Workshop equipment management method and system based on large model, terminal and storage medium

Abstract

The invention relates to a workshop equipment management method and system based on a large model, a terminal and a storage medium, and relates to the technical field of equipment management, and the method comprises the steps: obtaining a starting trigger signal of workshop equipment; acquiring equipment operation parameters and historical operation parameters in real time based on the starting trigger signal; analyzing the equipment operation parameters and the historical operation parameters to determine an equipment operation state; judging whether the equipment operation state meets the requirement of a normal operation state or not; if yes, continuing to obtain the equipment operation parameters and the historical operation parameters, and continuing to analyze the equipment operation parameters and the historical operation parameters to determine the equipment operation state for circular judgment; if not, controlling the large model to analyze the equipment operation parameters so as to determine the equipment fault reason; analyzing the equipment fault reason to determine an equipment maintenance plan; and distributing the equipment maintenance plan to maintenance personnel to maintain the workshop equipment. The method has the effect of improving the management efficiency of the workshop equipment.

Description

Technical Field

[0001] The present application relates to the field of equipment management technology, and in particular to a workshop equipment management method, system, terminal and storage medium based on a large model. Background Art

[0002] In modern industrial production, workshop equipment management is the core link to ensure production efficiency and normal operation of equipment.

[0003] In related technologies, workshop equipment usually has its own fault detection mechanism, which monitors various real-time data of the workshop equipment through sensors. When these data exceed the safe range, the corresponding fault code will be triggered. The workshop equipment will display these fault codes on a visual interface for maintenance personnel to view and repair.

[0004] Regarding the above-mentioned related technologies, the fault detection mechanism of workshop equipment can only rely on the set rules to operate. Faced with the complex dynamic environment in the workshop, once the fault of the workshop equipment is no longer within the set fault code, the workshop equipment cannot provide real-time management and decision support, resulting in low management efficiency of the workshop equipment. There is still room for improvement. Summary of the invention

[0005] In order to improve the management efficiency of workshop equipment, the present application provides a workshop equipment management method, system, terminal and storage medium based on a large model.

[0006] In the first aspect, the present application provides a workshop equipment management method based on a large model, which adopts the following technical solution: The workshop equipment management method based on the big model includes: Obtain the start trigger signal of the workshop equipment; Based on the start trigger signal, the equipment operating parameters and historical operating parameters of the workshop equipment are obtained in real time; Analyze equipment operating parameters and historical operating parameters to determine the equipment operating status of workshop equipment; Determine whether the equipment operation status meets the preset normal operation status requirements; If it is in compliance, then continue to obtain the equipment operating parameters and historical operating parameters, and continue to analyze the equipment operating parameters and historical operating parameters to determine the equipment operating status of the workshop equipment for cyclic judgment; If it does not meet the requirements, the preset large model is controlled to analyze the equipment operating parameters to determine the cause of the equipment failure; Analyze the causes of equipment failure to determine equipment maintenance plan; Distribute equipment maintenance plans to preset maintenance personnel to repair workshop equipment.

[0007] By adopting the above technical solution, after the workshop equipment is started, the equipment operating parameters and historical operating parameters are detected, and the equipment operating status is determined after analysis. When the equipment operating status does not meet the requirements of the normal operating status, the control model infers the cause of the equipment failure based on the equipment operating parameters and determines the equipment maintenance plan, thereby preventing the workshop equipment from failing to alarm when there is a fault without a fault code, thereby improving the management efficiency of the workshop equipment.

[0008] Optionally, the step of analyzing the equipment operating parameters and historical operating parameters to determine the equipment operating status of the workshop equipment includes: Obtain normal operating parameters of workshop equipment based on equipment operating parameters; Determine whether the equipment operating parameters meet the requirements of normal operating parameters; If it does not meet the requirements, the preset equipment abnormal state is defined as the equipment operation state of the workshop equipment; If it meets the requirements, the equipment operation status of the workshop equipment is determined according to the preset parameter change prediction method.

[0009] By adopting the above technical solution, the equipment operating parameters and normal operating parameters are compared. If the equipment operating parameters are not within the range of normal operating parameters, the workshop equipment must be in an abnormal state at this time, so the abnormal state of the equipment is defined as the equipment operating state; if it is within the range of normal operating parameters, it is necessary to further analyze the equipment operating parameters to determine the equipment operating state, thereby improving the convenience and accuracy of determining the equipment operating state.

[0010] Optionally, the step of determining the equipment operating status of the workshop equipment according to a preset parameter change prediction method includes: Obtain parameter deviation range values ​​based on equipment operating parameters; Analyze the equipment operating parameters and historical operating parameters to determine the operating parameter deviation value; Determine whether the operating parameter deviation value meets the parameter deviation range value requirements; If it is in compliance, the preset normal state of the equipment is defined as the equipment operation state of the workshop equipment; If it does not meet the requirements, the preset equipment abnormal state is defined as the equipment operation state of the workshop equipment.

