A digital monitoring method and system for production cost and inventory turnover of a textile mill, a storage medium and a program product

Abstract

The application provides a textile mill production cost and inventory turnover digital monitoring method and system, a storage medium and a program product, relates to the technical field of textile production management, and the method comprises the following steps: acquiring real-time yarn feeding speed sequences, real-time winding speed sequences and real-time tension sequences of a loom in a current production cycle; performing time domain conversion analysis on the real-time yarn feeding speed sequences and the real-time winding speed sequences respectively according to preset time sliding windows to obtain yarn feeding reference lengths and actual winding lengths; determining a target loss length according to the real-time tension sequences, the actual winding lengths and a preset yarn elastic modulus; accumulating the yarn feeding reference lengths and the target loss lengths of all preset time sliding windows respectively to obtain a total cycle material consumption and a total cycle waste loss; further determining a current account inventory and an actual production cost; and mapping the current account inventory and the actual production cost to a target digital monitoring interface.

Description

Technical Field

[0001] This application relates to the field of textile production management technology, and in particular to a digital monitoring method, system, storage medium and program product for textile factory production costs and inventory turnover. Background Technology

[0002] With the increasing demands for refined management in the textile industry, higher requirements are being placed on the precise monitoring of raw material consumption, inventory turnover, and production costs during the textile mill production process. Textile production is a continuous conversion process. The process of processing raw materials (such as various yarns) into rolls of fabric on the loom involves not only complex mechanical movements but also unavoidable waste and pollution losses. This makes it difficult to maintain an absolute linear synchronization between the actual material consumption and the theoretical output during production, resulting in significant challenges in obtaining real-time and accurate production flow data.

[0003] In related technologies, a theoretical calculation method based on standard consumption ratios is typically used. Specifically, a standard consumption ratio model for different fabric types and required raw materials is pre-established in the textile factory's management system. During actual production, the management system obtains the production length data of the wound fabric through the meter counter at the loom terminal; the collected fabric length data is substituted into the standard consumption ratio model to calculate the theoretical raw material consumption for producing that length of fabric; then, based on this theoretical consumption, the existing book inventory data of raw materials in the warehouse is simultaneously deducted to obtain the current book inventory of raw materials; finally, based on the theoretical consumption, the unit price of raw materials, and the preset waste loss rate and pollution allocation coefficient, the production cost is calculated.

[0004] However, the above-mentioned theoretical accounting method based on standard consumption ratio is problematic because the standard consumption ratio model is established based on fixed process parameters of the loom under ideal uniform speed operation. In actual continuous production, the operating status of the loom's transmission components and the physical traction process of the material are easily affected by multiple objective factors. This leads to a discrepancy between the theoretical consumption amount derived from the standard consumption ratio model and the actual physical consumption amount in the production process. Consequently, there is a significant difference between the book inventory amount obtained by deducting the theoretical consumption amount and the actual inventory turnover, resulting in a low accuracy rate of production cost and inventory monitoring in textile mills in related technologies. Summary of the Invention

[0005] This application provides a digital monitoring method, system, storage medium, and program product for textile mill production costs and inventory turnover, which can improve the accuracy of textile mill production cost and inventory monitoring.

[0006] Firstly, this application provides a digital monitoring method for production costs and inventory turnover in a textile mill, applied to a digital management system. The method includes: acquiring continuous operating status data of a loom during the current production cycle, the continuous operating status data including a real-time yarn feed speed sequence collected from the yarn supply end, a real-time winding speed sequence collected from the winding end, and a real-time tension sequence of the yarn on the transmission path of the loom; performing a first time-domain transformation analysis on the real-time yarn feed speed sequence according to a preset time sliding window to obtain the yarn feed reference length; and performing a second time-domain transformation analysis on the real-time winding speed sequence according to a preset time sliding window to obtain the actual winding length; and based on the real-time tension sequence, the actual winding length, and... The target loss length of the yarn within a preset time sliding window is determined by the preset yarn elastic modulus; the yarn feed reference length of all preset time sliding windows in the current production cycle is accumulated in the first step to obtain the total material consumption of the cycle; and the target loss length of all preset time sliding windows in the current production cycle is accumulated in the second step to obtain the total waste loss of the cycle; the raw material inventory data is updated according to the total material consumption of the cycle to obtain the current inventory level; and the actual production cost of the current production cycle is determined according to the total material consumption of the cycle, the total waste loss of the cycle, and the preset contaminant treatment rate; the current inventory level and the actual production cost are mapped to the target digital monitoring interface.

[0007] By adopting the above technical solution, continuous operating status data is synchronously acquired from the yarn feeding end and the take-up end of the loom. The real-time tension sequence of the yarn along the transmission path is used to physically correct for losses in the take-up length. This ensures that the total consumption of periodic materials and the total waste loss of periodic materials are obtained window-by-window based on the actual physical state of the loom during actual operation, rather than relying on a pre-established fixed standard consumption ratio model for theoretical deduction. Furthermore, the current book inventory updated based on the total consumption of periodic materials reflects the actual deduction of raw materials in the actual production flow. Simultaneously, based on the total consumption of periodic materials, the total waste loss of periodic materials, and the actual production cost determined by the preset pollutant treatment rate, material consumption and pollution treatment costs can be synchronously quantified and calculated. This makes the current book inventory and actual production cost mapped to the target digital monitoring interface more accurate and realistic, thus objectively presenting the micro-level material flow status within the production cycle. This solves the technical problem of low accuracy in monitoring production costs and inventory in textile mills, achieving the technical effect of improving the accuracy of production cost and inventory monitoring in textile mills.

[0008] Optionally, the target loss length of the yarn within a preset time sliding window is determined based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus. This includes: extracting the temporal features of the real-time tension sequence to obtain the tension fluctuation extreme points and high-frequency abrupt change intervals within the preset time sliding window; determining the tension compensation coefficient of the yarn within the preset time sliding window based on the tension fluctuation extreme points, high-frequency abrupt change intervals, and the preset yarn elastic modulus; using the tension compensation coefficient to perform length restoration processing on the actual winding length to obtain the theoretical tension-free winding length; and determining the length difference between the yarn feed reference length and the theoretical tension-free winding length as the target loss length.

[0009] By employing the above technical solution, the time-series features of the real-time tension sequence are extracted to obtain the extreme points of tension fluctuations and the high-frequency abrupt change range of tension. Based on the extreme points of tension fluctuations, the high-frequency abrupt change range of tension, and the preset yarn elastic modulus, the tension compensation coefficient is determined. This allows for the quantitative restoration of the elastic elongation of the yarn caused by tension during loom operation in the actual winding length, thus obtaining the theoretical tension-free winding length. Furthermore, the length difference between the yarn feed reference length and the theoretical tension-free winding length is used as the target loss length. This ensures that the target loss length eliminates the interference of yarn elastic deformation on the winding length, improving the accuracy of the target loss length calculation.

[0010] Optionally, the target loss length of the yarn within a preset time sliding window is determined based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus. This includes: performing stress accumulation analysis on the real-time tension sequence to obtain the cumulative tension load value borne by the yarn on the transmission path; matching the cumulative tension load value with a preset elastic-plastic critical mapping relationship to determine the plastic deformation rate of the yarn within the preset time sliding window; calculating the deformation increment based on the actual winding length and the plastic deformation rate to obtain the plastic extension increment; and calculating the loss deviation based on the yarn feed reference length, the actual winding length, and the plastic extension increment to obtain the target loss length.

[0011] By employing the above technical solution, stress accumulation analysis is performed on the real-time tension sequence to obtain the cumulative tension load value. This cumulative tension load value is then matched with a preset elastic-plastic critical mapping relationship to determine the plastic deformation rate. This allows for the identification of irreversible plastic deformation of the yarn exceeding its elastic range due to continuous load along the transmission path. The plastic extension increment is obtained by calculating the deformation increment based on the actual winding length and the plastic deformation rate. Combined with the yarn feed reference length and the actual winding length, loss deviation calculations are performed. This ensures that the target loss length reflects the true physical loss of the yarn after deducting the plastic extension increment, avoiding the miscalculation of the yarn's plastic extension length as waste material loss and improving the accuracy of target loss length calculation under high load conditions.

[0012] Optionally, after updating the raw material inventory data based on the total consumption of periodic materials to obtain the current inventory level, and determining the actual production cost of the current production cycle based on the total consumption of periodic materials, the total loss of periodic waste, and the preset pollutant treatment rate, the method further includes: extracting the historical inventory consumption rate sequence within a preset traceability period from the current inventory level; determining the expected depletion time node of the current inventory level based on the historical inventory consumption rate sequence and preset production scheduling data; when the expected depletion time node is within a preset supply chain security warning range, using the actual production cost as a cost fluctuation feedback variable; performing parameter correction operations on the initial purchase batch optimization model using the cost fluctuation feedback variable to obtain the target purchase batch optimization model, and generating a target replenishment strategy based on the target purchase batch optimization model.

[0013] By employing the aforementioned technical solution, historical inventory consumption rate sequences within a preset traceability period are extracted from the current book inventory level. Combined with preset production scheduling data, the expected depletion time of the current book inventory level is determined, enabling a prediction of the remaining availability period of raw materials based on actual inventory consumption trends. When the expected depletion time falls within a preset supply chain security warning range, actual production costs are introduced as cost fluctuation feedback variables into the initial procurement batch optimization model to perform parameter correction operations. This ensures that the generated target replenishment strategy not only considers the time urgency of inventory but also incorporates cost fluctuations during actual production into procurement decisions, improving the matching degree between the target replenishment strategy and the actual production status.

