Textile yarn production monitoring, regulating and controlling method

Abstract

The invention relates to a spinning yarn production monitoring regulation and control method, and relates to the field of production line intelligent management and control, and the method comprises the steps: carrying out the difference feature analysis of a plurality of similar devices in a plurality of device domains based on preset yarn features and preset spinning parameters, and determining a plurality of height difference device domains; according to preset yarn characteristics, fault probability trend analysis is carried out on the multiple height difference equipment domains, multiple probability trend coefficient sets are obtained to compensate the multiple reference fault rates, and multiple adaptive fault rate sets are obtained; and configuring a plurality of adaptive monitoring parameter sets according to the plurality of adaptive fault rate sets, and performing production monitoring and regulation and control on the plurality of height difference equipment domains. The technical problem that a traditional method lacks dynamic adjustment capability and cannot set an adaptive monitoring regulation scheme according to the actual state of equipment and the yarn product type under the condition that monitoring resources are limited, so that the monitoring regulation effect is poor can be solved, monitoring resource distribution can be optimized, and the monitoring efficiency is improved. And the monitoring and regulating effects of yarn production are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management and control of production lines, and in particular to a method for monitoring and controlling textile yarn production. Background Art

[0002] Traditional monitoring and control methods for textile yarn production usually adopt a full coverage monitoring strategy, that is, real-time monitoring of each device in the workshop. This method ignores the differences in actual production tasks and changes in equipment operating status. All equipment is at the same monitoring level, resulting in some equipment being over-monitored, while some key equipment has not received enough attention. Due to limited monitoring resources, this monitoring method often leads to low monitoring efficiency and cannot effectively detect potential quality problems or equipment failures in the production process.

[0003] At present, with the expansion of production scale and the diversification of textile product types, traditional monitoring methods have gradually exposed their shortcomings, especially when facing complex production environments, the lack of dynamic adjustment capabilities is particularly prominent.

[0004] In summary, the traditional yarn production monitoring and control methods lack dynamic adjustment capabilities. When monitoring resources are limited, it is impossible to set up appropriate monitoring and control plans according to the actual status of the equipment and the type of yarn products, which leads to technical problems such as poor monitoring and control effects. Summary of the invention

[0005] The present invention provides a textile yarn production monitoring and control method to solve the technical problem that traditional yarn production monitoring and control methods lack dynamic adjustment capability and cannot set a suitable monitoring and control scheme according to the actual status of the equipment and the type of yarn products when monitoring resources are limited, resulting in poor monitoring and control effects.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: In the first aspect, the present invention provides a method for monitoring and controlling textile yarn production, comprising: clustering spinning equipment in a textile workshop according to predetermined equipment characteristics, determining multiple equipment domains, and performing fault rate statistics on the multiple equipment domains to determine multiple benchmark fault rates; based on predetermined yarn characteristics and predetermined spinning parameters, performing difference feature analysis on multiple similar equipment in the multiple equipment domains to determine multiple high-difference equipment domains; performing fault probability trend analysis on the multiple high-difference equipment domains according to the predetermined yarn characteristics, obtaining multiple probability trend coefficient sets to compensate for the multiple benchmark failure rates, and obtaining multiple adaptive fault rate sets; configuring multiple adaptive monitoring parameter sets according to the multiple adaptive fault rate sets, performing production monitoring on the multiple high-difference equipment domains based on the multiple adaptive monitoring parameter sets, and regulating the abnormal monitoring equipment according to a predetermined feedback control mechanism.

[0007] In a second aspect, the present invention further provides an electronic device, comprising: At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.

