Fault prediction method and system for automated production line of electronic control firmware

Abstract

The present application relates to the technical field of equipment detection, and provides a method and system for predicting faults of an automated production line of electronically controlled firmware, the method comprising obtaining the operating status data information of the automated production line of electronically controlled firmware in real time; judging whether the operating status data information has an abnormality based on preset standard operating status data information; if an abnormality exists, determining the abnormal data information of the operating status based on the standard operating status data information; scoring each preset fault type based on the abnormal data information and preset scoring rules, and obtaining the score corresponding to each fault type; obtaining the probability of occurrence of each fault type based on the score of each fault type, and generating the fault diagnosis result of the automated production line of electronically controlled firmware based on the probability of occurrence of each fault type. The method realizes real-time and comprehensive prediction of potential faults on the automated production line of electronically controlled firmware.

Description

Technical Field

[0001] The present application relates to the field of equipment detection technology, and in particular to a fault prediction method and system for an automated production line of electronic control firmware. Background Art

[0002] With the improvement of industrial automation level, electronic control firmware is increasingly used in automated production lines. These production lines achieve efficient product production through complex electronic control systems and firmware drivers. However, due to the complexity of various devices and modules, failures are often inevitable. Existing fault detection methods lack real-time data analysis capabilities and cannot effectively predict potential failures, making it difficult to take effective countermeasures in a timely manner when production problems occur. Summary of the invention

[0003] The present application provides a fault prediction method and system for an automated production line of electronic control firmware to solve the problems raised by the above background technology.

[0004] In a first aspect, the present application provides a fault prediction method for an electronic control firmware automation production line, comprising:

[0005] Obtain the operating status data information of the electronic control firmware automation production line in real time;

[0006] Determining whether the operating status data information is abnormal based on preset standard operating status data information;

[0007] If there is an abnormality, determining abnormal data information of the operating state based on the standard operating state data information;

[0008] Scoring each preset fault type based on the abnormal data information and preset scoring rules to obtain a score corresponding to each fault type;

[0009] The occurrence probability of each of the fault types is obtained based on the score of each of the fault types, and the fault diagnosis result of the electronic control firmware automation production line is generated based on the occurrence probability of each of the fault types.

[0010] In a possible implementation, the operating status data information includes operating values ​​of multiple parameters, the standard operating status data information includes operating threshold ranges of multiple parameters, and judging whether the operating status data information is abnormal based on preset standard operating status data information includes:

[0011] Determine whether the operating value of each parameter is within the corresponding operating threshold range;

[0012] If the operating value of any parameter is not within the corresponding operating threshold range, it is determined that the operating status data information is abnormal.

[0013] In a possible implementation manner, the determining the abnormal data information of the operating state based on the standard operating state data information includes:

[0014] For each of the parameters, if the operating value of the parameter is not within the operating threshold range corresponding to the parameter, determine the operating deviation coefficient of the parameter based on the operating value of the parameter and the operating threshold range corresponding to the parameter, and establish a corresponding relationship between the parameter and the operating deviation coefficient;

[0015] Integrate the corresponding relationships to obtain the abnormal data information;

[0016] Wherein, determining the operation deviation coefficient of the parameter based on the operation value of the parameter and the operation threshold range corresponding to the parameter includes:

[0017] Obtain a first target value by subtracting a minimum operating value from a maximum operating value within the operating threshold range;

[0018] The second target value is obtained by subtracting the endpoint value of the operating threshold range closest to the operating value from the operating value;

[0019] The percentage between the second target value and the first target value is determined as the operation deviation coefficient.

