Fault diagnosis diagnosis method of thermal management system, vehicle and cloud device

Abstract

The application relates to the technical field of thermal management systems, in particular to a fault diagnosis method of a thermal management system, a vehicle and a cloud device, wherein the method comprises the following steps: identifying a characteristic signal of an operating parameter of the thermal management system; when the characteristic signal meets a preset abnormal condition, then storing related data of a preset time length before and after the characteristic signal; and diagnosing a fault of the thermal management system based on the related data of the preset time length before and after the characteristic signal. Therefore, the problems in the prior art that only the fault checking and positioning are performed when the vehicle system fails, the early warning is not timely, serious consequences are caused, the vehicle safety performance is low, and the user experience is poor are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of thermal management systems, in particular to a fault diagnosis method of a thermal management system, a vehicle and a cloud device. BACKGROUND

[0002] With the popularization of automobile electrification and the continuous improvement of new energy vehicle penetration rate, the requirements for vehicle heat management, low-temperature endurance, and battery safety are becoming higher and higher, and the importance and complexity of the thermal management system are also increasing. In particular, the application of multi-heat source heat pumps, super-fast charging and other system functions requires more sensors and actuators, and complex software algorithms to support system operation.

[0003] In related technologies, current vehicle diagnosis functions are mostly based on unified diagnostic services using fault code formats, and the objects are mostly key components. However, there are the following shortcomings: (1) only single component faults are diagnosed, and systematic faults of multiple components cannot be diagnosed; (2) only when actual faults occur in components, alarms are given, and pre-alarm before possible faults cannot be achieved; (3) when faults or failures occur, only post-fault frozen frames are available, and no information about the running states of the system and related systems before and after the faults, which is not conducive to fault troubleshooting and positioning; (4) when the system is abnormal, the driver cannot be reminded and guided to intervene in time, so as to avoid further faults and greater losses. SUMMARY

[0004] The present application provides a thermal management system diagnosis method and device, electronic equipment and storage medium, to solve the problems in the related art that only when vehicle system faults occur, fault troubleshooting and positioning are performed, early warning is not timely, serious consequences occur, vehicle safety performance is low, and user experience is poor.

[0005] The first aspect of the present application provides a fault diagnosis method of a thermal management system, which is applied to a vehicle. The method includes the following steps: identifying a characteristic signal of an operating parameter of the thermal management system; when the characteristic signal meets a preset abnormal condition, storing related data of a preset time period before and after the characteristic signal; and diagnosing a fault of the thermal management system based on the related data of the preset time period before and after the characteristic signal.

[0006] Optionally, the characteristic signal meets the preset abnormal condition, including: querying a relationship matrix table of the thermal management system with the characteristic signal, wherein the relationship matrix table includes risk trigger thresholds of multiple characteristic signals and correlation thresholds between the multiple characteristic signals; if at least one of the characteristic signal is greater than the risk trigger threshold and / or the characteristic signal is greater than the correlation threshold, the characteristic signal meets the preset abnormal condition.

[0007] Optionally, before the identifying the characteristic signal of the operating parameter of the thermal management system, the method further comprises: obtaining historical fault data of the thermal management system; determining a risk trigger threshold and a correlation threshold of a risk item corresponding to the characteristic signal according to the historical fault data; and generating a relation matrix table according to the risk item, the risk trigger threshold of the risk item, and the correlation threshold of the risk item.

[0008] The second aspect of the present application provides a fault diagnosis method of a thermal management system, the method being applied to a cloud device, and the method comprising the following steps: obtaining the related data of the thermal management system according to the above-mentioned embodiments; identifying a signal type of a target signal in the related data of the thermal management system and a system operating parameter of the thermal management system triggering the target signal; and analyzing a diagnosis result of the thermal management system according to the signal type and the system operating parameter.

[0009] Optionally, the diagnosis result comprises a thermal management system fault or a potential fault of the thermal management system.

[0010] Optionally, after the analyzing the diagnosis result of the thermal management system according to the signal type and the system operating parameter, the method further comprises: generating a corresponding alarm action of the thermal management system according to the diagnosis result.