[0008] The beneficial effects of the present invention are as follows: by clustering the spinning equipment in a textile workshop according to predetermined equipment characteristics, a plurality of equipment domains are determined, and the failure rate statistics of the plurality of equipment domains are respectively performed to determine a plurality of benchmark failure rates; then, based on predetermined yarn characteristics and predetermined spinning parameters, a plurality of similar equipment in the plurality of equipment domains are respectively analyzed for difference characteristics to determine a plurality of height difference equipment domains; then, according to the predetermined yarn characteristics, a failure probability trend analysis is performed on the plurality of height difference equipment domains respectively, and a plurality of probability trend coefficient sets are obtained to compensate the plurality of benchmark failure rates to obtain a plurality of adapted failure rate sets; finally, a plurality of adapted monitoring parameter sets are configured according to the plurality of adapted failure rate sets, and production monitoring is performed on the plurality of height difference equipment domains based on the plurality of adapted monitoring parameter sets, and abnormal monitoring equipment is regulated according to a predetermined feedback regulation mechanism; that is, by introducing an intelligent monitoring and control mechanism, the monitoring scheme can be adaptively adjusted according to the real-time status of the equipment, the production tasks and the characteristics of the yarn products, and the allocation of monitoring resources can be optimized, so that the monitoring and control effect of yarn production can be significantly improved under the condition of limited monitoring resources, and the high quality and consistency of yarn products can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of a process for monitoring and controlling textile yarn production provided by the present invention; Figure 2 A schematic diagram of a process for determining multiple equipment domains in a textile yarn production monitoring and control method provided by the present invention; Figure 3 This is a schematic structural diagram of an electronic device provided by the present invention.

[0010] In the accompanying drawings, the components represented by the reference numerals are described as follows: Electronic device 500 , memory 510 , processor 520 , first computer program 511 . DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0014] Embodiment 1, as Figure 1 As shown, the embodiment of the present invention provides a method for monitoring and controlling textile yarn production, which specifically includes the following steps: S100: Clustering the spinning equipment in the textile workshop according to predetermined equipment characteristics to determine a plurality of equipment domains, and performing failure rate statistics on the plurality of equipment domains respectively to determine a plurality of benchmark failure rates.

[0015] In one embodiment, Figure 2 As shown, step S100 of the present application also includes: S110: Obtaining predetermined equipment characteristics, wherein the predetermined equipment characteristics include equipment model, equipment service life and equipment wear characteristics; S120: Clustering the spinning equipment in the textile workshop once according to the equipment model to obtain a primary clustering result; S130: Clustering the primary clustering result twice according to the equipment service life to obtain a secondary clustering result; S140: Clustering the secondary clustering result three times according to the equipment wear characteristics to obtain the multiple equipment domains.

[0016] Specifically, first, the predetermined equipment characteristics are obtained, wherein the predetermined equipment characteristics include equipment model, equipment service life and equipment wear characteristics. The equipment model refers to the specific model or category of each spinning equipment. The equipment model usually determines the performance parameters, scope of application and production capacity of the equipment, and is an important feature to distinguish different equipment; the equipment service life refers to the cumulative working time or use cycle of the equipment since it was put into use. The service life of the equipment can reflect the aging degree of the equipment and the possible risk of failure. Equipment used for a long time may have problems such as decreased efficiency and increased failure rate. Among them, the longer the service life of the equipment, the potential failure probability of the equipment will usually increase. Therefore, as the service life of the equipment increases, the required monitoring resources should also increase accordingly to ensure the normal operation of the equipment and timely detection of faults; equipment wear characteristics refer to the wear of the equipment caused by friction, collision and other factors during the long-term operation of the equipment. These wears are usually reflected in the performance degradation of key components, such as transmission systems, mechanical gears, cutting tools, etc. Among them, the more serious the equipment wear, the more monitoring resources are required.

[0017] Then, the spinning equipment in the textile workshop is clustered once according to the equipment model. At this stage, all equipment is divided into several groups according to similar equipment models. The equipment contained in each group has similar working principles and performance characteristics, and a clustering result is obtained. The first clustering result is further clustered according to the service life of the equipment. That is, by grouping the service life of the equipment, the equipment can be divided into different aging stages (such as new equipment and second-hand equipment). For example, equipment with a service life of 0 to 1000 hours is classified as new equipment, and equipment with a service life of 1000 to 3000 hours is classified as second-hand equipment, etc., to obtain a secondary clustering result. Then, the secondary clustering result is clustered three times according to the wear characteristics of the equipment. By analyzing the wear degree of each equipment, the equipment group is further subdivided. The wear characteristics include the wear degree of mechanical parts, the performance degradation of the transmission system, the wear of gears, etc., which all affect the normal operation of the equipment, and multiple equipment domains are obtained. In the same equipment domain, the equipment has similar equipment models, functional characteristics and performance performance. Through the unified management of the equipment in the equipment domain, the monitoring resources can be configured more efficiently, and the monitoring and control effect of yarn production can be improved.