[0020] In a possible implementation, scoring each preset fault type based on the abnormal data information and a preset scoring rule to obtain a score corresponding to each fault type includes:

[0021] For each of the fault types, obtaining theoretical abnormal data information corresponding to the fault type in a database, and generating a score for the fault type based on the theoretical abnormal data information and the abnormal data information;

[0022] The theoretical abnormal data information includes theoretical operation deviation coefficients corresponding to multiple parameters and weights corresponding to the theoretical operation deviation coefficients. The generating of the score of the fault type based on the theoretical abnormal data information and the abnormal data information includes:

[0023] respectively determining whether there are operation deviation coefficients corresponding to the respective theoretical operation deviation coefficients in the abnormal data information;

[0024] If there is any of the theoretical operation deviation coefficients but no corresponding operation deviation coefficient exists in the abnormal data information, determining that the score corresponding to the fault type is 0;

[0025] If each of the theoretical operation deviation coefficients has a corresponding operation deviation coefficient in the abnormal data information, for each of the theoretical operation deviation coefficients, calculating a first absolute value of a difference between the theoretical operation deviation coefficient and the corresponding operation deviation coefficient;

[0026] For each of the first absolute values, subtract the first absolute value from the preset target value to obtain a target difference corresponding to the first absolute value, and calculate the product of the weight of the theoretical operation deviation coefficient corresponding to the first absolute value and the target difference; wherein the target value is greater than the first absolute value;

[0027] The products are added together to obtain the score of the fault type.

[0028] In a possible implementation, the respectively determining whether there are operation deviation coefficients corresponding to the respective theoretical operation deviation coefficients in the abnormal data information includes:

[0029] For each of the theoretical operating deviation coefficients, a target operating deviation coefficient is determined in the abnormal data information based on the parameters corresponding to the theoretical operating deviation coefficient, and it is determined whether the digital characteristics of the target operating deviation coefficient are consistent with the digital characteristics of the operating deviation coefficient. If they are consistent, the target operating deviation coefficient is determined to be the operating deviation coefficient corresponding to the theoretical operating deviation coefficient; wherein the digital characteristics are positive or negative numbers.

[0030] In a possible implementation, for each of the fault types, a method for acquiring theoretical abnormal data information corresponding to the fault type includes:

[0031] Constructing a virtual model of the electronic control firmware automated production line;

[0032] After the fault type is set on the virtual model, the operation of the virtual model is controlled by using the standard control parameter information of the electric control firmware automation production line, and the simulated operation status data information of the virtual model is obtained during the operation of the virtual model;

[0033] Determine standard abnormal data information of the fault type based on the standard operation status data information and the simulated operation status data information; the standard abnormal data information includes a standard abnormal operation value of at least one parameter;

[0034] For each parameter in the standard abnormal data information, determining a theoretical operation deviation coefficient of the parameter based on a standard abnormal operation value of the parameter and an operation threshold range of the parameter;

[0035] Adding the second absolute values ​​of the theoretical operation deviation coefficients of the parameters in the standard abnormal data information to obtain a sum of the absolute values;

[0036] For each parameter in the standard abnormal data information, taking the percentage of the third absolute value of the theoretical operation deviation coefficient of the parameter and the sum of the absolute values ​​as the weight of the theoretical operation deviation coefficient of the parameter;

[0037] Wherein, the determining of the theoretical operation deviation coefficient of the parameter based on the standard abnormal operation value of the parameter and the operation threshold range of the parameter includes:

[0038] Obtain a first target value by subtracting a minimum operating value from a maximum operating value within the operating threshold range;

[0039] The third target value is obtained by subtracting the endpoint value of the operating threshold range closest to the standard abnormal operating value;

[0040] The percentage between the third target value and the first target value is determined as the theoretical operation deviation coefficient.

[0041] In a possible implementation, the method further includes:

[0042] The operating status data information is bound to the fault diagnosis result to obtain a binding relationship, and the binding relationship is stored in a preset database.