[0014] Optionally, the initial purchase batch optimization model is modified using cost fluctuation feedback variables to obtain a target purchase batch optimization model. A target replenishment strategy is then generated based on this model, including: determining the waste cost based on the total periodic waste loss and yarn unit cost; determining the proportion of waste cost in the yarn waste cost within the cost fluctuation feedback variables; determining the target tensile strength compensation coefficient for the next batch of yarn based on the mapping relationship between the yarn waste cost proportion and the preset yarn quality tolerance; modifying the yarn unit price weight parameter and quality benefit parameter in the initial purchase batch optimization model using the target tensile strength compensation coefficient to obtain the target purchase batch optimization model; inputting the expected depletion time, current book inventory, and the average operating tension value of the loom in the current production cycle as joint state constraints into the target purchase batch optimization model for iterative optimization to obtain the target purchase batch and target yarn quality grade; and combining the target purchase batch and target yarn quality grade to generate the target replenishment strategy.

[0015] By adopting the above technical solution, the waste loss cost is determined based on the total amount of waste loss in the cycle and the unit cost of yarn. The proportion of waste loss cost in the yarn waste cost within the cost fluctuation feedback variable is also determined, enabling a quantitative assessment of the impact of waste loss on overall production costs. Furthermore, the target tensile strength compensation coefficient is determined based on the mapping relationship between the proportion of yarn waste cost and the preset yarn quality tolerance. This target tensile strength compensation coefficient is then used to correct the yarn unit price weight parameter and quality benefit parameter in the initial purchase batch optimization model, allowing the target purchase batch optimization model to dynamically balance purchase costs and yarn quality. By inputting the expected depletion time, current inventory level, and the average operating tension value of the loom in the current production cycle as joint state constraints into the target purchase batch optimization model for iterative optimization, the target purchase batch and target yarn quality level in the generated target replenishment strategy simultaneously adapt to the time constraints of inventory and the actual operating conditions of the loom, improving the overall rationality of the target replenishment strategy.

[0016] Optionally, after mapping the current book inventory and actual production costs to the target digital monitoring interface, the method further includes: extracting the actual production cost time series within a preset historical display period from the target digital monitoring interface, and decomposing the actual production cost time series into a basic material cost series and an environmental depreciation cost series based on the total waste loss of the period and the preset pollutant treatment rate; determining the second change slope of the environmental depreciation cost series based on the first change slope of the basic material cost series; determining the pollution discharge marginal index based on the first change slope and the second change slope; when the pollution discharge marginal index is greater than the preset green production tolerance threshold, performing phase difference analysis on the real-time yarn feeding speed series and the real-time winding speed series to obtain the resonant speed frequency band; determining the target operating speed range based on the resonant speed frequency band and generating a loom speed limit operation command; and issuing the speed limit operation command to the loom to control the loom to perform weaving operations according to the target operating speed range in the next production cycle.

[0017] By adopting the above technical solution, the actual production cost time series is decomposed into a basic material cost series and an environmental depreciation cost series. The first and second slopes of change are determined separately to calculate the pollution discharge marginal index. This allows for a quantitative assessment of the growth trend of environmental depreciation costs relative to basic material costs from a cost trend perspective. When the pollution discharge marginal index exceeds the preset green production tolerance threshold, phase difference analysis of the real-time yarn feed speed series and real-time winding speed series is performed to obtain the resonant speed frequency band. Based on this, the target operating speed range is determined to generate a loom speed-limiting command. This ensures that the loom can avoid operating in the resonant speed frequency band that easily causes drastic fluctuations in yarn tension in the next production cycle. This reduces waste generation and pollution discharge caused by speed mismatch at the source, achieving a linkage feedback between production cost monitoring and green production control.

[0018] Optionally, when the pollution discharge marginal index exceeds a preset green production tolerance threshold, phase difference analysis is performed on the real-time yarn feeding speed sequence and the real-time winding speed sequence to obtain the resonant speed frequency band. This includes: performing a first frequency domain transformation analysis on the real-time yarn feeding speed sequence to obtain a yarn feeding discrete frequency band sequence and a yarn feeding phase angle sequence; performing a second frequency domain transformation analysis on the real-time winding speed sequence to obtain a winding discrete frequency band sequence and a winding phase angle sequence; aligning the yarn feeding discrete frequency band sequence and the winding discrete frequency band sequence to obtain an aligned discrete frequency band sequence; determining a phase angle difference sequence that matches the aligned discrete frequency band sequence based on the yarn feeding phase angle sequence and the winding phase angle sequence; performing timestamp alignment mapping between the phase angle difference sequence and the fluctuation extreme value range of the real-time tension sequence to extract a candidate phase angle difference set located in the fluctuation extreme value range from the phase angle difference sequence; and determining the frequency band assignment of the candidate phase angle difference set based on the aligned discrete frequency band sequence to obtain the resonant speed frequency band.

[0019] By employing the aforementioned technical solution, frequency domain transformation analysis is performed on the real-time yarn feeding speed sequence and the real-time winding speed sequence to obtain their respective discrete frequency band sequences and phase angle sequences. Based on frequency band alignment, the phase angle difference sequence is determined, enabling precise identification of the speed phase deviation between the yarn feeding end and the winding end from a frequency domain perspective. By aligning the phase angle difference sequence with the extreme fluctuation range of the real-time tension sequence using timestamps, a candidate phase angle difference set is extracted. Furthermore, the candidate phase angle difference set is assigned to a specific frequency band based on the aligned discrete frequency band sequence. This ensures that the final resonant speed frequency band is a specific frequency band causally related to the extreme fluctuation range of the yarn tension, improving the accuracy of resonant speed frequency band positioning.

[0020] In a second aspect, embodiments of this application provide a digital management system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to cause the digital management system to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including program instructions that, when executed on a digital management system, cause the digital management system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a digital management system, cause the digital management system to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. This application synchronously collects real-time yarn feed speed sequences, real-time winding speed sequences, and real-time tension sequences of the yarn along the transmission path from the yarn feeding end and winding end of the loom. Within a preset time sliding window, it performs time-domain conversion analysis and tension-based loss correction respectively. The total consumption of periodic materials and the total loss of periodic waste are obtained by accumulating window by window. This ensures that the current book inventory updated based on the total consumption of periodic materials and the actual production cost determined based on the total loss of periodic waste can accurately reflect the real physical state data of the loom during actual continuous operation, rather than relying on theoretical back-calculation based on a pre-established fixed standard consumption ratio model. This improves the accuracy of production cost and inventory monitoring in textile mills.

[0025] 2. This application predicts the expected depletion time by extracting the historical inventory consumption rate sequence from the current book inventory. When the expected depletion time falls within the preset supply chain security warning range, the actual production cost is used as a cost fluctuation feedback variable and introduced into the initial purchase batch optimization model for parameter correction and iterative optimization. This ensures that the target purchase batch and target yarn quality grade in the generated target replenishment strategy are simultaneously adapted to the time constraints of inventory, cost fluctuation status, and actual operating conditions of the loom, thus achieving a closed-loop linkage from production monitoring to procurement decision-making.

[0026] 3. This application calculates the pollution marginal index by decomposing the actual production cost time series into a basic material cost series and an environmental depreciation cost series. When the pollution marginal index exceeds the preset green production tolerance threshold, the resonant speed frequency band is located based on the phase difference analysis of the yarn feeding speed series and the winding speed series. Based on this, a loom speed limit operation command is generated to control the loom to avoid the resonant speed frequency band. This realizes the linkage feedback between production cost monitoring and green production control, reducing waste generation and pollution discharge while taking into account the compliance requirements of green production. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a digital monitoring method for textile factory production costs and inventory turnover in an embodiment of this application.

[0028] Figure 2 This is a schematic diagram of the physical device structure of a digital management system in an embodiment of this application. Detailed Implementation

[0029] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0031] This application provides a digital monitoring method for production costs and inventory turnover in textile mills, see reference. Figure 1 , Figure 1 This is a flowchart illustrating a digital monitoring method for textile mill production costs and inventory turnover in this application, comprising the following steps:

[0032] Step S101: Obtain the continuous operating status data of the loom in the current production cycle. The continuous operating status data includes the real-time yarn feeding speed sequence collected from the yarn feeding end, the real-time winding speed sequence collected from the winding end, and the real-time tension sequence of the yarn on the transmission path of the loom.

[0033] Step S102: Perform a first time-domain transformation analysis on the real-time yarn feed speed sequence according to a preset time sliding window to obtain the yarn feed reference length; and perform a second time-domain transformation analysis on the real-time winding speed sequence according to a preset time sliding window to obtain the actual winding length; determine the target loss length of the yarn within the preset time sliding window based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus.

[0034] Step S103: Perform a first accumulation process on the yarn feed reference length of all preset time sliding windows in the current production cycle to obtain the total material consumption of the cycle; and perform a second accumulation process on the target loss length of all preset time sliding windows in the current production cycle to obtain the total waste material loss of the cycle.

[0035] Step S104: Update the raw material inventory data according to the total consumption of periodic materials to obtain the current inventory level, and determine the actual production cost of the current production cycle based on the total consumption of periodic materials, the total loss of periodic waste materials, and the preset pollutant treatment fee rate.

[0036] Step S105: Map the current book inventory and actual production cost to the target digital monitoring interface.

[0037] In this context, "loom" refers to production equipment in a textile factory used to process yarn into fabric, including but not limited to air-jet looms and water-jet looms. The current production cycle refers to the continuous time period from the start of a loom's weaving operation to its end, or a complete production period divided according to a preset production schedule. Continuous operating status data refers to the time-series data set continuously collected by the digital management system at a preset sampling frequency by sensors deployed at key locations on the loom within the current production cycle. Real-time yarn feed speed sequence refers to the time series composed of the linear velocity values ​​of yarn entering the loom, continuously collected at a preset sampling frequency by speed sensors deployed at the yarn feed end of the loom within the current production cycle. The yarn feed end refers to the entry point on the loom where the yarn is drawn from the yarn frame or yarn bobbin and enters the weaving area. Real-time winding speed sequence refers to the time sequence of fabric winding linear speed values ​​continuously collected by speed sensors deployed at the winding end of the loom at a preset sampling frequency during the current production cycle. The winding end refers to the exit position where the woven fabric on the loom is wound and collected after passing through the metering roller.

