Embedded fault diagnosis system

Abstract

The invention provides an embedded fault diagnosis system, which is characterized in that a software diagnosis unit runs a preset fault detection program related to an embedded module to obtain a running result, and performs keyword extraction on the running result to obtain a target keyword; when the target keyword exists in the preset keyword set, a pin corresponding to the target keyword is determined, and a target pin is obtained; the hardware diagnosis unit obtains a circuit design drawing of the electronic equipment, and determines m circuit paths corresponding to the target pin according to the circuit design drawing and a preset fault detection program; determining hardware working parameters corresponding to the m circuit paths to obtain m hardware working parameters; the network diagnosis unit obtains network parameters of each circuit path in the m circuit paths to obtain m network parameters; and the fault analysis unit performs fault diagnosis according to the m hardware working parameters and the m network parameters to obtain a target fault diagnosis result. According to the invention, the fault detection accuracy of the embedded module can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, chip technology, or artificial intelligence technology, and specifically to an embedded-based fault diagnosis system. Background Art

[0002] With the rapid development of science and technology, embedded modules are also widely used in electronic devices (automotive controllers, mobile phones, drones, robots, etc.). Therefore, fault detection of embedded modules has also become a focus of users. Therefore, how to improve the accuracy of fault detection of embedded modules needs to be solved urgently. Summary of the invention

[0003] The embodiment of the present application provides an embedded-based fault diagnosis system, which can improve the fault detection accuracy of embedded modules.

[0004] In a first aspect, an embodiment of the present application provides an embedded-based fault diagnosis system, which is applied to an electronic device, wherein the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit, and a fault analysis unit;

[0005] The software diagnosis unit is used to run a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtain an operation result, and perform keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, determine a pin corresponding to the target keyword to obtain a target pin, and the target pin is a pin among the n pins;

[0006] The hardware diagnosis unit is used to obtain a circuit design diagram of the electronic device, determine m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determine a hardware operating parameter of each circuit path in the m circuit paths to obtain m hardware operating parameters; m is a positive integer;

[0007] The network diagnosis unit is used to obtain the network parameters of each circuit path in the m circuit paths to obtain m network parameters;

[0008] The fault analysis unit is used to perform fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result.

[0009] In a second aspect, an embodiment of the present application provides an embedded-based fault diagnosis method, which is applied to the fault diagnosis system as described in the first aspect, and the fault diagnosis system is applied to an electronic device, wherein the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit, and a fault analysis unit, and the method includes:

[0010] When the embedded module is in a power-on state, the software diagnosis unit runs a preset fault detection program related to the embedded module to obtain an operation result, and performs keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, a pin corresponding to the target keyword is determined to obtain a target pin, and the target pin is a pin among the n pins;

[0011] Obtaining a circuit design diagram of the electronic device through the hardware diagnosis unit, determining m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determining a hardware operating parameter of each circuit path in the m circuit paths to obtain m hardware operating parameters; m is a positive integer;

[0012] Acquire a network parameter of each circuit path in the m circuit paths through the network diagnosis unit to obtain m network parameters;

[0013] The fault analysis unit performs fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the second aspect of the embodiment of the present application.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the second aspect of the embodiment of the present application.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the second aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0017] The implementation of the embodiments of the present application has the following beneficial effects:

[0018] It can be seen that the embedded-based fault diagnosis system described in the embodiment of the present application is applied to an electronic device, and the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit and a fault analysis unit, wherein the software diagnosis unit is used to run a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtain the running result, and perform keyword extraction on the running result to obtain a target keyword; when the target keyword exists in a preset keyword set, determine the pin corresponding to the target keyword to obtain the target pin, and the target pin is a pin among the n pins; the hardware diagnosis unit is used to obtain a circuit design diagram of the electronic device, determine m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determine the hardware working order of each circuit path in the m circuit paths as parameters, and obtain m hardware working parameters; m is a positive integer; a network diagnosis unit is used to obtain the network parameters of each circuit path in the m circuit paths, and obtain m network parameters; a fault analysis unit is used to perform fault diagnosis according to the m hardware working parameters and the m network parameters, and obtain a target fault diagnosis result. On the one hand, by running a preset fault detection program related to the embedded module, and extracting keywords for the corresponding running results, the keywords are used to locate the corresponding pins, and further hardware fault diagnosis is performed based on the pins. On the other hand, since the hardware working parameters reflect the working conditions of each circuit path to a certain extent, and the network parameters reflect the network conditions of each circuit path to a certain extent (such as network stability and network security), fault diagnosis can be accurately performed based on the working conditions and network conditions of each circuit path, thereby improving the fault detection accuracy of the embedded module. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a structural diagram of an embedded-based fault diagnosis system provided in an embodiment of the present application;

[0021] Figure 2 It is a flowchart of an embedded-based fault diagnosis method provided in an embodiment of the present application;

[0022] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but in a possible example also includes steps or units that are not listed, or in a possible example also includes other steps or units inherent to these processes, methods, products or devices.

[0024] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In an embodiment of the present application, the electronic device may be any computer device including an embedded module and an embedded-based fault diagnosis system, and the computer device may include at least one of the following: a smart phone, a car domain controller, an automobile processing equipment, a vehicle-mounted device, a server, a tablet computer, a desktop computer, a drone, a robot, a medical device, etc., without limitation herein.

[0027] In an embodiment of the present application, the embedded module may include at least one of the following: an embedded multimedia card (EMMC), an ARM embedded chip, a field programmable gate array (FPGA), a domain controller (such as an automotive domain controller), a neural network chip, a processor, a system on chip (SoC), etc., without limitation herein.

[0028] In the embodiment of the present application, the embedded module may include an embedded system, which may be composed of hardware and software, and the embedded system may be a device that can operate independently. The software content of the embedded system may include the software operating environment and its operating system, and the hardware content of the embedded system may include signal processors, memory, communication modules, and other aspects.

[0029] See also Figure 1 , Figure 1 This is a structural diagram of an embedded-based fault diagnosis system provided by an embodiment of the present application. As shown in the figure, it is applied to an electronic device, and the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit, and a fault analysis unit;

[0030] The software diagnosis unit is used to run a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtain an operation result, and perform keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, determine a pin corresponding to the target keyword to obtain a target pin, wherein the target pin is a pin among the n pins;

[0031] The hardware diagnosis unit is used to obtain a circuit design diagram of the electronic device, determine m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determine a hardware operating parameter of each circuit path in the m circuit paths to obtain m hardware operating parameters; m is a positive integer;

[0032] The network diagnosis unit is used to obtain the network parameters of each circuit path in the m circuit paths to obtain m network parameters;

[0033] The fault analysis unit is used to perform fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result.

