Textile equipment fault prediction control method, device, medium and equipment
By regularly obtaining the completion parameters and wear levels of textile equipment for prediction, identifying equipment chains with fluctuations exceeding the threshold, and achieving intelligent online monitoring and control of textile equipment failures, solving the problems of low efficiency and poor accuracy in traditional methods, realizing the realization of the problem of low efficiency and poor accuracy in traditional methods. Efficient fault prediction and control.
Patent Information
- Application Number
- CN202410481501.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-22
- Publication Date
- 2024-12-20
- Estimated Expiration
- 2044-04-22
AI Technical Summary
Traditional textile equipment fault prediction methods are inefficient and difficult to accurately detect potential faults, and lack intelligent and automated online monitoring methods.
The current completion parameters of the textile equipment are obtained every time interval, and the equipment wear level is combined to predict the future completion. If the fluctuation value exceeds the threshold, the key equipment chain will be determined and run control will be carried out to achieve fault prediction and online monitoring.
It realizes accurate prediction and control of textile equipment failures, saves resources, is simple and efficient in the process, and avoids the inefficiency of regular maintenance and manual inspection in traditional methods.
Smart Images
Figure CN118333607B_ABST
Abstract
Description
Background Art
[0002] With the rapid development of the textile industry and the improvement of its intelligence level, the requirements for the operational stability and efficiency of textile equipment are becoming increasingly higher. Traditional methods for predicting textile equipment faults mainly rely on regular maintenance and manual inspections, which is not only inefficient but also difficult to accurately detect potential faults. With the rapid development of sensor technology, data analysis, and artificial intelligence technology, there is an urgent need for an intelligent, automated online monitoring method for predicting textile equipment faults. Summary of the invention
[0003] The technical problem to be solved by this application is: how to provide an automatic and highly accurate method for predicting and controlling faults of textile equipment.
[0004] In view of the above technical problems, according to the first aspect of the present application, a textile equipment fault prediction control method is provided, comprising the following steps:
[0005] S100, at every first preset time period, obtain the current completion parameters of each target textile equipment chain to obtain a current completion parameter list; the above current completion parameters include the current operating equipment identifier and the current operating equipment progress; the initial textile capacity, textile task type and textile task volume of the equipment corresponding to any two of the above target textile equipment chains are the same.
[0006] S200, obtain a prediction control completion parameter list based on the above-mentioned current completion parameter list; the above-mentioned prediction control completion parameter list includes the prediction control completion parameters corresponding to each target textile equipment chain at a preset time point obtained based on the equipment wear level of each target textile equipment in each target textile equipment chain and the corresponding current completion parameters; the estimated time point is after the current time point.
[0007] S300, obtaining corresponding prediction control completion parameter fluctuation values according to the above prediction control completion parameter list.
[0008] S400: If the fluctuation value of the above-mentioned prediction control completion parameter is greater than the first preset fluctuation value threshold, a key target textile equipment chain is determined according to the prediction control completion parameter of each target textile equipment chain.
[0009] S500, performs operation control on the above-mentioned key target textile equipment chain.
[0010] According to a second aspect of the present application, a textile equipment fault prediction control device is provided, the device comprising:
[0011] The current completion parameter acquisition unit is configured to acquire the current completion parameters of each target textile equipment chain at intervals of a first preset time period to obtain a current completion parameter list; the above-mentioned current completion parameters include the current operating equipment identifier and the current operating equipment progress; the initial textile capacity, textile task type and textile task volume of the equipment corresponding to any two target textile equipment chains are the same.
[0012] The prediction control completion parameter acquisition unit is configured to obtain a prediction control completion parameter list based on the above-mentioned current completion parameter list; the above-mentioned prediction control completion parameter list includes the prediction control completion parameters corresponding to each target textile equipment chain at a preset time point obtained based on the equipment wear level of each target textile equipment in each target textile equipment chain and the corresponding current completion parameters; the estimated time point is after the current time point.
[0013] The fluctuation value acquisition unit is configured to obtain the corresponding prediction control completion parameter fluctuation value according to the above prediction control completion parameter list.
[0014] The key equipment chain determination unit is configured to determine the key target textile equipment chain according to the predicted control completion parameter of each target textile equipment chain if the predicted control completion parameter fluctuation value is greater than a first preset fluctuation value threshold.