[0038] The real-time tension sequence refers to the time series of tension values ​​borne by the yarn, continuously collected by tension sensors deployed along the yarn transport path of the loom at a preset sampling frequency within the current production cycle. The transport path refers to the physical route the yarn takes from the yarn supply end through the weaving area to the take-up end. The preset time sliding window refers to the time window used by the digital management system to segment the continuous operating status data of the current production cycle according to a preset fixed time length, such as extracting a segment of data every 1 second or every 5 seconds for analysis. The first time-domain transformation analysis refers to performing time integration on each speed sample value in the real-time yarn feed speed sequence within the preset time sliding window to convert the speed-time sequence into a length value. The yarn feed reference length refers to the length of yarn delivered from the yarn supply end within the preset time sliding window, obtained after the first time-domain transformation analysis. The second time-domain transformation analysis refers to performing time integration on each speed sample value in the real-time take-up speed sequence within the preset time sliding window to convert the speed-time sequence into a length value.

[0039] The actual winding length refers to the length of fabric actually wound up at the winding end within the preset time sliding window, obtained after the second time-domain transformation analysis. The preset yarn elastic modulus is a pre-set physical constant characterizing the ratio of stress to strain of the yarn material used in current production within its elastic deformation range; its value is determined by the material properties of the yarn. The target loss length refers to the physical loss length of the yarn obtained by subtracting the actual effective output length after tension correction from the yarn feed reference length within the preset time sliding window. This target loss length includes yarn waste loss caused by yarn breakage, fly waste, tension fluctuations, etc. The first accumulation process involves summing the yarn feed reference lengths corresponding to each preset time sliding window within the current production cycle. The total material consumption in the cycle refers to the total length of yarn delivered by the yarn supply end within the current production cycle, obtained after the first accumulation process, and is used to characterize the actual total consumption of raw materials within the current production cycle. The second accumulation process involves summing the target loss lengths corresponding to each preset time sliding window within the current production cycle.

[0040] The total waste loss in a given cycle refers to the total length of yarn physically lost during the current production cycle, obtained after the second accumulation process. Raw material inventory data refers to the book inventory data of raw materials recorded in the system storage module of the digital management system before the start of the current production cycle. Current book inventory refers to the book inventory value of raw materials obtained by subtracting the total consumption of materials in the cycle from the book inventory data. The preset pollutant treatment fee rate refers to the pre-set pollutant treatment fee corresponding to each unit of waste loss, determined based on the actual wastewater treatment, waste recycling, and other environmental protection fee standards of the textile mill. Actual production cost refers to the comprehensive production cost value obtained by summing the product of the total consumption of materials in the cycle and the unit cost of yarn, and the product of the total waste loss in the cycle and the preset pollutant treatment fee rate, within the current production cycle. The target digital monitoring interface refers to the visual display interface provided by the data visualization module of the digital management system, used to display production flow data such as current book inventory and actual production cost to operators.

[0041] In the above embodiment, the weaving operation of an air-jet loom during the current production cycle is taken as an example. After the loom is started, the digital management system acquires the continuous operating status data of the loom during the current production cycle. Specifically, the digital management system continuously collects the linear velocity value of the yarn drawn from the yarn bobbin at a preset sampling frequency of one sampling point every 100 milliseconds through a first rotary encoder deployed at the yarn feeding end of the loom, to obtain a real-time yarn feed speed sequence. For example, several consecutive sampling values ​​in this real-time yarn feed speed sequence are {2.05 m / s, 2.03 m / s, 2.08 m / s, 2.01 m / s, ...}. A second rotary encoder, deployed at the position of the metering roller at the take-up end of the loom, continuously collects the linear speed value of the fabric take-up at the same preset sampling frequency to obtain a real-time take-up speed sequence. For example, several sampled values ​​at corresponding moments in this real-time take-up speed sequence are {1.98 m / s, 1.96 m / s, 2.00 m / s, 1.95 m / s, …}. The metering roller is a cylindrical roller shaft at the take-up end of the loom used to measure the take-up length of the fabric. The fabric passes over the surface of the metering roller during the take-up process, and the metering roller rotates as the fabric moves. The digital management system uses a second rotary encoder deployed at the end of the metering roller shaft to collect the rotation angle and speed of the metering roller in real time. Specifically, the second rotary encoder outputs a pulse signal for each revolution. Based on the number of pulse signals and the circumference of the metering roller, the actual winding length of the fabric is calculated. For example, if the circumference of the metering roller is 1 meter, and the second rotary encoder outputs 9.90 pulse signals within a preset time sliding window, then the actual winding length within that preset time sliding window is 9.90 meters. The rotation status of the metering roller (including rotation angle, rotation speed, and number of pulse signals) is stored in the system storage module for subsequent data analysis and traceability. Tension sensors deployed on the yarn guide rollers along the yarn transport path of the loom continuously collect the tension value of the yarn at the same preset sampling frequency, obtaining a real-time tension sequence. For example, several sampled values ​​at corresponding moments in this real-time tension sequence are {3.2 N, 3.5 N, 4.8 N, 3.1 N, …}.

[0042] In the above embodiments, the data acquisition module of the digital management system also includes equipment operation status monitoring functionality. Specifically, through an operation status acquisition unit deployed in the loom control cabinet, the start-up time, stop time, and running duration of the loom are recorded in real time. For example, in the current production cycle, the digital management system records the loom's start-up time as 8:00 AM, stop time as 4:00 PM, and continuous running duration as 8 hours. The start-up time, stop time, and continuous running duration of the loom are stored in the system storage module so that operators can formulate equipment maintenance plans based on the loom's cumulative running duration. For example, when the loom's cumulative running duration reaches a preset maintenance threshold (e.g., 500 hours), the digital management system generates an equipment maintenance reminder on the target digital monitoring interface, prompting operators to perform regular maintenance on the loom. The digital management system simultaneously calculates the consumption of various raw materials based on the fabric production type and production process. Specifically, the system storage module pre-stores a table showing the correspondence between different fabric types and the required raw material types and their theoretical consumption ratios. For example, for polyester plain weave fabric A, the corresponding table states that theoretically, producing 1 meter of this fabric requires 1.05 meters of polyester yarn (considering warp and weft density and shrinkage during weaving). Within the current production cycle, based on the actual winding length and fabric type, the theoretical consumption ratio is extracted from the corresponding table to calculate the theoretical raw material consumption. For example, when the actual winding length is 9.90 meters, the theoretical raw material consumption is 9.90 × 1.05 = 10.395 meters. This theoretical raw material consumption is compared with the baseline yarn feed length of 10.25 meters calculated based on the real-time yarn feed speed sequence to assess the deviation between actual and theoretical consumption.

[0043] In the above embodiments, the digital management system sets the length of the preset time sliding window to 5 seconds, meaning each 5-second interval is considered an analysis unit. Within each preset time sliding window, a first time-domain transformation analysis is performed on the real-time yarn feed speed sequence, which involves integrating all sampled yarn feed speed values ​​within the preset time sliding window over a time step to obtain the baseline yarn feed length. For example, within a certain preset time sliding window, a trapezoidal integral is performed on 50 sampled yarn feed speed values ​​(one sample point every 100 milliseconds) within 5 seconds, yielding a baseline yarn feed length of 10.25 meters for that preset time sliding window. A second time-domain transformation analysis is performed on the real-time winding speed sequence, which involves integrating all sampled winding speed values ​​within the preset time sliding window over a time step to obtain the actual winding length. For example, within the same preset time sliding window, a trapezoidal integral is performed on 50 sampled winding speed values ​​within 5 seconds, yielding an actual winding length of 9.90 meters for that preset time sliding window. The target loss length of the yarn within the preset time sliding window is determined based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus. Specifically, the preset yarn elastic modulus is set to 10 gigapascals (GPa) based on the physical properties of the polyester yarn material currently used. Tension correction calculations are performed based on the real-time tension sequence within this preset time sliding window, the actual winding length of 9.90 meters, and the preset yarn elastic modulus of 10 gigapascals. This determines the illusory length increase of the yarn due to tension stretching within this preset time sliding window. This illusory length increase is then removed from the actual winding length, resulting in a target loss length of 0.20 meters for this preset time sliding window.

[0044] In the above embodiment, it is assumed that the total duration of the current production cycle is 8 hours, or 28,800 seconds. Dividing the cycle into 5-second preset time sliding windows, the current production cycle contains 5,760 preset time sliding windows. The digital management system performs a first accumulation process on the yarn feed reference length corresponding to each of the 5,760 preset time sliding windows within the current production cycle to obtain the total material consumption for the cycle. For example, after the first accumulation process, the total material consumption for the cycle is 58,000 meters. A second accumulation process is then performed on the target loss length corresponding to each of the 5,760 preset time sliding windows within the current production cycle to obtain the total waste material loss for the cycle. For example, after the second accumulation process, the total waste material loss for the cycle is 1,200 meters. The raw material inventory data is updated based on the total material consumption of 58,000 meters. Specifically, it is assumed that before the start of the current production cycle, the raw material inventory data for polyester yarn recorded in the system storage module of the digital management system is 500,000 meters. Subtracting the total cyclical material consumption of 58,000 meters from the raw material inventory of 500,000 meters yields a current inventory of 442,000 meters. The digital management system determines the actual production cost for the current production cycle based on the total cyclical material consumption of 58,000 meters, the total cyclical waste loss of 1,200 meters, and the preset contaminant treatment rate. Specifically, assuming the unit cost of polyester yarn is 0.05 yuan / meter and the preset contaminant treatment rate is 0.02 yuan / meter of waste, the actual production cost equals the sum of the total cyclical material consumption multiplied by the unit cost of yarn and the total cyclical waste loss multiplied by the preset contaminant treatment rate, i.e., 58,000 × 0.05 + 1,200 × 0.02 = 2,924 yuan. The current inventory of 442,000 meters and the actual production cost of 2,924 yuan are mapped to the target digital monitoring interface. Specifically, the target digital monitoring interface displays the trend of current book inventory and actual production cost changes for each current production cycle in the past seven days in the form of a line graph, while displaying the current book inventory and actual production cost for the most recent current production cycle in numerical form.