[0011] Optionally, the generating the alarm action of the thermal management system according to the diagnosis result comprises: if the diagnosis result is a thermal management system fault, identifying a fault level of the thermal management system, generating a first alarm action if the fault level is greater than a first fault level and a probability of fault recovery is lower than a preset probability, and generating a second alarm action if the fault level is less than or equal to a second fault level and the probability of fault recovery is higher than the preset probability; and if the diagnosis result is a potential fault of the thermal management system, generating the second alarm action.

[0012] Optionally, after the analyzing the diagnosis result of the thermal management system according to the signal type and the system operating parameter, the method further comprises: pushing an operating state, a health degree state, fault information, and a guide operation of the thermal management system to a user terminal.

[0013] The third aspect of the present application provides a vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to perform the fault diagnosis method of the thermal management system according to the above-mentioned embodiments.

[0014] The fourth aspect of the present application provides a cloud device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the fault diagnosis method of the thermal management system as described in the above embodiments.

[0015] Therefore, the present application has at least the following beneficial effects:

[0016] (1) The embodiments of the present application can store the related data of a predetermined time length before and after the feature signal when the feature signal collected at the vehicle end meets the preset abnormal condition; diagnose the fault of the thermal management system based on the related data of the predetermined time length before and after the feature signal, identify and warn the abnormal condition or system fault of the thermal management system, and store the related data of the thermal management system related components and related systems running in a period of time before and after the abnormal trigger, which can improve the analysis and countermeasure efficiency of engineers and support the continuous upgrading and optimization of the thermal management system.

[0017] (2) The embodiments of the present application can identify the signal type of the target signal in the related data of the thermal management system and the system running parameters of the thermal management system triggering the target signal in the cloud, determine the abnormal level and health degree state of the thermal management system according to the signal type and system running parameters, generate optimization operation instructions for recoverable faults and potential faults, and timely warn the non-recoverable faults to avoid more serious consequences and improve the safety of the vehicle and the user experience.

[0018] (3) The embodiments of the present application can push the warning reminder and guide processing suggestion to the passengers at the vehicle end or mobile phone end, intervene in the processing before the fault occurs or serious failure, and improve the safety of the vehicle and the user experience.

[0019] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter in the description of the application. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A flowchart of a fault diagnosis method of a thermal management system according to an embodiment of the present application is provided.

[0022] Figure 2 A schematic diagram of a thermal management system of a certain vehicle model according to an embodiment of the present application is provided.

[0023] Figure 3 A flowchart of a fault diagnosis method of a thermal management system according to another embodiment of the present application is provided.

[0024] Figure 4 A thermal management system health diagnosis framework diagram is provided according to an embodiment of the present application.

[0025] Figure 5 A thermal management system diagnosis flowchart is provided according to an embodiment of the present application.

[0026] Figure 6 A system abnormality level evaluation and fault analysis logic diagram is provided according to an embodiment of the present application.

[0027] Figure 7 A structural schematic diagram of a vehicle is provided according to an embodiment of the present application.

[0028] Figure 8 A structural schematic diagram of a cloud device is provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0030] The linkage system of the vehicle end, the cloud end and the user terminal constructed based on the method of the present application judges whether the state of the system operation is in a healthy and efficient state by cleaning and analyzing the operating parameters of each sensor and actuator of the thermal management system. If the system operation is abnormal, further system fault identification is performed. Through the pre-embedding points and snapshot mechanism in the software, the system operating parameters and the whole vehicle associated data of a period of time before and after the abnormal trigger point are stored in the vehicle end cache area, and are simultaneously uploaded to the cloud data center through the vehicle communication terminal. The intelligent diagnosis algorithm of the thermal management system deployed in the cloud determines the health degree level to select the early warning scheme. At the same time, for system faults, the fault causes are analyzed to form system optimization suggestions and operation guidelines to guide passengers to intervene and reduce the risk of failure.

[0031] The fault diagnosis method of the thermal management system, the vehicle and the cloud device of the embodiments of the present application are described below with reference to the accompanying drawings. Specifically, Figure 1 A flowchart of a fault diagnosis method of a thermal management system is provided according to an embodiment of the present application.

[0032] As shown in Figure 1 The fault diagnosis method of the thermal management system, the method is applied to a vehicle, wherein the method comprises the following steps:

[0033] In step S101, the characteristic signal of the operating parameter of the thermal management system is identified.