[0034] In a specific implementation, the preset fault detection program can be pre-set or system default, and the preset fault detection program can be used to implement software fault diagnosis of the embedded module, which may include at least one of the following: embedded software operating system fault diagnosis, pin function diagnosis, code fault diagnosis, etc., which are not limited here.

[0035] The running result may include at least one of the following: code running result, running time, fault detection result, etc., which are not limited here.

[0036] The preset keyword set may include at least one keyword, and each keyword may be preset or set by system default. The keyword may include at least one of the following: a character string, a pattern, a Chinese character, etc., which are not limited here.

[0037] In an embodiment of the present application, the software diagnosis unit can run a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtain an operation result, and perform keyword extraction on the operation result to obtain a target keyword. When the target keyword exists in the preset keyword set, it means that the embedded module is very likely to have a related hardware failure. Then, the mapping relationship between the preset keyword and the pin can be pre-stored, and then, the pin corresponding to the target keyword can be determined based on the mapping relationship to obtain the target pin. The target pin is a pin among n pins, and the target pin can include one or more pins. In this way, by running the preset fault detection program related to the embedded module and extracting keywords for the corresponding operation result, the corresponding pin can be located using the keyword, and further hardware fault diagnosis can be performed based on the pin, thereby ensuring the accuracy of fault diagnosis.

[0038] In a specific implementation, the hardware diagnosis unit can obtain a circuit design diagram of an electronic device, which includes a circuit diagram formed by connecting an embedded module and other modules of the electronic device. Then, the m circuit paths corresponding to the target pins can be determined based on the circuit design diagram and a preset fault detection program. Since the preset fault detection program involves parameters of the corresponding circuit paths or function scheduling processes, if a corresponding hardware fault occurs, it will also affect the operation of the program corresponding to the pin.

[0039] Among them, the hardware operating parameters may include at least one of the following: operating current, operating voltage, operating power, operating pulse, operating frequency, operating duty cycle, operating position, operating path, etc., which are not limited here. The hardware operating parameters can be understood as the hardware operating parameters at a moment, or they can also be understood as the hardware operating parameters over a period of time. The hardware operating parameters reflect the working conditions of each circuit path to a certain extent. The hardware operating parameters can be the hardware operating parameters of a detection point (for example, a port, a pin) or a path.

[0040] The network parameters may include at least one of the following: network bandwidth, network speed, packet loss rate, disconnection rate, throughput, etc., which are not limited here. The network parameters reflect the network status of each circuit path (such as network stability, network security, etc.) to a certain extent.

[0041] The target fault diagnosis result may include at least one of the following: fault cause, fault degree, fault location, fault solution, etc., which are not limited here.

[0042] Furthermore, the hardware diagnosis unit can determine the hardware working parameters of each circuit path in the m circuit paths, and obtain m hardware working parameters; m is a positive integer, that is, m is 1, or m is an integer greater than 1, and the network diagnosis unit can obtain the network parameters of each circuit path in the m circuit paths, and obtain m network parameters. Finally, the fault analysis unit can perform fault diagnosis based on the m hardware working parameters and the m network parameters to obtain the target fault diagnosis result, that is, since the hardware working parameters reflect the working conditions of each circuit path to a certain extent, and the network parameters reflect the network conditions of each circuit path to a certain extent (such as network stability, network security), the fault diagnosis can be accurately performed based on the working conditions and network conditions of each circuit path, thereby improving the fault detection accuracy of the embedded module.

[0043] In some possible examples, the software diagnosis unit is further specifically used for:

[0044] Obtaining target software environment parameters of the embedded module;

[0045] The preset fault detection procedure corresponding to the target software environment parameter is determined.

[0046] Among them, the target software environment parameters can be used to characterize the software operating environment of the embedded module. The target software environment parameters may include at least one of the following: operating system version, system path, memory size, operating system environment variables, setting parameters in the application configuration file, etc., which are not limited here.

[0047] In a specific implementation, different software environment parameters may correspond to different fault detection programs, that is, the mapping relationship between the preset software environment parameters and the fault detection program may be pre-stored, and then, the preset fault detection program corresponding to the target software environment parameters may be determined based on the mapping relationship, that is, the appropriate fault detection program may be adapted based on different software environment parameters, thereby improving the fault detection accuracy of the embedded module.

[0048] In some possible examples, in determining the m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program, the hardware diagnosis unit is specifically used to:

[0049] Acquire a portion of the circuit design diagram corresponding to the preset fault detection program from the circuit design diagram;

[0050] A feasible path analysis is performed based on the partial circuit design diagram and the target pin to obtain the m circuit paths.

[0051] In the specific implementation, different fault detection programs are associated with different circuit design drawings. Furthermore, a partial circuit design drawing corresponding to the preset fault detection program can be obtained from the circuit design drawing, and then a feasible path analysis is performed based on the partial circuit design drawing and the target pin to obtain m circuit paths. That is, since the partial circuit design drawing is known and the target pin is also known, the circuit path related to the target pin can also be directly determined. In this way, the pin corresponding to the keyword and the related circuit corresponding to the preset fault detection program can be determined, thereby improving the fault detection accuracy of the embedded module.

[0052] In some possible examples, the above steps of performing feasible path analysis according to the partial circuit design diagram and the target pin to obtain the m circuit paths may be implemented as follows:

[0053] Determine a target circuit design drawing identifier corresponding to the preset fault detection program according to a mapping relationship between the preset fault detection program and the circuit design drawing identifier, wherein the circuit design drawing identifier is used to identify different partial circuit design drawings;

[0054] Acquire a target partial circuit design diagram corresponding to the preset fault detection program from the circuit design diagram based on the target circuit design diagram identifier;

[0055] A feasible path analysis is performed based on the target partial circuit design diagram and the target pins to obtain m circuit paths.

[0056] Specifically, the mapping relationship between the preset fault detection program and the circuit design diagram identifier can be pre-stored, and then the target circuit design diagram identifier corresponding to the preset fault detection program can be determined based on the mapping relationship, wherein the circuit design diagram identifier can be used to identify different partial circuit design diagrams. Then, based on the target circuit design diagram identifier, the target partial circuit design diagram corresponding to the preset fault detection program is obtained from the circuit design diagram, and a feasible path analysis is performed based on the target partial circuit design diagram and the target pin to obtain m circuit paths, that is, since the partial circuit design diagram is known and the target pin is also known, the circuit path related to the target pin can also be directly determined, so that the pin corresponding to the keyword and the related circuit corresponding to the preset fault detection program can be determined, thereby improving the fault detection accuracy of the embedded module.