[0015] The control unit is configured to perform operation control on the above-mentioned key target textile equipment chain.
[0016] According to a third aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned textile equipment fault prediction and control methods.
[0017] According to a fourth aspect of the present application, an electronic device is provided, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.
[0018] This application has at least the following beneficial effects:
[0019] The textile equipment fault prediction and control method provided by the present application, first, for each target textile equipment chain, the corresponding current completion parameter is obtained every first preset time period. Here, since the initial textile capacity, textile task type and textile task amount of the equipment corresponding to each target textile equipment chain are the same, if the equipment wear is not considered, when no failure occurs, at the same time point, the textile progress corresponding to each target textile equipment chain should be basically the same; however, once the textile equipment is put into use, it will inevitably cause a certain amount of wear and tear, and the wear of the equipment will have different degrees of impact on the textile progress. Therefore, according to the current completion parameter list, a prediction control completion parameter list is obtained, and the prediction control completion parameter list includes each target textile equipment chain predicted according to the equipment wear level of each target textile equipment in each target textile equipment chain and the corresponding current completion parameter. Assume the prediction control completion parameter corresponding to the time point; here, considering the equipment wear of each target textile equipment, the completion corresponding to each target textile equipment chain at the preset time point in the current textile task may not be exactly the same, but its corresponding fluctuation value should be within a certain threshold (the first preset fluctuation value threshold); the present application obtains the prediction control completion parameter at the preset time point according to the current completion parameter of each target textile equipment chain. If the fluctuation value of the prediction control completion parameter corresponding to all target textile equipment chains at the preset time point is greater than the first preset fluctuation value threshold, it means that the target textile equipment in one or more current target textile equipment chains may have a fault, resulting in a large difference between its textile progress and the progress of other target textile equipment chains, thereby determining the key target textile equipment chain that may have a fault and giving an alarm, thus realizing the online monitoring and control adjustment of the prediction of textile equipment faults. The control method provided by the present application can determine the key target textile equipment chain that may have a fault in the target textile equipment chain with the same initial textile capacity, textile task type and textile task amount by obtaining the current overall progress and predicted progress of each target textile equipment chain, and perform operation control without obtaining the operation parameters of each target textile equipment, saving resources, and having a simple and efficient process. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0021] Figure 1 A flowchart of a textile equipment fault prediction control method provided by one embodiment of the present application;
[0022] Figure 2A structural block diagram of a textile equipment fault prediction control device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.
[0024] like Figure 1 As shown, an embodiment of the present application provides a textile equipment fault prediction control method, comprising:
[0025] S100, at every first preset time period, obtain the current completion parameters of each target textile equipment chain to obtain a current completion parameter list; the above current completion parameters include the current operating equipment identifier and the current operating equipment progress; the initial textile capacity, textile task type and textile task volume of the equipment corresponding to any two of the above target textile equipment chains are the same.
[0026] In some embodiments, in order to achieve online monitoring of textile equipment failure prediction, the current completion parameters of each target textile equipment chain are obtained at intervals of the first preset time period to obtain a current completion parameter list. As an example: the first preset time period can be 5 minutes. Here, the initial textile capacity, textile task type and textile task amount of the equipment corresponding to any two of the above-mentioned target textile equipment chains are the same, and the initial textile capacity of the equipment is the textile capacity when the equipment is not worn. That is, if the equipment wear of each target textile equipment in the target textile equipment chain is not considered, when each target textile equipment in each target textile equipment chain has not failed, when processing the same textile task (the textile task type and textile task amount are the same), at the same time point, the textile progress corresponding to each target textile equipment chain should be basically the same.
[0027] S200, obtain a prediction control completion parameter list based on the above-mentioned current completion parameter list; the above-mentioned prediction control completion parameter list includes the prediction control completion parameters corresponding to each target textile equipment chain at a preset time point obtained based on the equipment wear level of each target textile equipment in each target textile equipment chain and the corresponding current completion parameters; the estimated time point is after the current time point.
[0028] In some embodiments, step S200 includes:
[0029] S210, obtaining an equipment wear level list of the target equipment textile chain corresponding to each current completion parameter in the current completion parameter list; the above equipment wear level list includes the wear level of each key part of each target textile equipment in the corresponding target textile equipment chain.
