Artificial intelligence-based garment production flow management system

Abstract

The present application relates to the technical field of intelligent manufacturing and supply chain collaborative management of clothing, in particular to a clothing production process management system based on artificial intelligence, comprising: a production network management center for obtaining node execution entropy; a demand dynamic analysis unit for performing feature extraction analysis on collected unstructured design data to obtain an order complexity coefficient; a strategy game matching unit for obtaining a steady-state scheduling signal or a recovery scheduling signal; when the steady-state scheduling signal is generated, a collaborative scheduling execution unit is used to generate a global optimal scheduling instruction with the optimization objective of minimizing the turnaround time; when the recovery scheduling signal is generated, the collaborative scheduling execution unit is used to generate a risk repair scheduling instruction with the optimization objective of minimizing the node execution entropy and actively suppressing the pursuit of turnaround time; the present application effectively solves the problem that the prior art cannot perceive the hidden risks of nodes, and provides accurate quantitative basis for subsequent preventive scheduling.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and supply chain collaborative management technology for textiles and garments, specifically to an artificial intelligence-based garment production process management system. Background Technology

[0002] In the high-pressure production environment of the ultra-fast fashion supply chain, distributed production nodes need to frequently interact and process massive task flows. Their operation process not only includes explicit physical capacity data, but also implicit game behavior characteristics that reflect the nodes' willingness to cooperate and stability.

[0003] Existing production process management solutions generally adopt a linear scheduling architecture based on static physical capacity, that is, scheduling tasks solely based on the work progress reported by the MES system and equipment status. Although this solution has a certain resource allocation capability under normal steady-state conditions, it lacks the ability to quantify and analyze the complexity of unstructured design data and cannot perceive the implicit default tendency and trust entropy fluctuations caused by long-term high-pressure operations at nodes. As a result, the system mechanically dispatches high-difficulty or high-urgency orders even when nodes are in a high-risk critical state. This blind scheduling that ignores the trust dimension of human-machine coupling is prone to inducing false work reports at nodes, quality control failures, and even triggering a chain reaction of collapse in supply chain trust relationships. It is difficult to achieve a balance between pursuing ultimate turnover efficiency and maintaining the long-term sustainability of the system. Therefore, how to build a dynamic scheduling mechanism that integrates entropy perception and risk game logic, and achieve differentiated scheduling based on real-time and accurate quantification of node trust status, has become an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an artificial intelligence-based garment production process management system. Specifically, the technical solution of this invention includes:

[0005] Production network management center, entropy perception and quantification unit, demand dynamic analysis unit, strategy game matching unit, and collaborative scheduling and execution unit;

[0006] The production network management center is used to retrieve historical interaction data and current task flow data of distributed production nodes, and send the historical interaction data to the entropy value perception and quantification unit for trust quantification analysis to obtain the node execution entropy value;

[0007] The demand dynamic parsing unit is used to perform feature extraction and analysis on the collected unstructured design data, obtain the order complexity coefficient, and send the order complexity coefficient to the strategy game matching unit;

[0008] The strategy game matching unit is used to compare and analyze the node execution entropy value with the preset entropy value critical threshold to obtain a steady-state scheduling signal or a recovery scheduling signal.

[0009] When a steady-state scheduling signal is generated, the collaborative scheduling execution unit is used to generate a globally optimal scheduling instruction based on the order complexity coefficient and capacity constraints. The globally optimal scheduling instruction aims to minimize turnaround time.

[0010] When a recovery scheduling signal is generated, the collaborative scheduling execution unit generates a risk repair scheduling instruction based on the node execution entropy value. The risk repair scheduling instruction aims to minimize the node execution entropy value and actively suppresses the pursuit of turnaround time.

[0011] Preferably, the trust quantification analysis process is as follows: Historical operation periods of production nodes are collected and set as evaluation time windows. Command response behavior data of production nodes within the evaluation time window is obtained, including reporting delay perturbation values ​​and yield fluctuation variance values. The reporting delay perturbation values ​​and yield fluctuation variance values ​​are normalized. The normalized reporting delay perturbation values ​​and normalized yield fluctuation variance values ​​are weighted and summed to obtain the basic entropy value. The number of implicit defaults of production nodes within the evaluation time window is obtained. The number of implicit defaults is set as the trust decay coefficient. The value obtained by multiplying the basic entropy value by the trust decay coefficient is set as the node execution entropy value.

[0012] Preferably, the process for obtaining the number of implicit defaults is as follows: Obtain the equipment failure downtime reported by the production node within the evaluation time window, and at the same time obtain the energy consumption data curve of the production node in the same period; compare and analyze the energy consumption data curve with the preset standard downtime energy consumption characteristics; if the energy consumption data curve shows that it is in the running state but is reported as the down state, then generate a false report anomaly record; count the total number of false report anomaly records within the evaluation time window, and set the total number as the number of implicit defaults.