[0043] In a second aspect, the present application provides a fault prediction system for an automated production line of electronic control firmware, comprising:

[0044] Acquisition module, used to obtain the operation status data information of the electronic control firmware automation production line in real time;

[0045] A judgment module, used for judging whether the running status data information is abnormal based on preset standard running status data information;

[0046] A determination module, configured to determine abnormal data information of the operating state based on the standard operating state data information if the operating state data information is abnormal;

[0047] A scoring module, used to score each preset fault type based on the abnormal data information and preset scoring rules, and obtain a score corresponding to each fault type;

[0048] A generation module is used to obtain the occurrence probability of each of the fault types based on the score of each of the fault types, and to generate a fault diagnosis result of the electronic control firmware automation production line based on the occurrence probability of each of the fault types.

[0049] The present application provides a fault prediction method and system for an automated production line of electronic control firmware, the method comprising: obtaining the operating status data information of the automated production line of electronic control firmware in real time; judging whether the operating status data information has an abnormality based on the preset standard operating status data information; if an abnormality exists, determining the abnormal data information of the operating status based on the standard operating status data information; scoring each preset fault type based on the abnormal data information and the preset scoring rules, and obtaining the score corresponding to each fault type; obtaining the probability of occurrence of each fault type based on the score of each fault type, and generating the fault diagnosis result of the automated production line of electronic control firmware based on the probability of occurrence of each fault type. The method realizes real-time and comprehensive prediction of potential faults on the automated production line of electronic control firmware, and can troubleshoot potential faults before production problems occur, thereby reducing unnecessary losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0051] Figure 1 A schematic flow chart of a fault prediction method for an automated production line of electronically controlled firmware provided in an embodiment of the present application;

[0052] Figure 2 A schematic block diagram of the structure of a fault prediction system for an automated production line of electronically controlled firmware provided in an embodiment of the present application;

[0053] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0056] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0057] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0059] See also Figure 1 , Figure 1 A flow chart of a fault prediction method for an automated production line of electronically controlled firmware provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the fault prediction method for the electronic control firmware automated production line provided in the embodiment of the present application includes steps S1 to S5.

[0060] Step S1, obtaining the operating status data information of the electronic control firmware automation production line in real time.

[0061] Among them, the operating status data information is obtained through various sensors set on the electronic control firmware automation production line, including but not limited to the temperature, voltage, current, vibration frequency, motor speed, etc. of each device or component on the electronic control firmware automation production line.

[0062] Step S2: judging whether the running status data information is abnormal based on preset standard running status data information.

[0063] The operating status data information includes operating values ​​of multiple parameters, the standard operating status data information includes operating threshold ranges of multiple parameters, and judging whether the operating status data information is abnormal based on the preset standard operating status data information includes the following steps:

[0064] respectively judging whether the operating value of each parameter is within its corresponding operating threshold range; specifically, for each parameter, determining the target operating threshold range of the parameter in the standard operating status data information, and judging whether the operating value of the parameter is within the target operating threshold range;

[0065] If the operating value of any parameter is not within the corresponding operating threshold range, it is determined that the operating status data information is abnormal;

[0066] If the operating values ​​of the various parameters are within the corresponding operating threshold ranges, it is determined that there is no abnormality in the operating status data information.

[0067] Step S3: If there is an abnormality, determine the abnormal data information of the operating state based on the standard operating state data information.

[0068] Wherein, determining the abnormal data information of the operating state based on the standard operating state data information comprises the following steps:

[0069] For each of the parameters, if the operating value of the parameter is not within the operating threshold range corresponding to the parameter, determine the operating deviation coefficient of the parameter based on the operating value of the parameter and the operating threshold range corresponding to the parameter, and establish a corresponding relationship between the parameter and the operating deviation coefficient;

[0070] Integrate the corresponding relationships to obtain the abnormal data information;

[0071] Wherein, determining the operation deviation coefficient of the parameter based on the operation value of the parameter and the operation threshold range corresponding to the parameter includes:

[0072] Obtain a first target value by subtracting a minimum operating value from a maximum operating value within the operating threshold range;

[0073] The second target value is obtained by subtracting the endpoint value of the operating threshold range closest to the operating value from the operating value; specifically, the relationship between the operating value and the operating threshold range is determined; if the operating value is less than the minimum operating value of the operating threshold range, the second target value is obtained by subtracting the minimum operating value of the operating threshold range from the operating value, and at this time, the second target value is a negative number; if the operating value is greater than the maximum operating value of the operating threshold range, the second target value is obtained by subtracting the maximum operating value of the operating threshold range from the operating value, and at this time, the second target value is a positive number;

[0074] Determine the percentage between the second target value and the first target value as the operation deviation coefficient; specifically, divide the second target value by the first target value and multiply by 100% to obtain the operation deviation coefficient.