[0034] It can be understood that the embodiment of the present application can identify the characteristic signal of the operating parameter of the thermal management system, so as to subsequently store the related data of the preset time length before and after the characteristic signal when the characteristic signal meets the preset abnormal condition.

[0035] It should be noted that the characteristic signal can include a temperature signal, a pressure signal, a flow signal, etc. of the thermal management system, without specific limitation.

[0036] In the embodiment of the present application, before identifying the characteristic signal of the operating parameter of the thermal management system, the following steps are included: obtaining historical fault data of the thermal management system; determining a risk triggering threshold and a correlation threshold of a risk item corresponding to the characteristic signal according to the historical fault data; and generating a relationship matrix table according to the risk item, the risk triggering threshold of the risk item, and the correlation threshold of the risk item.

[0037] It can be understood that the embodiment of the present application can determine the risk triggering threshold and the correlation threshold of the risk item corresponding to the characteristic signal according to the historical fault data of the thermal management system; and generate a relationship matrix table according to the risk item, the risk triggering threshold of the risk item, and the correlation threshold of the risk item, so as to subsequently determine an abnormal risk item according to the characteristic signal.

[0038] It should be noted that the risk triggering threshold and the correlation threshold can be determined according to the fault threshold of the historical fault data. Generally, the abnormal condition can be found in time when the value is lower than the fault threshold, without specific limitation.

[0039] Specifically, taking a thermal management system scheme of a certain vehicle model as an example, the system architecture is shown in Figure 2 , which includes 11 sensors and 16 actuators.

[0040] The health risk warning set contains different abnormal or risk items, and corresponds to the working state characteristics of each sensor, actuator and associated system in the system that needs to be monitored and evaluated; the relationship matrix table sets appropriate risk triggering thresholds of each characteristic signal and reasonable thresholds of the relationship between multiple signals; the embedding point is performed in advance in the thermal management system control software; the sensor signals, actuators and associated working states during system operation are continuously monitored; when the related system abnormality or system failure in the setting occurs, the corresponding system abnormal signal is triggered, wherein, based on Figure 2 The corresponding relationship matrix table is generated based on the schematic diagram of the thermal management system of the certain vehicle model shown in

[0041] Table 1 Relationship Matrix Table

[0042]

[0043] In step S102, when the feature signal meets the preset abnormal condition, the related data of the preset time length before and after the feature signal is stored.

[0044] The preset abnormal condition is at least one of a signal that the feature signal is greater than a risk trigger threshold and a signal that the feature signal is greater than a correlation threshold, and the preset time length can be set according to actual needs and is not limited.

[0045] It can be understood that the embodiment of the application can store the related data of the preset time length before and after the feature signal when the feature signal meets the preset abnormal condition, so as to store the original data before and after the system anomaly or system failure occurs, so as to improve the efficiency of subsequent analysis and countermeasures of engineers, and support continuous upgrading and optimization of the thermal management system.

[0046] It should be noted that the feature signal queries the relationship matrix table of the thermal management system, wherein the relationship matrix table includes a plurality of risk trigger thresholds of feature signals and a plurality of correlation thresholds between the plurality of feature signals; if at least one of the feature signal is greater than the risk trigger threshold and the feature signal is greater than the correlation threshold, the feature signal meets the preset abnormal condition.

[0047] Specifically, when the abnormal signal is triggered, the original data of the thermal management system and the associated system before the abnormal trigger T1 and after the trigger T2 is stored.

[0048] In step S103, the fault of the thermal management system is diagnosed based on the related data of the feature signal before and after the preset time length.

[0049] It can be understood that the embodiment of the application can diagnose the fault of the thermal management system based on the related data of the feature signal before and after the preset time length, so as to intervene in processing before the fault occurs or serious failure, and improve the safety of the vehicle and the use experience of the user.

[0050] It should be noted that the vehicle controller arbitration module is triggered based on the abnormal information, whether the abnormality of the thermal management system overlaps with the fault of other systems of the vehicle is judged, if there is overlap and the associated fault caused by the thermal management abnormality can be clearly judged as the reason of other systems, the thermal management system no longer performs fault warning.

