brake pads will wear down, and this hardware wear information can be the thickness of the brake pads. The thickness wear of the brake pads can be measured using sensors such as laser thickness gauges and profilometers.

[0119] In other embodiments, the braking device can convert the vehicle's kinetic or potential energy into heat energy loss through friction, and this heat loss information can be the amount of heat lost. In some embodiments, an energy dissipation model can be used to establish a theoretical correlation between heat loss information and the wear of the braking device, so as to determine the wear of the braking device caused by a single braking operation. Heat loss information can be measured by sensors such as infrared thermometers and thermocouples installed on the braking device.

[0120] In the embodiments of the present invention, the flexible selection of a single-dimensional or dual-dimensional combination of reference loss information acquisition methods can adapt to the sensing and monitoring capabilities of different open-air operation scenarios and the operation and maintenance needs of different target vehicle models, thereby enhancing the adaptability and practicality of health status assessment.

[0121] According to an embodiment of the present invention, the method further includes: when the road surface slippage information is road surface slippage information at a future time after the current time, correcting the baseline loss information determined based on the estimated operating information using the loss increment parameter, and determining the health status of the vehicle component based on the difference between the corrected baseline loss information and the current health information, wherein the estimated operating information is determined based on the operation to be performed by the target vehicle at a future time.

[0122] In real-world mining operations, the tasks performed by the target vehicle can be predetermined, as can road surface slippage information, such as based on weather forecasts. Therefore, by utilizing road surface slippage information from future moments after the current time, along with estimated operational information, a predictive assessment of the target vehicle's health status in the future can be achieved. The method for determining this health status is similar to the real-time health status assessment described above and will not be repeated here.

[0123] For example, the future time could be a time 4 hours from now. At the current time, information on road surface slipperiness (such as weather information) 4 hours from now can be obtained from a weather station deployed at the mine. Based on the target vehicle's shift schedule, the tasks to be performed 4 hours from now, as well as the estimated operational information required to perform those tasks, such as estimated load, estimated road surface type, and estimated road surface slope, can be determined. Then, based on the aforementioned road surface slipperiness information and estimated operational information, the health status of the target vehicle after performing the aforementioned tasks can be predicted.

[0124] In this embodiment, by predicting the health status in advance, it is possible to predict the sudden reduction in the lifespan of vehicle components due to sudden climate changes, effectively avoiding sudden failures during operation, reducing unplanned downtime, preventing over-maintenance, and lowering operating costs.

[0125] According to an embodiment of the present invention, the method further includes: generating warning information for the target vehicle when the health status meets the warning conditions.

[0126] Warning conditions could be, for example, a health status of severe wear and tear, requiring repair, or requiring replacement, or, for a numerical health status, such as H above. rest The warning condition can be that the health status value is less than a predetermined threshold. The warning information can be a descriptive information about the health status or an immediate warning.

[0127] In one embodiment, an early warning can be issued immediately based on the relationship between the health status determined by the current real-time operating conditions and the warning conditions.

[0128] In another preferred embodiment, when the road surface slipperiness information is the road surface slipperiness information at a future time and the health status meets the warning conditions, a warning message is generated so as to repair the target vehicle based on the warning message before performing the operation to be performed.

[0129] For example, taking a future time that could be 4 hours later as an example, if the predicted health status of the target vehicle after the operation meets the warning conditions, then the target vehicle can be repaired in advance before the operation is performed, thus achieving pre-operational maintenance.

[0130] In this embodiment, the above-mentioned health status determination method can provide a quantitative relationship model of "slippery road surface (climate) - wear - lifespan" for open-air sites, making it possible to make preventive resource allocation (such as spare parts and personnel) based on weather forecasts, and improving the overall intelligence level of operation and maintenance management.

[0131] Furthermore, by using operational information or estimated operational information to determine the health status after a single braking operation, feedback can be provided to the autonomous driving control system of the target vehicle. This prompts the control system to automatically adopt a smoother and more conservative control strategy under slippery road conditions (weather), reducing wear from the source and improving operational safety, thus forming a proactive health management closed loop of "assessment-early warning-control".

[0132] Figure 5 A schematic diagram of the vehicle health status determination device according to an embodiment of the present invention is shown. Figure 5 As shown, the vehicle health status determination device 500 includes: an acquisition module 510, used to acquire the operating information, road surface slippage information, and current health information of a target vehicle, wherein the target vehicle includes vehicles performing operations in open-air locations, the current health information characterizes the health status of the vehicle components of the target vehicle, and the operating information is collected when the target vehicle performs operations; a first determination module 520, used to determine a loss increment parameter matching the road surface slippage information; and a second determination module 530, used to correct the baseline loss information determined based on the operating information using the loss increment parameter, and determine the health status of the vehicle components based on the difference between the corrected baseline loss information and the current health information.

[0133] It should be noted that the data processing system part in the embodiments of the present invention corresponds to the data processing method part in the embodiments of the present invention. The specific description of the data processing system part is referred to in the data processing method part, and will not be repeated here.

[0134] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention, or at least part of the functions of any one or more of them, can be implemented in a single module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be implemented by being divided into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be at least partially implemented as hardware circuits, such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits (ASICs), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, and firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0135] For example, any plurality of the acquisition module 510, the first determination module 520, and the second determination module 530 can be combined into one module / unit / subunit, or any one of these modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits can be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of the present invention, at least one of the acquisition module 510, the first determination module 520, and the second determination module 530 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the acquisition module 510, the first determination module 520, and the second determination module 530 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0136] One aspect of the present invention provides an autonomous vehicle, including: a processor configured to execute the vehicle health status determination method described above. The autonomous vehicle may be the target vehicle in the vehicle health status determination method, such as... Figure 1 Vehicle 101 in the middle.