[0075] Step S4: scoring each preset fault type based on the abnormal data information and preset scoring rules to obtain a score corresponding to each fault type.

[0076] Specifically, step S4 includes the following steps:

[0077] For each of the fault types, obtaining theoretical abnormal data information corresponding to the fault type in a database, and generating a score for the fault type based on the theoretical abnormal data information and the abnormal data information;

[0078] The theoretical abnormal data information includes theoretical operation deviation coefficients corresponding to multiple parameters and weights corresponding to the theoretical operation deviation coefficients. The method of generating the score of the fault type based on the theoretical abnormal data information and the abnormal data information includes the following steps:

[0079] Determine respectively whether there is an operation deviation coefficient corresponding to each of the theoretical operation deviation coefficients in the abnormal data information; specifically, for each of the theoretical operation deviation coefficients, determine a target operation deviation coefficient in the abnormal data information based on a parameter corresponding to the theoretical operation deviation coefficient, and determine whether a digital characteristic of the target operation deviation coefficient is consistent with a digital characteristic of the operation deviation coefficient; if they are consistent, determine that the target operation deviation coefficient is the operation deviation coefficient corresponding to the theoretical operation deviation coefficient; wherein the digital characteristic is a positive number or a negative number;

[0080] If there is any of the theoretical operation deviation coefficients but no corresponding operation deviation coefficient exists in the abnormal data information, determining that the score corresponding to the fault type is 0;

[0081] If each of the theoretical operation deviation coefficients has a corresponding operation deviation coefficient in the abnormal data information, for each of the theoretical operation deviation coefficients, calculating a first absolute value of a difference between the theoretical operation deviation coefficient and the corresponding operation deviation coefficient;

[0082] For each of the first absolute values, subtract the first absolute value from the preset target value to obtain a target difference corresponding to the first absolute value, and calculate the product of the weight of the theoretical operation deviation coefficient corresponding to the first absolute value and the target difference; wherein the target value is greater than the first absolute value;

[0083] The products are added together to obtain the score of the fault type.

[0084] Wherein, for each of the fault types, a method for obtaining theoretical abnormal data information corresponding to the fault type comprises the following steps:

[0085] Constructing a virtual model of the electronic control firmware automated production line;

[0086] After the fault type is set on the virtual model, the operation of the virtual model is controlled by using the standard control parameter information of the electric control firmware automation production line, and the simulated operation status data information of the virtual model is obtained during the operation of the virtual model;

[0087] Determine the standard abnormal data information of the fault type based on the standard operation status data information and the simulated operation status data information; the standard abnormal data information includes a standard abnormal operation value of at least one parameter; specifically, for each parameter in the simulated operation status data, determine whether the simulated operation value of the parameter is within the corresponding operation threshold range; if the simulated operation value of the parameter is not within the operation threshold range corresponding to the parameter, determine that the simulated operation value corresponding to the parameter is a standard abnormal operation value;

[0088] For each parameter in the standard abnormal data information, determining a theoretical operation deviation coefficient of the parameter based on a standard abnormal operation value of the parameter and an operation threshold range of the parameter;

[0089] Adding the second absolute values ​​of the theoretical operation deviation coefficients of the parameters in the standard abnormal data information to obtain a sum of the absolute values;

