Intelligent diagnosis method, device and equipment of processing equipment and storage medium

Abstract

The application provides an intelligent diagnosis method, device and equipment of processing equipment and a storage medium, relates to the technical field of production management, and can improve processing management efficiency. The specific scheme comprises the following steps: acquiring actual processing efficiency and reference processing efficiency of a production line in a preset area within a preset time period; if the actual processing efficiency is lower than the reference processing efficiency, acquiring a pulled empty time of each processing equipment in the preset area, and determining a target processing equipment from the multiple processing equipments according to the pulled empty time of each processing equipment, the pulled empty time being a waiting processing time of the processing equipment, and the processing equipment being a processing equipment included in each production line in the preset area; acquiring time information of the target processing equipment for processing one object within the preset time period, obtaining processing beat information of the target processing equipment within the preset time period; and determining a diagnosis result of the target processing equipment according to the processing beat information of the target processing equipment.

Description

Technical Field

[0001] This application relates to the field of production management technology, and in particular to an intelligent diagnostic method, apparatus, equipment and storage medium for processing equipment. Background Technology

[0002] With the increasing demand for customized products, product manufacturing has partially shifted from traditional mass production to customized production. Customized production, characterized by smaller order quantities and more frequent changes in customer requirements, makes production organization and execution more complex, leading to reduced efficiency, uneven worker allocation, and significant capacity fluctuations. Therefore, traditional methods relying on manual diagnosis and management of the production process are no longer suitable for the current product manufacturing model. Summary of the Invention

[0003] This application provides an intelligent diagnostic method, apparatus, device, and storage medium for processing equipment, which can improve processing management efficiency.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] In a first aspect, this application provides an intelligent diagnostic method for processing equipment, the method comprising:

[0006] Obtain the actual and reference processing efficiency of the production line in the preset area within a preset time period;

[0007] If the actual processing efficiency is lower than the reference processing efficiency, the idle time of each processing device in the preset area is obtained, and the target processing device is determined from multiple processing devices based on the idle time of each processing device. The idle time is the waiting time of the processing device, and the processing device is the processing device included in each production line in the preset area.

[0008] Obtain the time information of the target processing equipment for each object processed within a preset time period, and obtain the processing cycle information of the target processing equipment within the preset time period;

[0009] The diagnostic results for the target processing equipment are determined based on the processing cycle time information of the target processing equipment.

[0010] In one embodiment, obtaining the actual processing efficiency of a production line in a preset area within a preset time period includes:

[0011] Obtain the processing output, product qualification rate, logistics and distribution efficiency, and process execution rate of the production lines included in the preset area;

[0012] The actual processing efficiency is obtained based on the processing output, processing qualification rate, logistics and distribution efficiency, and process execution rate.

[0013] In one embodiment, obtaining the reference processing efficiency of a production line in a preset area within a preset time period includes:

[0014] Obtain information on the processing objects of the production line included in the preset area and the reference processing cycle of the processing equipment included in the production line;

[0015] Obtain the corresponding reference processing technology and reference logistics route based on the information of the processing object;

[0016] The reference processing efficiency is obtained based on the reference processing cycle time, reference processing technology, and reference logistics route of the processing equipment.

[0017] In one embodiment, determining a target processing device from a plurality of processing devices based on the idle time of each processing device includes:

[0018] Obtain the idle time of the processing equipment included in each production line, and determine the production line with the largest idle time as the target production line.

[0019] The processing equipment with the longest idle time in the target production line is identified as the target processing equipment.

[0020] In one embodiment, determining the diagnostic result of the target processing equipment based on the processing cycle time information of the target processing equipment includes:

[0021] Generate a cycle fluctuation curve based on the cycle information of the target processing equipment within a preset time period;

[0022] The diagnostic result of the target processing equipment is determined based on the magnitude of the beat fluctuation in the beat fluctuation curve and the target time period, where the beat value in the beat fluctuation curve is greater than a preset threshold.

[0023] In one embodiment, after determining the target processing device from a plurality of processing devices based on the idle time of each processing device, the method further includes:

[0024] Obtain the overall efficiency of the target processing equipment within a preset time period, and determine the diagnostic results of the target processing equipment based on the overall efficiency.