[0011] By adopting the above technical solution, the equipment operating parameters and historical operating parameters are analyzed to determine the operating parameter deviation value, and the operating parameter deviation value is compared with the parameter deviation range value. If the operating parameter deviation value exceeds the parameter deviation range value, it indicates that the workshop equipment is developing towards an abnormal state. Therefore, the abnormal state of the equipment is defined as the equipment operating state, thereby improving the accuracy of determining the equipment operating state.

[0012] Optionally, the steps of controlling the preset large model to analyze the equipment operating parameters to determine the cause of the equipment failure include: Determine whether the equipment operating parameters meet the requirements of the preset fault code parameters; If it meets the requirements, the equipment operating parameters are analyzed to determine the equipment fault code; Determine the cause of equipment failure based on the equipment failure code and the preset code failure relationship; If it does not meet the requirements, the cause of the equipment failure will be predicted based on the equipment operating parameter control model.

[0013] By adopting the above technical scheme, when the equipment operating parameters meet the requirements of the fault code parameters, the equipment fault code is obtained after analyzing the equipment operating parameters, so as to find the cause of the equipment failure in the code fault relationship according to the equipment fault code; when it does not meet the requirements of the fault code parameters, the large model is directly controlled to predict the cause of the equipment failure, thereby improving the convenience and accuracy of determining the cause of the equipment failure.

[0014] Optionally, the steps of predicting the cause of equipment failure according to the equipment operation parameter control model include: Obtain predicted operating parameters of workshop equipment based on equipment operating parameters; Determine whether the equipment operating parameters meet the requirements of the predicted operating parameters; If it does, the equipment operating parameters are input into the large model for reasoning to determine the cause of the equipment failure; If it does not meet the requirements, the large model is controlled according to the preset operating parameter prediction method to predict the cause of the equipment failure.

[0015] By adopting the above technical scheme, when the equipment operating parameters meet the requirements of the predicted operating parameters, it indicates that the equipment operating parameters themselves are abnormal. Therefore, the equipment operating parameters are input into the large model to infer the cause of the equipment failure. When the equipment operating parameters do not meet the requirements of the predicted operating parameters, the large model is controlled according to the operating parameter prediction method to predict the cause of the equipment failure, thereby improving the accuracy of determining the cause of the equipment failure.

[0016] Optionally, the step of controlling the large model to predict the cause of the equipment failure according to the preset operating parameter prediction method includes: Obtain the actual change trend of equipment operating parameters; Analyze the actual parameter change trend, equipment operating parameters and predicted operating parameters to determine the predicted abnormal operating parameters; The predicted abnormal operation parameters are input into the large model for prediction to determine the cause of equipment failure.

[0017] By adopting the above technical scheme, the actual parameter change trend, equipment operating parameters and predicted operating parameters are analyzed to determine the predicted abnormal operating parameters after the development of the equipment operating parameters, and then the predicted abnormal operating parameters are input into the large model to predict the cause of the equipment failure, so as to prevent the equipment operating parameters from being temporarily normal, resulting in the large model being unable to infer the cause of the failure, thereby improving the accuracy of the cause of the equipment failure.

[0018] Optionally, the steps of analyzing the cause of equipment failure to determine the equipment maintenance plan include: Determine the basic maintenance plan based on the cause of equipment failure and the preset failure maintenance relationship; A fault determination method for obtaining the cause of equipment failure; Determine whether the fault determination method meets the requirements of the preset fault prediction type; If not, the basic maintenance plan is defined as the equipment maintenance plan; If it is in compliance, the equipment preventive maintenance plan is determined based on the equipment failure cause and the preset failure prevention relationship; Obtain parameter change trends and maintenance operation parameters; Analyze equipment operating parameters, parameter change trends and maintenance operating parameters to determine equipment maintenance time; Link the basic maintenance plan and equipment maintenance time to generate the actual maintenance plan; Link the equipment preventive maintenance plan and actual maintenance plan to generate an equipment maintenance plan.

[0019] By adopting the above technical scheme, the basic maintenance plan is found in the fault maintenance relationship according to the cause of the equipment failure. When the fault determination method does not meet the requirements of the fault prediction type, the basic maintenance plan is directly defined as the equipment maintenance plan. When it meets the requirements, it indicates that the equipment has not failed yet. Therefore, the equipment preventive maintenance plan is found in the fault prevention relationship according to the cause of the equipment failure, so that the maintenance personnel first maintain the workshop equipment, and then perform maintenance according to the actual maintenance plan, thereby improving the accuracy of the equipment maintenance plan.