[0051] Specifically, a risk early warning set of the thermal management system health degree is established based on a potential failure mode analysis list and a design prevention information base, dimensions and characteristic points of system state abnormal monitoring are determined; an abnormal condition judgment pre-embedding point is performed through a thermal management system control module, and original data of system operation in a period before and after the point is stored when a condition is triggered; a system operation state and fault information related to the thermal management are confirmed through a vehicle control unit when the point is stored, and original data of the related systems are stored according to the same mechanism.

[0052] According to the fault diagnosis method of the thermal management system provided in the embodiments of the present application, when the characteristic signal collected at the vehicle end meets the preset abnormal condition, the related data in a preset time period before and after the characteristic signal is stored; the fault of the thermal management system is diagnosed based on the related data in the preset time period before and after the characteristic signal, and the abnormal condition or the system fault of the thermal management system is identified and warned, so that the abnormal state of the thermal management system can be found in time, and the related data of the thermal management system related components and the related systems in a period before and after the abnormal trigger are stored, which can improve the analysis and countermeasure efficiency of engineers and support the continuous upgrading and optimization of the thermal management system.

[0053] Next, a fault diagnosis method of a thermal management system according to another embodiment of the present application is described with reference to the accompanying drawings.

[0054] Figure 3 is a flowchart of the fault diagnosis method of the thermal management system according to the embodiments of the present application.

[0055] As Figure 3 indicated, the fault diagnosis method of the thermal management system, the method is applied to a vehicle, wherein the method comprises the following steps:

[0056] In step S201, the related data of the thermal management system of the above embodiments is acquired.

[0057] It can be understood that the embodiments of the present application can acquire the related data of the thermal management system of the above embodiments, so as to identify the signal type of the target signal in the related data of the thermal management system and the system operation parameter of the thermal management system triggering the target signal subsequently.

[0058] In step S202, the signal type of the target signal in the related data of the thermal management system and the system operation parameter of the thermal management system triggering the target signal are identified.

[0059] It can be understood that the embodiments of the present application can identify the signal type of the target signal in the related data of the thermal management system and the system operation parameter of the thermal management system triggering the target signal, so as to analyze the diagnosis result of the thermal management system according to the signal type and the system operation parameter subsequently.

[0060] It should be noted that the target signal is a signal that shows an abnormality of the feature signal; the signal type can be an abnormal condition of the thermal management system caused by a thermal management system failure and a vehicle other system failure, without specific limitation.

[0061] In step S203, the diagnostic result of the thermal management system is analyzed according to the signal type and the system operation parameter.

[0062] The diagnostic result includes a thermal management system failure or a potential failure of the thermal management system.

[0063] It can be understood that the embodiments of the present application can analyze the diagnostic result of the thermal management system according to the signal type and the system operation parameter, thereby determining the abnormal risk classification, analyzing the failure problem, and guiding system optimization, and improving the efficiency of engineers in analyzing the problem and countermeasures.

[0064] It should be noted that, as shown in Figure 6 The abnormal condition level of the thermal management system is determined based on the thermal management system health evaluation model deployed in the cloud, and the cause of the system abnormal condition or system failure is analyzed.

[0065] The abnormal condition level is determined based on the analysis of specific parameter indicators of the system, including the deviation and development trend of the indicators.

[0066] The normal reference value of the determined indicator is derived from the calculation result of the cloud health recognition self-training model. The model first cleans up a large amount of parameter data of the same vehicle type collected by the cloud, and then performs data normalization and standardization processing. The data is grouped according to the defined characteristics, and the model is continuously trained to ensure the accuracy of the output indicators, such as neural networks. The output indicators can be generated by multi-dimensional data comprehensive analysis to determine a judgment parameter indicator. The parameter indicator values of the same vehicle type calculated by the model are statistically distributed and error calculated based on statistics to define a reasonable interval value. The deviation of the corresponding indicators of the target vehicle and the deviation trend of the judgment indicators are compared to classify the abnormal conditions, and different severity levels are used for subsequent operation requirements.

[0067] When a systematic failure occurs, the expert experience system is used to analyze and locate the failure cause based on a decision tree model to quickly guide user operation or after-sales maintenance. The expert experience system has a self-iterative upgrading function, which can be corrected by infusing offline analysis results of the problem, and can also be analyzed by targeting feedback from user operation or system response when the failure is detected by the failure identification model during actual vehicle operation, thereby continuously learning the model to improve the accuracy and efficiency of the expert experience system.