[0090] For each parameter in the standard abnormal data information, the percentage of the third absolute value of the theoretical operation deviation coefficient of the parameter and the sum of the absolute values ​​is used as the weight of the theoretical operation deviation coefficient of the parameter; the third absolute value of the theoretical operation deviation coefficient of the parameter is divided by the sum of the absolute values ​​and then multiplied by 100% to obtain the weight of the theoretical operation deviation coefficient of the parameter;

[0091] Wherein, the determining of the theoretical operation deviation coefficient of the parameter based on the standard abnormal operation value of the parameter and the operation threshold range of the parameter includes:

[0092] Obtain a first target value by subtracting a minimum operating value from a maximum operating value within the operating threshold range;

[0093] The third target value is obtained by subtracting the endpoint value of the operating threshold range closest to the standard abnormal operating value; specifically, the relationship between the standard abnormal operating value and the operating threshold range is determined; if the standard abnormal operating value is less than the minimum operating value of the operating threshold range, the third target value is obtained by subtracting the minimum operating value of the operating threshold range from the standard abnormal operating value, and at this time, the third target value is a negative number; if the standard abnormal operating value is greater than the maximum operating value of the operating threshold range, the third target value is obtained by subtracting the maximum operating value of the operating threshold range from the standard abnormal operating value, and at this time, the third target value is a positive number;

[0094] The percentage between the third target value and the first target value is determined as the theoretical operation deviation coefficient; specifically, the third target value is divided by the first target value and then multiplied by 100% to obtain the operation deviation coefficient.

[0095] Step S5: acquiring the occurrence probability of each of the fault types based on the scores of each of the fault types, and generating a fault diagnosis result of the electronic control firmware automation production line based on the occurrence probability of each of the fault types.

[0096] Wherein, obtaining the occurrence probability of each of the fault types based on the score of each of the fault types comprises the following steps:

[0097] Adding the scores of each fault type to obtain a sum of the scores;

[0098] For each of the fault types, a ratio of the score of the fault type to the sum of the scores is determined as the occurrence probability of the fault type.

[0099] The present embodiment provides a method, which obtains the operating status data information of the electronic control firmware automation production line in real time, and when there is an abnormality in the operating status data information, scores each preset fault type respectively according to the abnormal information of the operating status data information and a preset scoring rule, obtains the score corresponding to each fault type, and obtains the occurrence probability of each fault type based on the score of each fault type, thereby realizing real-time and comprehensive prediction of potential faults on the electronic control firmware automation production line, and being able to troubleshoot potential faults before production problems occur, thereby reducing unnecessary losses.

[0100] In some embodiments, the method further comprises:

[0101] The operating status data information is bound to the fault diagnosis result to obtain a binding relationship, and the binding relationship is stored in a preset database.

[0102] The step of binding the operating status data information with the fault diagnosis result to obtain a binding relationship comprises the following steps:

[0103] Performing matrix encoding on the operating status data information based on a preset matrix encoding rule to obtain a first encoding matrix, and performing matrix encoding on the fault diagnosis result based on the matrix encoding rule to obtain a second encoding matrix; matrix elements of the first encoding matrix and the second encoding matrix both include numbers and letters;

[0104] Randomly generate a first letter vector; the number of first letters in the first letter vector is consistent with the number of columns of the first encoding matrix, each of the first letters is different from each other, and each of the first letters does not exist in the first encoding matrix;

[0105] Multiplying the first encoding matrix by the first letter vector to obtain a first target encoding matrix;

[0106] Randomly generate a second letter vector; the number of second letters in the second letter vector is consistent with the number of columns of the second encoding matrix, each second letter is different from each other, and each second letter does not exist in the second encoding matrix;

[0107] Multiplying the second encoding matrix by the second letter vector to obtain a second target encoding matrix;

[0108] The first target coding matrix and the second target coding matrix are bound to obtain the binding relationship.