[0025] In one embodiment, obtaining the overall equipment efficiency of the target processing equipment within a preset time period includes:

[0026] Obtain the time information of the target processing equipment for each object processed within the reference time period to obtain the reference processing cycle information of the target processing equipment;

[0027] Calculate the median value of each cycle value in the reference processing cycle information to obtain the theoretical processing cycle of the target processing equipment;

[0028] The overall efficiency of the target processing equipment is determined based on the theoretical processing cycle time.

[0029] A second aspect of this application provides an intelligent diagnostic device for processing equipment, the device comprising:

[0030] The first acquisition module is used to acquire the actual processing efficiency and reference processing efficiency of the production line in the preset area within a preset time period.

[0031] The first determining module is used to obtain the idle time of each processing device in the preset area if the actual processing efficiency is lower than the reference processing efficiency, and determine the target processing device from multiple processing devices based on the idle time of each processing device. The idle time is the waiting time of the processing device, and the processing device is the processing device included in each production line in the preset area.

[0032] The second acquisition module is used to acquire the time information of the target processing equipment completing the processing of each object within a preset time period, and to obtain the processing cycle information of the target processing equipment within the preset time period.

[0033] The second determining module is used to determine the diagnostic result of the target processing equipment based on the processing cycle information of the target processing equipment.

[0034] In a third aspect of this application, an electronic device is provided, comprising a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the intelligent diagnostic method for the processing equipment described in the first aspect of this application.

[0035] In a fourth aspect of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the intelligent diagnostic method for processing equipment described in the first aspect of this application.

[0036] The beneficial effects of the technical solutions provided in this application include at least the following:

[0037] The intelligent diagnostic method for processing equipment provided in this application embodiment obtains the actual processing efficiency and reference processing efficiency of the production line in a preset area within a preset time period. If the actual processing efficiency is lower than the reference processing efficiency, the idle time of each processing device in the preset area is obtained. Based on the idle time of each processing device, a target processing device is determined from multiple processing devices. The idle time is the waiting time of the processing device, and the processing device is the processing device included in each production line in the preset area. The time information of the target processing device for completing processing one object within the preset time period is obtained to obtain the processing cycle information of the target processing device within the preset time period. Finally, the diagnostic result of the target processing device is determined based on the processing cycle information of the target processing device. The intelligent diagnostic method for processing equipment provided in this application embodiment can identify the target processing device with low processing efficiency from multiple processing devices and obtain the diagnostic result of the target processing device. At the same time, the target processing device can be improved based on the diagnostic result, thereby improving the processing management efficiency of the product. Attached Figure Description

[0038] Figure 1 This application provides a schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0039] Figure 2 A flowchart illustrating an intelligent diagnostic method for processing equipment provided in this application embodiment;

[0040] Figure 3 This application provides a schematic diagram of a diagnostic result report.

[0041] Figure 4 A schematic diagram of a management process provided for an embodiment of this application;

[0042] Figure 5 A schematic diagram of a data architecture for intelligent diagnosis of a processing equipment provided in an embodiment of this application;

[0043] Figure 6 This is a structural diagram of an intelligent diagnostic device for a processing equipment provided in an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0045] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0046] In addition, the use of “based on” or “according to” implies openness and inclusivity, because processes, steps, calculations or other actions “based on” or “according to” one or more conditions or values ​​can in practice be based on additional conditions or values ​​beyond those conditions.

[0047] With the increasing demand for customized products, product manufacturing has partially shifted from traditional mass production to customized production. Customized production, characterized by smaller order quantities and more frequent changes in customer requirements, makes production organization and execution more complex, leading to reduced efficiency, uneven worker allocation, and significant capacity fluctuations. Therefore, traditional methods relying on manual diagnosis and management of the production process are no longer suitable for the current product manufacturing model.