[0018] Through multi-level cluster analysis, the equipment in the textile workshop can be more accurately grouped according to model, service life and degree of wear, thus providing a basis for subsequent production monitoring and fault prediction.

[0019] In one embodiment, step S100 of the present application further includes: S150: randomly select a first device domain, query the device operation logs of multiple devices in the first device domain, and obtain multiple fault numbers and multiple operation durations of the multiple devices within a predetermined period; S160: set the ratio of the sum of the multiple fault numbers to the sum of the multiple operation durations as a first failure rate, obtain a first benchmark failure rate after normalization, and add it to the multiple benchmark failure rates.

[0020] Specifically, any one of the multiple device domains is randomly selected as the first device domain, and then the device operation logs of multiple devices in the first device domain are queried to obtain multiple fault numbers and multiple operation durations of multiple devices within a predetermined period. The predetermined period can be set according to actual needs. For example, the device operation logs in the past month are queried, where the number of faults refers to the number of faults that occurred in the device within the predetermined period, and the operation duration refers to the total operation duration of the device within the predetermined period. Then, the multiple fault numbers and multiple operation durations are summed to obtain the total number of faults and the total operation duration, and the ratio of the total number of faults to the total operation duration is set as the first failure rate. Further, in order to facilitate comparison and analysis, the calculated first failure rate is normalized. The purpose of normalization is to scale the failure rate values ​​between different device domains to a unified range. The normalized failure rate is called the first benchmark failure rate, which represents the overall failure trend of the devices in the first device domain, and the first benchmark failure rate is added to the multiple benchmark failure rates.

[0021] By calculating multiple benchmark failure rates of multiple device domains, a basis is provided for subsequent targeted monitoring of the equipment, and a suitable monitoring strategy can be customized for each device domain. For example, device domains with higher failure rates may require more frequent monitoring and early warning, while device domains with lower failure rates can appropriately reduce the monitoring frequency. This dynamic adjustment method based on failure rate can effectively optimize the allocation of monitoring resources and avoid resource waste.

[0022] S200: Based on predetermined yarn characteristics and predetermined spinning parameters, respectively analyze the difference characteristics of a plurality of similar devices in the plurality of device domains to determine a plurality of high-difference device domains.

[0023] In one embodiment, step S200 of the present application further includes: S210: randomly select a first device domain, read a predetermined yarn feature set and a predetermined spinning parameter set of multiple spinning devices in the first device domain in a future period, wherein the yarn features include type, material, fineness, tensile strength, raw material cost and spinning complexity, and the spinning parameters include twist, tension, spinning speed and spinning temperature; S220: randomly select a first spinning device from multiple spinning devices, wherein the first spinning device has a first yarn feature and a first spinning parameter; S230: perform a similarity comparison between the first yarn feature and other yarn features in the predetermined yarn feature set, and count the number of features whose comprehensive similarity is less than a predetermined similarity threshold, and set it as a first difference; S240: if the If the first difference is greater than a predetermined difference threshold, the first spinning parameter is compared with other spinning parameters in the predetermined spinning parameter set for similarity, and the number of features whose comprehensive similarity is less than the predetermined similarity threshold is counted and set as the second difference; S250: If the second difference is greater than the predetermined difference threshold, the first spinning equipment is added to the first height difference equipment domain; S260: Determine whether the number of equipment in the first height difference equipment domain is less than a predetermined scalar, and if it is greater than or equal to, adjust the predetermined difference threshold for re-screening, wherein the predetermined scalar is one tenth of the number of spinning equipment in the first equipment domain; S270: If it is less than, add the first height difference equipment domain to the multiple height difference equipment domains.

[0024] Specifically, a first device domain is randomly selected from the multiple device domains, and then a predetermined yarn feature set and a predetermined spinning parameter set of multiple spinning devices in the first device domain within a future period (such as the next 24 hours) are read, wherein the yarn characteristics include type (such as cotton yarn, wool yarn, chemical fiber yarn, etc.), material (such as cotton, wool, polyester, etc.), fineness (such as 20s, 40s, etc.), tensile strength, raw material cost and spinning complexity, and the spinning parameters include twist, tension, spinning speed and spinning temperature.