[0137] Figure 6A block diagram of an autonomous vehicle suitable for implementing the method described above, according to an embodiment of the present invention, is shown. Figure 6 The autonomous vehicle shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention. In this autonomous vehicle, devices other than the processor can be regarded as devices that implement data input, processing and output.

[0138] like Figure 6 As shown, the autonomous vehicle 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in ROM (Read-Only Memory) 602 or a program loaded from storage portion 608 into RAM (Random Access Memory) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0139] RAM 603 stores various programs and data required for the operation of the autonomous vehicle 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Processor 601 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.

[0140] According to an embodiment of the present invention, the autonomous vehicle 600 may further include an input / output (I / O) interface 605, which is also connected to a bus 604. The autonomous vehicle 600 may also include one or more of the following components connected to the input / output (I / O) interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A driver 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the driver 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0141] According to embodiments of the present invention, the method flow according to embodiments of the present invention can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by processor 601, it performs the functions defined in the system of the embodiments of the present invention. According to embodiments of the present invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0142] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0143] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0144] For example, according to embodiments of the present invention, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0145] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of the present invention. When the computer program product is run on an autonomous vehicle, the program code is used to enable the autonomous vehicle to implement the methods provided in the embodiments of the present invention.

[0146] When the computer program is executed by the processor 601, it performs the functions defined in the system / apparatus of this embodiment of the invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0147] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and / or installed from the removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0148] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or pairings fall within the scope of this invention.

[0150] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A method for determining the health status of a vehicle, characterized in that, include: The system acquires the target vehicle's operating information, road surface wetness information, and current health information. The target vehicle includes vehicles performing operations in open-air locations. The current health information represents the health status of the target vehicle's components. The operating information is collected when the target vehicle performs the operation. Determine the loss increment parameters that match the road surface slippage information; and The baseline loss information determined based on the operating information is corrected using the loss increment parameter, and the health status of the vehicle component is determined based on the difference between the corrected baseline loss information and the current health information.

2. The method according to claim 1, characterized in that, The loss increment parameter includes a first sub-parameter corresponding to the road surface condition; The determination of the loss increment parameter matching the road surface slipperiness information includes: Determine the road surface condition information that matches the road surface slipperiness information; The first sub-parameter is determined based on the relative difference between the road surface condition information and the reference road surface condition information, wherein the reference road surface condition information includes the road surface condition of the open-air site under the reference road surface slippery information.

3. The method according to claim 1, characterized in that, The loss increment parameter includes a second sub-parameter corresponding to the braking performance of the target vehicle; The determination of the loss increment parameter matching the road surface slipperiness information includes: If the road surface slippery information meets the predetermined conditions, determine the reference braking information of the target vehicle when running in the operating state indicated by the reference road surface slippery information and the operating information; The second sub-parameter is determined based on the degree of deviation between the braking information in the operation information and the reference braking information.

4. The method according to claim 3, characterized in that, The braking information includes the number of braking operations and braking performance parameters; determining the second sub-parameter based on the deviation between the braking information in the operational information and the reference braking information includes: The second sub-parameter is determined based on the first degree of deviation between the number of braking operations and the reference number of braking operations, and the second degree of deviation between the braking performance parameter and the reference braking performance parameter; The second sub-parameter is obtained by weighted summation of the first deviation degree and the second deviation degree, and the weights of the first deviation degree and the second deviation degree are determined based on the historical operating information of the target vehicle.

5. The method according to claim 1, characterized in that, The loss increment parameter includes a first sub-parameter corresponding to the road surface condition and a second sub-parameter corresponding to the braking performance of the target vehicle. Determining the loss increment parameter that matches the road surface slipperiness information includes: By combining the first sub-parameter and the second sub-parameter, the loss increment parameter is obtained. Among them, the first sub-parameter has a greater impact on the loss increment parameter than the second sub-parameter has on the loss increment parameter.

6. The method according to any one of claims 1 to 5, characterized in that, The vehicle component includes a braking device; the step of correcting the baseline loss information determined based on the operating information using the loss increment parameter, and determining the health status of the vehicle component based on the difference between the corrected baseline loss information and the current health information, includes: The reference loss information for a single braking operation is corrected using the loss increment parameter to obtain the corrected reference loss information for a single braking operation. The health status of the braking device is determined based on the difference between the corrected baseline loss information for a single braking operation and the current health information.

7. The method according to claim 6, characterized in that, The baseline loss information for a single braking operation includes: the difference in hardware loss information of the braking device before and after performing a single braking operation when the target vehicle is running in the operating state indicated by the operating information; and / or The baseline loss information includes: heat loss information caused by the braking device performing a single braking operation when the target vehicle is running in the operating state indicated by the operating information.

8. The method according to claim 1, characterized in that, The method further includes: When the road surface slippage information is road surface slippage information at a future time after the current time, the baseline loss information determined based on the estimated operating information is corrected using the loss increment parameter, and the health status of the vehicle component is determined based on the difference between the corrected baseline loss information and the current health information, wherein the estimated operating information is determined based on the operation to be performed by the target vehicle at the future time.

9. The method according to claim 1 or 8, characterized in that, The method further includes: If the health status meets the warning conditions, generate a warning message for the target vehicle; and / or If the road surface slippery information is future road surface slippery information and the health status meets the warning conditions, the warning information is generated so that the target vehicle can be repaired based on the warning information before the operation to be performed.

10. An unmanned vehicle, characterized in that, include: A processor configured to perform the method according to any one of claims 1 to 9.

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