[0020] In the second aspect, the present application provides a workshop equipment management system based on a large model, which adopts the following technical solutions: The workshop equipment management system based on the large model includes: An acquisition module is used to acquire a start trigger signal, equipment operating parameters and historical operating parameters; A memory, used to store the program of the workshop equipment management method based on the large model; The program in the processor and the memory can be loaded and executed by the processor to implement the above-mentioned workshop equipment management method based on the large model.

[0021] By adopting the above technical solution, the processor loads and executes the program of the workshop equipment management method based on the big model stored in the memory, so that the acquisition module obtains a series of data related to the workshop equipment management based on the big model, so that after the workshop equipment is started, the equipment operating parameters and historical operating parameters are detected, and the equipment operating status is determined after analysis. When the equipment operating status does not meet the requirements of the normal operating status, the control big model infers the cause of the equipment failure according to the equipment operating parameters, and determines the equipment maintenance plan, so as to prevent the workshop equipment from failing to alarm when there is a fault without a fault code, thereby improving the management efficiency of the workshop equipment.

[0022] In a third aspect, the present application provides a smart terminal, which adopts the following technical solution: An intelligent terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes any of the above-mentioned large model-based workshop equipment management methods.

[0023] By adopting the above technical solution, by operating the intelligent terminal, the processor loads and executes the computer program stored in the memory, so that after the workshop equipment is started, the equipment operating parameters and historical operating parameters are detected, and the equipment operating status is determined after analysis. When the equipment operating status does not meet the requirements of the normal operating status, the control large model infers the cause of the equipment failure based on the equipment operating parameters and determines the equipment maintenance plan, thereby preventing the workshop equipment from being unable to alarm when there is a fault without a fault code, thereby improving the management efficiency of the workshop equipment.

[0024] In a fourth aspect, the present application provides a computer storage medium capable of storing corresponding programs, which is convenient for improving the management efficiency of workshop equipment and adopts the following technical solutions: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the above-mentioned workshop equipment management method based on a large model.

[0025] By adopting the above technical solution, a computer program of a workshop equipment management method based on a large model is stored in a computer-readable storage medium, so that the processor loads and executes the computer program in the storage medium, so that after the workshop equipment is started, the equipment operating parameters and historical operating parameters are detected, and the equipment operating status is determined after analysis. When the equipment operating status does not meet the requirements of the normal operating status, the large model is controlled to infer the cause of the equipment failure according to the equipment operating parameters, and determine the equipment maintenance plan, so as to prevent the workshop equipment from failing to alarm when there is a fault without a fault code, thereby improving the management efficiency of the workshop equipment.

[0026] In summary, the present application includes at least one of the following beneficial technical effects: After the workshop equipment is started, the equipment operating parameters and historical operating parameters are detected and analyzed to determine the equipment operating status. When the equipment operating status does not meet the requirements of the normal operating status, the control model infers the cause of the equipment failure based on the equipment operating parameters and determines the equipment maintenance plan, thereby preventing the workshop equipment from failing to alarm when there is no fault code, thereby improving the management efficiency of the workshop equipment; When the equipment operating parameters meet the requirements of the fault code parameters, the equipment operating parameters are analyzed to obtain the equipment fault code, so as to find the cause of the equipment fault in the code fault relationship according to the equipment fault code. When the equipment operating parameters do not meet the requirements of the fault code parameters, the large model is directly controlled to predict the cause of the equipment fault, thereby improving the convenience and accuracy of determining the cause of the equipment fault; By analyzing the actual change trend of parameters, equipment operating parameters and predicted operating parameters, the predicted abnormal operating parameters after the development of the equipment operating parameters are determined, and then the predicted abnormal operating parameters are input into the large model to predict the cause of the equipment failure, so as to prevent the equipment operating parameters from being temporarily normal, resulting in the large model being unable to infer the cause of the failure, thereby improving the accuracy of the cause of the equipment failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the workshop equipment management method based on the large model in an embodiment of the present application.

[0028] Figure 2 It is a flowchart of the steps of analyzing equipment operating parameters and historical operating parameters in an embodiment of the present application to determine the equipment operating status of the workshop equipment.

[0029] Figure 3 It is a flowchart of the steps of determining the equipment operating status of workshop equipment according to a preset parameter change prediction method in an embodiment of the present application.

[0030] Figure 4 It is a flowchart of the steps of controlling a preset large model in an embodiment of the present application to analyze equipment operating parameters to determine the cause of equipment failure.

[0031] Figure 5 It is a flowchart of the steps of predicting the cause of equipment failure based on the equipment operating parameter control model in an embodiment of the present application.

[0032] Figure 6 It is a flowchart of the steps of controlling a large model to predict the cause of equipment failure according to a preset operating parameter prediction method in an embodiment of the present application.