[0030] Specifically, once the textile equipment is put into use, it will inevitably cause a certain amount of wear and tear, and the wear and tear of the equipment will have different degrees of impact on the textile progress. The wear level of each key part of each target textile equipment in the textile chain of each target equipment is obtained to obtain a list of equipment wear levels. Here, the key parts are parts that will affect the textile progress. As an example: the key parts can be rollers, gears, bearings, belts and chains, etc. The above-mentioned equipment wear level list is updated once every third preset time period. Here, the third preset time period can be determined based on the relevant experience of those skilled in the art, as an example: it can be 30 days.
[0031] S220, determining the textile capacity of each target textile equipment in each target textile equipment chain according to the above-mentioned equipment wear level list and the preset mapping table; the above-mentioned preset mapping table includes the textile capacity reduction corresponding to the wear level of each key part of each target textile equipment.
[0032] Specifically, the preset mapping table includes the reduction in textile capacity corresponding to the wear level of each key part of each target textile equipment. That is, according to the wear level of each key part of each target textile equipment in each target equipment textile chain, the reduction in textile capacity of each target textile equipment can be obtained, and then according to the initial textile capacity of each target textile equipment (100%), the textile capacity of each target textile equipment in each target textile equipment chain can be obtained.
[0033] S230, obtaining a corresponding prediction control completion parameter list according to the weaving capacity and current completion parameter of each target weaving equipment in each target weaving equipment chain.
[0034] Specifically, the above step S230 includes:
[0035] S231, determining a first textile equipment according to a currently running equipment identifier corresponding to each target textile equipment chain; the first textile equipment is a textile equipment whose preset working order in the target textile equipment chain is after the currently running equipment.
[0036] Here, since the entire weaving process is completed in sequence by multiple devices, that is, each target weaving device chain includes a plurality of target weaving devices arranged in a working order, a plurality of first weaving devices that have not yet been operated are determined according to the currently operating device identifier.
[0037] S232, if any target textile equipment chain has a first textile equipment, determine the prediction control completion parameters of each target textile equipment chain with a first textile equipment according to the textile capacity of the current operating equipment and each first textile equipment, the progress of the current operating equipment and the preset time point.
[0038] Here, since the current running device may be the last target textile device, the first textile device may be 0, and if the current running device is not the last device in the target textile device chain, at least one first textile device can be determined. Then, according to the textile capacity of the current running device and each first textile device, the progress of the current running device and the preset time point, the prediction control completion parameter of each target textile device chain with a first textile device is determined. Here, the prediction control completion parameter can be determined based on a pre-trained prediction model. The prediction model is not limited here, and technicians in the field can select a suitable prediction model for training and application according to specific needs.
[0039] S233, obtaining a list of prediction control completion parameters according to each prediction control completion parameter.
[0040] S300, obtaining corresponding prediction control completion parameter fluctuation values according to the above prediction control completion parameter list.
[0041] In some embodiments, the predicted completion fluctuation value may be the variance or standard deviation of all predicted control completion parameters in the predicted control completion parameter list.
[0042] S400: If the fluctuation value of the above-mentioned prediction control completion parameter is greater than the first preset fluctuation value threshold, a key target textile equipment chain is determined according to the prediction control completion parameter of each target textile equipment chain.
[0043] In some embodiments, step S400 includes:
[0044] S410, if the fluctuation value of the above-mentioned prediction control completion parameter is greater than the first preset fluctuation value threshold, the prediction control completion parameters in the above-mentioned prediction control completion parameter list are clustered based on the preset clustering algorithm to obtain a target completion cluster set; the number of prediction control completion parameters in the above-mentioned target completion cluster set is less than the preset number.
[0045] Specifically, if the fluctuation value of the above-mentioned prediction control completion parameter is greater than the first preset fluctuation value threshold, it means that the target textile equipment in one or more current target textile equipment chains may have a fault, resulting in a large difference between its textile progress and the progress of other target textile equipment chains. At this time, the prediction control completion parameters in the above-mentioned prediction control completion parameter list are clustered based on the preset clustering algorithm, and the prediction control completion parameters corresponding to the target textile equipment chains whose progress differs greatly from the progress of other target textile equipment chains are clustered into one category, namely, the target completion cluster set. Here, the preset clustering algorithm is not specifically limited, and technicians in the field can select a suitable clustering algorithm for application according to specific needs.