[0025] Then, any one of the multiple spinning devices in the first device domain is randomly selected as the first spinning device, wherein the first spinning device has a first yarn feature and a first spinning parameter. Then, the first yarn feature is similarly compared with other yarn features in the predetermined yarn feature set to obtain multiple comprehensive similarities, wherein the comprehensive similarity refers to the average of the similarities of multiple attribute features in the yarn feature (type, material, fineness, tensile strength, raw material cost and spinning complexity); further, the number of features whose comprehensive similarity is less than a predetermined similarity threshold is counted and set as the first difference, and the predetermined similarity threshold can be set according to the actual scenario, such as setting the predetermined similarity threshold to 50%.

[0026] The first difference is judged according to a predetermined difference threshold. If the first difference is greater than the predetermined difference threshold, the first spinning parameter is compared with other spinning parameters in the predetermined spinning parameter set for similarity, and the number of features whose comprehensive similarity of the spinning parameters is less than the predetermined similarity threshold is counted and set as the second difference. If the second difference is greater than the predetermined difference threshold, the first spinning equipment is added to the first height difference equipment domain, wherein the height difference equipment refers to equipment with a large overall difference in yarn characteristics and spinning parameters from similar equipment in the equipment domain. It is further judged whether the number of equipment in the first height difference equipment domain is less than a predetermined scalar, wherein the predetermined scalar is one tenth of the number of spinning equipment in the first equipment domain. If the number of equipment in the first height difference equipment domain is greater than or equal to the predetermined scalar, it is necessary to appropriately increase the predetermined difference threshold and perform re-screening; if the number of equipment in the first height difference equipment domain is less than the predetermined scalar, the first height difference equipment domain is added to the multiple height difference equipment domains.

[0027] S300: According to the predetermined yarn characteristics, respectively perform fault probability trend analysis on the multiple height difference equipment domains, obtain multiple probability trend coefficient sets to compensate the multiple reference failure rates, and obtain multiple adaptation failure rate sets.

[0028] In one embodiment, step S300 of the present application further includes: S310: Randomly select a first height difference device domain, and select any device in the first height difference device domain as a first height difference device.

[0029] Specifically, any one height difference device domain is randomly selected from the plurality of height difference device domains as the first height difference device domain, and any one device is selected from the first height difference device domain as the first height difference device.

[0030] S320: Acquire the first height difference yarn feature of the first height difference device, input the first height difference yarn feature into a fault probability predictor to perform fault probability trend analysis, output a first probability trend coefficient, and add it to a first probability trend coefficient set.

[0031] In one embodiment, step S320 of the present application further includes: S321: Query historical spinning operation records to obtain a sample yarn feature set, and statistically analyze the mean failure probability of multiple spinning equipment with different sample yarn features within a predetermined historical period as the sample probability trend coefficient to obtain a sample probability trend coefficient set; S322: Take the sample yarn feature as input and the sample probability trend coefficient as output, use the sample yarn feature set and the sample probability trend coefficient set as training data, perform supervised training on the BP neural network until convergence, and obtain the fault probability predictor.

[0032] Specifically, first, the historical spinning operation records are queried, and the yarn feature data in different production cycles are extracted from the historical spinning records to obtain a sample yarn feature set; then, the mean failure probabilities of multiple spinning equipment with different sample yarn features within a predetermined historical period are counted, that is, for each sample yarn feature, the failure probability of different spinning equipment within a predetermined historical period is counted, and the mean of multiple failure probabilities is calculated and set as the sample probability trend coefficient to obtain a sample probability trend coefficient set, in which the sample yarn features and the sample probability trend coefficients correspond one to one.

[0033] Next, a fault probability predictor is constructed based on the BP neural network. The fault probability predictor is a neural network model that can be iteratively optimized in machine learning, including an input layer, a hidden layer and an output layer, wherein the input data of the input layer is the sample yarn feature, and the output data is the sample probability trend coefficient; then, the sample yarn feature is used as input, the sample probability trend coefficient is used as output, and the sample yarn feature set and the sample probability trend coefficient set are used as training data to perform supervised training on the fault probability predictor. In each training, the sample yarn feature in the training set is input into the neural network. After calculation by the input layer and the hidden layer, the output layer generates a predicted value (i.e., the trend coefficient predicted value of the fault probability); then, the fault probability predictor is used The mean square error (MSE) or other suitable loss function is used to calculate the error between the network prediction output and the actual sample probability trend coefficient; the back propagation algorithm calculates the gradient of each parameter (weight and bias) according to the loss function, and uses the chain rule to gradually propagate the error from the output layer to the input layer, calculates the gradient of each layer, and updates the weights and biases; gradient descent is used to update the weights and biases in the network. The update process will continuously adjust the parameters of the network so that the prediction error gradually decreases. Through multiple rounds of iterations, the network parameters are gradually optimized to reduce the loss function value and improve the prediction accuracy. Iterative training is continuously performed until the predetermined number of iterations is reached or the loss function converges to a certain threshold, and a trained fault probability predictor is obtained.