[0033] Figure 7 It is a flowchart of the steps of analyzing the cause of equipment failure in an embodiment of the present application to determine an equipment maintenance plan. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-Figure 7 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0035] The embodiment of the present application discloses a workshop equipment management method based on a large model, and specifically discloses an Internet of Things sensor, a SCADA system and a processing terminal. The Internet of Things sensor, the SCADA system and the processing terminal are connected through data wires and the Internet of Things to achieve data interaction and control. When the processing terminal receives a start trigger signal of the workshop equipment, the processing terminal controls the Internet of Things sensor and the SCADA system to collect the equipment operating parameters and call the historical operating parameters. The processing terminal determines the equipment operating status after analyzing the equipment operating parameters and the historical operating parameters. When the equipment operating status does not meet the requirements of the normal operating status, the processing terminal controls the large model to infer the cause of the equipment failure according to the equipment operating parameters, and then determines the equipment maintenance plan according to the cause of the equipment failure, so as to distribute the equipment maintenance plan to the maintenance personnel to remind them to repair the workshop equipment, thereby preventing the failure of alarm prompts due to faults other than fault codes in the workshop equipment, thereby improving the management efficiency of the workshop equipment.

[0036] Reference Figure 1 The present application embodiment discloses a workshop equipment management method based on a large model, comprising the following steps: Step S100: Acquire a start trigger signal of a workshop device.

[0037] Among them, the start trigger signal refers to the signal for starting the workshop equipment. When the personnel turn on the workshop equipment, the subsystem of the workshop equipment sends the signal to the processing terminal.

[0038] Step S101: acquiring equipment operating parameters and historical operating parameters of workshop equipment in real time based on a start trigger signal.

[0039] Among them, equipment operating parameters refer to the operating parameters of workshop equipment during operation, such as temperature, vibration, noise, etc., which are collected by IoT sensors and SCADA systems, and the collected data are pre-processed, aligned, synchronized, feature extracted and data fused through multi-source data fusion technology. Historical operating parameters refer to the parameters of previous workshop equipment operation, which are called by the processing terminal in the operating parameter database.

[0040] Step S102: Analyze the equipment operating parameters and historical operating parameters to determine the equipment operating status of the workshop equipment.

[0041] The equipment operation status refers to the state of the workshop equipment during operation, including normal state and abnormal state, which is determined by the processing terminal after analyzing the equipment operation parameters and historical operation parameters. The specific analysis method is referred to Figure 3 steps.

[0042] Step S103: Determine whether the equipment operation state meets the preset normal operation state requirement.

[0043] Among them, the normal operating state refers to the state in which the workshop equipment is in normal operation, and the normal operating state requirement refers to being consistent with the normal operating state.

[0044] By processing the terminal, it is determined whether the equipment operation status is consistent with the normal operation status, thereby determining whether the current status of the workshop equipment is normal.

[0045] Step S1031: If it is in compliance, continue to obtain the equipment operating parameters and the historical operating parameters, and continue to analyze the equipment operating parameters and the historical operating parameters to determine the equipment operating status of the workshop equipment for cyclic judgment.

[0046] Among them, if the processing terminal determines that the equipment operating status is consistent with the normal operating status, it means that the operating status of the workshop equipment is normal. Therefore, the equipment operating parameters and historical operating parameters continue to be detected and called, and the equipment operating parameters and historical operating parameters continue to be analyzed to determine the equipment operating status, so as to continue to pay attention to changes in the equipment operating status.

[0047] Step S1032: If not, the preset large model is controlled to analyze the equipment operating parameters to determine the cause of the equipment failure.

[0048] If the processing terminal determines that the equipment operation status is inconsistent with the normal operation status, it means that the workshop equipment is in an abnormal state and the workshop equipment may fail. Therefore, the control model analyzes the equipment operation parameters to determine the cause of the equipment failure. For specific methods, refer to Figure 4 steps.

[0049] The big model refers to a general big model, which uses the semantic understanding and associative reasoning capabilities of the general big model for multi-source equipment data combined with the equipment's operating parameters to diagnose the cause of equipment failure. The cause of equipment failure refers to the specific cause of equipment failure, which is inferred by the big model based on the equipment's operating parameters.

[0050] Step S104: Analyze the cause of the equipment failure to determine an equipment maintenance plan.

[0051] Among them, the equipment maintenance plan refers to the maintenance plan for workshop equipment, which is determined by the processing terminal after analyzing the cause of equipment failure. The specific method is referred to Figure 7 steps.

[0052] Step S105: Distribute the equipment maintenance plan to the preset maintenance personnel to repair the workshop equipment.

[0053] Among them, after the processing terminal obtains the equipment maintenance plan, the equipment maintenance plan is distributed to the maintenance personnel, so that the maintenance personnel can repair the workshop equipment in time, and the situation where the workshop equipment failure is not within the fault code range and the workshop equipment cannot alarm will not occur, thereby improving the management efficiency of the workshop equipment.