[0048] To address the aforementioned issues, this application provides an intelligent diagnostic method for processing equipment. This method acquires the actual and reference processing efficiency of a production line within a preset area over a preset time period. If the actual processing efficiency is lower than the reference efficiency, it acquires the idle time of each processing device within the preset area. Based on this idle time, a target processing device is identified from multiple processing devices. The idle time is the waiting time for processing, and the processing devices are those included in each production line within the preset area. The method acquires the time information for each object processed by the target processing device within the preset time period, obtaining the processing cycle information of the target processing device within that time period. Finally, the diagnostic result of the target processing device is determined based on its processing cycle information. This intelligent diagnostic method for processing equipment can identify a target processing device with low processing efficiency from multiple processing devices and obtain a diagnostic result for that device. Furthermore, improvements can be made to the target processing device based on this diagnostic result, thereby improving product processing management efficiency.

[0049] The intelligent diagnostic method for processing equipment provided in this application can be executed by a computer device, a terminal device, or a server. The terminal device can be various personal computers, laptops, smartphones, tablets, and portable wearable devices, etc. This application does not make any specific limitations.

[0050] Figure 1This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Figure 1 As shown, the computer device includes a processor and a memory connected via a system bus. The processor provides computing and control capabilities. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. These computer programs can be executed by the processor to implement the steps of the intelligent diagnostic method for a processing device provided in the above embodiments. The internal memory provides a cached operating environment for the operating system and computer programs in the non-volatile storage medium.

[0051] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0052] Based on the aforementioned implementing entity, embodiments of this application provide an intelligent diagnostic method for processing equipment. For example... Figure 2 As shown, the method includes the following steps:

[0053] Step 201: Obtain the actual processing efficiency and reference processing efficiency of the production line in the preset area within the preset time period.

[0054] The production line in the preset area can be a production line in a production workshop. Each production line includes multiple processing equipment. The processing equipment in the production workshop is used to process various objects, such as parts for products, like vehicle parts or mobile phone parts.

[0055] The actual processing efficiency of a production line in a preset area within a preset time period can be understood as the actual processing efficiency of a production line in a production workshop within that preset time period. Similarly, the reference processing efficiency of a production line in a preset area within a preset time period can be understood as the theoretical processing efficiency of a production line in the same production workshop within that preset time period.

[0056] Step 202: If the actual processing efficiency is lower than the reference processing efficiency, obtain the idle time of each processing device in the preset area, and determine the target processing device from multiple processing devices based on the idle time of each processing device.

[0057] Here, the idle time refers to the waiting time of the processing equipment, and the processing equipment refers to the processing equipment included in each production line within the preset area. The target processing equipment can be understood as the processing equipment with low efficiency or processing bottlenecks.

[0058] Understandably, if the actual processing efficiency is lower than the reference processing efficiency, it indicates that there is a processing bottleneck in the production workshop. In this case, it is necessary to identify the production line with the lower processing efficiency in the production workshop and the less efficient processing equipment in the production line with the lower processing efficiency, so as to obtain the target processing equipment.

[0059] Step 203: Obtain the time information of the target processing equipment for each object processed within a preset time period, and obtain the processing cycle information of the target processing equipment within the preset time period.

[0060] Specifically, the processing equipment generates a status signal after processing each part. By collecting the status signals of the target equipment within a preset time period, the processing cycle information can be obtained.

[0061] Step 204: Determine the diagnostic results of the target processing equipment based on the processing cycle information of the target processing equipment.

[0062] Optionally, a beat fluctuation curve can be generated based on the processing beat information of the target processing equipment within a preset time period. Then, the diagnostic result of the target processing equipment can be determined based on the beat fluctuation magnitude in the beat fluctuation curve and the target time period. The target time period is the time period in the beat fluctuation curve where the beat value is greater than a preset threshold.

[0063] In other words, by generating a cycle time fluctuation curve from the processing cycle time information obtained within a preset time period, the horizontal axis of the processing cycle time curve represents the time of the preset time period, and the vertical axis of the processing cycle curve represents the time required to process each part within the preset time period. Then, based on the fluctuation of the processing cycle time curve, the diagnostic result of the target processing equipment is determined.

[0064] Optionally, the actual processing efficiency of the production line in the preset area within a preset time period can be obtained, including: obtaining the processing output, product qualification rate, logistics and distribution efficiency, and process execution rate of the production line included in the preset area, and then obtaining the actual processing efficiency based on the processing output, processing qualification rate, logistics and distribution efficiency, and process execution rate.