[0013] Preferably, the feature extraction and analysis process is as follows: Virtual sample garment images and process description text are obtained from unstructured design data; a preset multimodal generation model is used to perform structural analysis on the virtual sample garment images to obtain the process feature vector to be produced; the process feature vector to be produced is matched with a preset standard process database to calculate the distance and obtain the process deviation; the current social media attention growth rate of the design data is obtained; the process deviation and the attention growth rate are weighted and summed after data normalization and set as the order complexity coefficient.

[0014] Preferably, the comparison and analysis process of the strategy game matching unit is as follows: the node execution entropy value is compared with the preset entropy value critical threshold; if the node execution entropy value is less than the preset entropy value critical threshold, the production node is determined to be in the stable execution zone and a steady-state scheduling signal is generated; if the node execution entropy value is greater than or equal to the preset entropy value critical threshold, the production node is determined to be in the high-risk critical zone and a recovery scheduling signal is generated.

[0015] Preferably, the risk repair scheduling instruction generation process is as follows: In response to the recovery scheduling signal, obtain the profit margin value and order complexity coefficient of all pending orders in the current task pool; filter out orders with a profit margin value greater than a preset profit threshold and an order complexity coefficient less than a preset difficulty threshold, and set them as reassurance orders; prioritize the dispatch of reassurance orders to the corresponding production nodes, lock the production capacity time slot of the production node, and prohibit the insertion of other orders with high urgency or high complexity, until the node execution entropy value of the production node falls back below the preset recovery threshold.

[0016] Preferably, the process of generating the globally optimal scheduling instruction is as follows: in response to the steady-state scheduling signal, the real-time idle time periods of multiple production nodes are obtained; multiple orders with similar process feature vectors are clustered and merged to form a combined production package; the combined production package is allocated to the corresponding production node to maximize the output quantity per unit time and minimize the production line changeover loss.

[0017] Preferably, it also includes a network decoupling early warning unit; the network decoupling early warning unit is used to count in real time the proportion of nodes in the entire network whose execution entropy value is greater than a preset entropy value critical threshold, and set the node proportion as the network decoupling index; the network decoupling index is compared and analyzed with a preset circuit breaker threshold; if the network decoupling index is greater than the preset circuit breaker threshold, a supply chain interruption signal is generated, all new order dispatches are forcibly stopped and an emergency inventory transfer procedure is initiated; if the network decoupling index is less than or equal to the preset circuit breaker threshold, the current scheduling strategy is maintained.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. This invention introduces an entropy-sensing quantification unit to transform non-standard trust relationships in the supply chain into calculable scalars. It uses historical interaction data, work reporting delays, and energy consumption curves to identify implicit default behaviors. This mechanism can accurately capture the sub-healthy state of production nodes that have not actually defaulted but whose trust levels are unstable. It effectively solves the problem that existing technologies cannot detect the implicit risks of nodes and provides accurate quantitative basis for subsequent preventive scheduling.

[0020] 2. This invention utilizes a demand dynamic analysis unit to extract multimodal features from unstructured design data, and combines the process structure of virtual sample garments with social media attention to generate an order complexity coefficient. This method deconstructs emotional fashion elements and market urgency into machine-readable manufacturing difficulty indicators, accurately quantifies the impact of external demand on the production system, and avoids emotional breakdowns at production nodes caused by ignoring the pressure of order differentiation.

[0021] 3. This invention constructs a dynamic scheduling mechanism based on strategic game theory. When a node is detected to be in a high-risk critical zone, it automatically switches to a risk repair mode. By prioritizing the dispatch of high-profit, low-difficulty appeasing orders and shielding high-pressure tasks, the system actively suppresses the pursuit of short-term efficiency in exchange for long-term system stability. This realizes a paradigm shift from simple physical capacity scheduling to human-machine coupled trust scheduling, effectively repairing damaged trust relationships.

[0022] 4. This invention sets up a network decoupling early warning unit, which assesses the fragility of the supply chain by monitoring the proportion of high-risk nodes across the entire network in real time. When a systemic trust crisis is detected, it can decisively trigger the circuit breaker mechanism, forcibly blocking the dispatch of new orders and initiating inventory allocation. This macro-risk control method prevents local trust crises from evolving into systemic avalanches, providing the last line of defense for the survival of the supply chain infrastructure in extreme situations. Attached Figure Description

[0023] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0024] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0026] Example 1:

[0027] Please see Figure 1 An AI-based garment production process management system includes a production network management center, an entropy perception and quantification unit, a demand dynamic analysis unit, a strategy game matching unit, and a collaborative scheduling and execution unit.