[0034] The first height difference yarn feature of the first height difference device is obtained, wherein the first height difference yarn feature includes a first type, a first material, a first fineness, a first tensile strength, a first raw material cost, and a first spinning complexity; then the first height difference yarn feature is input into a fault probability predictor for fault probability trend analysis, and a first probability trend coefficient is output and added to the first probability trend coefficient set. By constructing a fault probability predictor based on a BP neural network for fault probability trend analysis, the efficiency and accuracy of the probability trend can be improved.

[0035] In one embodiment, step S300 of the present application further includes: S330: Using 1 plus the probability trend coefficient as the compensation coefficient, multiple compensation coefficient sets are calculated based on the multiple probability trend coefficient sets; S340: Multiplying the multiple benchmark failure rates based on the multiple compensation coefficient sets to obtain the multiple adaptation failure rate sets.

[0036] Specifically, first, 1 plus the probability trend coefficient is used as the compensation coefficient, and the multiple probability trend coefficient sets and 1 are summed to obtain multiple compensation coefficient sets. Then, the multiple benchmark failure rates are multiplied according to the multiple compensation coefficient sets, and the product of the two is used as the adaptation failure rate to obtain multiple adaptation failure rate sets. By performing a failure probability trend analysis based on the sample yarn characteristics and compensating the benchmark failure rate according to the probability trend coefficient to obtain an adaptation failure rate set, the scientificity, accuracy and reliability of the adaptation failure rate setting can be improved, thereby providing a more accurate and scientific basis for subsequent production monitoring, and helping to improve the accuracy of equipment failure prediction and the utilization efficiency of monitoring resources.

[0037] S400: configuring a plurality of adaptive monitoring parameter sets according to the plurality of adaptive fault rate sets, performing production monitoring on the plurality of height difference equipment domains based on the plurality of adaptive monitoring parameter sets, and regulating the abnormal monitoring equipment according to a predetermined feedback regulation mechanism.

[0038] In one embodiment, step S400 of the present application further includes: S410: Obtaining the initial monitoring parameters of the spinning equipment, wherein the initial monitoring parameters include an initial monitoring frequency, an initial early warning threshold and an initial monitoring model complexity, the monitoring model is a machine learning model for abnormal monitoring of the operation process of the spinning equipment, and the monitoring model complexity is the number of analysis units of the monitoring model; S420: According to a predetermined adjustment plan, the initial monitoring frequency, the initial early warning threshold and the initial monitoring model complexity are proportionally optimized and adjusted according to the multiple adaptive failure rate sets to obtain the multiple adaptive monitoring parameter sets, wherein the adaptive failure rate and the adaptive monitoring frequency, the adaptive early warning threshold and the adaptive monitoring model complexity are positively correlated.

[0039] Specifically, first, the initial monitoring parameters of the spinning equipment are obtained, where the initial monitoring parameters include the initial monitoring frequency, the initial warning threshold and the initial monitoring model complexity. The initial monitoring frequency refers to the frequency of monitoring data collection and analysis during the operation of the spinning equipment. It determines the time interval at which the system collects data for analysis so as to promptly detect abnormalities in the operation of the equipment. It can be set according to the actual scenario, for example, the initial monitoring frequency is set to 1 minute each time; the initial warning threshold refers to the threshold during the operation of the equipment. If the monitoring data exceeds the threshold, the system will issue a warning to indicate that the equipment may have a malfunction or abnormality, such as the yarn quality warning threshold or the spinning parameter warning threshold. The yarn quality warning threshold is used to monitor and control key quality indicators of the yarn (such as fineness, tensile strength, uniformity, etc.) to prevent yarn that does not meet quality standards from entering the production process, thereby ensuring the qualified rate of yarn products.