[0065] For example, the preset time period can be 5 hours in a certain day. The actual processing efficiency is obtained by the processing efficiency, delivery efficiency and process execution efficiency of the target workshop within these 5 hours. The processing efficiency can be obtained based on the processing output and product qualification rate of these 5 hours.

[0066] Optionally, the process of obtaining the reference processing efficiency of the production line in the preset area within the preset time period can be as follows: obtain the processing object information of the production line included in the preset area and the reference processing cycle of the processing equipment included in the production line, obtain the corresponding reference processing technology and reference logistics route according to the processing object information, and finally obtain the reference processing efficiency according to the reference processing cycle, reference processing technology and reference logistics route of the processing equipment.

[0067] In actual implementation, the reference processing efficiency can be calculated through simulation. Specifically, a three-dimensional simulation model of the production workshop can be constructed, and then the reference processing efficiency can be obtained through simulation calculation based on the reference processing cycle, reference processing technology, and reference logistics route of the processing equipment.

[0068] Furthermore, the processing status of the processing equipment and the position of the processing personnel in the production workshop can be displayed in the form of 3D animation, and the processing process in the production workshop can be monitored by watching the 3D animation of the production workshop.

[0069] In one embodiment, the process of determining the target processing equipment from multiple processing equipment based on the idle time of each processing equipment can be as follows: obtaining the idle time of the processing equipment included in each production line, obtaining the idle time of each production line, determining the production line with the largest idle time as the target production line, and determining the processing equipment with the largest idle time in the target production line as the target processing equipment.

[0070] Understandably, when the actual processing efficiency is lower than the reference processing efficiency, it is necessary to find the bottleneck in the processing process. Specifically, this can be achieved by obtaining the idle time of the processing equipment included in each production line, identifying the production line with the longest idle time as the target production line, and identifying the processing equipment with the longest idle time in the target production line as the target processing equipment. This target processing equipment is the bottleneck equipment, which is the equipment with lower processing efficiency.

[0071] In one embodiment, the process of determining the diagnostic result of the target processing equipment based on the processing cycle information of the target processing equipment can be as follows: generate a cycle fluctuation curve based on the processing cycle information of the target processing equipment within a preset time period, and determine the diagnostic result of the target processing equipment based on the magnitude of the cycle fluctuation in the cycle fluctuation curve and the target time period, wherein the target time period is the time period in which the cycle value in the cycle fluctuation curve is greater than a preset threshold.

[0072] In practical implementation, an intelligent diagnostic model can be trained using a large number of cycle time fluctuation curves and bottleneck cause diagnosis results from processing equipment. Then, by inputting the cycle time fluctuation curves into the trained intelligent diagnostic model, the bottleneck cause diagnosis results are obtained. Furthermore, corresponding improvement suggestions can be derived based on the diagnostic results. For example... Figure 3 As shown, taking the welding yard as an example, this provides a schematic diagram of a diagnostic result report for an embodiment of this application.

[0073] In one embodiment, after determining the target processing equipment from multiple processing equipment based on the idle time of each processing equipment, the method further includes: obtaining the overall equipment efficiency of the target processing equipment within a preset time period, and determining the diagnostic result of the target processing equipment based on the overall equipment efficiency.

[0074] Specifically, obtaining the overall efficiency of the target processing equipment within a preset time period can be achieved by: obtaining the time information of the target processing equipment for completing the processing of each object within the reference time period to obtain the reference processing cycle information of the target processing equipment; then calculating the median value of each cycle value in the reference processing cycle information to obtain the theoretical processing cycle of the target processing equipment; then calculating the time utilization rate, performance utilization rate, and yield rate of the processing equipment based on the theoretical processing cycle; and finally obtaining the overall efficiency of the processing equipment based on the time utilization rate, performance utilization rate, and yield rate of the processing equipment.

[0075] To facilitate understanding by those skilled in the art, this application provides an intelligent diagnostic method for processing equipment, using a computer device as the execution subject as an example. Specifically, the method includes:

[0076] (1) Obtain the processing output, product qualification rate, logistics and distribution efficiency and process execution rate of the production lines included in the preset area;

[0077] (2) The actual processing efficiency is obtained based on the processing output, processing qualification rate, logistics and distribution efficiency and process execution rate.