[0028] The production network management center is used to retrieve historical interaction data and current task flow data of distributed production nodes, and send the historical interaction data to the entropy value perception and quantification unit for trust quantification analysis to obtain the node execution entropy value;

[0029] The demand dynamic parsing unit is used to perform feature extraction and analysis on the collected unstructured design data, obtain the order complexity coefficient, and send the order complexity coefficient to the strategy game matching unit;

[0030] The strategy game matching unit is used to compare and analyze the node execution entropy value with the preset entropy value critical threshold to obtain a steady-state scheduling signal or a recovery scheduling signal.

[0031] When a steady-state scheduling signal is generated, the collaborative scheduling execution unit is used to generate a globally optimal scheduling instruction based on the order complexity coefficient and capacity constraints. The globally optimal scheduling instruction aims to minimize turnaround time.

[0032] When a recovery scheduling signal is generated, the collaborative scheduling execution unit generates a risk repair scheduling instruction based on the node execution entropy value. The risk repair scheduling instruction aims to minimize the node execution entropy value and actively suppresses the pursuit of turnaround time.

[0033] This embodiment details the architecture and operation mechanism of the aforementioned AI-based apparel production process management system. This system aims to solve the problem of node trust collapse caused by high-pressure production in the ultra-fast fashion supply chain. The production network management center, as the data aggregation hub, connects to the MES systems of various contract manufacturers in real time through encrypted API interfaces. This center not only captures explicit production progress but also focuses on mining implicit interactive behavior data, aiming to build a digital twin mapping of the entire network. The entropy perception and quantification unit introduces the concept of thermodynamic entropy, transforming non-standard supply chain trust relationships into a computable scalar. This unit continuously monitors the disorder of node behavior, i.e., the node execution entropy value, through a specific algorithm model. This value directly reflects the probability density of production nodes defaulting or implicitly defaulting.

[0034] Building upon this foundation, the demand dynamic analysis unit utilizes deep learning models to process design drawings and process sheets, deconstructing intuitive fashion elements into machine-readable manufacturing difficulty indicators, namely, the order complexity coefficient, thereby quantifying the impact of external demand on the production system. The strategy game matching unit, acting as the system's decision-making brain, executes state-based game logic, the core of which lies in determining whether the current node is in a steady state suitable for high-pressure operations or in a recovery state requiring load relief. The collaborative scheduling execution unit dynamically switches the scheduling objective function based on the decision results, seeking a Nash equilibrium point between pursuing efficiency and maintaining system sustainability.

[0035] This embodiment achieves a paradigm shift in supply chain management by constructing a closed-loop control system that includes entropy perception and strategic game theory. This shift is from simple physical capacity scheduling to human-machine coupled scheduling that includes a trust dimension. The system can automatically identify and repair production relationships on the verge of collapse while ensuring overall capacity output. This risk control mechanism, which actively suppresses short-term efficiency in exchange for long-term system stability, effectively avoids the supply chain avalanche effect caused by a single point of trust collapse.

[0036] Example 2:

[0037] The trust quantification analysis process is as follows:

[0038] The historical operation periods of production nodes are collected and set as evaluation time windows. The instruction response behavior data of production nodes within the evaluation time window are obtained. The instruction response behavior data includes the reporting delay perturbation value and the yield fluctuation variance value.

[0039] The reporting delay perturbation value and the yield fluctuation variance value are normalized. The normalized reporting delay perturbation value and the normalized yield fluctuation variance value are weighted and summed to obtain the basic entropy value.

[0040] The number of implicit defaults of production nodes within the evaluation time window is obtained. The number of implicit defaults is set as the trust decay coefficient, and the value obtained by multiplying the base entropy value by the trust decay coefficient is set as the node execution entropy value.

[0041] This embodiment further refines the specific calculation logic of the entropy value sensing and quantification unit in Embodiment 1, aiming to establish a mathematical model capable of sensitively capturing psychological anomalies at production nodes. The system performs data cleaning and time window slicing operations, locking the most recent production cycle as the evaluation time window, such as the past 72 hours, and extracting micro-behavioral features from it; among which, the reporting delay perturbation value... The aim is to quantify the node's tendency to delay responding to instructions from higher levels, while the yield fluctuation variance value... This reflects the stability of the work quality, and together they constitute the basic profile of the node coordination degree; the reporting delay perturbation value The calculation formula is defined as follows: ; here This is the timestamp of when the node actually submits the work order in the MES system. The standard completion timestamp specified in the process sheet, with the unit uniformly in minutes; if Indicates a delay. Indicating early completion, this embodiment takes the actual algebraic value before subsequent normalization processing to retain the incentive attribute of early completion.