[0040] Among them, the monitoring model is a machine learning model for abnormal monitoring of the operation process of the spinning equipment, such as a yarn quality monitoring model or a spinning parameter monitoring model. A variety of machine learning models can be selected to monitor the abnormality of the spinning equipment, such as decision trees, random forests, neural networks, etc. The specific model to be selected depends on the characteristics of the equipment data and the monitoring requirements. The complexity of the monitoring model is the number of analysis units of the monitoring model. The more analysis units there are, the higher the complexity of the monitoring model. The higher the model complexity, the more equipment operation characteristics that can be captured and analyzed, and the higher the accuracy may be, but the corresponding computing and storage requirements will also increase.

[0041] According to the predetermined adjustment scheme, the initial monitoring frequency, initial warning threshold and initial monitoring model complexity are proportionally optimized and adjusted according to the multiple adaptive failure rate sets, wherein there is a positive correlation between the adaptive failure rate and the monitoring parameters (monitoring frequency, warning threshold, monitoring model complexity), that is, as the failure rate increases, the corresponding monitoring frequency, warning threshold and monitoring model complexity also need to be increased accordingly to ensure that the monitoring of the equipment is more accurate and timely, wherein the predetermined adjustment scheme includes the step size of each adjustment, that is, the proportional amplitude of each adjustment, including the monitoring frequency step size, the warning threshold step size and the model complexity step size, and obtains the adjusted multiple adaptive monitoring parameter sets, wherein the adaptive monitoring parameters include the adaptive monitoring frequency, the adaptive warning threshold and the adaptive monitoring model complexity. Finally, based on the multiple adaptive monitoring parameter sets, the multiple height difference equipment domains are subjected to production monitoring.

[0042] Through the predetermined adjustment plan combined with the step control mechanism, the monitoring frequency, warning threshold and monitoring model complexity can be dynamically optimized by reasonably adjusting the step size when monitoring resources are limited. This can not only improve the monitoring efficiency of spinning equipment and reduce unnecessary waste of resources, but also enhance the early warning capability of faults, thereby ensuring efficient and stable operation of the equipment.

[0043] In one embodiment, step S400 of the present application further includes: S430: Establish a first mapping between the equipment domain and the height difference equipment domain; S440: Randomly select a first height difference equipment domain, and based on the first mapping, obtain a first mapping equipment domain of the first height difference equipment domain; S450: Count the abnormal types and abnormal proportions of abnormal monitoring equipment in the first height difference equipment domain within a predetermined time window, and if the abnormal proportion of the same abnormal type is greater than a first predetermined threshold, all spinning equipment in the first mapping equipment domain is regulated according to a predetermined feedback control mechanism.

[0044] Specifically, first, a first mapping relationship between a device domain and a height difference device domain is established; then, a first height difference device domain is randomly selected from multiple height difference device domains, and based on the first mapping, a first mapping device domain of the first height difference device domain is obtained. Then, in the process of production monitoring of the multiple height difference device domains, within the selected time window, the abnormal types and their occurrence proportions of all abnormal monitoring devices in the first height difference device domain are counted, and the abnormal types may include equipment failure, performance degradation, process abnormalities, etc. When the abnormal proportion of the same abnormal type is greater than a first predetermined threshold value (such as 50%), indicating that the abnormal type has a higher occurrence frequency in the device domain, all spinning equipment in the first mapping device domain is regulated according to a predetermined feedback control mechanism, wherein the predetermined feedback control mechanism includes a variety of control schemes for abnormal states, that is, when an abnormality is detected in the production process, corresponding adjustments and processing schemes are made according to preset rules, and can be set according to historical abnormal maintenance records.