[0057] In a specific implementation, a mapping relationship between preset pins and circuit path sets may be pre-stored, and then, a target circuit path set corresponding to a target pin may be determined based on the mapping relationship. The target circuit path set may include multiple circuit paths, and each circuit path may correspond to at least one keyword. Then, a circuit path corresponding to the target keyword may be selected from the multiple circuit paths to obtain m circuit paths.

[0058] In a specific implementation, different circuit modules in a partial circuit design diagram can be regarded as a node (for example, a resistor can be regarded as a node, and for example, an amplifier can be regarded as a node), that is, different nodes are connected together to obtain a partial circuit design diagram, and path planning can also be performed based on the target pin as the starting point to obtain multiple circuit paths, and then these circuit paths are screened to obtain m circuit paths.

[0059] Among them, the circuit design diagram identifier can be used to identify different partial circuit design diagrams in the circuit design diagram.

[0060] In some possible examples, in performing fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result, the fault analysis unit is specifically used to:

[0061] Obtaining a reference hardware operating parameter corresponding to each circuit path of the m circuit paths to obtain m reference hardware operating parameters;

[0062] Determining m first deviations according to the m hardware operating parameters and the m reference hardware operating parameters;

[0063] Determining m second deviations according to the m first deviations and the m network parameters;

[0064] Obtaining a deviation threshold range corresponding to each circuit path in the m circuit paths, to obtain m deviation threshold ranges;

[0065] The target fault diagnosis result is determined according to the m second deviations and the m deviation threshold ranges.

[0066] Among them, the reference hardware operating parameters may include at least one of the following: operating current, operating voltage, operating power, operating pulse, operating frequency, operating duty cycle, operating position, operating path, etc., which are not limited here. The reference hardware operating parameters can be understood as hardware operating parameters at a moment, or they can also be understood as hardware operating parameters within a period of time. The reference hardware operating parameters reflect the working conditions of each circuit path to a certain extent, etc. The reference hardware operating parameters can be hardware operating parameters of a detection point (for example, a port, a pin) or a path. The reference hardware operating parameters can be understood as hardware operating parameters corresponding to the circuit path when there is no fault (normal). The reference hardware operating parameters can be pre-set or system default. For example, the reference hardware operating parameters can be related to the target software environment parameters of the embedded module, and the mapping relationship between the preset software environment parameters of the embedded module and the reference hardware operating parameters can be pre-stored, and the corresponding reference hardware operating parameters can be determined based on the mapping relationship.

[0067] In a specific implementation, different circuit paths may correspond to different hardware operating parameters, different circuit paths may correspond to different path identifiers, and different path identifiers may correspond to different reference hardware operating parameters. For example, a mapping relationship between a preset path identifier and a reference hardware operating parameter may be pre-stored, and then, based on the mapping relationship, the reference hardware operating parameter corresponding to each circuit path in the m circuit paths may be obtained to obtain m reference hardware operating parameters, and m first deviations may be determined based on the m hardware operating parameters and the m reference hardware operating parameters, that is, the corresponding first deviation may be determined based on each hardware operating parameter in the m hardware operating parameters and the corresponding reference hardware operating parameter, that is, the first deviation = (hardware operating parameter - reference hardware operating parameter) / reference hardware operating parameter, and the first deviation reflects the degree of hardware operating abnormality and the direction of abnormality.

[0068] Next, m second deviations can be determined based on the m first deviations and m network parameters, that is, the network parameters reflect the network conditions of the circuit path to a certain extent, that is, the first deviation can be dynamically optimized based on the actual network conditions to make the deviation more in line with the actual situation.

[0069] Next, the deviation threshold range corresponding to each circuit path in the m circuit paths is obtained to obtain m deviation threshold ranges. Different circuit paths may correspond to different deviation threshold ranges. Finally, the target fault diagnosis result is determined according to the m second deviations and the m deviation threshold ranges. In this way, fault diagnosis can be accurately performed based on the working conditions and network conditions of each circuit path, thereby improving the fault detection accuracy of the embedded module.

[0070] In a specific implementation, different degrees of deviation may correspond to different fault diagnosis results, such as overvoltage, undervoltage, overcurrent, etc., which are not limited here.

[0071] In some possible examples, in determining the target fault diagnosis result according to the m second deviations and the m deviation threshold ranges, the fault analysis unit is specifically used to:

[0072] When the target second deviation is within the target deviation threshold range, it is determined that the circuit path corresponding to the target second deviation is normal; the target second deviation is any second deviation among the m second deviations; the target deviation threshold range is the deviation threshold range corresponding to the target second deviation; the target deviation threshold range includes an upper threshold and a lower threshold;

[0073] When the target second deviation is not within the target deviation threshold range, if the target second deviation is greater than the upper threshold, determining a first relative deviation between the target second deviation and the upper threshold, and determining the target fault diagnosis result corresponding to the first relative deviation;

[0074] If the target second deviation is less than the lower limit threshold, a second relative deviation between the lower limit threshold and the target second deviation is determined, and the target fault diagnosis result corresponding to the second relative deviation is determined.

[0075] In the specific implementation, taking the target second deviation as an example, the target second deviation is any second deviation among the m second deviations, the target deviation threshold range is the deviation threshold range corresponding to the target second deviation, and the target deviation threshold range includes an upper threshold and a lower threshold, and the upper threshold is greater than the lower threshold.

[0076] In a specific implementation, when the target second deviation is within the target deviation threshold range, it indicates that the circuit path is normal, that is, it is determined that the circuit path corresponding to the target second deviation is working normally (without abnormality or fault).

[0077] Furthermore, when the target second deviation is not within the target deviation threshold range, if the target second deviation is greater than the upper threshold, the first relative deviation between the target second deviation and the upper threshold can be determined, that is, the first relative deviation = (target second deviation-upper threshold) / upper threshold. The first relative deviation reflects the deviation direction and deviation size, and the first mapping relationship between the preset relative deviation and the fault diagnosis result can be pre-stored. Then, the target fault diagnosis result corresponding to the first relative deviation can be determined based on the first mapping relationship. Since the target deviation threshold range reflects the normal condition of the corresponding circuit path, the first relative deviation reflects the degree of difference between the actual working condition and the normal condition. The corresponding diagnosis result is determined based on the degree of difference, thereby improving the fault detection accuracy of the embedded module.

[0078] Correspondingly, if the target second deviation is less than the lower limit threshold, the second relative deviation between the lower limit threshold and the target second deviation can be determined, that is, the second relative deviation = (lower limit threshold-target second deviation) / lower limit threshold. The second relative deviation reflects the deviation direction and deviation size. The second mapping relationship between the preset relative deviation and the fault diagnosis result can also be pre-stored. Then, the target fault diagnosis result corresponding to the second relative deviation can be determined based on the second mapping relationship. Since the target deviation threshold range reflects the normal condition of the corresponding circuit path, the second relative deviation reflects the difference between the actual working condition and the normal condition. The corresponding diagnosis result is determined based on the difference. Therefore, the fault detection accuracy of the embedded module can be improved.