[0042] The system employs the Min-Max standardization method to eliminate dimensional differences and determines extreme value boundaries based on historical statistical data. Specifically, it statistically analyzes extreme values. Set as historical data distribution and Percentiles were used to remove outliers and normalized reporting delays were calculated. and normalized yield variance ,in, The statistical extreme value of the delay perturbation value. The statistical extreme values ​​of the yield variance are used; and the basic entropy value is calculated using the weighted summation formula. Weighting factors The determination of the system is achieved using principal component analysis: constructing a system containing... Dataset matrix of historical time window samples Its dimensions are The column vectors correspond to the historical normalized reporting delays, respectively. and normalized yield variance ;calculate covariance matrix Solve Eigenvalues ​​and eigenvectors; select the eigenvector corresponding to the largest eigenvalue. Calculate weights and ,satisfy The formula is expressed as:

[0043]

[0044] in, To normalize the reporting delay, To normalize the variance of yield, Preset weighting factors;

[0045] Based on this, in order to strictly follow the laws of physical dimensions and solve the formulas The issue of dimensional consistency in [the context of this text] Since the implicit default count is a dimensionless scalar, while the number of implicit defaults has a dimension of "times", the system introduces a reference benchmark normalization step in its physical implementation: setting a reference default benchmark value. Its physical dimensions are set to secondary, for example, taking The number of implicit defaults obtained from statistics Its dimension is subdivision, and as the numerator, it is related to the denominator. Its dimension is also subdivision. By performing a division operation, the dimension is physically eliminated, resulting in a truly dimensionless confidence attenuation coefficient. ,Right now ,in, The trust decay coefficient, The number of implicit defaults. To reference the default baseline value, the system generates the final node execution entropy value through multiplication. ,Right now This calculation process not only rigorously eliminates dimensional conflicts in terms of mathematical principles, but also reflects the nonlinear risk amplification logic, that is, once a default occurs, the system's trust rating will deteriorate exponentially.

[0046] This embodiment constructs a highly sensitive trust quantification model by combining minor behavioral disturbances with serious default records. This model can identify the sub-healthy state of production nodes that, although they have not officially defaulted, are already dissatisfied, i.e., a high-entropy state. This provides accurate quantitative basis for subsequent preventive recovery scheduling and ensures the timeliness of risk warning.

[0047] Example 3:

[0048] The process for obtaining the number of implicit defaults is as follows:

[0049] The system obtains the equipment failure downtime reported by the production node within the evaluation time window, and also obtains the energy consumption data curve of the production node during the same period.

[0050] The energy consumption data curve is compared and analyzed with the preset standard shutdown energy consumption characteristics. If the energy consumption data curve shows that it is in the running state but is reported as the shutdown state, a false alarm record is generated.

[0051] The total number of falsely reported abnormal records within the statistical evaluation time window is set as the number of implicit defaults.

[0052] This embodiment further specifies the physical implementation details of the implicit default count acquisition step in Embodiment 2, aiming to verify the authenticity of the digital world using objective data from the physical world; the system synchronously collects the operating status logs of production equipment and the real-time power data of smart meters through an IoT gateway to construct a multi-dimensional data cube; the system executes a feature matching algorithm to convert the real-time energy consumption data curve Compared with the preset standard shutdown energy consumption characteristics Perform temporal alignment and comparison here. Defined as production equipment in time The instantaneous active power, measured in kW. Defined as the baseline power threshold of the equipment under standard shutdown conditions, in kW;

[0053] In response to the time period during which the node reports the outage. Inside, detected Significantly higher than It also possesses typical load characteristics, and the specific judgment rule is: when it meets the following conditions... Among them, the determination of the multiple Set to 1.2, and during the duration Power standard deviation within ,in The standard deviation of power fluctuation, If the minimum fluctuation threshold is set to 0.05kW, the system determines that the node has engaged in false work reporting behavior, i.e., fake shutdown for private purposes or deliberate idleness. Based on this, the system triggers the abnormal event recording mechanism to generate false abnormal records with timestamps. The system iterates through all records within the evaluation time window and accumulates the number of implicit defaults through a counter, which serves as a key penalty factor for subsequent entropy value calculation.

[0054] This embodiment utilizes the physical principle of the law of conservation of energy to construct a trust verification mechanism based on objective physical data. This mechanism effectively identifies the phenomenon of false reporting of equipment operating status in distributed factories, transforms hidden violations into quantifiable default risk indicators, and greatly enhances the system's ability to perceive potential systemic risks in the supply chain.

[0055] Example 4:

[0056] The feature extraction and analysis process is as follows:

[0057] The virtual sample garment image and process description text are obtained from the unstructured design data. The structure of the virtual sample garment image is analyzed using a preset multimodal generation model to obtain the feature vector of the process to be produced.

[0058] The process deviation degree is obtained by matching the feature vector of the process to be produced with the preset standard process database and calculating the distance. The current social media attention growth rate of the design data is obtained. The process deviation degree and the attention growth rate are weighted and summed after data normalization and set as the order complexity coefficient.