[0046] S420, determining the target textile equipment chain corresponding to each prediction control completion parameter in each of the target completion clusters as a key target textile equipment chain.
[0047] Specifically, the key target textile equipment chain is a target textile equipment chain whose progress differs greatly from that of other target textile equipment chains, and one or more corresponding target textile equipment may have failed.
[0048] S500, performs operation control on the above-mentioned key target textile equipment chain.
[0049] In some embodiments, step S500 includes:
[0050] S510, obtaining working parameter curves of the current running equipment and each second textile equipment in the key target textile equipment chain when executing the current textile task; the above-mentioned second textile equipment is a textile equipment whose preset working order in the key target textile equipment chain is before the current running equipment.
[0051] Specifically, in order to determine which target textile equipment has failed, the working parameter curves of the current operating equipment and each second textile equipment in the key target textile equipment chain when performing the current textile task are obtained.
[0052] S520, based on the weaving capacity of the current operating equipment and each second textile equipment in the key target textile equipment chain, the weaving type and the textile task amount of the current textile task, obtain the preset working parameter curve of the current operating equipment and each second textile equipment when performing the current textile task.
[0053] Specifically, the currently operating equipment and each second textile equipment in the key target textile equipment chain have corresponding preset working parameter curves, that is, based on their textile capacity (determined according to their equipment wear level), the textile type and textile task volume of the current textile task, their preset working parameter curves when no failure occurs can be determined. This can be determined based on historical data or based on a preset model, and no specific limitation is made here.
[0054] S530, determining the key target textile equipment according to the similarity between the working parameter curves of the current operating equipment and each second textile equipment in the key target textile equipment chain when executing the current textile task and the corresponding preset working parameter curves; the similarity between the working parameter curves of the above-mentioned key target textile equipment when executing the current textile task and the corresponding preset working parameter curves is less than a preset similarity threshold.
[0055] Specifically, if the similarity between the working parameter curves of the current operating equipment and each second textile equipment in the key target textile equipment chain when performing the current textile task and the corresponding preset working parameter curves is greater than the preset similarity threshold, it means that the target textile equipment may not have failed and is in normal operation.
[0056] S540, suspending the operation of key textile equipment in the key target textile equipment chain.
[0057] Specifically, a fault prediction alarm can be performed on the key textile equipment of the key target textile equipment chain, and the content of the fault prediction alarm can include the key target textile equipment chain identifier and the key target textile equipment identifier. At the same time, the key textile equipment is suspended from operation.
[0058] In an exemplary embodiment of the present application, after step S300, the method further includes:
[0059] S600, if the above-mentioned predicted control completion parameter fluctuation value is greater than the second preset fluctuation value threshold and less than the first preset fluctuation value threshold, then starting from the current time, at every second preset time period, obtain the current completion parameter of each target textile equipment chain to obtain a current completion parameter list; and jump to step S200; the length of the above-mentioned second preset time period is less than the length of the first preset time period.
[0060] Specifically, if the fluctuation value of the above-mentioned prediction control completion parameter is greater than the second preset fluctuation value threshold and less than the first preset fluctuation value threshold, it means that the textile progress of one or more current target textile equipment chains is different from the progress of other target textile equipment chains, but the existing differences cause the fluctuation value to be not large enough, and it cannot be directly determined as a key target textile equipment chain, which may be caused by some temporary special circumstances. At this time, in order to continue verification, shorten the monitoring time, that is, from the current time, every second preset time period, obtain the current completion parameter of each target textile equipment chain, and obtain the current completion parameter list; and jump to step S200; so as to pay closer attention to the situation of each target textile equipment chain, so as to realize timely alarm and operation control when problems occur.
[0061] The following are embodiments of the device disclosed herein, which can be used to execute the method embodiments disclosed herein. For details not disclosed in the device embodiments disclosed herein, please refer to the method embodiments disclosed herein.
[0062] In an exemplary embodiment of the present application, a textile equipment fault prediction control device 100 is also provided. Figure 2 As shown, the device comprises:
[0063] The current completion parameter acquisition unit 110 is configured to acquire the current completion parameters of each target textile equipment chain at intervals of a first preset time period to obtain a current completion parameter list; the above-mentioned current completion parameters include the current operating equipment identifier and the current operating equipment progress; the initial textile capacity, textile task type and textile task volume of the equipment corresponding to any two target textile equipment chains are the same.