[0068] In the embodiments of the present application, after analyzing the diagnostic result of the thermal management system according to the signal type and the system operation parameter, the corresponding alarm action of the thermal management system is generated according to the diagnostic result.

[0069] It can be understood that the embodiments of the present application can generate the corresponding alarm action of the thermal management system according to the diagnostic result, and can generate optimized operation instructions for recoverable faults and potential faults, and remind the customer to operate according to the guidance at the vehicle end and the mobile phone end, so as to intervene in the treatment before the fault occurs or serious failure, and improve the vehicle safety and use experience of the user.

[0070] In the embodiments of the present application, the alarm action of the thermal management system is generated according to the diagnostic result, including: if the diagnostic result is a fault of the thermal management system, identifying the fault level of the thermal management system, if the fault level is greater than a first fault level and the probability of fault recovery is lower than a preset probability, generating a first alarm action, if the fault level is less than or equal to a second fault level and the probability of fault recovery is higher than the preset probability, generating a second alarm action; if the diagnostic result is that the thermal management system has a potential fault, generating the second alarm action.

[0071] The first fault level, the second fault level and the preset probability can be set according to actual needs, the first fault level is higher than the second fault level, and no specific limitation is made.

[0072] It can be understood that the embodiments of the present application can generate optimized operation instructions for recoverable faults and potential faults, and remind the customer to operate according to the guidance at the vehicle end and the mobile phone end, so as to intervene in the treatment before the fault occurs or serious failure, and improve the vehicle safety and use experience of the user.

[0073] It should be noted that the first alarm action can be to generate a fault prompt, for example: "the evaporator of the thermal management system is faulty, please repair as soon as possible!", etc., which can be set according to actual needs; the second alarm action can generate a suggestion strategy or an optimized operation instruction to prompt the user to operate according to the guidance, so as to intervene in the treatment before the fault occurs or serious failure; different response mechanisms are formed for system abnormalities or faults; for example, high-level unrecoverable faults remind passengers through the vehicle-mounted communication terminal and take necessary safety measures; for example, low-level recoverable faults and potential faults generate system optimization guidance programs to remind the customer to avoid risks to avoid causing unrecoverable damage.

[0074] Specifically, for a high-risk level unrecoverable system failure, through the vehicle-mounted network terminal, the driver is prompted to take corresponding measures and failure maintenance in time to avoid greater system damage; for a low-risk level recoverable abnormal situation or system failure, a system operation optimization and guidance program is generated to remind the user to operate as suggested to let the system recover to normal state as soon as possible.

[0075] In the embodiment of the present application, after analyzing the diagnostic result of the thermal management system according to the signal type and the system running parameter, the running state, the health degree state, the fault information and the guidance operation of the thermal management system are pushed to the user terminal.

[0076] It can be understood that the embodiment of the present application can enable the user to learn about the abnormality of the thermal management system in time and obtain operation guidance to reduce anxiety; the health degree of the thermal management system can be viewed in real time to improve user experience.

[0077] Specifically, the system abnormality or failure system is sent to the user through the mobile phone APP, the user responds to the abnormality or failure, confirms the abnormality, failure or feedbacks misjudgment, the user feedback signal is returned to the thermal management system health assessment module for self-learning and iterative upgrading to continuously improve the fault analysis accuracy; at the same time, the problem library updated locally can also be used for self-learning and upgrading of the health assessment module.

[0078] According to the fault diagnosis method of the thermal management system proposed in the embodiment of the present application, the signal type of the target signal in the related data of the thermal management system and the system running parameter triggering the target signal of the thermal management system are identified, the abnormality level and the health degree state of the thermal management system are determined according to the signal type and the system running parameter, the optimal operation guidance can be generated for recoverable failure and potential failure, and timely warning can be achieved for unrecoverable failure to avoid more serious consequences and improve the safety of the vehicle and the user experience.

[0079] The fault diagnosis method of the thermal management system of the present application will be described in detail below. Figure 4 and Figure 5 The fault diagnosis method of the thermal management system of the present application will be described in detail below.