[0059] This embodiment further specifies the workflow of the dynamic demand analysis unit in Embodiment 1, aiming to transform abstract fashion design into pressure indicators for the production end; the system deploys a multimodal generative model based on the Transformer architecture, the specific structure of which includes an image encoder, and employs... - The model employs a / 16 architecture and a text encoder, utilizing the BERT-Base architecture. Both are fused through a three-layer cross-attention module. Trained on a private dataset containing millions of sample garment image-process text pairs using contrastive loss, the model simultaneously understands the spatial structure of visual images and the semantic information of text descriptions, thus mapping unstructured design data to a high-dimensional feature space. The output dimension is [missing information]. Feature vector of the process to be manufactured The system calculates the vector and compares it with the feature vector of the standard base model. The Euclidean distance between them, i.e., the process deviation. ;in, Defined as the arithmetic mean of all historical feature vectors under the basic style category in the standard process database, i.e. ,in Based on historical vectors, The total number of samples;

[0060] Based on this, the system connects to social media APIs to obtain the current growth rate of attention to this design data on social media. Its specific calculation logic is as follows: setting a calculation time window. The interaction data collected within this window will last for 24 hours, including the number of likes. Number of comments and number of reposts Construct a heat function The interaction weight factor is set to a fixed value: The growth rate is obtained by calculating the first derivative using the discrete difference method. The unit is To eliminate the influence of time dimension on order complexity coefficients, the system performs dimensionless normalization on the growth rate: obtaining the maximum growth rate from historical statistics. The unit is Calculate the normalized growth rate of attention This step cancels out the time unit by dividing by units of the same dimension;

[0061] Considering the independent contributions of manufacturing difficulty and market urgency to system stress, the system uses a weighted summation model to calculate the order complexity coefficient; the specific formula is: ;in, As a preset weighting factor, this embodiment sets With a focus on manufacturing considerations, this calculation method avoids the problem of losing the overall risk signal due to a single indicator value being too low. The higher the coefficient, the more it means that the order combines high manufacturing difficulty or high market urgency, posing a huge challenge to the stability of the production node.

[0062] Meanwhile, in order to unify the dimensions, the system measures the process deviation. Normalization is also performed: the maximum process deviation in the historical database is obtained. Calculate the normalized process deviation ,in, To normalize the process deviation, This represents the deviation from the original process. This represents the largest historical deviation in manufacturing processes; the system integrates manufacturing difficulty with market urgency through a formula. Calculate the order complexity factor here. All are dimensionless scalars; the higher the coefficient, the more difficult and urgent the order is, posing a huge challenge to the stability of the production node.

[0063] This embodiment redefines the meaning of order complexity by incorporating cross-modal feature fusion and market data. The solution not only considers the physical manufacturing difficulty but also combines the time-dimensional delivery pressure, thereby accurately identifying those "poisonous" orders that are highly likely to trigger negative emotions at production nodes, providing data support for subsequent differentiated scheduling.

[0064] Example 5:

[0065] The comparative analysis process of matching units in strategy games is as follows:

[0066] The node execution entropy value is compared with a preset entropy threshold. If the node execution entropy value is less than the preset entropy threshold, the production node is determined to be in a stable execution zone, and a steady-state scheduling signal is generated.

[0067] If the node's execution entropy value is greater than or equal to the preset entropy threshold, the production node is determined to be in a high-risk critical zone, and a recovery scheduling signal is generated.

[0068] This embodiment further specifies the decision-making logic of the strategy game matching unit in Embodiment 1. This logic constructs a binary switching mechanism based on trust states; the system loads a preset entropy threshold. The threshold is determined as follows: Select a set of nodes from the historical database that have experienced breach of contract. and the set of normal performance nodes We statistically analyzed the entropy distribution of the two types of nodes at behavioral inflection points and used ROC curve analysis to calculate the Youden index. Select The entropy point corresponding to the maximum value is taken as Or set directly to Distribution Percentile values ​​are used to ensure highly sensitive identification of risks.

[0069] The comparator module receives the latest node execution entropy value in real time. And execute the threshold discrimination logic; in response to If the system determines that the node's current trust state is stable and it is willing to undertake high-load tasks, then the system state is locked into a stable execution zone and a steady-state scheduling signal is output; otherwise, in response to... The system determines that the node has accumulated excessive negative emotions or speculative tendencies and is in a high-risk state where the chain may break at any time, i.e., a high-risk critical zone, thereby triggering an emergency intervention mechanism and outputting a recovery scheduling signal.

[0070] This embodiment introduces the concept of risk threshold, giving the scheduling system the ability to read between the lines. This mechanism breaks the limitation of traditional scheduling that only focuses on physical capacity. It can proactively perceive the risk threshold before a node defaults, thus providing a precise triggering time for the system to switch from the exploitation mode to the appeasement mode, reflecting the humanization and adaptability of the management system.