[0078] (3) Obtain information on the processing objects of the production line included in the preset area and the reference processing cycle of the processing equipment included in the production line;

[0079] (4) Obtain the corresponding reference processing technology and reference logistics route based on the information of the processing object;

[0080] (5) Obtain the reference processing efficiency based on the reference processing cycle time, reference processing technology and reference logistics route of the processing equipment;

[0081] (6) If the actual processing efficiency is lower than the reference processing efficiency, then obtain the time that each processing device in the preset area is idle;

[0082] (7) Obtain the idle time of the processing equipment included in each production line, obtain the idle time of each production line, and determine the production line with the largest idle time as the target production line;

[0083] (8) Identify the processing equipment with the longest idle time in the target production line as the target processing equipment;

[0084] (9) Obtain the time information of the target processing equipment for each object processed within a preset time period, and obtain the processing cycle information of the target processing equipment within the preset time period;

[0085] (10) Generate a cycle fluctuation curve based on the processing cycle information of the target processing equipment within a preset time period;

[0086] (11) Determine the diagnostic result of the target processing equipment based on the magnitude of the beat fluctuation in the beat fluctuation curve and the target time period. The target time period is the time period in the beat fluctuation curve where the beat value is greater than the preset threshold.

[0087] (12) Obtain the time information of the target processing equipment for each object processed within the reference time period, and obtain the reference processing cycle information of the target processing equipment;

[0088] (13) Calculate the median value of each cycle value in the reference processing cycle information to obtain the theoretical processing cycle of the target processing equipment;

[0089] (14) Determine the overall equipment efficiency of the target processing equipment based on the theoretical processing cycle time;

[0090] (15) Determine the diagnostic results of the target processing equipment based on the overall efficiency of the equipment.

[0091] Based on the above execution method, it can be understood that the execution process can be divided into four modules: bottleneck identification module, deep root cause analysis module, benchmarking management module, and intelligent diagnosis module. Each module can be executed through a corresponding model, specifically, such as... Figure 4 As shown, it can include corresponding production line bottleneck identification models, deep root cause analysis models, benchmarking management models, and intelligent diagnostic models.

[0092] Specifically, the production line bottleneck identification model utilizes the Theory of Bottlenecks (TOC) to focus on five steps, establishing a bottleneck data model by retrieving data from production and logistics management systems, such as production line inventory levels and buffer emptying frequency, to scientifically calculate the precise location of the bottleneck. The deep-root cause analysis model retrieves efficiency loss data at bottleneck locations, filters and ranks the top five problems, and analyzes the frequency, timing, and fluctuations of each problem to identify underlying patterns and specificities. This targeted approach addresses the root causes of problems, moving beyond piecemeal solutions to resolve systemic issues at minimal cost, achieving both short-term and long-term relief. The benchmarking management model, deployed through a cloud platform, allows for data sharing across various bases, standardizing the calculation standards and formulas for key indicators and their data sources. This enables accurate and effective benchmarking and difference analysis, truly ensuring the effectiveness and accuracy of benchmarking management. The intelligent diagnostic model extracts rules and simulates matching with an expert knowledge base to derive hypothetical diagnoses. These hypothetical diagnoses are then verified against the bottleneck location and simulation model, resulting in diagnostic conclusions. Furthermore, it extracts solutions from the expert database to provide diagnostic solutions. Figure 5 This is a schematic diagram of a data architecture for intelligent diagnosis of a processing equipment provided in an embodiment of this application.

[0093] It should be understood that while the steps in the flowchart of the above embodiments are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the above flowchart may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the steps or stages in other steps.

[0094] like Figure 6 As shown in the figure, this application embodiment provides an intelligent diagnostic device for processing equipment, the device comprising:

[0095] The first acquisition module 11 is used to acquire the actual processing efficiency and reference processing efficiency of the production line in the preset area within a preset time period.