[0109] The method provided in this embodiment, on the one hand, realizes modular management of the operating status data information and the fault diagnosis results, which is convenient for subsequent data retrieval, analysis and tracking; on the other hand, in the process of binding the operating status data information and the fault diagnosis results, it realizes automatic encryption of the operating status data information and the fault diagnosis results, which helps to improve data security; on still another hand, storing the binding relationship helps to generate a fault mode library, thereby helping to improve the intelligence of fault prediction.

[0110] See also Figure 2 , Figure 2 A schematic block diagram of the structure of a fault prediction system 100 for an automated production line of electronically controlled firmware provided in an embodiment of the present application, such as Figure 2 As shown, the fault prediction system 100 of the electric control firmware automatic production line provided in the embodiment of the present application includes:

[0111] The acquisition module 110 is used to acquire the operation status data information of the electric control firmware automation production line in real time.

[0112] The judgment module 120 is used to judge whether the running status data information is abnormal based on preset standard running status data information.

[0113] The determination module 130 is configured to determine abnormal data information of the operating state based on the standard operating state data information if the operating state data information is abnormal.

[0114] The scoring module 140 is used to score each preset fault type based on the abnormal data information and preset scoring rules to obtain a score corresponding to each fault type.

[0115] The generating module 150 is used to obtain the occurrence probability of each of the fault types based on the score of each of the fault types, and to generate the fault diagnosis result of the electronic control firmware automatic production line based on the occurrence probability of each of the fault types.

[0116] It should be noted that technical personnel in the relevant technical field can clearly understand that, for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned fault prediction method embodiment of the electronic control firmware automation production line, and will not be repeated here.

[0117] The fault prediction system 100 for the electric control firmware automatic production line provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in Figure 3 The system is run on the terminal device 200 shown.

[0118] See also Figure 3 , Figure 3 The present invention provides a schematic block diagram of the structure of a terminal device 200 according to an embodiment of the present application. The terminal device 200 includes a processor 201 and a memory 202. The processor 201 and the memory 202 are connected via a device bus 203, wherein the memory 202 may include a non-volatile storage medium and an internal memory.

[0119] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 201, the processor 201 can execute any of the above-mentioned fault prediction methods for the automatic production line of electric control firmware.

[0120] The processor 201 is used to provide computing and control capabilities to support the operation of the entire terminal device 200.

[0121] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 201, the processor 201 can execute any of the above-mentioned fault prediction methods for the automatic production line of electronic control firmware.

[0122] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the terminal device 200 involved in the scheme of the present application. The specific terminal device 200 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0123] It should be understood that the processor 201 may be a central processing unit (CPU), and the processor 201 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0124] In some embodiments, the processor 201 is used to run a computer program stored in the memory to implement the following steps:

[0125] Obtain the operating status data information of the electronic control firmware automation production line in real time;

[0126] Determining whether the operating status data information is abnormal based on preset standard operating status data information;

[0127] If there is an abnormality, determining abnormal data information of the operating state based on the standard operating state data information;

[0128] Scoring each preset fault type based on the abnormal data information and preset scoring rules to obtain a score corresponding to each fault type;

[0129] The occurrence probability of each of the fault types is obtained based on the score of each of the fault types, and the fault diagnosis result of the electronic control firmware automation production line is generated based on the occurrence probability of each of the fault types.

[0130] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the terminal device 200 described above can refer to the corresponding process of the fault prediction method of the aforementioned electronic control firmware automation production line, and will not be repeated here.

[0131] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the fault prediction method for the electronic control firmware automation production line provided in the embodiment of the present application.

[0132] The computer-readable storage medium may be an internal storage unit of the terminal device 200 in the aforementioned embodiment, such as a hard disk or memory of the terminal device 200. The computer-readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the terminal device 200.