[0045] A textile yarn production monitoring and control method provided by an embodiment of the present invention has at least the following technical effects: The spinning equipment in the textile workshop is clustered according to the predetermined equipment characteristics, a plurality of equipment domains are determined, and the failure rate statistics of the plurality of equipment domains are respectively performed to determine a plurality of benchmark failure rates; then, based on the predetermined yarn characteristics and the predetermined spinning parameters, the difference characteristics of a plurality of similar equipment in the plurality of equipment domains are respectively analyzed to determine a plurality of high-difference equipment domains; then, according to the predetermined yarn characteristics, the failure probability trend analysis of the plurality of high-difference equipment domains is respectively performed, and a plurality of probability trend coefficient sets are obtained to compensate the plurality of benchmark failure rates to obtain a plurality of adapted failure rate sets; finally, a plurality of adapted monitoring parameter sets are configured according to the plurality of adapted failure rate sets, and production monitoring is performed on the plurality of high-difference equipment domains based on the plurality of adapted monitoring parameter sets, and the abnormal monitoring equipment is regulated according to the predetermined feedback regulation mechanism; that is, by introducing the intelligent monitoring and control mechanism, the monitoring scheme can be adaptively adjusted according to the real-time status of the equipment, the production tasks and the characteristics of the yarn products, and the allocation of monitoring resources can be optimized, so that the monitoring and control effect of yarn production can be significantly improved under the condition of limited monitoring resources, and the high quality and consistency of yarn products can be ensured.

[0046] For example 2, please refer to Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: clustering the spinning equipment in the textile workshop according to predetermined equipment characteristics, determining multiple equipment domains, and performing fault rate statistics on the multiple equipment domains respectively to determine multiple benchmark fault rates; based on predetermined yarn characteristics and predetermined spinning parameters, performing difference feature analysis on multiple similar equipment in the multiple equipment domains respectively to determine multiple high-difference equipment domains; according to the predetermined yarn characteristics, performing fault probability trend analysis on the multiple high-difference equipment domains respectively, obtaining multiple probability trend coefficient sets to compensate for the multiple benchmark fault rates, and obtaining multiple adaptive fault rate sets; configuring multiple adaptive monitoring parameter sets according to the multiple adaptive fault rate sets, performing production monitoring on the multiple high-difference equipment domains based on the multiple adaptive monitoring parameter sets, and regulating the abnormal monitoring equipment according to the predetermined feedback control mechanism.

[0047] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0048] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for monitoring and controlling textile yarn production, characterized in that: Methods include: Clustering the spinning equipment in the textile workshop according to predetermined equipment characteristics to determine multiple equipment domains, and performing failure rate statistics on the multiple equipment domains respectively to determine multiple benchmark failure rates; Based on the predetermined yarn characteristics and the predetermined spinning parameters, respectively performing difference characteristic analysis on a plurality of similar devices in the plurality of device domains to determine a plurality of high-difference device domains; According to the predetermined yarn characteristics, respectively performing fault probability trend analysis on the multiple height difference equipment domains, obtaining multiple probability trend coefficient sets to compensate the multiple reference fault rates, and obtaining multiple adaptation fault rate sets; Multiple adaptive monitoring parameter sets are configured according to the multiple adaptive fault rate sets, and based on the multiple adaptive monitoring parameter sets, production monitoring is performed on the multiple height difference equipment domains, and the abnormal monitoring equipment is regulated according to a predetermined feedback regulation mechanism.

2. A textile yarn production monitoring and control method according to claim 1, characterized in that: The spinning equipment in the textile workshop is clustered according to the predetermined equipment characteristics to determine multiple equipment domains, including: Acquiring predetermined equipment characteristics, wherein the predetermined equipment characteristics include equipment model, equipment service life, and equipment wear characteristics; Clustering the spinning equipment in the textile workshop according to the equipment model to obtain a clustering result; Performing secondary clustering on the primary clustering result according to the service life of the equipment to obtain a secondary clustering result; The secondary clustering result is clustered thirdly according to the equipment wear characteristics to obtain the multiple equipment domains.

3. A textile yarn production monitoring and control method according to claim 1, characterized in that: Performing failure rate statistics on the multiple device domains respectively to determine multiple benchmark failure rates includes: Randomly selecting a first device domain, querying device operation logs of multiple devices in the first device domain, and obtaining multiple fault numbers and multiple operation durations of the multiple devices within a predetermined period; The ratio of the sum of the multiple fault numbers to the sum of the multiple operating time periods is set as a first failure rate, and a first reference failure rate is obtained after normalization processing, and added to the multiple reference failure rates.