[0054] Reference Figure 2 , the steps of analyzing the equipment operating parameters and historical operating parameters to determine the equipment operating status of the workshop equipment include: Step S200: obtaining normal operating parameters of workshop equipment based on the equipment operating parameters.

[0055] Among them, the normal operating parameters refer to the parameters of the workshop equipment when it is operating normally, and are obtained by the processing terminal by correspondingly calling the operating parameter database according to the equipment model included in the equipment operating parameters.

[0056] Step S201: Determine whether the equipment operating parameters meet the requirements of normal operating parameters.

[0057] The normal operating parameter requirement refers to being within the parameter range of the normal operating parameter. The processing terminal determines whether the equipment operating parameter is within the parameter range of the normal operating parameter, thereby determining whether the workshop equipment is likely to be in a normal operating state.

[0058] Step S2011: If not, the preset equipment abnormal state is defined as the equipment operation state of the workshop equipment.

[0059] If the processing terminal determines that the equipment operation parameters are not within the parameter range of the normal operation parameters, it indicates that the workshop equipment is definitely not in a normal state, so the abnormal state of the equipment is defined as the equipment operation state.

[0060] The abnormal state of the equipment refers to the state in which the equipment is in abnormal operation, which is stored in the processing terminal by the operator.

[0061] Step S2012: If it is in compliance, the equipment operation status of the workshop equipment is determined according to a preset parameter change prediction method.

[0062] If the processing terminal determines that the equipment operating parameters are within the parameter range of the normal operating parameters, it indicates that the operating parameters of the workshop equipment are normal, but the status of the workshop equipment needs further analysis and determination. Therefore, the equipment operating status of the workshop equipment is determined according to the parameter change prediction method. The specific method is referred to Figure 3 steps.

[0063] The parameter change prediction method refers to a method for determining the operating status of the equipment based on the changes in the equipment operating parameters, which is stored in the processing terminal by the operator.

[0064] Reference Figure 3 The step of determining the equipment operation status of the workshop equipment according to the preset parameter change prediction method includes: Step S300: Obtain parameter deviation range values ​​based on equipment operating parameters.

[0065] The parameter deviation range value refers to the normal range of the deviation value between the current operating parameters and the historical operating parameters, which is obtained by calling the parameter deviation database by the processing terminal according to the device model corresponding to the device operating parameters.

[0066] Step S301: Analyze the equipment operating parameters and historical operating parameters to determine the operating parameter deviation value.

[0067] The operating parameter deviation value refers to the deviation value between the equipment operating parameter and the historical operating parameter, which is obtained by the processing terminal by subtracting the data of the corresponding type of the equipment operating parameter and the historical operating parameter.

[0068] Step S302: Determine whether the operating parameter deviation value meets the parameter deviation range value requirement.

[0069] The parameter deviation range value requirement refers to being within the parameter deviation range value. The processing terminal determines whether the operating parameter deviation value is within the parameter deviation range value, thereby determining whether the operating parameters of the workshop equipment tend to be abnormal.

[0070] Step S3021: If it is in compliance, the preset normal state of the equipment is defined as the equipment operation state of the workshop equipment.

[0071] If the processing terminal determines that the operating parameter deviation value is within the parameter deviation range, it indicates that the change of the operating parameter of the workshop equipment tends to be normal, so the normal state of the equipment is defined as the equipment operating state of the workshop equipment.

[0072] The normal state of the equipment refers to the state in which the equipment is in normal operation, which is stored in the processing terminal by the operator.

[0073] Step S3022: If not, the preset equipment abnormal state is defined as the equipment operation state of the workshop equipment.

[0074] If the processing terminal determines that the operating parameter deviation value exceeds the parameter deviation range value, it indicates that the change of the operating parameter of the workshop equipment tends to be abnormal, so the equipment abnormal state is defined as the equipment operating state of the workshop equipment.

[0075] The abnormal state of the device in this step is consistent with the abnormal state of the device in step S2011, and will not be repeated here.

[0076] Reference Figure 4 The steps of controlling the preset large model to analyze the equipment operating parameters to determine the cause of equipment failure include: Step S400: Determine whether the equipment operating parameters meet the requirements of the preset fault code parameters.

[0077] The fault code parameter refers to the type of operating parameter that carries the fault code, and the requirement for the fault code parameter refers to the consistency of the parameter type corresponding to the fault code parameter.

[0078] The processing terminal determines whether the equipment operation parameters are consistent with the parameter type corresponding to the fault code parameters, thereby determining whether the equipment operation parameters carry the fault code.

[0079] Step S401: If it is met, the equipment operating parameters are analyzed to determine the equipment fault code.