[0045] In the above embodiments, the production data statistics module of the digital management system performs statistical analysis on the data collected by the data acquisition module. Specifically, the production data statistics module reads the actual winding length of each preset time sliding window within the current production cycle from the system storage module and categorizes and summarizes it according to fabric type. For example, in the current production cycle, the loom produced two types of fabric: polyester plain weave fabric A and polyester twill fabric B. The production data statistics module calculates that the cumulative winding length of polyester plain weave fabric A is 35,000 meters, and the cumulative winding length of polyester twill fabric B is 22,000 meters. The production data statistics module matches each fabric type with its corresponding cumulative winding length, generates a fabric production summary table, and maps this summary table to the target digital monitoring interface so that operators can observe the production status of each type of fabric. The production data statistics module also performs statistics on the loom's operation. Specifically, the production data statistics module reads the loom's start-up time, downtime, and continuous running time within the current production cycle from the system storage module, and calculates the loom's Overall Equipment Effectiveness (OEE). For example, if the planned running time of the loom in the current production cycle is 8 hours (480 minutes), the actual running time is 7.5 hours (450 minutes), and the downtime is 0.5 hours (30 minutes, including yarn changing, troubleshooting, etc.), then the loom's operating time rate is 450 / 480 = 93.75%. The production data statistics module maps the loom's operating data, such as operating time rate, average running speed, and cumulative winding length, to the target digital monitoring interface so that operators can evaluate the loom's operating efficiency.

[0046] In the above embodiments, the system storage module of the digital management system is used to store all data generated during the production process. Specifically, the system storage module uses a relational database (such as MySQL or PostgreSQL) as the data storage carrier. The database includes multiple data tables, each used to store different types of data. For example, the raw material inventory data table stores information such as batch number, quantity received, receipt time, and current book inventory of various raw materials; the production cycle data table stores information such as start-up time, downtime, continuous running time, total consumption of cycle materials, total waste loss of cycle materials, and actual production cost for each current production cycle; the fabric production data table stores information such as cumulative winding length, production time, and corresponding loom number for each fabric model. The system storage module is also responsible for periodically sending data from the past seven days to the data visualization module. Specifically, the system storage module has a data synchronization task that is automatically executed every preset synchronization period (e.g., hourly). When the data synchronization task is executed, the system storage module queries the database for the production cycle data, raw material inventory data, and fabric production data from the past seven days, and sends the query results to the data visualization module in the form of data packets. After receiving the data packet, the data visualization module parses and converts the data, and then maps the converted data to various visualization display areas of the target digital monitoring interface.

[0047] Through the above steps, the digital management system directly and synchronously acquires continuous operating status data from the yarn feeding and take-up ends of the loom. It then uses the real-time tension sequence of the yarn along the transmission path to perform physical-level loss correction on the take-up length. This ensures that the total consumption of periodic materials and the total waste loss are obtained window-by-window based on the actual physical state of the loom during operation, rather than relying on a pre-established fixed standard consumption ratio model for theoretical deduction. Furthermore, the current book inventory updated based on the total consumption of periodic materials reflects the actual deduction of raw materials in the actual production flow. Simultaneously, based on the total consumption of periodic materials, the total waste loss, and the actual production cost determined by the preset pollutant treatment rate, material consumption and pollution treatment costs can be synchronously quantified and calculated. This makes the current book inventory and actual production cost mapped to the target digital monitoring interface more accurate and realistic, thus objectively presenting the micro-level material flow status within the production cycle. This solves the technical problem of low accuracy in monitoring production costs and inventory in textile mills, achieving the technical effect of improving the accuracy of production cost and inventory monitoring in textile mills.

[0048] The entity performing the above steps can be a system, such as a digital management system or a platform, such as a digital management system, or a controller or processor in the system or platform, or a separate controller or processor, or other processing devices or processing units with similar processing functions, but is not limited to these.

[0049] In an optional embodiment, determining the target loss length of the yarn within a preset time sliding window based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus includes: extracting temporal features from the real-time tension sequence to obtain the tension fluctuation extreme points and high-frequency abrupt change intervals within the preset time sliding window; determining the tension compensation coefficient of the yarn within the preset time sliding window based on the tension fluctuation extreme points, the high-frequency abrupt change intervals, and the preset yarn elasticity; performing length restoration processing on the actual winding length using the tension compensation coefficient to obtain the theoretical tension-free winding length; and determining the length difference between the yarn feed reference length and the theoretical tension-free winding length as the target loss length.

[0050] The temporal feature extraction process involves statistically analyzing sampled data from the real-time tension sequence within a preset time sliding window to identify extreme value distribution characteristics and high-frequency variation features. Tension fluctuation extreme points refer to the sampling times and tension values ​​corresponding to the local maximum and minimum values ​​in the real-time tension sequence within the preset time sliding window. These extreme points reflect the maximum tension change amplitude experienced by the yarn within that window. The high-frequency abrupt change interval refers to the continuous time period within the preset time sliding window where the rate of change of tension value in the real-time tension sequence exceeds a preset rate of change threshold. This interval reflects rapid and frequent tension changes experienced by the yarn during this time period. The tension compensation coefficient is a dimensionless coefficient determined based on the tension fluctuation extreme points, the high-frequency abrupt change interval, and the preset yarn elastic modulus. It characterizes the proportion of elastic elongation of the yarn due to tension within the preset time sliding window. Length restoration processing involves using the tension compensation coefficient to reverse-correct the actual winding length, deducting the artificially increased length due to yarn elastic stretching. The theoretical tension-free winding length refers to the winding length value obtained after length reduction treatment, which is the theoretical winding length value of the yarn under no tension.

[0051] In the above embodiment, the analysis process of the air-jet loom within a preset time sliding window (5 seconds) is taken as an example. The digital management system extracts the temporal features of the real-time tension sequence within the preset time sliding window. Specifically, the 50 tension sample values ​​within the 5 seconds are compared point by point. Points where both the preceding and following sample values ​​are less than the current sample value are marked as local maxima, and points where both the preceding and following sample values ​​are greater than the current sample value are marked as local minima. This yields the extreme points of tension fluctuation within the preset time sliding window. For example, within the preset time sliding window, three local maxima points with tension values ​​of 5.2 N, 4.8 N, and 5.0 N are identified, along with three local minima points with tension values ​​of 2.1 N, 2.3 N, and 2.0 N. The digital management system calculates the rate of tension change between adjacent sample values ​​and marks continuous time periods where the rate of tension change exceeds a preset rate of change threshold (e.g., 10 N / s) as high-frequency abrupt change intervals of tension. For example, the periods from 1.5 seconds to 2.0 seconds and from 3.8 seconds to 4.1 seconds within the preset time sliding window are identified as two high-frequency tension abrupt change intervals. Based on the extreme tension fluctuation points, the high-frequency tension abrupt change intervals, and the preset yarn elastic modulus of 10 gigapascals, the yarn tensile compensation coefficient within the preset time sliding window is determined. Specifically, based on the maximum tension value of 5.2 N at the extreme tension fluctuation points and the cross-sectional area of ​​the yarn (e.g., 0.01 mm² for polyester yarn), the maximum stress is calculated to be 5.2 / (0.01 × 10⁻⁶). －6 The yarn elastic modulus is 520 MPa. Based on the preset yarn elastic modulus of 10 GPa, the maximum strain is calculated to be 520 / 10000 = 0.052, meaning the maximum elastic elongation is 5.2%. Combining the time proportion of the high-frequency tension abrupt change range and the distribution characteristics of the tension fluctuation extreme points, a weighted average correction is applied to the maximum elastic elongation, resulting in a tension compensation coefficient of 1.032. Using the tension compensation coefficient of 1.032, the actual winding length of 9.90 meters is restored to its theoretical tension-free winding length. Specifically, the theoretical tension-free winding length equals the actual winding length divided by the tension compensation coefficient, i.e., 9.90 / 1.032 = 9.59 meters. The length difference between the yarn feed reference length of 10.25 meters and the theoretical tension-free winding length of 9.59 meters is determined as the target loss length, i.e., the target loss length is 10.25 - 9.59 = 0.66 meters.

[0052] In an optional embodiment, determining the target loss length of the yarn within a preset time sliding window based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus includes: performing stress accumulation analysis on the real-time tension sequence to obtain the cumulative tension load value borne by the yarn on the transmission path; matching the cumulative tension load value with a preset elastic-plastic critical mapping relationship to determine the plastic deformation rate of the yarn within the preset time sliding window; calculating the deformation increment based on the actual winding length and the plastic deformation rate to obtain the plastic extension increment; and calculating the loss deviation based on the yarn feed reference length, the actual winding length, and the plastic extension increment to obtain the target loss length.

[0053] The stress accumulation analysis refers to the summation of stress values ​​at each sampling moment within a preset time sliding window of the real-time tension sequence to obtain the total stress load borne by the yarn over the entire time span of the preset time sliding window. The accumulated tension load value is the total load obtained after stress accumulation analysis, calculated by integrating the tension values ​​borne by the yarn along the transmission path over time. Its physical meaning is the stress-time combined load value experienced by the yarn within the preset time sliding window. The preset elastoplastic critical mapping relationship is a pre-established correspondence between the accumulated tension load value and the probability of plastic deformation of the yarn, based on the mechanical property test data of the yarn material. This preset elastoplastic critical mapping relationship is stored in the digital management system in the form of a lookup table or piecewise function. The plastic deformation rate is the proportion of irreversible permanent deformation that occurs in the yarn within the preset time sliding window due to the accumulated tension load exceeding the elastic limit, obtained by matching the preset elastoplastic critical mapping relationship. Deformation increment conversion refers to the process of multiplying the actual winding length with the plastic deformation rate to convert the plastic deformation rate into a plastic elongation with length dimensions. Plastic elongation increment refers to the irreversible elongation length of the yarn due to plastic deformation, obtained after deformation increment conversion. Loss deviation calculation refers to the process of comprehensively calculating the yarn feed reference length, actual winding length, and plastic elongation increment to determine the true physical loss length of the yarn after deducting the influence of the plastic elongation increment; that is, the target loss length equals the yarn feed reference length minus the actual winding length plus the plastic elongation increment.