[0079] In some possible examples, when m is greater than 1, the fault analysis unit is further specifically configured to:

[0080] Identify a normal circuit path and an abnormal circuit path among the m circuit paths; the normal circuit path is a circuit path without abnormalities; the abnormal circuit path is a circuit path with abnormalities;

[0081] Detecting whether there is a common path between the first circuit path and the second circuit path; the first circuit path is a normal circuit path of the m circuit paths, and the second circuit path is an abnormal circuit path of the m circuit paths;

[0082] If so, determine other paths in the second circuit path except the common path, and determine that the other paths are abnormal.

[0083] In the specific implementation, the normal and abnormal conditions of m circuit paths can be identified. Of course, the following functions can also be realized:

[0084] Detecting whether there is a common path between the first circuit path and the second circuit path; the first circuit path is a circuit path without abnormalities among the m circuit paths, and the second circuit path is a circuit path with abnormalities among the m circuit paths;

[0085] If so, determine other paths in the second circuit path except the common path, and determine that the other paths are abnormal.

[0086] In an embodiment of the present application, taking the first circuit path and the second circuit path as an example, the first circuit path is a circuit path without abnormalities among m circuit paths, and the second circuit path is a circuit path with abnormalities among m circuit paths. It is detected whether there is a common path between the first circuit path and the second circuit path. If so, other paths in the second circuit path except the common path are determined, and it is determined that there are abnormalities in the other paths. In this way, this method can be used to locate the position where the abnormality may occur as much as possible, that is, the normal path can be marked and the abnormal path can be approached, and then, the abnormal position can be accurately and quickly locked, thereby improving the fault detection accuracy of the embedded module.

[0087] In some possible examples, in determining the target fault diagnosis result according to the m second deviations and the m deviation threshold ranges, the fault analysis unit is specifically used to:

[0088] Compare the m second deviations with the corresponding deviation threshold range to obtain k abnormal deviations, each abnormal deviation is not within the corresponding deviation threshold range;

[0089] Determine the abnormality degree of each abnormal deviation degree among the k abnormal deviation degrees to obtain k abnormality degrees;

[0090] Determine a target abnormality level according to the k abnormality levels;

[0091] The target fault diagnosis result is determined according to the target abnormality degree.

[0092] In a specific implementation, the m second deviations can be compared with the corresponding deviation threshold range to obtain k abnormal deviations, each of which is not within the corresponding deviation threshold range. The abnormal deviation can be understood as a second deviation that is not within the corresponding deviation threshold range.

[0093] Then, the degree of abnormality of each abnormal deviation among the k abnormal deviations can be determined to obtain k abnormal degrees, as follows: taking the target second deviation as an example, the target second deviation is any second deviation among the m second deviations, the target deviation threshold range is the deviation threshold range corresponding to the target second deviation, and the target deviation threshold range includes an upper threshold and a lower threshold, and the upper threshold is greater than the lower threshold. In a specific implementation, when the target second deviation is not within the target deviation threshold range, if the target second deviation is greater than the upper threshold, then the first relative deviation between the target second deviation and the upper threshold can be determined, that is, the first relative deviation = (target second deviation - upper threshold) / upper threshold, that is, the first mapping relationship between the preset relative deviation and the degree of abnormality can be pre-stored, and then, the degree of abnormality corresponding to the first relative deviation can be determined by the first mapping relationship. Correspondingly, if the target second deviation is less than the lower limit threshold, the second relative deviation between the lower limit threshold and the target second deviation can be determined, that is, the second relative deviation = (lower limit threshold - target second deviation) / lower limit threshold, that is, the second mapping relationship between the preset relative deviation and the degree of abnormality can be pre-stored, and then, the degree of abnormality corresponding to the first relative deviation can be determined by the second mapping relationship.

[0094] The first mapping relationship between the preset relative deviation and the abnormality degree and the second mapping relationship between the preset relative deviation and the abnormality degree may be the same or different.

[0095] Based on this approach, the m second deviations can be compared with the corresponding deviation threshold range to obtain k abnormal deviations, and each abnormal deviation is not within the corresponding deviation threshold range.

[0096] Next, the weight of each circuit path can be determined. The greater the degree of abnormality, the greater the weight. Based on this method, k weights can be determined, and the sum of the k weights is 1. For example, the sum of the abnormality degrees can be calculated, and then the ratio between each abnormality degree and the sum can be determined. Each ratio corresponds to a weight, that is, the ratio is regarded as a weight. In this way, the weight can be dynamically adjusted based on the disturbance situation between each circuit path, so that the final fault diagnosis result conforms to the correlation between the pin-related circuits.

[0097] Furthermore, a weighted operation can be performed based on k abnormality degrees and k weights to obtain a target abnormality degree, and then a target fault diagnosis result can be determined based on the target abnormality degree. Specifically, a mapping relationship between a preset abnormality degree and a fault diagnosis result can be pre-stored, and then a target fault diagnosis result corresponding to the target abnormality degree can be determined based on the mapping relationship. In this way, a corresponding fault diagnosis result can be dynamically determined based on the abnormal situation of a pin-related circuit path, thereby improving the fault detection accuracy of the embedded module.

[0098] In some possible examples, the above step of determining the target abnormality level according to the k abnormality levels may be implemented as follows:

[0099] Determine the sum of the k abnormality levels to obtain a total abnormality level;

[0100] Determine the ratio between the k abnormality levels and the total abnormality level to obtain k weights;

[0101] A weighted operation is performed according to the k abnormality degrees and the k weights to obtain the target abnormality degree.

[0102] In a specific implementation, the sum of k abnormality levels can be determined to obtain the total abnormality level, that is, the k abnormality levels are summed to obtain the total abnormality level, and then the ratio between each of the k abnormality levels and the total abnormality level is determined to obtain k weights. Finally, a weighted operation can be performed based on the k abnormality levels and the k weights to obtain the target abnormality level. In this way, the weights can be dynamically adjusted based on the disturbance conditions between each circuit path, so that the final fault diagnosis result conforms to the correlation between the pin-related circuits.

[0103] In some possible examples, the above step of determining the target abnormality level according to the k abnormality levels may be implemented as follows:

[0104] Obtaining k initial weights corresponding to the k abnormality levels;

[0105] Determine the sum of the k abnormality levels to obtain a total abnormality level;

[0106] Determine the ratios between the k abnormality levels and the second total abnormality level to obtain k ratios;

[0107] Determine k disturbance factors corresponding to the k ratios;

[0108] Determining k disturbance weights according to the k disturbance factors and the k initial weights;

[0109] Determine the sum of the k disturbance weights to obtain a total disturbance weight;

[0110] Determining k weights according to the k disturbance weights and the total disturbance weight;

[0111] A weighted operation is performed according to the k abnormality degrees and the k weights to obtain the target abnormality degree.