[0080] Among them, if the processing terminal determines that the parameter type corresponding to the equipment operating parameters is consistent with the parameter type corresponding to the fault code parameter, it indicates that the equipment operating parameters carry a fault code. Therefore, the equipment fault code is determined after analyzing the equipment operating parameters, providing data support for the subsequent determination of the cause of the equipment failure.

[0081] The equipment fault code refers to the code indicating the cause of the equipment fault, which is obtained by the processing terminal identifying and calling the code carried by the equipment operating parameters.

[0082] Step S4011: Determine the cause of the equipment failure according to the equipment failure code and the preset code failure relationship.

[0083] The code fault relationship refers to the correspondence between the code and the fault cause. The operator forms a mapping table by matching the fault code with the fault cause one by one. The device fault cause in this step is consistent with the device fault cause in step S1032, and is found by the processing terminal in the mapping table corresponding to the code fault relationship according to the device fault code.

[0084] Step S402: If not, predict the cause of the equipment failure based on the equipment operating parameter control model.

[0085] If the processing terminal determines that the equipment operating parameters and the parameter types corresponding to the fault code parameters are inconsistent, it means that the workshop equipment has a fault other than the fault code. Therefore, the cause of the equipment failure is predicted based on the equipment operating parameter control model. For specific methods, refer to Figure 5 steps.

[0086] Reference Figure 5The steps of predicting the cause of equipment failure based on the equipment operating parameter control model include: Step S500: Obtaining predicted operating parameters of workshop equipment based on the equipment operating parameters.

[0087] Among them, the predicted operating parameters refer to the operating parameters that the large model can directly combine to infer the cause of the fault, that is, the parameters other than the normal operating parameters, which are called from the operating parameter database by the processing terminal according to the equipment model corresponding to the equipment operating parameters.

[0088] Step S501: Determine whether the equipment operating parameters meet the requirements of the predicted operating parameters.

[0089] The requirement of the predicted operating parameters refers to being within the parameter range of the predicted operating parameters. The processing terminal determines whether the equipment operating parameters are within the parameter range of the predicted operating parameters, thereby determining whether the large model can infer the cause of the equipment failure in combination with the equipment operating parameters.

[0090] Step S5011: If it is met, the equipment operating parameters are input into the large model for reasoning to determine the cause of the equipment failure.

[0091] Among them, if the processing terminal determines that the equipment operating parameters are within the parameter range of the predicted operating parameters, it means that the equipment operating parameters are abnormal parameters. Therefore, the large model can infer the cause of the equipment failure in combination with the equipment operating parameters, so that the equipment operating parameters are input into the large model, so that the large model can infer the cause of the equipment failure corresponding to the equipment operating parameters.

[0092] Step S5012: If not, the large model is controlled to predict the cause of the equipment failure according to the preset operation parameter prediction method.

[0093] If the processing terminal determines that the equipment operating parameters are not within the parameter range of the predicted operating parameters, it indicates that the equipment operating parameters are normal operating parameters. Therefore, the large model combined with the equipment operating parameters cannot infer the cause of the equipment failure. Therefore, the large model is controlled according to the operating parameter prediction method to predict the cause of the equipment failure. The specific method is referred to Figure 6 steps.

[0094] The operating parameter prediction method refers to a method of inferring the cause of a fault by predicting operating parameters, which is stored in the processing terminal by the operator.

[0095] Reference Figure 6 The steps of predicting the cause of equipment failure by controlling the large model according to the preset operation parameter prediction method include: Step S600: Obtaining the actual change trend of the equipment operating parameters.

[0096] Among them, the actual change trend of the parameter refers to the change trend of the equipment operation parameter, including two trends of gradually increasing and gradually decreasing. It is determined by the processing terminal after analyzing the size of the historical operation parameters and the equipment operation parameters. If the equipment operation parameters are greater than the historical operation parameters, the actual change trend of the parameter is determined to be gradually increasing, otherwise it is gradually decreasing.

[0097] Step S601: Analyze the actual parameter change trend, equipment operating parameters and predicted operating parameters to determine the predicted abnormal operating parameters.

[0098] Among them, the predicted abnormal operating parameters refer to the abnormal operating parameters of the workshop equipment predicted based on the equipment operating parameters. The processing terminal gradually adjusts the size of the equipment operating parameters according to the actual change trend of the parameters until the equipment operating parameters enter the range corresponding to the predicted operating parameters. At this time, the equipment operating parameters are the predicted abnormal operating parameters.

[0099] Step S602: Input the predicted abnormal operation parameters into the large model for prediction to determine the cause of the equipment failure.

[0100] After the predicted abnormal operation parameters are determined, the predicted abnormal operation parameters are input into the large model, so that the large model can infer the cause of the equipment failure corresponding to the predicted abnormal operation parameters.