[0071] Example 6:

[0072] The process of generating risk remediation scheduling instructions is as follows:

[0073] In response to the resumption of scheduling signal, obtain the profit margin values ​​and order complexity coefficients of all pending orders in the current task pool;

[0074] Orders with a profit margin greater than a preset profit threshold and an order complexity coefficient less than a preset difficulty threshold are selected and designated as appeasement orders.

[0075] Prioritize the dispatch of reassuring orders to the corresponding production nodes and lock the production capacity slots of those nodes, prohibiting the insertion of other orders with high urgency or high complexity, until the node execution entropy value of those production nodes falls below the preset recovery threshold.

[0076] This embodiment further specifies the execution logic of the collaborative scheduling execution unit in repair mode in Embodiment 1, aiming to repair damaged trust relationships through the transfer of benefits and pressure blocking; the system activates the order filter, traverses the pending orders in the task pool, and extracts their financial attributes, i.e., profit margin values. Technical attributes, i.e., order complexity coefficient The system executes dual filtering logic to filter out those that meet the requirements. and Specific orders with preset profit thresholds Set as historical average profit rate Preset difficulty threshold Set as the historical average complexity coefficient Percentile values ​​indicate that these high-profit, low-difficulty orders are defined as appeasement orders.

[0077] Building upon this, the scheduling engine breaks the conventional first-come, first-served rule, forcibly inserting reassuring orders into the production queue of high-entropy nodes. The system activates a capacity locking mechanism, blocking any urgent or difficult orders that might push up entropy from entering that node, until the node's execution entropy value is detected to have significantly decreased and fallen below a preset recovery threshold. This closed-loop control logic for entropy reduction is manifested as follows: as reassuring orders are successfully delivered, the system generates a series of perfect fulfillment records with zero latency and zero fluctuations within the evaluation time window, gradually squeezing out historical high-entropy data using a first-in, first-out sliding window mechanism, while simultaneously introducing a recovery decay factor. For each appeasement order completed, execute The update operation; among which, the decay factor is restored. The value range is set to In this embodiment, a fixed value is used. ,in To restore the decay factor, thereby achieving rapid convergence of the entropy value;

[0078] This embodiment simulates the rest and recuperation mechanism of an organism and creatively proposes a restorative scheduling strategy that takes a step back to move forward. By actively sacrificing local turnover efficiency, delivering profitable orders to high-risk nodes and shielding them from interference, the system can quickly reduce the execution entropy of nodes, thereby restoring trust relationships without cutting off the supply chain and avoiding higher reconstruction costs caused by node loss.

[0079] Example 7:

[0080] The process of generating globally optimal scheduling instructions is as follows:

[0081] In response to steady-state scheduling signals, the real-time idle time periods of multiple production nodes are obtained;

[0082] Multiple orders with similar process feature vectors are clustered and merged to form combined production packages;

[0083] Assign combined production packages to the corresponding production nodes to maximize output per unit time and minimize production line changeover losses.

[0084] This embodiment further specifies the execution logic of the collaborative scheduling execution unit in steady-state mode in Embodiment 1, aiming to fully explore the production potential of nodes in the stable execution zone. The system scans the status registers of all nodes in the network and extracts the available time slice resources of all nodes marked as steady-state. The system calls an unsupervised clustering algorithm, such as the K-Means algorithm, to aggregate massive scattered nodes in the feature space. Before performing clustering, the system uses the silhouette coefficient method to traverse the network. To determine the optimal number of clusters And using Euclidean distance As a similarity metric, orders with similar spatial distances in the process feature vector space are packaged to generate combined production packages with economies of scale.

[0085] Based on this, the solver constructs a linear programming model to accurately allocate the combined production packages to the corresponding idle time slots. In order to eliminate ambiguity about the optimization objective in the technical solution, this embodiment clarifies that the core objective of the global optimal scheduling instruction is to minimize the turnaround time, and maximizing the output per unit time and minimizing the production line changeover loss are the specific paths to achieve this core objective.

[0086] Let the total number of production packages to be allocated be . ,Right now The total number of production nodes in steady state and available for scheduling is ,Right now In the specific modeling process, to resolve the issues of inconsistent physical dimensions and conflicting parameter signs, this embodiment introduces a normalization factor: defining the maximum theoretical production rate. The unit is units per hour as the rate benchmark, and the maximum tolerable handover time is defined. The unit is hours, and the symbol used here is... To distinguish it from the delay perturbation value in Example 2 As a time reference; construct a dimensionless modified objective function. Its mathematical expression is as follows:

[0087]

[0088] in, For the dimensionless total utility value, maximize This value corresponds to the optimal overall turnover efficiency of the system;

[0089] For the first The output quantity of each combined production package;

[0090] For the first The package in the first Estimated production time at each node;

[0091] The switching loss time is determined by a preset standard process database and specifically using a rolling window scheduling mode: at the current decision moment, the system locks the node. The last completed or currently executing task is used as a determined preceding reference point. The relationship between this reference point and the current candidate task is calculated. Differences in process characteristics between individual packages, thereby determining The numerical value; This model adopts a rolling time window strategy, which treats the current state of the node as a definite boundary condition at each scheduling time. This approach transforms the dynamic sequence dependency problem into a definite state dependency problem, ensuring the uniqueness of the cost coefficient in the linear programming model. For example, the switching loss for the same product in different colors is 0.1 hours, and the switching loss for different products is 0.5 hours.