[0112] In a specific implementation, each abnormality degree corresponds to a circuit path, and the weight of each circuit path can be determined. Since different circuit paths have different weights, the initial weight of each circuit path can be determined to obtain k initial weights, and the sum of the k initial weights is 1. Then, the greater the abnormality degree, the greater the disturbance factor, and the value of the disturbance factor can be 0~0.2. Based on this method, a mapping relationship between a preset ratio and a disturbance factor can be pre-stored, and then, k disturbance factors can be determined based on the mapping relationship. For example, the sum of the abnormality degrees, that is, the total abnormality degree, can be calculated, and then the ratio between each abnormality degree and the sum (total abnormality degree) can be determined. Each ratio corresponds to a disturbance factor, that is, the larger the ratio, the larger the disturbance factor. The corresponding initial weight is disturbed based on the disturbance factor, and the weight after disturbance processing can be obtained, that is, k disturbance weights, that is, each disturbance weight = (1+disturbance factor) * initial weight.

[0113] Next, determine the weight after disturbance processing, that is, the sum of k disturbance weights, to obtain the total disturbance weight, and determine the ratio between each disturbance weight of the k disturbance weights and the total disturbance weight. Each ratio corresponds to a weight, that is, the ratio is regarded as a weight. In this way, k weights can be obtained. In this way, the weights can be dynamically adjusted based on the circuit path and the disturbance conditions between each circuit path, so that the final fault diagnosis result not only conforms to the circuit characteristics itself but also conforms to the correlation between pin-related circuits.

[0114] Furthermore, a weighted operation can be performed based on k abnormality degrees and k weights to obtain a target abnormality degree, and then a target fault diagnosis result can be determined based on the target abnormality degree. Specifically, a mapping relationship between a preset abnormality degree and a fault diagnosis result can be pre-stored, and then a target fault diagnosis result corresponding to the target abnormality degree can be determined based on the mapping relationship. In this way, a corresponding fault diagnosis result can be dynamically determined based on the abnormal situation of a pin-related circuit path, thereby improving the fault detection accuracy of the embedded module.

[0115] In some possible examples, in determining the m second deviations according to the m first deviations and the m network parameters, the fault analysis unit is specifically configured to:

[0116] Determine m fitting straight lines and m fitting curve segments according to the m network parameters;

[0117] Determine m slopes according to the m fitted straight lines;

[0118] According to the mapping relationship between the preset slope and the adjustment parameter, the adjustment parameter corresponding to the m slopes is determined to obtain m adjustment parameters, and the value range of the adjustment parameter is -0.1~0.1;

[0119] Determine an extreme value set corresponding to each of the m fitting curve segments to obtain m extreme value sets; each extreme value set includes a plurality of extreme values, and the extreme values ​​may include a maximum value and a minimum value;

[0120] Determine the standard deviation corresponding to each extreme value set in the m extreme value sets to obtain m standard deviations;

[0121] According to the mapping relationship between the preset standard deviation and the fine-tuning parameter, the fine-tuning parameters corresponding to the m standard deviations are determined to obtain the m fine-tuning parameters, where the value range of the fine-tuning parameters is -0.02 to 0.02;

[0122] The m second deviations are determined according to the m adjustment parameters, the m fine-tuning parameters and the m first deviations.

[0123] Among them, the m network parameters can all be network parameters of a preset time period of each circuit path, and the network parameters of the preset time period can include multiple network parameters, each network parameter corresponds to a sampling time, and then, fitting can be performed based on the network parameters and sampling time of each circuit path, that is, each network parameter and its corresponding sampling time can be regarded as a coordinate point, and multiple coordinate points can be obtained based on this method, and then the multiple coordinates are mapped to a coordinate system, the horizontal axis of the coordinate system is time, and the vertical axis is the network parameter, that is, the corresponding fitting operation can be implemented, and the above m network parameters can determine m fitting lines and m fitting curve segments, and the network parameters of each circuit path can correspond to a fitting line and a fitting curve segment in the preset time period. The preset time period can be understood as a historical time period, and the historical time period may include the current time or may not include the current time.

[0124] Then, m slopes can be determined based on the m fitting lines. Each fitting line corresponds to a slope, and the slope reflects the trend of network changes.

[0125] Next, the mapping relationship between the preset slope and the adjustment parameter can be pre-stored, and then the adjustment parameters corresponding to the m slopes can be determined based on the mapping relationship to obtain m adjustment parameters. The fine-tuning parameters corresponding to each of the m fitting curve segments can also be determined, and the value range of the fine-tuning parameters can be -0.1~0.1.

[0126] Specifically, the extreme value points of each fitting curve segment can be selected to obtain multiple extreme value points, including maximum and minimum values. The multiple extreme value points can be subjected to standard deviation calculation to obtain the corresponding standard deviation. According to the mapping relationship between the preset standard deviation and the fine-tuning parameter, the fine-tuning parameter corresponding to the m fitting curve segments is determined to obtain m fine-tuning parameters. The value range of the fine-tuning parameter can be -0.02~0.02. The standard deviation reflects the network stability to a certain extent.

[0127] Finally, m second deviations can be determined based on m adjustment parameters, m fine-tuning parameters and m first deviations, that is, each first deviation is adjusted using the corresponding adjustment parameters and fine-tuning parameters to obtain the corresponding second deviation, second deviation = (1 + fine-tuning parameter) * (1 + adjustment parameter) * first deviation. Furthermore, the first deviation can be dynamically adjusted based on network change trends and network stability, so that the final second deviation depth conforms to the actual situation, thereby improving the fault detection accuracy of the embedded module.

[0128] It can be seen that the embedded-based fault diagnosis system described in the embodiment of the present application is applied to an electronic device, and the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit and a fault analysis unit, wherein the software diagnosis unit is used to run a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtain the running result, and perform keyword extraction on the running result to obtain a target keyword; when the target keyword exists in a preset keyword set, determine the pin corresponding to the target keyword to obtain the target pin, and the target pin is a pin among the n pins; the hardware diagnosis unit is used to obtain a circuit design diagram of the electronic device, determine m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determine the hardware working order of each circuit path in the m circuit paths as parameters, and obtain m hardware working parameters; m is a positive integer; a network diagnosis unit is used to obtain the network parameters of each circuit path in the m circuit paths, and obtain m network parameters; a fault analysis unit is used to perform fault diagnosis according to the m hardware working parameters and the m network parameters, and obtain a target fault diagnosis result. On the one hand, by running a preset fault detection program related to the embedded module, and extracting keywords for the corresponding running results, the keywords are used to locate the corresponding pins, and further hardware fault diagnosis is performed based on the pins. On the other hand, since the hardware working parameters reflect the working conditions of each circuit path to a certain extent, and the network parameters reflect the network conditions of each circuit path to a certain extent (such as network stability and network security), fault diagnosis can be accurately performed based on the working conditions and network conditions of each circuit path, thereby improving the fault detection accuracy of the embedded module.