[0064] The prediction control completion parameter acquisition unit 120 is configured to obtain a prediction control completion parameter list based on the above-mentioned current completion parameter list; the above-mentioned prediction control completion parameter list includes the prediction control completion parameters corresponding to each target textile equipment chain at a preset time point obtained based on the equipment wear level of each target textile equipment in each target textile equipment chain and the corresponding current completion parameters; the estimated time point is after the current time point.
[0065] The fluctuation value acquisition unit 130 is configured to obtain the corresponding prediction control completion parameter fluctuation value according to the above prediction control completion parameter list.
[0066] The key equipment chain determination unit 140 is configured to determine the key target textile equipment chain according to the predicted control completion parameter of each target textile equipment chain if the predicted control completion parameter fluctuation value is greater than the first preset fluctuation value threshold.
[0067] The control unit 150 is configured to perform operation control on the above-mentioned key target textile equipment chain.
[0068] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.
[0069] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.
[0070] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
[0071] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.
[0072] The electronic device according to this embodiment of the present application is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0073] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).
[0074] The storage device stores program codes, which can be executed by the processor, so that the processor executes the steps described in the “exemplary method” section of the specification according to various exemplary embodiments of the present application.
[0075] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).
[0076] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.
[0077] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.
[0078] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface. In addition, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.
[0079] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.
[0080] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the above program product is run on a terminal device, the above program code is used to enable the above terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Exemplary Method" section of the present specification.
[0081] The above-mentioned program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.
[0082] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0083] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.
[0084] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).
[0085] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.
[0086] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.
[0087] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A method for predicting and controlling faults of textile equipment, It is characterized in that The method comprises: S100, at intervals of a first preset time period, obtaining the current completion parameter of each target textile equipment chain to obtain a current completion parameter list; the current completion parameter includes the current operating equipment identifier and the current operating equipment progress; the initial textile capacity, textile task type and textile task amount of the equipment corresponding to any two of the target textile equipment chains are the same; S200, obtaining a prediction control completion parameter list according to the current completion parameter list; the prediction control completion parameter list includes prediction control completion parameters corresponding to each target textile equipment chain at a preset time point obtained according to the equipment wear level of each target textile equipment in each target textile equipment chain and the corresponding current completion parameter; the preset time point is after the current time point; S300, obtaining corresponding prediction control completion parameter fluctuation values according to the prediction control completion parameter list; S400, if the fluctuation value of the prediction control completion parameter is greater than a first preset fluctuation value threshold, determining a key target textile equipment chain according to the prediction control completion parameter of each target textile equipment chain; S500, performing operation control on the key target textile equipment chain; The step S200 includes: S210, obtaining an equipment wear level list of a target equipment textile chain corresponding to each current completion parameter in the current completion parameter list; the equipment wear level list includes the wear level of each key part of each target textile equipment in the corresponding target textile equipment chain; S220, determining the textile capacity of each target textile equipment of each target textile equipment chain according to the equipment wear level list and a preset mapping table; the preset mapping table includes a textile capacity reduction amount corresponding to the wear level of each key part of each target textile equipment; S230, obtaining a corresponding prediction control completion parameter list according to the weaving capacity and current completion parameter of each target weaving equipment in each target weaving equipment chain; The step S230 includes: S231, determining a first textile equipment according to a currently running equipment identifier corresponding to each target textile equipment chain; the first textile equipment is a textile equipment whose preset working order in the target textile equipment chain is after the currently running equipment; S232, if any target textile equipment chain has a first textile equipment, determining the prediction control completion parameter of each target textile equipment chain having the first textile equipment according to the textile capacity of the current operating equipment and each first textile equipment, the progress of the current operating equipment and the preset time point; S233, obtaining a list of prediction control completion parameters according to each prediction control completion parameter.
2. The textile equipment fault prediction and control method according to claim 1, It is characterized in that The step S400 includes: S410, if the fluctuation value of the prediction control completion parameter is greater than a first preset fluctuation value threshold, clustering the prediction control completion parameters in the prediction control completion parameter list based on a preset clustering algorithm to obtain a target completion cluster set; the number of prediction control completion parameters in the target completion cluster set is less than a preset number; S420, determining the target textile equipment chain corresponding to each predicted control completion parameter in each target completion cluster as a key target textile equipment chain.