[0101] Reference Figure 7 , the steps to analyze the cause of equipment failure and determine the equipment maintenance plan include: Step S700: Determine a basic maintenance plan according to the equipment failure cause and the preset failure maintenance relationship.

[0102] Among them, the fault maintenance relationship refers to the correspondence between the fault cause and the maintenance plan. For example, if the fault cause is motor overheating, the maintenance plan is to replace the motor lubricating oil and check the motor cooling system. The operator matches the fault cause with the maintenance plan one by one to form a mapping table.

[0103] Step S701: Obtain a fault determination method for the cause of the equipment fault.

[0104] Among them, the fault determination method refers to the method of determining the cause of the equipment failure, including prediction and reasoning. When the equipment operating parameters carry a fault code or exceed the normal parameter range, it is inference. When the equipment operating parameters do not exceed the normal parameter range, it is prediction.

[0105] Step S702: Determine whether the fault determination method meets the requirements of the preset fault prediction type.

[0106] The fault prediction type refers to a type in which the fault cause is determined by a prediction method, and the requirement for the fault prediction type refers to consistency with the fault prediction type.

[0107] The processing terminal determines whether the fault determination method is consistent with the fault prediction type, thereby determining whether the workshop equipment is currently damaged.

[0108] Step S7021: If not, the basic maintenance plan is defined as an equipment maintenance plan.

[0109] Among them, if the processing terminal determines that the fault determination method is inconsistent with the fault prediction type, it means that the cause of the fault is inferred and the workshop equipment has failed. Therefore, the basic maintenance plan is defined as the equipment maintenance plan, so that maintenance is carried out immediately.

[0110] Step S7022: If it is in compliance, determine the equipment preventive maintenance plan according to the equipment failure cause and the preset failure prevention relationship.

[0111] Among them, if the processing terminal determines that the fault determination method is consistent with the fault prediction type, it means that the current workshop equipment has not yet failed, but the workshop equipment is developing towards a failure trend. Therefore, the equipment preventive maintenance plan is determined based on the cause of the equipment failure and the fault prevention relationship, providing data support for the subsequent determination of the equipment maintenance plan.

[0112] Fault prevention relationship refers to the correspondence between fault causes and preventive maintenance plans. For example, if the motor is overheated, the preventive maintenance method is to clean the cooling system and reduce the load. The operator forms a mapping table by matching the fault causes with the preventive maintenance plans. Equipment preventive maintenance plan refers to the maintenance method for workshop equipment before the workshop equipment fails. The processing terminal finds it in the mapping table corresponding to the fault prevention relationship according to the equipment failure cause.

[0113] Step S703: Obtain parameter change trends and maintenance operation parameters.

[0114] The parameter change trend in this step is consistent with the actual parameter change trend in step S600, which will not be described in detail. The maintenance operation parameters in this step are consistent with the predicted operation parameters in step S500, that is, the parameters are beyond the normal range.

[0115] Step S704: Analyze the equipment operation parameters, parameter change trends and maintenance operation parameters to determine the equipment maintenance time.

[0116] Among them, equipment maintenance time refers to the time when workshop equipment needs to be repaired according to the basic maintenance plan. The processing terminal adjusts the equipment operating parameters based on the equipment operating parameters and the parameter change trend until the equipment operating parameters reach the maintenance operating parameters, and records the change time.

[0117] Step S705: Associating the basic maintenance plan with the equipment maintenance time to generate an actual maintenance plan.

[0118] The actual maintenance plan refers to the actual maintenance plan for the workshop equipment. The processing terminal stores the basic maintenance plan and the equipment maintenance time in the same data packet, providing a specific time point for the maintenance of the workshop equipment.

[0119] Step S706: Associating the equipment preventive maintenance plan and the actual maintenance plan to generate an equipment maintenance plan.

[0120] The equipment maintenance plan in this step is consistent with the equipment maintenance plan in step S104, and the processing terminal stores the equipment preventive maintenance plan and the actual maintenance plan in the same data packet.

[0121] Based on the same inventive concept, the embodiment of the present application provides a workshop equipment management system based on a large model, including: An acquisition module is used to acquire a start trigger signal, equipment operating parameters, historical operating parameters, normal operating parameters, parameter deviation range values, predicted operating parameters, actual parameter change trends, fault determination methods, parameter change trends, and maintenance operating parameters; A memory for storing a program of a workshop equipment management method based on a large model; The program in the processor memory can be loaded and executed by the processor to implement the workshop equipment management method based on the large model.

[0122] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0123] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute a workshop equipment management method based on a large model.

[0124] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0125] Based on the same inventive concept, an embodiment of the present application provides an intelligent terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes a workshop equipment management method based on a large model.

