A full-chain risk monitoring and coordination system and method for clothing manufacturing

Abstract

The present invention relates to the technical field of data analysis, and discloses a full-chain risk monitoring and coordination system and method for garment manufacturing. The system includes a two-way risk monitoring tree construction module, a risk factor calculation module, a risk probability calculation module, a node coordination sequence generation module, and a risk coordination module, which constructs a two-way risk monitoring tree for the full chain of garment manufacturing; monitors the cyclic monitoring data of each data node, calculates the risk factor, risk tolerance, and risk probability corresponding to each data node through the cyclic monitoring data; calculates the urgency of each data node according to the association relationship, and determines the risk coordination degree of each data node according to the urgency and risk probability; generates a coordination factor for each data coordination node according to the risk coordination degree, generates a coordination strategy for the data coordination node according to the coordination factor, and performs risk coordination on the data coordination node according to the coordination strategy. The present invention can improve the accuracy of full-chain risk monitoring and coordination in garment manufacturing.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a full-chain risk monitoring and coordination system and method for garment manufacturing. Background Art

[0002] With the complex and changeable market environment in the garment industry, the risk management of the supply chain is crucial for enterprises. From raw material procurement to product launch, every link hides unknown risks. Therefore, it is necessary to accurately identify, effectively manage, and reduce the unknown risks in the supply chain, make the supply chain operate steadily, reduce the occurrence probability and impact degree of risks, and improve the elasticity and sustainability of the supply chain.

[0003] The existing full-chain risk monitoring and coordination technology for garment manufacturing is to monitor the status of production equipment, inventory levels, and logistics by integrating information from all links of the supply chain. In practical applications, relying solely on equipment to collect data separately results in inaccurate data collection and the risks brought by not monitoring the full-chain data in real time, thus resulting in low accuracy when conducting full-chain risk monitoring and coordination for garment manufacturing. Summary of the Invention

[0004] The present invention provides a full-chain risk monitoring and coordination system and method for garment manufacturing, and its main purpose is to solve the problem of accuracy when conducting full-chain risk monitoring and coordination for garment manufacturing.

[0005] To achieve the above object, a full-chain risk monitoring and coordination system for garment manufacturing provided by the present invention includes:

[0006] The system includes a two-way risk monitoring tree construction module, a risk factor calculation module, a risk probability calculation module, a node coordination sequence generation module, and a risk coordination module. Among them,

[0007] The two-way risk monitoring tree construction module is used to obtain the data nodes of the full chain of garment manufacturing and the association relationships between the nodes, and construct a two-way risk monitoring tree for the full chain of garment manufacturing according to the data nodes and the association relationships;

[0008] The risk factor calculation module is used to monitor the cyclic monitoring data of each data node in the two-way risk monitoring tree according to a preset two-way cyclic time node, and calculate the risk factors corresponding to the leaf nodes in the two-way risk monitoring tree through the cyclic monitoring data;

[0009] The risk probability calculation module is used to calculate the risk tolerance of the leaf nodes according to the target node data corresponding to the leaf nodes in the two-way risk monitoring tree, and is used for: determining the data collection time period corresponding to the leaf nodes according to the time points corresponding to the two-way loop nodes in the two-way risk monitoring tree and the preset time collection points; extracting the historical critical data corresponding to the leaf nodes through the data collection time period; calculating the risk tolerance of the leaf nodes according to the target node data and the historical critical data, where the risk tolerance calculation formula is:

[0010]

[0011] where R kα is the risk tolerance corresponding to the kth leaf node at the αth time point, S kα is the target node data corresponding to the kth leaf node at the αth time point, L kα is the minimum value among the historical critical data corresponding to the kth leaf node at the αth time point, H kα is the maximum value among the historical critical data corresponding to the kth leaf node at the αth time point, and min is the minimum value function;

[0012] Calculating the risk probability of each data node according to the risk factor and the risk tolerance;

[0013] The node coordination sequence generation module is used to calculate the urgency of each data node according to the association relationship through a preset dynamic hierarchical association algorithm, determine the risk coordination degree of each data node according to the urgency and the risk probability, and generate a node coordination sequence of the two-way risk monitoring tree according to the risk coordination degree;

[0014] The risk coordination module is used to generate a coordination factor for each data coordination node in the node coordination sequence according to the risk coordination degree, generate a coordination strategy for the data coordination node according to the coordination factor, and perform risk coordination on the data coordination node according to the coordination strategy.