[0054] In the above embodiment, taking the analysis process of the air-jet loom within a preset time sliding window (time length of 5 seconds) as an example, another method for determining the target loss length will be explained. The digital management system performs stress accumulation analysis on the real-time tension sequence within the preset time sliding window. Specifically, the 50 tension sampling values ​​within the 5 seconds are summed one by one according to the product of each sampling value and its corresponding time step (100 milliseconds = 0.1 seconds) to obtain the cumulative tension load value borne by the yarn on the transmission path. For example, if the average of the 50 tension sampling values ​​is approximately 3.5 N, then the cumulative tension load value is approximately 3.5 × 50 × 0.1 = 17.5 N·s. The digital management system matches the cumulative tension load value of 17.5 N·s with a preset elastoplastic critical mapping relationship. Specifically, the preset elastoplastic critical mapping relationship is pre-stored in the digital management system in the form of a lookup table, which records the plastic deformation rate corresponding to different cumulative tension load value intervals. For example, the lookup table records that the cumulative tension load value is between 15 N·s and 20 N·s, corresponding to a plastic deformation rate of 0.3%. The digital management system matches the cumulative tension load value of 17.5 N·s to this range, determining that the plastic deformation rate of the yarn within this preset time sliding window is 0.3%. Based on the actual winding length of 9.90 meters and the plastic deformation rate of 0.3%, the deformation increment is calculated to obtain the plastic extension increment. Specifically, the plastic extension increment equals the actual winding length multiplied by the plastic deformation rate, i.e., 9.90 × 0.003 = 0.0297 meters. Based on the yarn feed reference length of 10.25 meters, the actual winding length of 9.90 meters, and the plastic extension increment of 0.0297 meters, the loss deviation is calculated to obtain the target loss length. Specifically, the target loss length equals the yarn feed reference length minus the actual winding length plus the plastic extension increment, i.e., 10.25 - 9.90 + 0.0297 = 0.3797 meters. The reason for adding back the plastic extension increment is that the actual winding length of 9.90 meters already includes 0.0297 meters of artificial increase due to yarn plastic deformation. Although this part is reflected as an increase in the length of the winding end, it is not the actual output length of the yarn material, but the result of the yarn being permanently stretched. Therefore, this part needs to be deducted from the effective output when calculating the actual physical loss.

[0055] In an optional embodiment, after updating the raw material inventory data based on the total periodic material consumption to obtain the current inventory level, and determining the actual production cost of the current production cycle based on the total periodic material consumption, the total periodic waste loss, and the preset pollutant treatment rate, the method further includes: extracting the historical inventory consumption rate sequence within a preset traceability period from the current inventory level; determining the expected depletion time node of the current inventory level based on the historical inventory consumption rate sequence and preset production scheduling data; when the expected depletion time node is within a preset supply chain security warning range, using the actual production cost as a cost fluctuation feedback variable; performing parameter correction operations on the initial purchase batch optimization model using the cost fluctuation feedback variable to obtain the target purchase batch optimization model, and generating a target replenishment strategy based on the target purchase batch optimization model.

[0056] The preset traceability period refers to the time length pre-set by the digital management system for tracing historical inventory consumption data, such as 7 days or 14 days. The historical inventory consumption rate sequence refers to the time series calculated by the digital management system cycle by cycle within the preset traceability period, based on the change in current book inventory recorded after the end of each current production cycle and the duration of each current production cycle. A single inventory consumption rate equals the decrease in current book inventory within a given current production cycle divided by the duration of that current production cycle. Pre-set production scheduling data refers to the planned production tasks and corresponding loom scheduling information for various fabrics over a future period, pre-entered into the digital management system by textile mill managers. The expected depletion time point refers to the future point in time when the current book inventory will drop to zero, predicted by the digital management system based on the changing trend of the historical inventory consumption rate sequence and the planned consumption in the preset production scheduling data. The preset supply chain security warning interval refers to a security warning time range pre-set by the digital management system, starting from the current time, such as 7 to 14 days in the future.

[0057] Specifically, when the expected depletion time falls within the preset supply chain security warning range, it indicates that, based on the current consumption trend, raw material inventory will be exhausted within this warning period, requiring the triggering of a replenishment mechanism. Cost fluctuation feedback variables refer to using the actual production cost of the current production cycle as a parameter reflecting cost fluctuations during actual production, used to dynamically adjust the parameters of the procurement model. The initial procurement batch optimization model is a pre-established mathematical optimization model in the digital management system used to determine the optimal procurement batch. This initial procurement batch optimization model aims to minimize the total procurement cost, and its initial parameters are set based on historical procurement data and standard process parameters. Parameter correction operation refers to the computational operation of updating and adjusting the relevant parameter values ​​in the initial procurement batch optimization model using the cost fluctuation feedback variable. The target procurement batch optimization model is the procurement batch optimization model obtained after parameter correction operations, with parameter values ​​updated according to actual production cost fluctuations. The target replenishment strategy is a procurement suggestion scheme generated based on the output of the target procurement batch optimization model, containing information such as recommended procurement batch and recommended yarn quality grade.

[0058] In the above embodiments, the raw material and fabric flow monitoring module of the digital management system is used to monitor the consumption of various raw materials in the warehouse and the production status of fabric. Specifically, when raw materials are received into the warehouse, the operator inputs information such as the type of raw material, batch number, quantity, and time of receipt into the system storage module through the input module of the digital management system. For example, the operator inputs: the type of raw material is polyester yarn, the batch number is 20110101-A, the quantity is 100,000 meters, and the receipt time is 10:00 AM on January 1, 2011. The digital management system accumulates and updates this receipt information with the raw material inventory data to obtain the updated raw material inventory data. During the production process, the raw material and fabric flow monitoring module performs real-time calculations based on the actual consumption of each raw material in the data acquisition module. Specifically, the raw material and fabric flow monitoring module calculates the raw material consumption rate for the current hour, using an hourly unit as the statistical unit. For example, in the first hour of the current production cycle, the digital management system accumulates the yarn feed baseline lengths of all preset time sliding windows within that hour, obtaining a raw material consumption of 7250 meters for that hour. Therefore, the raw material consumption rate for that hour is 7250 meters per hour. The raw material fabric flow monitoring module stores the raw material consumption rate for each hour in the system storage module and maps the raw material consumption rate for each hour over the past seven days to the target digital monitoring interface as a line graph, allowing operators to observe the changing trend of the raw material consumption rate.

[0059] In the above embodiment, the inventory management scenario of polyester yarn will continue to be used as an example. After the digital management system updates the raw material inventory data to 442,000 meters based on the total material consumption of 58,000 meters in the cycle, and determines the actual production cost of 2,924 yuan for the current production cycle, the historical inventory consumption rate sequence within a preset traceability period is extracted from the current inventory. Specifically, the preset traceability period is set to 7 days. The current inventory records at the end of each current production cycle in the past 7 days are read from the system storage module, and the inventory consumption rate for each current production cycle is calculated. For example, there were 14 current production cycles in the past 7 days (assuming two current production cycles per day, each current production cycle being 8 hours). The inventory depletion rates for each current production cycle were {7000 m / h, 7200 m / h, 7100 m / h, 7300 m / h, 7250 m / h, 7150 m / h, 7400 m / h, 7350 m / h, 7200 m / h, 7100 m / h, 7250 m / h, 7300 m / h, 7200 m / h, 7250 m / h}, thus obtaining the historical inventory depletion rate sequence. Based on the historical inventory depletion rate sequence and the preset production scheduling data, the expected depletion time of the current book inventory is determined. Specifically, the weighted moving average of the historical inventory depletion rate sequence is calculated, resulting in an average depletion rate of 7220 m / h. The preset production schedule data indicates that the future production task is continuous weaving of polyester fabric, with a daily production schedule of 16 hours. Dividing the current inventory of 442,000 meters by the average consumption rate of 7,220 meters / hour yields approximately 61.2 hours of remaining operational time. Based on a daily production schedule of 16 hours, this translates to approximately 3.8 days of remaining operational time. Therefore, the estimated depletion point is determined to be 3.8 days from the current time.

[0060] In the above embodiment, it is determined whether the expected depletion time point is within a preset supply chain security warning range. Specifically, the preset supply chain security warning range is set to within the next 7 days. Since the expected depletion time point (day 3.8) is within the preset supply chain security warning range (within 7 days), the actual production cost of the current production cycle, 2924 yuan, is used as a cost fluctuation feedback variable. The cost fluctuation feedback variable of 2924 yuan is used to perform parameter correction operations on the initial procurement batch optimization model. Specifically, the optimization objective of the initial procurement batch optimization model is to minimize the total procurement cost, and its initial parameters include the yarn unit price weight parameter (initial value 1.0) and the quality benefit parameter (initial value 1.0). The digital management system adjusts the yarn unit price weight parameter and the quality benefit parameter in the initial procurement batch optimization model according to the deviation ratio between the cost fluctuation feedback variable of 2924 yuan and the historical average production cost (e.g., 2800 yuan), to obtain the target procurement batch optimization model. A target replenishment strategy is generated based on the target procurement batch optimization model, which includes a recommended procurement batch and a recommended yarn quality grade.

[0061] In an optional embodiment, the initial purchase batch optimization model is modified using cost fluctuation feedback variables to obtain a target purchase batch optimization model. A target replenishment strategy is then generated based on this model, including: determining the waste cost based on the total periodic waste loss and yarn unit cost; determining the proportion of waste cost in the yarn waste cost within the cost fluctuation feedback variables; determining the target tensile strength compensation coefficient for the next batch of yarn based on the mapping relationship between the yarn waste cost proportion and the preset yarn quality tolerance; modifying the yarn unit price weight parameter and quality benefit parameter in the initial purchase batch optimization model using the target tensile strength compensation coefficient to obtain the target purchase batch optimization model; inputting the expected depletion time, current book inventory, and the average operating tension value of the loom in the current production cycle as joint state constraints into the target purchase batch optimization model for iterative optimization to obtain the target purchase batch and target yarn quality grade; and combining the target purchase batch and target yarn quality grade to generate the target replenishment strategy.