[0126] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0127] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A workshop equipment management method based on a large model, characterized in that: include: Obtain the start trigger signal of the workshop equipment; Based on the start trigger signal, the equipment operating parameters and historical operating parameters of the workshop equipment are obtained in real time; Analyze equipment operating parameters and historical operating parameters to determine the equipment operating status of workshop equipment; Determine whether the equipment operation status meets the preset normal operation status requirements; If it is in compliance, then continue to obtain the equipment operating parameters and historical operating parameters, and continue to analyze the equipment operating parameters and historical operating parameters to determine the equipment operating status of the workshop equipment for cyclic judgment; If it does not meet the requirements, the preset large model is controlled to analyze the equipment operating parameters to determine the cause of the equipment failure; Analyze the causes of equipment failure to determine equipment maintenance plan; Distribute equipment maintenance plans to preset maintenance personnel to repair workshop equipment.

2. The workshop equipment management method based on a large model according to claim 1 is characterized in that: The steps of analyzing the equipment operating parameters and historical operating parameters to determine the equipment operating status of the workshop equipment include: Obtain normal operating parameters of workshop equipment based on equipment operating parameters; Determine whether the equipment operating parameters meet the requirements of normal operating parameters; If it does not meet the requirements, the preset equipment abnormal state is defined as the equipment operation state of the workshop equipment; If it meets the requirements, the equipment operation status of the workshop equipment is determined according to the preset parameter change prediction method.

3. The workshop equipment management method based on a large model according to claim 2 is characterized in that: The steps of determining the equipment operation status of the workshop equipment according to the preset parameter change prediction method include: Obtain parameter deviation range values ​​based on equipment operating parameters; Analyze the equipment operating parameters and historical operating parameters to determine the operating parameter deviation value; Determine whether the operating parameter deviation value meets the parameter deviation range value requirements; If it is in compliance, the preset normal state of the equipment is defined as the equipment operation state of the workshop equipment; If it does not meet the requirements, the preset equipment abnormal state is defined as the equipment operation state of the workshop equipment.

4. The workshop equipment management method based on a large model according to claim 1 is characterized in that: The steps of controlling the preset large model to analyze the equipment operating parameters to determine the cause of equipment failure include: Determine whether the equipment operating parameters meet the requirements of the preset fault code parameters; If it meets the requirements, the equipment operating parameters are analyzed to determine the equipment fault code; Determine the cause of equipment failure based on the equipment failure code and the preset code failure relationship; If it does not meet the requirements, the cause of the equipment failure will be predicted based on the equipment operating parameter control model.

5. The workshop equipment management method based on a large model according to claim 4 is characterized in that: The steps to predict the cause of equipment failure based on the equipment operating parameter control model include: Obtain predicted operating parameters of workshop equipment based on equipment operating parameters; Determine whether the equipment operating parameters meet the requirements of the predicted operating parameters; If it does, the equipment operating parameters are input into the large model for reasoning to determine the cause of the equipment failure; If it does not meet the requirements, the large model is controlled according to the preset operating parameter prediction method to predict the cause of the equipment failure.

6. The workshop equipment management method based on a large model according to claim 5 is characterized in that: The steps of controlling the large model to predict the cause of equipment failure according to the preset operating parameter prediction method include: Obtain the actual change trend of equipment operating parameters; Analyze the actual parameter change trend, equipment operating parameters and predicted operating parameters to determine the predicted abnormal operating parameters; The predicted abnormal operation parameters are input into the large model for prediction to determine the cause of equipment failure.

7. The workshop equipment management method based on a large model according to claim 1 is characterized in that: The steps to analyze the cause of equipment failure and determine the equipment maintenance plan include: Determine the basic maintenance plan based on the cause of equipment failure and the preset failure maintenance relationship; A fault determination method for obtaining the cause of equipment failure; Determine whether the fault determination method meets the requirements of the preset fault prediction type; If not, the basic maintenance plan is defined as the equipment maintenance plan; If it is in compliance, the equipment preventive maintenance plan is determined based on the equipment failure cause and the preset failure prevention relationship; Obtain parameter change trends and maintenance operation parameters; Analyze equipment operating parameters, parameter change trends and maintenance operating parameters to determine equipment maintenance time; Link the basic maintenance plan and equipment maintenance time to generate the actual maintenance plan; Link the equipment preventive maintenance plan and actual maintenance plan to generate an equipment maintenance plan.

8. The workshop equipment management system based on the large model is characterized by: include: An acquisition module is used to acquire a start trigger signal, equipment operating parameters and historical operating parameters; A memory for storing a program of a workshop equipment management method based on a large model according to any one of claims 1 to 7; The program in the memory can be loaded and executed by the processor to implement the workshop equipment management method based on a large model as described in any one of claims 1 to 7.

9. An intelligent terminal, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the large model-based workshop equipment management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the workshop equipment management method based on a large model as claimed in any one of claims 1 to 7.

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