[0080] Step 1, establish a thermal management system health degree risk warning set based on a design potential failure mode analysis list and a design prevention information library, and determine the dimension and characteristic point of system state abnormality monitoring;

[0081] Taking a certain vehicle thermal management system scheme as an example, the system architecture is as shown in Figure 2 which includes 11 sensors and 16 actuators.

[0082] The health risk early warning set contains different abnormal or risk items, and a relationship matrix table corresponding to the working state characteristics of each sensor, actuator and associated system in the system that needs to be monitored and evaluated is generated. The relationship matrix table sets appropriate risk trigger thresholds for each characteristic signal, and reasonable relationship thresholds between multiple signals; and the pre-buried points are performed in advance in the thermal management system control software.

[0083] Step 2, abnormal condition judgment pre-buried point is performed by the thermal management system control module, and the system running original data before and after the point is stored for a period of time when the condition is triggered.

[0084] The system running original data before and after the point is stored for a period of time when the condition is triggered.

[0085] Step 3, the running state and fault information of the system associated with the thermal management at the time of the point are confirmed by the vehicle controller, and the original data of the associated system of the vehicle is stored according to the same mechanism in step 2.

[0086] According to the triggered abnormal signal, the original data of the thermal management system and the associated system before the triggering of the abnormal signal T1 and after the triggering of the abnormal signal T2 is stored.

[0087] Step 4, the stored related original data is uploaded to the cloud big data platform through the vehicle communication terminal.

[0088] Step 5, based on the system health training model deployed in the cloud, the risk level of the system abnormality is classified, and the cause of the related abnormality is analyzed.

[0089] Step 6, different response mechanisms are formed for system abnormalities or faults; for example, high-level non-recoverable faults remind passengers through the vehicle communication terminal and take necessary safety measures; for example, low-level recoverable faults and potential faults generate system optimization guide programs to remind customers to avoid risks to avoid causing non-recoverable damage.

[0090] Step 7, the user terminal deploys a system health monitoring module, the user can observe the system running health status and fault information based on real-time dashboard, the user responds to the state of abnormality or failure, confirms the abnormality, failure or feedback error, the user feedback signal is returned to the thermal management system health assessment module, so that it is self-learning and iterative upgrading, and the fault analysis accuracy is continuously improved; meanwhile, the problem library can be updated locally to be used for self-learning and upgrading of the health assessment module.

[0091] Meanwhile, the system optimization guide generated in step 6 is received, reminding the user to operate as suggested, so that the system can be restored to normal state as soon as possible.

[0092] In summary, the present application can identify and warn abnormal conditions or system failures of the thermal management system, rather than being limited to the existing fault diagnosis of parts based on unified diagnostic services; the present application provides protection for the benign and efficient operation of the system; the vehicle and the cloud are linked, and the original signals of the thermal management system related components and related systems within a period of time before and after the abnormal trigger can be stored according to the demand; the subsequent abnormal risk grading, fault problem analysis and system optimization guidance are facilitated; through user information confirmation and external fault import, the system health assessment model deployed in the cloud can be self-learning and upgrading, and the system fault detection and reason judgment ability is continuously improved; for recoverable faults and potential faults, optimization operation guidance can be generated, and the customer is reminded to operate according to the guidance on the vehicle end and the mobile phone end, so as to intervene and handle before the failure occurs or the serious failure.

[0093] Figure 7 A schematic diagram of the structure of the vehicle is provided for the embodiments of the present application. The vehicle can include:

[0094] The memory 701, the processor 702 and the computer program stored in the memory 701 and executable on the processor 702.

[0095] The processor 702 implements the fault diagnosis method of the thermal management system provided in the above embodiments when executing the program.

[0096] Further, the vehicle further includes:

[0097] The communication interface 703 is used for communication between the memory 701 and the processor 702.

[0098] The memory 701 is used to store the computer program executable on the processor 702.

[0099] The memory 701 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0100] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 7 Only one thick line is used to represent the bus in the middle, but it does not mean that there is only one bus or only one type of bus.

[0101] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can complete communication between each other through an internal interface.

[0102] The processor 702 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0103] Figure 8 A structural schematic diagram of a cloud device provided by the embodiments of the present application is provided. The cloud device can include:

[0104] The memory 801, the processor 802 and a computer program stored in the memory 801 and executable on the processor 802.