4. A textile yarn production monitoring and control method according to claim 1, characterized in that: Based on the predetermined yarn characteristics and the predetermined spinning parameters, the difference characteristics of multiple similar devices in the multiple device domains are analyzed respectively to determine multiple high-difference device domains, including: Randomly select a first device domain, and read a predetermined yarn feature set and a predetermined spinning parameter set of multiple spinning devices in the first device domain in a future period, wherein the yarn features include type, material, fineness, tensile strength, raw material cost and spinning complexity, and the spinning parameters include twist, tension, spinning speed and spinning temperature; Randomly selecting a first spinning device from a plurality of spinning devices, wherein the first spinning device has a first yarn characteristic and a first spinning parameter; Performing a similarity comparison between the first yarn feature and other yarn features in the predetermined yarn feature set, and counting the number of features whose comprehensive similarity is less than a predetermined similarity threshold, as a first difference degree; If the first difference is greater than a predetermined difference threshold, the first spinning parameter is compared with other spinning parameters in the predetermined spinning parameter set for similarity, and the number of features whose comprehensive similarity is less than the predetermined similarity threshold is counted and set as a second difference; If the second difference is greater than a predetermined difference threshold, adding the first spinning device to the first height difference device domain; Determine whether the number of devices in the first height difference device domain is less than a predetermined scalar, and if it is greater than or equal to, adjust the predetermined difference threshold to perform another screening, wherein the predetermined scalar is one tenth of the number of spinning devices in the first device domain; If it is less, the first height difference device domain is added to the multiple height difference device domains.

5. A textile yarn production monitoring and control method according to claim 4, characterized in that: According to the predetermined yarn characteristics, the failure probability trend analysis is performed on the multiple height difference equipment domains respectively, including: Randomly select a first height difference device domain, and select any device in the first height difference device domain as the first height difference device; The first height difference yarn feature of the first height difference device is obtained, the first height difference yarn feature is input into a fault probability predictor to perform fault probability trend analysis, a first probability trend coefficient is output, and the coefficient is added to a first probability trend coefficient set.

6. A textile yarn production monitoring and control method according to claim 5, characterized in that: Build a failure probability predictor, including: Query historical spinning operation records to obtain a sample yarn feature set, and statistically calculate the mean value of the failure probability of multiple spinning equipment with different sample yarn features within a predetermined historical period as a sample probability trend coefficient to obtain a sample probability trend coefficient set; The sample yarn features are used as input, the sample probability trend coefficient is used as output, the sample yarn feature set and the sample probability trend coefficient set are used as training data, and the BP neural network is supervised and trained until convergence, so as to obtain the fault probability predictor.

7. A textile yarn production monitoring and control method according to claim 1, characterized in that: The multiple reference failure rates are compensated to obtain multiple adaptation failure rate sets, including: Taking 1 plus the probability trend coefficient as the compensation coefficient, a plurality of compensation coefficient sets are calculated according to the plurality of probability trend coefficient sets; The multiple reference failure rates are multiplied by the multiple compensation coefficient sets to obtain the multiple adaptation failure rate sets.

8. A textile yarn production monitoring and control method according to claim 1, characterized in that: Configuring multiple adaptation monitoring parameter sets according to the multiple adaptation failure rate sets includes: Obtaining initial monitoring parameters of the spinning equipment, wherein the initial monitoring parameters include an initial monitoring frequency, an initial warning threshold, and an initial monitoring model complexity, the monitoring model is a machine learning model for abnormal monitoring of the operation process of the spinning equipment, and the monitoring model complexity is the number of analysis units of the monitoring model; According to a predetermined adjustment plan, the initial monitoring frequency, initial warning threshold and initial monitoring model complexity are proportionally optimized and adjusted according to the multiple adaptation failure rate sets to obtain the multiple adaptation monitoring parameter sets, wherein the adaptation failure rate and adaptation monitoring frequency, the adaptation warning threshold and adaptation monitoring model complexity are positively correlated.

9. A textile yarn production monitoring and control method according to claim 1, characterized in that: The abnormal monitoring equipment is regulated according to the predetermined feedback control mechanism, including: Establishing a first mapping between the device domain and the height difference device domain; Randomly select a first height difference device domain, and based on the first mapping, obtain a first mapping device domain of the first height difference device domain; The abnormal types and abnormal proportions of abnormal monitoring equipment in the first height difference equipment domain within a predetermined time window are counted; if the abnormal proportion of the same abnormal type is greater than a first predetermined threshold, all spinning equipment in the first mapping equipment domain is regulated according to a predetermined feedback control mechanism.

10. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of a textile yarn production monitoring and control method as described in any one of claims 1 to 9.

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