[0133] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A fault prediction method for an automated production line of electronic control firmware, characterized in that: include: Obtain the operating status data information of the electronic control firmware automation production line in real time; Determining whether the operating status data information is abnormal based on preset standard operating status data information; If there is an abnormality, determining abnormal data information of the operating state based on the standard operating state data information; Scoring each preset fault type based on the abnormal data information and preset scoring rules to obtain a score corresponding to each fault type; Obtaining the probability of occurrence of each of the fault types based on the scores of each of the fault types, and generating a fault diagnosis result of the electronic control firmware automation production line based on the probability of occurrence of each of the fault types; The step of scoring each preset fault type based on the abnormal data information and the preset scoring rules to obtain the score corresponding to each fault type includes: For each of the fault types, obtaining theoretical abnormal data information corresponding to the fault type in a database, and generating a score for the fault type based on the theoretical abnormal data information and the abnormal data information; The theoretical abnormal data information includes theoretical operation deviation coefficients corresponding to multiple parameters and weights corresponding to each of the theoretical operation deviation coefficients, the abnormal data information includes operation deviation coefficients corresponding to multiple parameters, and the scoring of the fault type generated based on the theoretical abnormal data information and the abnormal data information includes: respectively determining whether there are operation deviation coefficients corresponding to the respective theoretical operation deviation coefficients in the abnormal data information; If there is any of the theoretical operation deviation coefficients but no corresponding operation deviation coefficient exists in the abnormal data information, determining that the score corresponding to the fault type is 0; If each of the theoretical operation deviation coefficients has a corresponding operation deviation coefficient in the abnormal data information, for each of the theoretical operation deviation coefficients, calculating a first absolute value of a difference between the theoretical operation deviation coefficient and the corresponding operation deviation coefficient; For each of the first absolute values, subtract the first absolute value from the preset target value to obtain a target difference corresponding to the first absolute value, and calculate the product of the weight of the theoretical operation deviation coefficient corresponding to the first absolute value and the target difference; wherein the target value is greater than the first absolute value; The products are added together to obtain the score of the fault type.

2. The fault prediction method for the electronic control firmware automatic production line according to claim 1, characterized in that: The operation status data information includes operation values ​​of multiple parameters, the standard operation status data information includes operation threshold ranges of multiple parameters, and judging whether the operation status data information is abnormal based on preset standard operation status data information includes: Determine whether the operating value of each parameter is within the corresponding operating threshold range; If the operating value of any parameter is not within the corresponding operating threshold range, it is determined that the operating status data information is abnormal.

3. The fault prediction method for the electronic control firmware automatic production line according to claim 2, characterized in that: The determining of the abnormal data information of the operating state based on the standard operating state data information includes: For each of the parameters, if the operating value of the parameter is not within the operating threshold range corresponding to the parameter, determine the operating deviation coefficient of the parameter based on the operating value of the parameter and the operating threshold range corresponding to the parameter, and establish a corresponding relationship between the parameter and the operating deviation coefficient; Integrate the corresponding relationships to obtain the abnormal data information; Wherein, determining the operation deviation coefficient of the parameter based on the operation value of the parameter and the operation threshold range corresponding to the parameter includes: Obtain a first target value by subtracting a minimum operating value from a maximum operating value within the operating threshold range; The second target value is obtained by subtracting the endpoint value of the operating threshold range closest to the operating value from the operating value; The percentage between the second target value and the first target value is determined as the operation deviation coefficient.

4. The fault prediction method for the electronic control firmware automatic production line according to claim 1, characterized in that: The respectively judging whether there are operation deviation coefficients corresponding to the respective theoretical operation deviation coefficients in the abnormal data information includes: For each of the theoretical operating deviation coefficients, a target operating deviation coefficient is determined in the abnormal data information based on the parameters corresponding to the theoretical operating deviation coefficient, and it is determined whether the digital characteristics of the target operating deviation coefficient are consistent with the digital characteristics of the operating deviation coefficient. If they are consistent, the target operating deviation coefficient is determined to be the operating deviation coefficient corresponding to the theoretical operating deviation coefficient; wherein the digital characteristics are positive or negative numbers.