[0062] Here, yarn unit cost refers to the purchase price per unit length of yarn, for example, measured in yuan / meter. Scrap cost refers to the product of the total scrap loss in a given cycle and the yarn unit cost, i.e., the direct material loss amount caused by yarn scrap loss during the current production cycle. Yarn scrap cost ratio refers to the percentage of scrap cost loss relative to cost fluctuation feedback variables, used to quantify the impact of scrap loss on overall production costs. Preset yarn quality tolerance refers to a pre-set maximum acceptable percentage threshold for yarn scrap cost ratio for the textile mill. When the yarn scrap cost ratio exceeds the preset yarn quality tolerance, it indicates that the scrap loss caused by the quality level of the current batch of yarn has exceeded the acceptable range. Target tensile strength compensation coefficient is an adjustment coefficient determined based on the mapping relationship between the yarn scrap cost ratio and the preset yarn quality tolerance, used to correct the tensile strength requirements for the next batch of yarn procurement. The higher the yarn scrap cost ratio, the larger the target tensile strength compensation coefficient, indicating that the next batch should purchase yarn with higher tensile strength.

[0063] The yarn unit price weight parameter refers to the parameter used in the initial purchase batch optimization model to measure the weight of the yarn purchase unit price in the optimization objective function. The quality benefit parameter refers to the parameter used in the initial purchase batch optimization model to measure the weight of the benefit of reduced waste loss due to improved yarn quality in the optimization objective function. The joint state constraint condition refers to a set of comprehensive constraint parameters, including the expected depletion time, current inventory level, and the average operating tension value of the loom in the current production cycle, which are input into the target purchase batch optimization model to limit the feasible region for optimization. The average operating tension value is the arithmetic mean of all tension samples in the real-time tension sequence of the loom in the current production cycle. Iterative optimization refers to the solution process of the target purchase batch optimization model, under the constraints of the joint state constraint condition, gradually approaching the optimal solution through repeated iterative calculations. The target purchase batch refers to the recommended yarn purchase quantity determined after iterative optimization. The target yarn quality grade refers to the recommended yarn quality grade determined after iterative optimization.

[0064] In the above embodiment, taking the procurement decision scenario of polyester yarn as an example, the specific process of using cost fluctuation feedback variables to perform parameter correction operations on the initial procurement batch optimization model and generate a target replenishment strategy is explained. The digital management system determines the waste loss cost based on the total periodic waste loss of 1200 meters and the yarn unit cost of 0.05 yuan / meter. Specifically, the waste loss cost equals the total periodic waste loss multiplied by the yarn unit cost, i.e., 1200 × 0.05 = 60 yuan. The proportion of the waste loss cost of 60 yuan in the cost fluctuation feedback variable of 2924 yuan is determined. Specifically, the proportion of the yarn waste cost equals the waste loss cost divided by the cost fluctuation feedback variable, i.e., 60 / 2924 ≈ 2.05%. Based on the mapping relationship between the yarn waste cost proportion of 2.05% and the preset yarn quality tolerance, the target tensile strength compensation coefficient for the next batch of yarn is determined. Specifically, the preset yarn quality tolerance is set to 1.5%. Since the cost of yarn waste accounts for 2.05%, which is greater than the preset yarn quality tolerance of 1.5%, the digital management system determines the target tensile strength compensation coefficient to be 1.1 based on the pre-set mapping relationship (e.g., for every 0.5 percentage point increase in the cost of yarn waste exceeding the preset yarn quality tolerance, the target tensile strength compensation coefficient increases by 0.1).

[0065] In the above embodiment, the yarn unit price weight parameter and quality benefit parameter in the initial procurement batch optimization model are modified using a target tensile strength compensation coefficient of 1.1. Specifically, the digital management system adjusts the yarn unit price weight parameter from the initial value of 1.0 to 1.0 / 1.1=0.91, that is, reducing the weight of the yarn unit price in the optimization objective, allowing for a higher unit price. The digital management system adjusts the quality benefit parameter from the initial value of 1.0 to 1.0×1.1=1.1, that is, increasing the weight of the yarn quality benefit in the optimization objective, favoring the selection of high-quality yarn. After the above parameter modification, the target procurement batch optimization model is obtained. The expected depletion time node (day 3.8), the current book inventory of 442,000 meters, and the average operating tension value of the loom in the current production cycle (e.g., 3.5 Newtons) are used as joint state constraints and input into the target procurement batch optimization model for iterative optimization. Specifically, under the constraints of the joint state conditions, the target procurement batch optimization model performs iterative calculations with the optimization objective of minimizing the total procurement cost. Considering the relatively short expected depletion date (day 3.8), the target purchase batch optimization model is solved while meeting the time constraint; considering the average running tension value of 3.5 Newtons, the model is solved while meeting the yarn tensile strength requirement. After iterative optimization, the target purchase batch is determined to be 600,000 meters, and the target yarn quality grade is Grade A (polyester yarn with a tensile strength of not less than 800 MPa). Combining the target purchase batch of 600,000 meters and the target yarn quality grade A, a target replenishment strategy is generated. This strategy recommends purchasing 600,000 meters of Grade A polyester yarn within 2 days.

[0066] In an optional embodiment, after mapping the current book inventory and actual production costs to the target digital monitoring interface, the method further includes: extracting the actual production cost time series within a preset historical display period from the target digital monitoring interface, and decomposing the actual production cost time series into a basic material cost series and an environmental depreciation cost series based on the total waste loss of the period and the preset pollutant treatment rate; determining the second change slope of the environmental depreciation cost series based on the first change slope of the basic material cost series; determining the pollution discharge marginal index based on the first change slope and the second change slope; performing phase difference analysis on the real-time yarn feeding speed series and the real-time winding speed series when the pollution discharge marginal index is greater than the preset green production tolerance threshold to obtain the resonant speed frequency band; determining the target operating speed range based on the resonant speed frequency band and generating a loom speed limit operation command; and issuing the speed limit operation command to the loom to control the loom to perform weaving operations according to the target operating speed range in the next production cycle.

[0067] The preset historical display period refers to the time span pre-set in the target digital monitoring interface for displaying historical production cost data, such as the last 7 days. The actual production cost time series refers to the time series composed of the actual production cost values ​​corresponding to each current production cycle within the preset historical display period, arranged in chronological order. The basic material cost series is a time series separated from the actual production cost time series, consisting only of the product of the total material consumption and the unit cost of yarn within each current production cycle. The environmental depreciation cost series is a time series separated from the actual production cost time series, consisting only of the product of the total waste loss within each current production cycle and the preset pollutant treatment rate. The first slope refers to the linear trend slope of the basic material cost series over time, reflecting the rate of increase or decrease in basic material costs. The second slope refers to the linear trend slope of the environmental depreciation cost series over time, reflecting the rate of increase or decrease in environmental depreciation costs. The pollution marginal index is a ratio indicator determined based on the first and second slopes, used to quantify the rate of increase in environmental depreciation costs relative to the rate of increase in basic material costs.

[0068] Among them, a larger pollution margin index indicates a higher rate of increase in environmental damage costs relative to the cost of basic materials, meaning that waste loss and pollution problems are accelerating. The preset green production tolerance threshold refers to the maximum acceptable value of the pollution margin index. When the pollution margin index exceeds this preset green production tolerance threshold, it indicates that the current level of waste loss and pollution has exceeded the tolerance range of green production. Phase difference analysis refers to the process of analyzing the phase relationship between the real-time yarn feed speed sequence and the real-time winding speed sequence in the frequency domain to identify specific frequency ranges where the speeds are asynchronous. The resonant speed band refers to the speed frequency range obtained after phase difference analysis where the phase difference between the real-time yarn feed speed sequence and the real-time winding speed sequence is the largest and is associated with the extreme range of tension fluctuations. When operating in this resonant speed band, the degree of speed asynchrony between the yarn supply end and the winding end is the greatest, resulting in the most severe yarn tension fluctuations and the highest waste generation. The target operating speed range refers to the speed range within which the loom should operate in the next production cycle, determined based on the resonant speed frequency band. This target operating speed range excludes the resonant speed frequency band to avoid the loom operating within a speed range that is prone to causing severe tension fluctuations. The loom speed-limiting operation command refers to the control command generated by the digital management system used to control the loom to operate within the target operating speed range.

[0069] In the above embodiments, the data visualization module of the digital management system is used to display the data calculated by each module in a visual manner. Specifically, the data visualization module reads data such as the current book inventory, actual production cost, total cycle material consumption, total cycle waste loss, and loom running time for each current production cycle in the past seven days from the system storage module, and maps this data to the target digital monitoring interface in the form of line charts, bar charts, or numerical tables. The target digital monitoring interface includes multiple visualization display areas. The first display area displays the trend of the current book inventory in each current production cycle in the past seven days in the form of a line chart, with the horizontal axis representing time (in the current production cycle) and the vertical axis representing the current book inventory (in meters). The second display area displays the trend of the actual production cost in each current production cycle in the past seven days in the form of a line chart, with the horizontal axis representing time and the vertical axis representing the actual production cost (in yuan). The third display area displays the cumulative winding length of each fabric type in the past seven days in the form of a bar chart, with the horizontal axis representing the fabric type and the vertical axis representing the cumulative winding length (in meters). The fourth display area presents key data for the most recent production cycle in a numerical table format, including current inventory levels, actual production costs, total cycle material consumption, total cycle waste loss, loom runtime, and overall equipment efficiency. Operators can intuitively observe various data points during the production process through the target digital monitoring interface and make production decisions based on data trends.