[0092] For binary decision variables, when The time indicates that the package Assigned to node Otherwise, it is 0;

[0093] These are dimensionless weighting coefficients used to balance output efficiency and switching costs; in this embodiment, The value system is obtained through offline simulation optimization: the system selects historical order data from the past year as the training set, and sets... exist Within the interval Perform traversal simulation scheduling for each step size, and calculate different... For the order on-time delivery rate and average equipment utilization rate under the given values, select the value corresponding to the highest weighted score of these two indicators. The value is used as the preset weighting coefficient of this system. In order to ensure output while moderately suppressing the disturbance caused by frequent product changes, the coefficient is set in the range of [0.3, 0.7], and the preferred value in this embodiment is 0.5. The formula eliminates the difference in physical units by dividing by the benchmark value, so that the first normalized output rate and the second normalized loss rate are additive and subtractable. At the same time, the model must meet the following constraints:

[0094] That is, each production package can and must be assigned to only one production node;

[0095] ,in, For the first The remaining capacity period of each node, this value is based on the node's remaining capacity obtained from the production network management center. The capacity calendar is calculated by summing all available fragmented idle time slices for future scheduled periods, such as within a 24-hour period; to ensure the feasibility of physical production, the calculation... A continuity constraint filtering mechanism is introduced: the system traverses nodes. All idle time slices Only those that meet the single-episode duration requirement are selected. The effective time slices are accumulated, or logical judgments are added to the constraints to ensure that each assigned task can fall completely into a continuous idle time window, preventing tasks from being incorrectly assigned to physically discontinuous time fragments;

[0096] This embodiment returns to the core logic of lean manufacturing. After confirming that the node is in a trustworthy steady state, it uses algorithms to reconstruct fragmented personalized needs into quasi-standardized batch tasks. This strategy ensures flexible production capabilities while maximizing the scale advantages of industrialized mass production, achieving the ultimate improvement in turnover efficiency.

[0097] Example 8:

[0098] This system also includes a network decoupling early warning unit;

[0099] The network decoupling early warning unit is used to count in real time the proportion of nodes in the entire network whose execution entropy value is greater than the preset entropy value critical threshold, and set the node proportion as the network decoupling index.

[0100] The network decoupling index is compared with the preset circuit breaker threshold. If the network decoupling index is greater than the preset circuit breaker threshold, a supply chain disruption signal is generated, forcibly stopping the dispatch of all new orders and initiating an emergency inventory transfer procedure. If the network decoupling index is less than or equal to the preset circuit breaker threshold, the current scheduling strategy is maintained.

[0101] This embodiment details the macro-level risk control mechanism of the network decoupling early warning unit, aiming to prevent local trust crises from escalating into systemic collapse. The system initiates a full network scan process, continuously counting the number of nodes in a high-entropy state (i.e., a high-risk critical zone) among all currently online nodes. ; Calculate this quantity as a percentage of the total number of nodes. The percentage is defined as the network decoupling index. This index characterizes the fragility of the entire supply chain network; based on this, the system will... With preset circuit breaker threshold Perform real-time comparison; respond to If a large-scale trust disruption risk is detected in the network, the system determines that the conventional repair strategy has failed and immediately triggers the highest level of circuit breaker mechanism; the system generates a supply chain disruption signal, cuts off all new order delivery channels at the physical layer, and simultaneously activates the backup logistics plan, starts the emergency inventory transfer procedure, and directly calls upon finished goods inventory to meet market demand; otherwise, if the index does not exceed the limit, the system continues to maintain the existing dynamic game scheduling strategy.