[0096] The first determining module 12 is used to obtain the idle time of each processing device in the preset area if the actual processing efficiency is lower than the reference processing efficiency, and determine the target processing device from multiple processing devices based on the idle time of each processing device. The idle time is the waiting time of the processing device, and the processing device is the processing device included in each production line in the preset area.

[0097] The second acquisition module 13 is used to acquire the time information of the target processing equipment completing the processing of each object within a preset time period, and to obtain the processing cycle information of the target processing equipment within the preset time period.

[0098] The second determining module 14 is used to determine the diagnostic result of the target processing equipment based on the processing cycle information of the target processing equipment.

[0099] In one embodiment, the first acquisition module 11 is specifically used for:

[0100] Obtain the processing output, product qualification rate, logistics and distribution efficiency, and process execution rate of the production lines included in the preset area;

[0101] The actual processing efficiency is obtained based on the processing output, processing qualification rate, logistics and distribution efficiency, and process execution rate.

[0102] In one embodiment, the first acquisition module 11 is specifically used for:

[0103] Obtain information on the processing objects of the production line included in the preset area and the reference processing cycle of the processing equipment included in the production line;

[0104] Obtain the corresponding reference processing technology and reference logistics route based on the information of the processing object;

[0105] The reference processing efficiency is obtained based on the reference processing cycle time, reference processing technology, and reference logistics route of the processing equipment.

[0106] In one embodiment, the first determining module 12 is specifically used for:

[0107] Obtain the idle time of the processing equipment included in each production line, and determine the production line with the largest idle time as the target production line.

[0108] The processing equipment with the longest idle time in the target production line is identified as the target processing equipment.

[0109] In one embodiment, the second determining module 14 is specifically used for:

[0110] Generate a cycle fluctuation curve based on the cycle information of the target processing equipment within a preset time period;

[0111] The diagnostic result of the target processing equipment is determined based on the magnitude of the beat fluctuation in the beat fluctuation curve and the target time period, where the beat value in the beat fluctuation curve is greater than a preset threshold.

[0112] In one embodiment, the second determining module 14 is further configured to:

[0113] Obtain the overall efficiency of the target processing equipment within a preset time period, and determine the diagnostic results of the target processing equipment based on the overall efficiency.

[0114] In one embodiment, the second determining module 14 is specifically used for:

[0115] Obtain the time information of the target processing equipment for each object processed within the reference time period to obtain the reference processing cycle information of the target processing equipment;

[0116] Calculate the median value of each cycle value in the reference processing cycle information to obtain the theoretical processing cycle of the target processing equipment;

[0117] The overall efficiency of the target processing equipment is determined based on the theoretical processing cycle time.

[0118] The intelligent diagnostic device for the processing equipment provided in this embodiment can execute the above-described method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0119] Specific limitations regarding the intelligent diagnostic device for processing equipment can be found in the limitations of the intelligent diagnostic method for processing equipment described above, and will not be repeated here. Each module in the aforementioned intelligent diagnostic device for processing equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the server, or stored in the server's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0120] In another embodiment of this application, a computer device is also provided, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the intelligent diagnostic method for the processing equipment as described in the embodiments of this application.

[0121] In another embodiment of this application, a computer-readable storage medium is also provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the intelligent diagnostic method for the processing equipment as described in the embodiments of this application are implemented.

[0122] In another embodiment of this application, a computer program product is also provided, which includes computer instructions that, when executed on an intelligent diagnostic device of a processing equipment, cause the intelligent diagnostic device of the processing equipment to perform each step of the intelligent diagnostic method of the processing equipment in the method flow shown in the above method embodiment.

[0123] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks, SSDs).