3. The textile equipment fault prediction and control method according to claim 2, It is characterized in that After step S300, the method further includes: S600, if the fluctuation value of the predicted control completion parameter is greater than the second preset fluctuation value threshold and less than the first preset fluctuation value threshold, then starting from the current time, at every second preset time period, obtain the current completion parameter of each target textile equipment chain to obtain a current completion parameter list; and jump to step S200; the length of the second preset time period is less than the length of the first preset time period.
4. The textile equipment fault prediction and control method according to claim 1, It is characterized in that The equipment wear level list is updated once every third preset time period.
5. The textile equipment fault prediction and control method according to claim 1, It is characterized in that The step S500 includes: S510, obtaining a working parameter curve of a currently running device and each second textile device in the key target textile device chain when executing the current textile task; the second textile device is a textile device in the key target textile device chain whose working sequence is preset before the currently running device; S520, obtaining preset working parameter curves of the current operating equipment and each second textile equipment when executing the current textile task according to the textile capacity of the current operating equipment and each second textile equipment in the key target textile equipment chain, the textile type and the textile task amount of the current textile task; S530, determining the key target textile equipment according to the similarity between the working parameter curves of the current operating equipment and each second textile equipment in the key target textile equipment chain when executing the current textile task and the corresponding preset working parameter curve; the similarity between the working parameter curves of the key target textile equipment when executing the current textile task and the corresponding preset working parameter curve is less than a preset similarity threshold; S540, suspending the operation of key textile equipment in the key target textile equipment chain.
6. A textile equipment fault prediction control device, It is characterized in that The device comprises: The current completion parameter acquisition unit is configured to acquire the current completion parameter of each target textile equipment chain at intervals of a first preset time period to obtain a current completion parameter list; the current completion parameter includes a current operating equipment identifier and a current operating equipment progress; the initial textile capacity, textile task type and textile task amount of the equipment corresponding to any two target textile equipment chains are the same; The prediction control completion parameter acquisition unit is configured to obtain a prediction control completion parameter list according to the current completion parameter list; the prediction control completion parameter list includes the prediction control completion parameter corresponding to each target textile equipment chain at a preset time point obtained according to the equipment wear level of each target textile equipment in each target textile equipment chain and the corresponding current completion parameter; the preset time point is after the current time point; a fluctuation value acquisition unit, configured to obtain a corresponding prediction control completion parameter fluctuation value according to the prediction control completion parameter list; a key equipment chain determination unit configured to determine a key target textile equipment chain according to the predicted control completion parameter of each target textile equipment chain if the predicted control completion parameter fluctuation value is greater than a first preset fluctuation value threshold; A control unit configured to perform operation control on the key target textile equipment chain; The predictive control completion parameter acquisition unit is configured to: Obtaining a list of equipment wear levels of a target equipment textile chain corresponding to each current completion parameter in the current completion parameter list; the equipment wear level list includes the wear level of each key part of each target textile equipment in the corresponding target textile equipment chain; Determine the textile capacity of each target textile equipment in each target textile equipment chain according to the equipment wear level list and a preset mapping table; the preset mapping table includes a textile capacity reduction amount corresponding to the wear level of each key part of each target textile equipment; According to the weaving capacity and current completion parameters of each target textile equipment in each target textile equipment chain, a corresponding prediction control completion parameter list is obtained; The corresponding prediction control completion parameter list is obtained according to the weaving capacity and current completion parameter of each target textile equipment in each target textile equipment chain, and is configured as follows: Determine a first textile equipment according to the current running equipment identifier corresponding to each target textile equipment chain; the first textile equipment is a textile equipment whose preset working order in the target textile equipment chain is after the current running equipment; If any target textile equipment chain has a first textile equipment, then the prediction control completion parameter of each target textile equipment chain having the first textile equipment is determined according to the textile capacity of the current operating equipment and each first textile equipment, the progress of the current operating equipment and the preset time point; According to each predictive control completion parameter, a predictive control completion parameter list is obtained.
7. A non-transitory computer-readable storage medium having stored therein at least one instruction, It is characterized in that The at least one instruction is loaded and executed by the processor to implement the method according to any one of claims 1-5.
8. An electronic device, It is characterized in that The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 7.