[0129] See also Figure 2 , Figure 2 This is a flow chart of an embedded fault diagnosis method provided by an embodiment of the present application. As shown in the figure, it is applied to Figure 1 The fault diagnosis system shown is applied to an electronic device, wherein the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit, and a fault analysis unit, wherein the embedded fault diagnosis method includes:

[0130] 201. When the embedded module is in a power-on state, the software diagnostic unit runs a preset fault detection program related to the embedded module to obtain an operation result, and performs keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, the pin corresponding to the target keyword is determined to obtain a target pin, and the target pin is a pin among the n pins.

[0131] 202. Obtain a circuit design diagram of the electronic device through the hardware diagnostic unit, and determine m circuit paths corresponding to the target pins according to the circuit design diagram and the preset fault detection program; determine the hardware operating parameters of each of the m circuit paths to obtain m hardware operating parameters; m is a positive integer.

[0132] 203. Obtain a network parameter of each circuit path in the m circuit paths through the network diagnosis unit to obtain m network parameters.

[0133] 204. Perform fault diagnosis according to the m hardware operating parameters and the m network parameters by the fault analysis unit to obtain a target fault diagnosis result.

[0134] The specific description of steps 201 to 204 can refer to the above Figure 1 The related steps of the described embedded fault diagnosis system will not be repeated here.

[0135] In some possible examples, the following steps may also be included:

[0136] Acquiring target software environment parameters of the embedded module through the software diagnostic unit;

[0137] The preset fault detection procedure corresponding to the target software environment parameter is determined.

[0138] Among them, the target software environment parameters can be used to characterize the software operating environment of the embedded module. The target software environment parameters may include at least one of the following: operating system version, system path, memory size, operating system environment variables, setting parameters in the application configuration file, etc., which are not limited here.

[0139] In a specific implementation, different software environment parameters may correspond to different fault detection programs, that is, the mapping relationship between the preset software environment parameters and the fault detection program may be pre-stored, and then, the preset fault detection program corresponding to the target software environment parameters may be determined based on the mapping relationship, that is, the appropriate fault detection program may be adapted based on different software environment parameters, thereby improving the fault detection accuracy of the embedded module.

[0140] In some possible examples, the above step of determining m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program may be implemented as follows:

[0141] Acquire a portion of the circuit design diagram corresponding to the preset fault detection program from the circuit design diagram;

[0142] A feasible path analysis is performed based on the partial circuit design diagram and the target pin to obtain the m circuit paths.

[0143] In a specific implementation, different fault detection programs are associated with different circuit design drawings. Specifically, a mapping relationship between a preset fault detection program and a circuit design drawing identifier can be pre-stored. Then, a target circuit design drawing identifier corresponding to the preset fault detection program can be determined based on the mapping relationship. Then, a partial circuit design drawing corresponding to the preset fault detection program is obtained from the circuit design drawing based on the target circuit design drawing identifier. A feasible path analysis is performed based on the partial circuit design drawing and the target pin to obtain m circuit paths. That is, since the partial circuit design drawing is known and the target pin is also known, the circuit path related to the target pin can also be directly determined. In this way, the pin corresponding to the keyword and the related circuit corresponding to the preset fault detection program can be determined, thereby improving the fault detection accuracy of the embedded module.

[0144] In a specific implementation, a mapping relationship between preset pins and circuit path sets may be pre-stored, and then, a target circuit path set corresponding to a target pin may be determined based on the mapping relationship. The target circuit path set may include multiple circuit paths, and each circuit path may correspond to at least one keyword. Then, a circuit path corresponding to the target keyword may be selected from the multiple circuit paths to obtain m circuit paths.

[0145] In a specific implementation, different circuit modules in a partial circuit design diagram can be regarded as a node (for example, a resistor can be regarded as a node, and for example, an amplifier can be regarded as a node), that is, different nodes are connected together to obtain a partial circuit design diagram, and path planning can also be performed based on the target pin as the starting point to obtain multiple circuit paths, and then these circuit paths are screened to obtain m circuit paths.

[0146] It can be seen that the method described in the embodiment of the present application is applied to an embedded-based fault diagnosis system, and the embedded-based fault diagnosis system is applied to an electronic device, and the electronic device includes an embedded module; the embedded module includes n pins, and n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit and a fault analysis unit, wherein the software diagnosis unit runs a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtains an operation result, and performs keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, determines the pin corresponding to the target keyword to obtain a target pin, and the target pin is a pin among the n pins; the hardware diagnosis unit obtains a circuit design diagram of the electronic device, and determines m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determines the m circuit paths The hardware working parameters of each circuit path in the m circuit paths are obtained to obtain m hardware working parameters; m is a positive integer; the network diagnosis unit obtains the network parameters of each circuit path in the m circuit paths to obtain m network parameters; the fault analysis unit performs fault diagnosis according to the m hardware working parameters and the m network parameters to obtain the target fault diagnosis result. On the one hand, by running a preset fault detection program related to the embedded module, and extracting keywords for the corresponding running results, the keywords are used to locate the corresponding pins, and further hardware fault diagnosis is performed based on the pins. On the other hand, since the hardware working parameters reflect the working conditions of each circuit path to a certain extent, and the network parameters reflect the network conditions of each circuit path to a certain extent (such as network stability and network security), fault diagnosis can be accurately performed based on the working conditions and network conditions of each circuit path, thereby improving the fault detection accuracy of the embedded module.

[0147] In accordance with the above embodiment, please refer to Figure 3 , Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device includes a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor. In the embodiment of the present application, the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the electronic device includes Figure 1 The embedded fault diagnosis system shown in the figure includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit and a fault analysis unit. The above program includes instructions for executing the following steps:

[0148] When the embedded module is in a power-on state, the software diagnosis unit runs a preset fault detection program related to the embedded module to obtain an operation result, and performs keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, a pin corresponding to the target keyword is determined to obtain a target pin, and the target pin is a pin among the n pins;

[0149] Obtaining a circuit design diagram of the electronic device through the hardware diagnosis unit, determining m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determining a hardware operating parameter of each circuit path in the m circuit paths to obtain m hardware operating parameters; m is a positive integer;

[0150] Acquire a network parameter of each circuit path in the m circuit paths through the network diagnosis unit to obtain m network parameters;

[0151] The fault analysis unit performs fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result.