[0105] The processor 802 implements the fault diagnosis method of the thermal management system provided in the above embodiments when executing the program.

[0106] Further, the cloud device further includes:

[0107] The communication interface 803 is used for communication between the memory 801 and the processor 802.

[0108] The memory 801 is used to store a computer program executable on the processor 802.

[0109] The memory 801 can include a high-speed RAM memory and can also include a non-volatile memory, such as at least one disk memory.

[0110] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 8 Only one thick line is used in the figure to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0111] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can complete communication between each other through an internal interface.

[0112] The processor 802 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0113] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0114] Furthermore, the terms "first", "second", etc. are used herein only to describe different steps or features and do not imply a relative importance or a specific order of steps or features. Thus, features defined with "first", "second" etc. can include one or more of the features implicitly or explicitly. In the description of the application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise expressly specified.

[0115] Any process or method descriptions or blocks in flow charts described herein and elsewhere can be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of the preferred embodiments of the present application in which the functions performed by the various processes described herein and elsewhere are allocated differently among the components of the preferred embodiments, such as according to the functions performed by the various components, in a substantially simultaneous manner, or according to a different order.

[0116] It should be understood that aspects of the present application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the various steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. As such, if desired, the various steps or methods can be implemented in hardware, as in another embodiment, using any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0117] Those of skill in the art would understand that information and signals can be represented using any of a variety of technologies and techniques. Further, those of skill in the art would understand that the preferred embodiments of the present application can be implemented by a variety of hardware elements and software elements, and that the preferred embodiments of the present application can be implemented in a variety of environments.

Claims

1. A fault diagnosis method for a thermal management system, characterized in that, The method is applied to cloud devices, and the method includes the following steps: Identify characteristic signals of the operating parameters of the thermal management system; When the feature signal meets the preset abnormal conditions, the relevant data before and after the feature signal for a preset time period are stored. Obtain relevant data from the thermal management system; Identify the signal type of the target signal in the relevant data of the thermal management system and the system operating parameters of the thermal management system that trigger the target signal; The diagnostic results of the thermal management system are analyzed based on the signal type and the system operating parameters. The diagnostic results include thermal management system malfunctions or potential malfunctions in the thermal management system. The step of generating an alarm action for the thermal management system based on the diagnostic results includes: If the diagnostic result is a thermal management system failure, the failure level of the thermal management system is identified. If the failure level is greater than the first failure level and the probability of failure recovery is lower than the preset probability, a first alarm action is generated. If the failure level is less than or equal to the second failure level and the probability of failure recovery is higher than the preset probability, a second alarm action is generated. If the diagnostic result indicates a potential fault in the thermal management system, a second alarm action is generated.

2. The fault diagnosis method for the thermal management system according to claim 1, characterized in that, The characteristic signal satisfies preset abnormal conditions, including: The relationship matrix table of the thermal management system is queried using the characteristic signals, wherein the relationship matrix table includes risk trigger thresholds for multiple characteristic signals and correlation thresholds between multiple characteristic signals; If the characteristic signal is greater than the risk trigger threshold signal, and / or at least one of the characteristic signal is greater than the correlation threshold signal, then the characteristic signal satisfies the preset abnormal condition.

3. The fault diagnosis method for the thermal management system according to claim 1, characterized in that, Prior to identifying the characteristic signals of the operating parameters of the thermal management system, the process includes: Acquire historical fault data of the thermal management system; Based on the historical fault data, determine the risk trigger threshold and correlation threshold for the risk item corresponding to the characteristic signal; A relationship matrix table is generated based on the risk item, the risk trigger threshold of the risk item, and the correlation threshold of the risk item.

4. The fault diagnosis method for the thermal management system according to claim 1, characterized in that, After analyzing the diagnostic results of the thermal management system based on the signal type and the system operating parameters, the process includes: Based on the diagnostic results, a corresponding alarm action of the thermal management system is generated.

5. The fault diagnosis method for a thermal management system according to claim 1, characterized in that, After analyzing the diagnostic results of the thermal management system based on the signal type and the system operating parameters, the method further includes: The system pushes the operating status, health status, fault information, and guidance operations of the thermal management system to the user terminal.

6. A cloud device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the fault diagnosis method for the thermal management system as described in any one of claims 1-5.

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