[0124] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for intelligent diagnosis of processing equipment, characterized in that, The method includes: Obtain the actual and reference processing efficiency of the production line in the preset area within a preset time period; If the actual processing efficiency is lower than the reference processing efficiency, the idle time of each processing device in the preset area is obtained, and the target processing device is determined from multiple processing devices based on the idle time of each processing device. The idle time is the waiting time of the processing device, and the processing device is the processing device included in each production line in the preset area. Obtain the time information of the target processing equipment completing the processing of each object within the preset time period, and obtain the processing cycle information of the target processing equipment within the preset time period; The diagnostic result of the target processing equipment is determined based on the processing cycle information of the target processing equipment; The acquisition of the actual processing efficiency of the production line in the preset area within a preset time period includes: The processing output, product qualification rate, logistics and distribution efficiency, and process execution rate of the production lines included in the preset area are obtained. The actual processing efficiency is obtained based on the processing output, the product qualification rate, the logistics and distribution efficiency, and the process execution rate. The process of obtaining the reference processing efficiency of the production line in the preset area within a preset time period includes: Obtain information on the processing objects of the production line included in the preset area and the reference processing cycle of the processing equipment included in the production line; Based on the information about the object being processed, obtain the corresponding reference processing technology and reference logistics route; The reference processing efficiency is obtained based on the reference processing cycle time, reference processing technology, and reference logistics route of the processing equipment. The step of determining the diagnostic result of the target processing equipment based on the processing cycle information of the target processing equipment includes: A cycle fluctuation curve is generated based on the processing cycle information of the target processing equipment within the preset time period; The diagnostic result of the target processing equipment is determined based on the magnitude of the beat fluctuation in the beat fluctuation curve and the target time period, wherein the target time period is the time period in the beat fluctuation curve where the beat value is greater than a preset threshold.

2. The method according to claim 1, characterized in that, The step of determining the target processing equipment from multiple processing equipment based on the idle time of each of the processing equipment includes: Obtain the idle time of the processing equipment included in each production line, and determine the production line with the largest idle time as the target production line. The processing equipment with the longest idle time in the target production line is identified as the target processing equipment.

3. The method according to claim 1, characterized in that, After determining the target processing device from multiple processing devices based on the idle time of each of the processing devices, the method further includes: Obtain the overall equipment efficiency of the target processing equipment within the preset time period, and determine the diagnostic result of the target processing equipment based on the overall equipment efficiency.

4. The method according to claim 3, characterized in that, The step of obtaining the overall efficiency of the target processing equipment within the preset time period includes: Obtain the time information of the target processing equipment for each object processed within a reference time period to obtain the reference processing cycle information of the target processing equipment; Calculate the median value of each cycle value in the reference processing cycle information to obtain the theoretical processing cycle of the target processing equipment; The overall efficiency of the target processing equipment is determined based on the theoretical processing cycle time.

5. An intelligent diagnostic device for processing equipment, characterized in that, The device includes: The first acquisition module is used to acquire the actual processing efficiency and reference processing efficiency of the production line in the preset area within a preset time period. The first determining module is used to, if the actual processing efficiency is lower than the reference processing efficiency, obtain the idle time of each processing device in the preset area, and determine the target processing device from multiple processing devices based on the idle time of each processing device, wherein the idle time is the waiting time of the processing device, and the processing device is the processing device included in each production line in the preset area; The second acquisition module is used to acquire time information of the target processing equipment completing the processing of one object within the preset time period, and to obtain the processing cycle information of the target processing equipment within the preset time period. The second determining module is used to determine the diagnostic result of the target processing equipment based on the processing cycle information of the target processing equipment; The first acquisition module is specifically used for: acquiring the processing output, product qualification rate, logistics and distribution efficiency, and process execution rate of the production line included in the preset area; obtaining the actual processing efficiency based on the processing output, product qualification rate, logistics and distribution efficiency, and process execution rate; acquiring the processing object information of the production line included in the preset area and the reference processing cycle time of the processing equipment included in the production line; acquiring the corresponding reference processing technology and reference logistics route based on the processing object information; and obtaining the reference processing efficiency based on the reference processing cycle time, reference processing technology, and reference logistics route of the processing equipment. The second determining module is specifically used to: generate a beat fluctuation curve based on the processing beat information of the target processing equipment within the preset time period; and determine the diagnostic result of the target processing equipment based on the beat fluctuation magnitude in the beat fluctuation curve and the target time period, wherein the target time period is the time period in the beat fluctuation curve where the beat value is greater than a preset threshold.

6. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, which, when executed by the processor, implements the intelligent diagnostic method for the processing equipment according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the intelligent diagnostic method for the processing equipment according to any one of claims 1 to 4.

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