5. The fault prediction method for the electronic control firmware automatic production line according to claim 1, characterized in that: For each of the fault types, a method for obtaining theoretical abnormal data information corresponding to the fault type includes: Constructing a virtual model of the electronic control firmware automated production line; After the fault type is set on the virtual model, the operation of the virtual model is controlled by using the standard control parameter information of the electric control firmware automation production line, and the simulated operation status data information of the virtual model is obtained during the operation of the virtual model; Determine standard abnormal data information of the fault type based on the standard operation status data information and the simulated operation status data information; the standard abnormal data information includes a standard abnormal operation value of at least one parameter; For each parameter in the standard abnormal data information, determining a theoretical operation deviation coefficient of the parameter based on a standard abnormal operation value of the parameter and an operation threshold range of the parameter; Adding the second absolute values ​​of the theoretical operation deviation coefficients of the parameters in the standard abnormal data information to obtain a sum of the absolute values; For each parameter in the standard abnormal data information, taking the percentage of the third absolute value of the theoretical operation deviation coefficient of the parameter and the sum of the absolute values ​​as the weight of the theoretical operation deviation coefficient of the parameter; Wherein, the determining of the theoretical operation deviation coefficient of the parameter based on the standard abnormal operation value of the parameter and the operation threshold range of the parameter includes: Obtain a first target value by subtracting a minimum operating value from a maximum operating value within the operating threshold range; The third target value is obtained by subtracting the endpoint value of the operating threshold range closest to the standard abnormal operating value; The percentage between the third target value and the first target value is determined as the theoretical operation deviation coefficient.

6. The fault prediction method for the electronic control firmware automatic production line according to claim 1, characterized in that: The method further comprises: The operating status data information is bound to the fault diagnosis result to obtain a binding relationship, and the binding relationship is stored in a preset database.

7. A fault prediction system for an automated production line of electronic control firmware, characterized in that: include: Acquisition module, used to obtain the operation status data information of the electronic control firmware automation production line in real time; A judgment module, used for judging whether the running status data information is abnormal based on preset standard running status data information; A determination module, configured to determine abnormal data information of the operating state based on the standard operating state data information if the operating state data information is abnormal; A scoring module, used to score each preset fault type based on the abnormal data information and preset scoring rules, and obtain a score corresponding to each fault type; A generating module, configured to obtain the occurrence probability of each of the fault types based on the score of each of the fault types, and generate a fault diagnosis result of the electronic control firmware automation production line based on the occurrence probability of each of the fault types; The step of scoring each preset fault type based on the abnormal data information and the preset scoring rules to obtain the score corresponding to each fault type includes: For each of the fault types, obtaining theoretical abnormal data information corresponding to the fault type in a database, and generating a score for the fault type based on the theoretical abnormal data information and the abnormal data information; The theoretical abnormal data information includes theoretical operation deviation coefficients corresponding to multiple parameters and weights corresponding to each of the theoretical operation deviation coefficients, the abnormal data information includes operation deviation coefficients corresponding to multiple parameters, and the scoring of the fault type generated based on the theoretical abnormal data information and the abnormal data information includes: respectively determining whether there are operation deviation coefficients corresponding to the respective theoretical operation deviation coefficients in the abnormal data information; If there is any of the theoretical operation deviation coefficients but no corresponding operation deviation coefficient exists in the abnormal data information, determining that the score corresponding to the fault type is 0; If each of the theoretical operation deviation coefficients has a corresponding operation deviation coefficient in the abnormal data information, for each of the theoretical operation deviation coefficients, calculating a first absolute value of a difference between the theoretical operation deviation coefficient and the corresponding operation deviation coefficient; For each of the first absolute values, subtract the first absolute value from the preset target value to obtain a target difference corresponding to the first absolute value, and calculate the product of the weight of the theoretical operation deviation coefficient corresponding to the first absolute value and the target difference; wherein the target value is greater than the first absolute value; The products are added together to obtain the score of the fault type.

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