[0015] Optionally, when the two-way risk monitoring tree construction module constructs a two-way risk monitoring tree for the entire clothing manufacturing chain according to the data nodes and the association relationship, it is used for:

[0016] Determining the two-way hierarchical association and one-way hierarchical association of the data nodes according to the association relationship;

[0017] Determining the two-way loop points of the entire clothing manufacturing chain through the two-way hierarchical association, and determining the one-way loop points of the entire clothing manufacturing chain according to the one-way hierarchical association;

[0018] Generate a two-way association path between the two-way cycle points according to the described two-way hierarchical association;

[0019] Generate a one-way association path between the one-way cycle points according to the described one-way hierarchical association;

[0020] Connect the two-way cycle points according to the two-way association path to obtain a two-way cycle subtree, and connect the one-way cycle points according to the one-way association path to obtain a one-way cycle subtree;

[0021] Associate the two-way cycle subtree with the one-way cycle subtree to obtain a two-way risk monitoring tree for the entire clothing manufacturing chain.

[0022] Optionally, when the risk factor calculation module monitors the cycle monitoring data of each data node in the two-way risk monitoring tree according to a preset two-way cycle time node, it is used for:

[0023] Trigger the two-way cycle time node according to a preset time sliding window;

[0024] Collect the real-time two-way monitoring data of the two-way cycle points and the real-time one-way monitoring data of the one-way cycle points in the two-way risk monitoring tree according to the time point corresponding to the triggered two-way cycle time node;

[0025] Attribute the real-time two-way monitoring data and the real-time one-way monitoring data to obtain real-time two-way monitoring attribute data and real-time one-way monitoring attribute data;

[0026] Collect the real-time two-way monitoring attribute data and the real-time one-way monitoring attribute data as cycle monitoring data.

[0027] Optionally, when the risk factor calculation module calculates the risk factors corresponding to the leaf nodes in the two-way risk monitoring tree through the cycle monitoring data, it is used for:

[0028] Obtain the target attribute data of the leaf node at the time point;

[0029] Calculate the two-way risk factor corresponding to the two-way cycle point in the leaf node according to the real-time two-way monitoring attribute data and the target attribute data in the cycle monitoring data by using the following preset two-way time factor algorithm:

[0030]

[0031] where f i is the two-way risk factor corresponding to the i-th two-way cycle point, p it is the positive feedback attribute value of the real-time two-way monitoring attribute data corresponding to the i-th two-way cycle point under the condition of time point t, q itis the target attribute value of the target attribute data corresponding to the i-th bidirectional cycle point under the condition of time point t, is the real-time attribute value of the real-time bidirectional monitoring attribute data corresponding to the i-th bidirectional cycle point under the condition of time point t, is the reverse feedback attribute value corresponding to the i-th bidirectional cycle point under the condition of time point t;

[0032] Calculate the unidirectional risk factor corresponding to the unidirectional cycle point in the leaf node according to the real-time unidirectional monitoring attribute data and the target attribute data in the cycle monitoring data by using the following preset unidirectional time factor algorithm:

[0033]

[0034] where g i is the unidirectional risk factor corresponding to the i-th unidirectional cycle point, a it is the real-time attribute value of the real-time unidirectional monitoring attribute data corresponding to the i-th unidirectional cycle point under the condition of time point t, b it is the target attribute value of the target attribute data corresponding to the i-th unidirectional cycle point under the condition of time point t.