[0070] In the above embodiment, the production management scenario of the aforementioned jet loom will continue to be used as an example to illustrate the specific process of the green production control closed loop. After the digital management system maps the current book inventory of 442,000 meters and the actual production cost of 2,924 yuan to the target digital monitoring interface, it extracts the time series sequence of the actual production cost within a preset historical display period from the target digital monitoring interface. Specifically, the preset historical display period is set to the past 7 days. The target digital monitoring interface displays the actual production cost of each current production cycle within the past 7 days in the form of a line graph. The digital management system extracts the time series sequence of the actual production cost from this line graph. For example, the actual production cost values ​​for the 14 current production cycles in the past 7 days are {2,780 yuan, 2,810 yuan, 2,830 yuan, 2,850 yuan, 2,870 yuan, 2,880 yuan, 2,890 yuan, 2,900 yuan, 2,905 yuan, 2,910 yuan, 2,915 yuan, 2,918 yuan, 2,920 yuan, 2,924 yuan}. Based on the total periodic waste loss and the preset pollutant treatment rate, the actual production cost time series is broken down into a basic material cost series and an environmental depreciation cost series. Specifically, the total periodic waste loss records for each current production cycle within the past 7 days are read from the system storage module, and the environmental depreciation cost for each current production cycle is calculated (equal to the total periodic waste loss multiplied by the preset pollutant treatment rate of 0.02 yuan / meter). The basic material cost is obtained by subtracting the environmental depreciation cost from the actual production cost. For example, the basic material cost sequence is {2760 yuan, 2787 yuan, 2805 yuan, 2822 yuan, 2840 yuan, 2848 yuan, 2855 yuan, 2862 yuan, 2865 yuan, 2868 yuan, 2871 yuan, 2873 yuan, 2875 yuan, 2900 yuan}, and the environmental protection depreciation cost sequence is {20 yuan, 23 yuan, 25 yuan, 28 yuan, 30 yuan, 32 yuan, 35 yuan, 38 yuan, 40 yuan, 42 yuan, 44 yuan, 45 yuan, 45 yuan, 24 yuan}.

[0071] In the above embodiment, the digital management system determines a first slope of change based on the basic material cost sequence and a second slope of change based on the environmental depreciation cost sequence. Specifically, a linear regression fitting is performed on the basic material cost sequence to obtain a first slope of 10 yuan / cycle. The digital management system performs a linear regression fitting on the environmental depreciation cost sequence to obtain a second slope of 1.8 yuan / cycle. A pollution discharge marginal index is determined based on the first slope of 10 yuan / cycle and the second slope of 1.8 yuan / cycle. Specifically, the pollution discharge marginal index equals the second slope divided by the first slope, i.e., 1.8 / 10 = 0.18. It is then determined whether the pollution discharge marginal index of 0.18 is greater than a preset green production tolerance threshold. Specifically, the preset green production tolerance threshold is set to 0.15. Since the pollution discharge marginal index of 0.18 is greater than the preset green production tolerance threshold of 0.15, the digital management system performs phase difference analysis on the real-time yarn feed speed sequence and the real-time winding speed sequence to obtain the resonant speed frequency band. For example, after phase difference analysis, the resonant speed frequency band is determined to be the frequency range corresponding to the loom's operating speed between 1.95 m / s and 2.10 m / s. Based on the resonant speed frequency band, the target operating speed range is determined, and a loom speed-limiting command is generated. Specifically, the operating speed range of 1.95 m / s to 2.10 m / s corresponding to the resonant speed frequency band is excluded from the loom's permissible operating speed range, determining the target operating speed range to be 1.80 m / s to 1.94 m / s. A loom speed-limiting command is generated, which includes the target operating speed range of 1.80 m / s to 1.94 m / s. The loom speed-limiting command is issued to the loom to control it to perform weaving operations within the target operating speed range of 1.80 m / s to 1.94 m / s in the next production cycle.

[0072] In an optional embodiment, when the pollution discharge marginal index is greater than a preset green production tolerance threshold, phase difference analysis is performed on the real-time yarn feeding speed sequence and the real-time winding speed sequence to obtain the resonant speed frequency band. This includes: performing a first frequency domain transformation analysis on the real-time yarn feeding speed sequence to obtain a yarn feeding discrete frequency band sequence and a yarn feeding phase angle sequence; performing a second frequency domain transformation analysis on the real-time winding speed sequence to obtain a winding discrete frequency band sequence and a winding phase angle sequence; aligning the yarn feeding discrete frequency band sequence and the winding discrete frequency band sequence to obtain an aligned discrete frequency band sequence; determining a phase angle difference sequence that matches the aligned discrete frequency band sequence based on the yarn feeding phase angle sequence and the winding phase angle sequence; performing timestamp alignment mapping between the phase angle difference sequence and the fluctuation extreme value range of the real-time tension sequence to extract a candidate phase angle difference set located in the fluctuation extreme value range from the phase angle difference sequence; and determining the frequency band assignment of the candidate phase angle difference set based on the aligned discrete frequency band sequence to obtain the resonant speed frequency band.

[0073] The first frequency domain transformation analysis refers to the process of performing a Discrete Fourier Transform (DFT) on the time-domain data of the real-time yarn feed speed sequence within the current production cycle, converting it from a time-domain representation to a frequency-domain representation. The yarn feed discrete frequency band sequence is the sequence formed by arranging the frequency values ​​corresponding to each discrete frequency component in the real-time yarn feed speed sequence according to their magnitude, obtained after the first frequency domain transformation analysis. The yarn feed phase angle sequence is the sequence formed by the phase angle values ​​corresponding one-to-one with each discrete frequency component in the yarn feed discrete frequency band sequence, obtained after the first frequency domain transformation analysis. The second frequency domain transformation analysis refers to the process of performing a Discrete Fourier Transform (DFT) on the time-domain data of the real-time winding speed sequence within the current production cycle, converting it from a time-domain representation to a frequency-domain representation. The winding discrete frequency band sequence is the sequence formed by arranging the frequency values ​​corresponding to each discrete frequency component in the real-time winding speed sequence according to their magnitude, obtained after the second frequency domain transformation analysis. The winding phase angle sequence is the sequence formed by arranging the phase angle values ​​corresponding one-to-one with each discrete frequency component in the winding discrete frequency band sequence, obtained after the second frequency domain transformation analysis. Frequency band alignment refers to the process of matching discrete frequency components with the same or similar frequency values ​​in the yarn feed discrete frequency band sequence with the winding discrete frequency band sequence one by one.

[0074] The aligned discrete frequency band sequence refers to the sequence of frequency values ​​corresponding to the discrete frequency components that are successfully matched in the yarn feed discrete frequency band sequence and the winding discrete frequency band sequence after frequency band alignment. The phase angle difference sequence refers to the sequence of phase angle differences obtained by subtracting the corresponding phase angle value in the winding phase angle sequence from the corresponding phase angle value in the yarn feed phase angle sequence for each frequency component in the aligned discrete frequency band sequence. The timestamp alignment mapping is the process of matching the time-domain representation (i.e., the time period during which each frequency component has a significant impact in the time domain) of each frequency component in the phase angle difference sequence with the timestamps of the fluctuation extreme value intervals in the real-time tension sequence. The fluctuation extreme value interval refers to the continuous time period in the real-time tension sequence where the tension value is near a local maximum or local minimum. The candidate phase angle difference value set refers to the set of phase angle difference values ​​extracted from the phase angle difference sequence after timestamp alignment mapping that have a temporal correspondence with the fluctuation extreme value intervals of the real-time tension sequence. Frequency band assignment determination refers to the process of determining the assigned frequency band of each candidate phase angle difference value in the candidate phase angle difference value set based on the aligned discrete frequency band sequence, in order to identify which frequency bands the phase difference between the yarn supply end and the take-up end causes drastic fluctuations in yarn tension.

[0075] In the above embodiments, the process of determining the resonant speed frequency band is further explained using the phase difference analysis scenario of the air-jet loom as an example. The digital management system performs a first frequency domain transformation analysis on the real-time yarn feed speed sequence. Specifically, a Discrete Fourier Transform is performed on all sampled data of the real-time yarn feed speed sequence within the current production cycle (8 hours × 10 sampling points / second = 288,000 sampling points) to obtain the yarn feed discrete frequency band sequence and the yarn feed phase angle sequence. For example, the yarn feed discrete frequency band sequence contains several discrete frequency components with frequency values ​​of {0.5 Hz, 1.0 Hz, 1.5 Hz, 2.0 Hz, 2.5 Hz, …}, and the corresponding yarn feed phase angle sequence is {30°, 45°, 60°, 75°, 90°, …}. The digital management system performs a second frequency domain transformation analysis on the real-time winding speed sequence. Specifically, a Discrete Fourier Transform is performed on all sampled data of the real-time winding speed sequence within the current production cycle to obtain the winding discrete frequency band sequence and the winding phase angle sequence. For example, the take-up discrete frequency band sequence contains several discrete frequency components with frequency values ​​of {0.5 Hz, 1.0 Hz, 1.5 Hz, 2.0 Hz, 2.5 Hz, …}, and the corresponding take-up phase angle sequence is {32°, 50°, 58°, 95°, 88°, …}. The yarn feed discrete frequency band sequence is then aligned with the take-up discrete frequency band sequence. Specifically, the digital management system matches the frequency components with the same frequency value in the two discrete frequency band sequences one-to-one, resulting in an aligned discrete frequency band sequence of {0.5 Hz, 1.0 Hz, 1.5 Hz, 2.0 Hz, 2.5 Hz, …}.