[0152] In some possible examples, the above program includes instructions for performing the following steps:

[0153] Acquiring target software environment parameters of the embedded module through the software diagnostic unit;

[0154] The preset fault detection procedure corresponding to the target software environment parameter is determined.

[0155] In some possible examples, in determining the m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program, the program includes instructions for performing the following steps:

[0156] Acquire a portion of the circuit design diagram corresponding to the preset fault detection program from the circuit design diagram;

[0157] A feasible path analysis is performed based on the partial circuit design diagram and the target pin to obtain the m circuit paths.

[0158] In some possible examples, in the aspect of performing feasible path analysis according to the partial circuit design diagram and the target pin to obtain the m circuit paths, the program includes instructions for executing the following steps:

[0159] Determine a target circuit design drawing identifier corresponding to the preset fault detection program according to a mapping relationship between the preset fault detection program and the circuit design drawing identifier, wherein the circuit design drawing identifier is used to identify different partial circuit design drawings;

[0160] Acquire a target partial circuit design diagram corresponding to the preset fault detection program from the circuit design diagram based on the target circuit design diagram identifier;

[0161] A feasible path analysis is performed based on the target partial circuit design diagram and the target pins to obtain m circuit paths.

[0162] In some possible examples, in terms of performing fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result, the program includes instructions for executing the following steps:

[0163] Obtaining a reference hardware operating parameter corresponding to each circuit path of the m circuit paths to obtain m reference hardware operating parameters;

[0164] Determining m first deviations according to the m hardware operating parameters and the m reference hardware operating parameters;

[0165] Determining m second deviations according to the m first deviations and the m network parameters;

[0166] Obtaining a deviation threshold range corresponding to each circuit path in the m circuit paths, to obtain m deviation threshold ranges;

[0167] The target fault diagnosis result is determined according to the m second deviations and the m deviation threshold ranges.

[0168] In some possible examples, in determining the target fault diagnosis result according to the m second deviations and the m deviation threshold ranges, the program includes instructions for executing the following steps:

[0169] When the target second deviation is within the target deviation threshold range, it is determined that the circuit path corresponding to the target second deviation is normal; the target second deviation is any second deviation among the m second deviations; the target deviation threshold range is the deviation threshold range corresponding to the target second deviation; the target deviation threshold range includes an upper threshold and a lower threshold;

[0170] When the target second deviation is not within the target deviation threshold range, if the target second deviation is greater than the upper threshold, determining a first relative deviation between the target second deviation and the upper threshold, and determining the target fault diagnosis result corresponding to the first relative deviation;

[0171] If the target second deviation is less than the lower limit threshold, a second relative deviation between the lower limit threshold and the target second deviation is determined, and the target fault diagnosis result corresponding to the second relative deviation is determined.

[0172] In some possible examples, in determining the target fault diagnosis result according to the m second deviations and the m deviation threshold ranges, the program includes instructions for executing the following steps:

[0173] Compare the m second deviations with the corresponding deviation threshold range to obtain k abnormal deviations, each abnormal deviation is not within the corresponding deviation threshold range;

[0174] Determine the abnormality degree of each abnormal deviation degree among the k abnormal deviation degrees to obtain k abnormality degrees;

[0175] Determine a target abnormality level according to the k abnormality levels;

[0176] The target fault diagnosis result is determined according to the target abnormality degree.

[0177] In some possible examples, in determining the m second deviations according to the m first deviations and the m network parameters, the program includes instructions for performing the following steps:

[0178] Determine m fitting straight lines and m fitting curve segments according to the m network parameters;

[0179] Determine m slopes according to the m fitted straight lines;

[0180] Determine adjustment parameters corresponding to the m slopes to obtain m adjustment parameters;

[0181] Determine the fine-tuning parameters corresponding to the m fitting curve segments to obtain m fine-tuning parameters;

[0182] The m second deviations are determined according to the m adjustment parameters, the m fine-tuning parameters and the m first deviations.

[0183] In some possible examples, in determining the target abnormality level according to the k abnormality levels, the program includes instructions for performing the following steps:

[0184] Determine the sum of the k abnormality levels to obtain a total abnormality level;

[0185] Determine the ratio between the k abnormality levels and the total abnormality level to obtain k weights;

[0186] A weighted operation is performed according to the k abnormality degrees and the k weights to obtain the target abnormality degree.

[0187] In some possible examples, in determining the target abnormality level according to the k abnormality levels, the program includes instructions for performing the following steps:

[0188] Obtaining k initial weights corresponding to the k abnormality levels;

[0189] Determine the sum of the k abnormality levels to obtain a total abnormality level;

[0190] Determine the ratios between the k abnormality levels and the total abnormality level to obtain k ratios;

[0191] Determine k disturbance factors corresponding to the k ratios;

[0192] Determining k disturbance weights according to the k disturbance factors and the k initial weights;

[0193] Determine the sum of the k disturbance weights to obtain a total disturbance weight;

[0194] Determining k weights according to the k disturbance weights and the total disturbance weight;

[0195] A weighted operation is performed according to the k abnormality degrees and the k weights to obtain the target abnormality degree.

[0196] In some possible examples, when m is greater than 1, the above program includes instructions for performing the following steps:

[0197] Identify a normal circuit path and an abnormal circuit path among the m circuit paths; the normal circuit path is a circuit path without abnormalities; the abnormal circuit path is a circuit path with abnormalities;

[0198] Detecting whether there is a common path between the first circuit path and the second circuit path; the first circuit path is a normal circuit path of the m circuit paths, and the second circuit path is an abnormal circuit path of the m circuit paths;

[0199] If so, determine other paths in the second circuit path except the common path, and determine that the other paths are abnormal.

[0200] It can be seen that the electronic device described in the embodiment of the present application includes an embedded-based fault diagnosis system, and the electronic device includes an embedded module; the embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit, and a fault analysis unit, wherein the software diagnosis unit runs a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtains an operation result, and performs keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, determines the pin corresponding to the target keyword to obtain a target pin, where the target pin is a pin among the n pins; the hardware diagnosis unit obtains a circuit design diagram of the electronic device, and determines m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determines the hardware of each circuit path in the m circuit paths The working parameters of the components are obtained to obtain m hardware working parameters; m is a positive integer; the network diagnosis unit obtains the network parameters of each circuit path in the m circuit paths to obtain m network parameters; the fault analysis unit performs fault diagnosis according to the m hardware working parameters and the m network parameters to obtain the target fault diagnosis result. On the one hand, by running a preset fault detection program related to the embedded module, and extracting keywords for the corresponding running results, the keywords are used to locate the corresponding pins, and further hardware fault diagnosis is performed based on the pins. On the other hand, since the hardware working parameters reflect the working conditions of each circuit path to a certain extent, and the network parameters reflect the network conditions of each circuit path to a certain extent (such as network stability and network security), fault diagnosis can be accurately performed based on the working conditions and network conditions of each circuit path, thereby improving the fault detection accuracy of the embedded module.