[0102] This embodiment constructs a circuit breaker mechanism for the supply chain system, providing the last line of defense against extreme systemic risks. When algorithmic repair fails to curb the spread of a crisis of trust, this mechanism can decisively stop the loss and prevent erroneous orders from further exacerbating the supply and demand contradiction through physical blocking, thereby protecting the supply chain infrastructure from being completely destroyed and preserving basic resources for subsequent system restart.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An AI-based garment production process management system, characterized in that: It includes a production network management center, an entropy perception and quantification unit, a demand dynamic analysis unit, a strategy game matching unit, and a collaborative scheduling and execution unit; The production network management center is used to retrieve historical interaction data and current task flow data of distributed production nodes, and send the historical interaction data to the entropy value perception and quantification unit for trust quantification analysis to obtain the node execution entropy value; The demand dynamic parsing unit is used to perform feature extraction and analysis on the collected unstructured design data, obtain the order complexity coefficient, and send the order complexity coefficient to the strategy game matching unit; The strategy game matching unit is used to compare and analyze the node execution entropy value with the preset entropy value critical threshold to obtain a steady-state scheduling signal or a recovery scheduling signal. When a steady-state scheduling signal is generated, the collaborative scheduling execution unit is used to generate a globally optimal scheduling instruction based on the order complexity coefficient and capacity constraints. The globally optimal scheduling instruction aims to minimize turnaround time. When a recovery scheduling signal is generated, the collaborative scheduling execution unit generates a risk repair scheduling instruction based on the node execution entropy value. The risk repair scheduling instruction aims to minimize the node execution entropy value and actively suppresses the pursuit of turnaround time. The trust quantification analysis process is as follows: Historical operation periods of production nodes are collected and set as evaluation time windows. Command response behavior data of production nodes within the evaluation time window is obtained, including reporting delay perturbation values ​​and yield fluctuation variance values. The reporting delay perturbation values ​​and yield fluctuation variance values ​​are normalized. The normalized reporting delay perturbation values ​​and normalized yield fluctuation variance values ​​are weighted and summed to obtain the basic entropy value. The number of implicit defaults of production nodes within the evaluation time window is obtained. The number of implicit defaults is set as the trust decay coefficient. The value obtained by multiplying the basic entropy value by the trust decay coefficient is set as the node execution entropy value.

2. The AI-based garment production process management system according to claim 1, characterized in that, The process for obtaining the number of implicit defaults is as follows: Obtain the equipment failure downtime reported by the production node within the evaluation time window, and simultaneously obtain the energy consumption data curve of the production node during the same period; compare and analyze the energy consumption data curve with the preset standard downtime energy consumption characteristics; if the energy consumption data curve shows that the device is in operation but is reported as being in a downtime state, then generate a false report anomaly record; count the total number of false report anomaly records within the evaluation time window, and set the total number as the number of implicit defaults.

3. The garment production process management system based on artificial intelligence according to claim 1, characterized in that, The feature extraction and analysis process is as follows: virtual sample garment images and process description texts are obtained from unstructured design data; the virtual sample garment images are structurally analyzed using a preset multimodal generation model to obtain the feature vectors of the process to be produced. The process deviation is obtained by matching the feature vector of the process to be produced with the preset standard process database and calculating the distance. The current social media attention growth rate of the design data is obtained. The weighted sum of the process deviation and the attention growth rate after data normalization is set as the order complexity coefficient.

4. The AI-based garment production process management system according to claim 1, characterized in that, The comparison and analysis process of the strategy game matching unit is as follows: the node execution entropy value is compared with the preset entropy value critical threshold; if the node execution entropy value is less than the preset entropy value critical threshold, the production node is determined to be in the stable execution zone, and a steady-state scheduling signal is generated. If the node's execution entropy value is greater than or equal to the preset entropy threshold, the production node is determined to be in a high-risk critical zone, and a recovery scheduling signal is generated.

5. The AI-based garment production process management system according to claim 1, characterized in that, The risk repair scheduling instruction is generated as follows: In response to the recovery scheduling signal, the profit margin and order complexity coefficient of all pending orders in the current task pool are obtained; orders with a profit margin greater than a preset profit threshold and an order complexity coefficient less than a preset difficulty threshold are selected and set as reassurance orders; reassurance orders are preferentially dispatched to the corresponding production nodes, and the production capacity time slot of the production node is locked, prohibiting the insertion of other orders with high urgency or high complexity, until the node execution entropy value of the production node falls back below the preset recovery threshold.

6. The AI-based garment production process management system according to claim 1, characterized in that, The process of generating the global optimal scheduling instruction is as follows: In response to the steady-state scheduling signal, the real-time idle time periods of multiple production nodes are obtained; multiple orders with similar process feature vectors are clustered and merged to form a combined production package; the combined production package is allocated to the corresponding production node to maximize the output quantity per unit time and minimize the production line changeover loss.

7. The garment production process management system based on artificial intelligence according to claim 1, characterized in that, It also includes a network decoupling early warning unit; the network decoupling early warning unit is used to count in real time the proportion of nodes in the entire network whose execution entropy value is greater than a preset entropy value critical threshold, and set the node proportion as the network decoupling index; the network decoupling index is compared and analyzed with a preset circuit breaker threshold. If the network decoupling index is greater than the preset circuit breaker threshold, a supply chain interruption signal is generated, all new order dispatches are forcibly stopped and an emergency inventory transfer procedure is initiated; if the network decoupling index is less than or equal to the preset circuit breaker threshold, the current scheduling strategy is maintained.

Images