[0076] In the above embodiments, the digital management system determines a phase angle difference sequence that matches the aligned discrete frequency band sequence based on the yarn feed phase angle sequence and the winding phase angle sequence. Specifically, for each frequency component in the aligned discrete frequency band sequence, the corresponding phase angle value in the yarn feed phase angle sequence is subtracted from the corresponding phase angle value in the winding phase angle sequence to obtain a phase angle difference sequence of {-2°, -5°, 2°, -20°, 2°, …}. The phase angle difference sequence is then timestamped and mapped to the fluctuation extreme value interval of the real-time tension sequence. Specifically, the fluctuation extreme value interval in the real-time tension sequence (i.e., the continuous time period when the tension value is near a local maximum or local minimum) is first determined, and then the time period in which each frequency component has a significant impact in the time domain is timestamped and matched with the fluctuation extreme value interval. After the timestamped alignment and mapping, a candidate phase angle difference set that falls within the fluctuation extreme value interval is extracted from the phase angle difference sequence, for example, the candidate phase angle difference set is {-20°, -18°, -22°}. The digital management system determines the frequency band assignment of the candidate phase angle difference set based on the aligned discrete frequency band sequence. Specifically, it determines the frequency component corresponding to each candidate phase angle difference value in the candidate phase angle difference set. For example, the frequency component corresponding to a candidate phase angle difference of -20° is 2.0 Hz, the frequency component corresponding to a candidate phase angle difference of -18° is 2.1 Hz, and the frequency component corresponding to a candidate phase angle difference of -22° is 1.9 Hz. The frequency range covered by the above frequency components is determined as the resonant speed band, that is, the resonant speed band is 1.9 Hz to 2.1 Hz. The loom running speed range corresponding to this resonant speed band is 1.95 m / s to 2.10 m / s.

[0077] It should be noted that the examples of all the specific values ​​mentioned above are merely exemplary embodiments, and the specific values ​​are not limited to the examples mentioned above.

[0078] Through the embodiments of this application, by synchronously collecting real-time yarn feed speed sequences, real-time winding speed sequences, and real-time tension sequences of the yarn on the transmission path from the yarn feeding end and winding end of the loom, time-domain transformation analysis is performed within a preset time sliding window to obtain the yarn feed reference length and actual winding length. Then, tension correction is performed by combining the real-time tension sequence and the preset yarn elastic modulus to determine the target loss length. The yarn feed reference length and target loss length of all preset time sliding windows in the current production cycle are then accumulated to obtain the total cycle material consumption and total cycle waste loss. Furthermore, the current book inventory is updated and the actual production cost is determined based on the total cycle material consumption and total cycle waste loss and mapped to the target digital monitoring interface. This achieves accurate digital monitoring of textile mill production costs and inventory turnover based on the actual continuous operation status data of the loom.

[0079] The digital management system in the embodiments of this invention is described below from the perspective of hardware processing. (See attached document.) Figure 2 , Figure 2 This is a schematic diagram of the physical device structure of a digital management system in an embodiment of this application.

[0080] It should be noted that, Figure 2 The structure of the digital management system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0081] like Figure 2 As shown, the digital management system includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 202 or programs loaded from storage section 208 into Random Access Memory (RAM) 203, such as executing the methods described in the above embodiments. Various programs and data required for platform operation are also stored in RAM 203. The CPU 201, ROM 202, and RAM 203 are interconnected via bus 204. I / O interface 205 is also connected to bus 204. The following components are connected to I / O interface 205: an input section 206 including audio input devices, push-button switches, etc.; an output section 207 including a Liquid Crystal Display (LCD) and audio output devices, indicator lights, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0082] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the various functions defined in the present invention.

[0083] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution platform, apparatus, or device.

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of platforms, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0085] Specifically, the digital management system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the digital monitoring method for textile factory production costs and inventory turnover provided in the above embodiment.

[0086] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the digital management system described in the above embodiments; or it may exist independently and not incorporated into the digital management system. The storage medium carries one or more computer programs that, when executed by a processor of the digital management system, enable the digital management system to implement the digital monitoring method for textile factory production costs and inventory turnover provided in the above embodiments.

[0087] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A digital monitoring method for production costs and inventory turnover in a textile mill, characterized in that, include: Acquire continuous operating status data of the loom during the current production cycle. The continuous operating status data includes real-time yarn feed speed sequence collected from the yarn feeding end, real-time winding speed sequence collected from the winding end, and real-time tension sequence of the yarn on the transmission path of the loom. The real-time yarn feed speed sequence is subjected to a first time-domain transformation analysis based on a preset time sliding window to obtain the yarn feed reference length; and the real-time winding speed sequence is subjected to a second time-domain transformation analysis based on a preset time sliding window to obtain the actual winding length. The target loss length of the yarn within the preset time sliding window is determined based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus. The yarn feed reference lengths of all preset time sliding windows in the current production cycle are first accumulated to obtain the total material consumption of the cycle. The target loss lengths of all preset time sliding windows in the current production cycle are second accumulated to obtain the total waste material loss of the cycle. The raw material inventory data is updated based on the total consumption of materials in the cycle to obtain the current inventory level. The actual production cost of the current production cycle is determined based on the total consumption of materials in the cycle, the total waste loss in the cycle, and the preset pollutant treatment rate. Map the current book inventory and the actual production cost to the target digital monitoring interface.

2. The method according to claim 1, characterized in that, Determining the target loss length of the yarn within the preset time sliding window based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus includes: Temporal features are extracted from the real-time tension sequence to obtain the tension fluctuation extreme points and high-frequency abrupt change intervals within the preset time sliding window; The tension compensation coefficient of the yarn under the preset time sliding window is determined based on the extreme point of tension fluctuation, the high-frequency abrupt change range of tension, and the preset yarn elasticity. The actual winding length is restored using the tension compensation coefficient to obtain the theoretical tension-free winding length. The length difference between the yarn feed reference length and the theoretical tension-free winding length is determined as the target loss length.

3. The method according to claim 1, characterized in that, Determining the target loss length of the yarn within the preset time sliding window based on the real-time tension sequence, the actual winding length, and the preset yarn elastic modulus includes: Stress accumulation analysis is performed on the real-time tension sequence to obtain the cumulative tension load value borne by the yarn on the transmission path; The cumulative tension load value is matched with a preset elastic-plastic critical mapping relationship to determine the rate of plastic deformation of the yarn within the preset time sliding window; The plastic elongation increment is obtained by calculating the deformation increment based on the actual winding length and the plastic deformation rate. The target loss length is obtained by calculating the loss deviation based on the yarn feed reference length, the actual winding length, and the plastic extension increment.

4. The method according to claim 1, characterized in that, After updating the raw material inventory data based on the total periodic material consumption to obtain the current inventory level, and determining the actual production cost of the current production cycle based on the total periodic material consumption, the total periodic waste loss, and the preset pollutant treatment rate, the method further includes: Extract the historical inventory consumption rate sequence within a preset traceability period from the current book inventory level; The estimated depletion time of the current book inventory is determined based on the historical inventory consumption rate sequence and the preset production schedule data. When the expected depletion time point falls within the preset supply chain security warning range, the actual production cost will be used as a cost fluctuation feedback variable. The initial purchase batch optimization model is modified using the cost fluctuation feedback variable to obtain the target purchase batch optimization model, and a target replenishment strategy is generated based on the target purchase batch optimization model.

5. The method according to claim 4, characterized in that, The step of performing parameter correction operations on the initial purchase batch optimization model using the cost fluctuation feedback variable to obtain the target purchase batch optimization model, and generating a target replenishment strategy based on the target purchase batch optimization model, includes: The waste loss cost is determined based on the total amount of waste material loss in the cycle and the unit cost of yarn. Determine the proportion of the waste material loss cost in the cost fluctuation feedback variables of yarn waste; Based on the mapping relationship between the proportion of yarn waste cost and the preset yarn quality tolerance, the target tensile strength compensation coefficient for the next batch of yarn is determined; The yarn unit price weight parameter and quality benefit parameter in the initial procurement batch optimization model are corrected using the target tensile strength compensation coefficient to obtain the target procurement batch optimization model; The expected depletion time point, the current book inventory, and the average operating tension value of the loom in the current production cycle are used as joint state constraints and input into the target purchase batch optimization model for iterative optimization to obtain the target purchase batch and the target yarn quality grade. The target purchase volume and the target yarn quality grade are combined to generate the target replenishment strategy.

6. The method according to claim 1, characterized in that, After mapping the current book inventory and the actual production cost to the target digital monitoring interface, the method further includes: Extract the actual production cost time series within the preset historical display period from the target digital monitoring interface, and decompose the actual production cost time series into a basic material cost series and an environmental protection loss cost series based on the total waste loss of the period and the preset pollutant treatment rate. Based on the first slope of change of the basic material cost series, the second slope of change of the environmental depreciation cost series is determined; The pollution discharge marginal index is determined based on the first slope and the second slope. When the pollution discharge marginal index is greater than the preset green production tolerance threshold, phase difference analysis is performed on the real-time yarn feed speed sequence and the real-time winding speed sequence to obtain the resonant speed frequency band; The target operating speed range is determined based on the resonant speed frequency band, and a speed-limiting operation command for the loom is generated. The speed-limiting operation command is sent to the loom to control the loom to perform weaving operations within the target operating speed range in the next production cycle.

7. The method according to claim 6, characterized in that, When the pollution discharge marginal index is greater than a preset green production tolerance threshold, phase difference analysis is performed on the real-time yarn feed speed sequence and the real-time winding speed sequence to obtain the resonant speed frequency band, including: The real-time yarn feeding speed sequence is subjected to a first frequency domain transformation analysis to obtain the yarn feeding discrete frequency band sequence and the yarn feeding phase angle sequence; A second frequency domain transformation analysis is performed on the real-time winding speed sequence to obtain the winding discrete frequency band sequence and the winding phase angle sequence; The yarn feed discrete frequency band sequence is aligned with the winding discrete frequency band sequence to obtain an aligned discrete frequency band sequence. A phase angle difference sequence matching the aligned discrete frequency band sequence is determined based on the yarn feed phase angle sequence and the winding phase angle sequence. The phase angle difference sequence is timestamped and mapped to the fluctuation extreme value range of the real-time tension sequence in order to extract the candidate phase angle difference set that is in the fluctuation extreme value range from the phase angle difference sequence. The candidate phase angle difference set is assigned a frequency band based on the aligned discrete frequency band sequence to obtain the resonant velocity frequency band.

8. A digital management system, characterized in that, The digital management system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the digital management system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising program instructions, characterized in that, When the program instructions are run on the digital management system, the digital management system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the digital management system, the digital management system performs the method as described in any one of claims 1-7.

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