[0201] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0202] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0203] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0204] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0205] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0206] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0207] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0208] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0209] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0210] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A fault diagnosis system based on embedded system, characterized in that: Applicable to electronic equipment, the electronic equipment comprising an embedded module; The embedded module includes n pins, where n is an integer greater than 1; the fault diagnosis system includes: a hardware diagnosis unit, a software diagnosis unit, a network diagnosis unit and a fault analysis unit; The software diagnosis unit is used to run a preset fault detection program related to the embedded module when the embedded module is in a power-on state, obtain an operation result, and perform keyword extraction on the operation result to obtain a target keyword; when the target keyword exists in a preset keyword set, determine a pin corresponding to the target keyword to obtain a target pin, and the target pin is a pin among the n pins; The hardware diagnosis unit is used to obtain a circuit design diagram of the electronic device, determine m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program; determine a hardware operating parameter of each circuit path in the m circuit paths to obtain m hardware operating parameters; m is a positive integer; The network diagnosis unit is used to obtain the network parameters of each circuit path in the m circuit paths to obtain m network parameters; The fault analysis unit is used to perform fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result.

2. The embedded fault diagnosis system according to claim 1, characterized in that: The software diagnostic unit is also specifically used for: Obtaining target software environment parameters of the embedded module; The preset fault detection procedure corresponding to the target software environment parameter is determined.

3. The embedded fault diagnosis system according to claim 2, characterized in that: In the aspect of determining the m circuit paths corresponding to the target pin according to the circuit design diagram and the preset fault detection program, the hardware diagnosis unit is specifically used for: Acquire a portion of the circuit design diagram corresponding to the preset fault detection program from the circuit design diagram; A feasible path analysis is performed based on the partial circuit design diagram and the target pin to obtain the m circuit paths.

4. The embedded fault diagnosis system according to claim 3, characterized in that: The performing feasible path analysis according to the partial circuit design diagram and the target pin to obtain the m circuit paths includes: Determine a target circuit design drawing identifier corresponding to the preset fault detection program according to a mapping relationship between the preset fault detection program and the circuit design drawing identifier, wherein the circuit design drawing identifier is used to identify different partial circuit design drawings; Acquire a target partial circuit design diagram corresponding to the preset fault detection program from the circuit design diagram based on the target circuit design diagram identifier; A feasible path analysis is performed based on the target partial circuit design diagram and the target pins to obtain m circuit paths.

5. The embedded fault diagnosis system according to claim 3 or 4, characterized in that: In the aspect of performing fault diagnosis according to the m hardware operating parameters and the m network parameters to obtain a target fault diagnosis result, the fault analysis unit is specifically used for: Obtaining a reference hardware operating parameter corresponding to each circuit path of the m circuit paths to obtain m reference hardware operating parameters; Determining m first deviations according to the m hardware operating parameters and the m reference hardware operating parameters; Determining m second deviations according to the m first deviations and the m network parameters; Obtaining a deviation threshold range corresponding to each circuit path in the m circuit paths, to obtain m deviation threshold ranges; The target fault diagnosis result is determined according to the m second deviations and the m deviation threshold ranges.

6. The embedded fault diagnosis system according to claim 5, characterized in that: In the aspect of determining the target fault diagnosis result according to the m second deviations and the m deviation threshold ranges, the fault analysis unit is specifically used for: When the target second deviation is within the target deviation threshold range, determining that the circuit path corresponding to the target second deviation is normal; the target second deviation is any second deviation among the m second deviations; The target deviation threshold range is the deviation threshold range corresponding to the target second deviation; the target deviation threshold range includes an upper threshold and a lower threshold; When the target second deviation is not within the target deviation threshold range, if the target second deviation is greater than the upper threshold, determining a first relative deviation between the target second deviation and the upper threshold, and determining the target fault diagnosis result corresponding to the first relative deviation; If the target second deviation is less than the lower limit threshold, a second relative deviation between the lower limit threshold and the target second deviation is determined, and the target fault diagnosis result corresponding to the second relative deviation is determined.

7. The embedded fault diagnosis system according to claim 5, characterized in that: In the aspect of determining the target fault diagnosis result according to the m second deviations and the m deviation threshold ranges, the fault analysis unit is specifically used for: Compare the m second deviations with the corresponding deviation threshold range to obtain k abnormal deviations, each abnormal deviation is not within the corresponding deviation threshold range; Determine the abnormality degree of each abnormal deviation degree among the k abnormal deviation degrees to obtain k abnormality degrees; Determine a target abnormality level according to the k abnormality levels; The target fault diagnosis result is determined according to the target abnormality degree.

8. The embedded fault diagnosis system according to claim 7, characterized in that: The step of determining a target abnormality level according to the k abnormality levels comprises: Determine the sum of the k abnormality levels to obtain a total abnormality level; Determine the ratio between the k abnormality levels and the total abnormality level to obtain k weights; A weighted operation is performed according to the k abnormality degrees and the k weights to obtain the target abnormality degree.

9. The embedded fault diagnosis system according to claim 7, characterized in that: The step of determining a target abnormality level according to the k abnormality levels comprises: Obtaining k initial weights corresponding to the k abnormality levels; Determine the sum of the k abnormality levels to obtain a total abnormality level; Determine the ratios between the k abnormality levels and the total abnormality level to obtain k ratios; Determine k disturbance factors corresponding to the k ratios; Determining k disturbance weights according to the k disturbance factors and the k initial weights; Determine the sum of the k disturbance weights to obtain a total disturbance weight; Determining k weights according to the k disturbance weights and the total disturbance weight; A weighted operation is performed according to the k abnormality degrees and the k weights to obtain the target abnormality degree.

10. The embedded fault diagnosis system according to any one of claims 1 to 4, characterized in that: When m is greater than 1, the fault analysis unit is further specifically used for: Identify a normal circuit path and an abnormal circuit path among the m circuit paths; the normal circuit path is a circuit path without abnormalities; the abnormal circuit path is a circuit path with abnormalities; Detecting whether there is a common path between the first circuit path and the second circuit path; the first circuit path is a normal circuit path of the m circuit paths, and the second circuit path is an abnormal circuit path of the m circuit paths; If so, determine other paths in the second circuit path except the common path, and determine that the other paths are abnormal.

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