[0035] Optionally, when calculating the risk probability of each data node according to the risk factor and the risk tolerance, the risk probability calculation module is used for:

[0036] Determine the target risk probability corresponding to the leaf node according to the risk factor and the risk tolerance:

[0037]

[0038] where Y k is the target risk probability of the k-th leaf node, f k is the bidirectional risk factor corresponding to the bidirectional cycle point in the k-th leaf node, g k is the unidirectional risk factor corresponding to the unidirectional cycle point in the k-th leaf node, R k is the risk tolerance of the k-th leaf node, ∨ is the or symbol, and ∧ is the and symbol;

[0039] Extract the node hierarchical relationship corresponding to the leaf node through the bidirectional risk monitoring tree;

[0040] Determine the hierarchical node corresponding to the leaf node according to the node hierarchical relationship;

[0041] Statistically calculate the node risk probability corresponding to the hierarchical node through the target risk probability;

[0042] Determine the risk probability of each data node according to the node risk probability.

[0043] Optionally, when calculating the urgency of each data node according to the association relationship through a preset dynamic hierarchical association algorithm, the node coordination sequence generation module is used for:

[0044] Determine the node level corresponding to each data node according to the association relationship;

[0045] Determine the hierarchical urgency coefficient of each data node according to the node level;

[0046] Determine the dynamic demand data of each data node through the cycle time point corresponding to the cycle time node;

[0047] Calculate the urgency of each data node according to the occurrence times of the dynamic demand data through the following preset dynamic hierarchical association algorithm:

[0048]

[0049] where d σ is the urgency of the σ-th data node, δ σ is the hierarchical urgency coefficient of the σ-th data node, c σε is the occurrence times of the σ-th data node at the cycle time point ε, e is the exponent, λ is the time decay rate, T is the final time of the cycle time point, ε σ is the cycle time point corresponding to the σ-th data node, and m is the number of cycles of the cycle time point.

[0050] Optionally, when determining the risk coordination degree of each data node according to the urgency and the risk probability, the node coordination sequence generation module is used for:

[0051] Extract the cycle urgency and cycle risk probability corresponding to each data node at the cycle time point corresponding to the cycle time node according to the urgency and the risk probability;

[0052] Select the best combination parameters corresponding to the cycle urgency and the cycle risk probability under the condition of the cycle time point;

[0053] Determine the risk coordination degree of each data node according to the best combination parameters.

[0054] Optionally, when generating the coordination factor of each data coordination node in the node coordination sequence according to the risk coordination degree, the risk coordination module is used for:

[0055] Identify the priority of each data coordination node in the node coordination sequence according to the risk coordination degree to obtain the node priority corresponding to each data coordination node;

[0056] Determine the risk coordination type corresponding to each data coordination node according to the node priority;

[0057] Generate a coordination factor for each data coordination node according to the risk coordination type.

[0058] Optionally, when generating the coordination strategy of the data coordination node according to the coordination factor, the risk coordination module is used for:

[0059] Determine the coordination content of the data coordination node according to the coordination factor;

[0060] Generate a coordination task for the data coordination node according to the coordination content;

[0061] Generate a coordination strategy for the data coordination node according to the coordination task.

[0062] To solve the above problems, the present invention also provides a full-chain risk monitoring and coordination method for clothing manufacturing, and the method includes:

[0063] Obtain the data nodes of the full chain of clothing manufacturing and the association relationship between the nodes, and construct a two-way risk monitoring tree for the full chain of clothing manufacturing according to the data nodes and the association relationship;

[0064] Monitor the cyclic monitoring data of each data node in the two-way risk monitoring tree according to the preset two-way cyclic time node, and calculate the risk factor corresponding to each data node through the cyclic monitoring data;

[0065] Calculate the risk tolerance of each data node according to the target node data corresponding to each data node in the two-way risk monitoring tree, and calculate the risk probability of each data node according to the risk factor and the risk tolerance;

[0066] Calculate the urgency of each data node according to the association relationship through the preset dynamic hierarchical association algorithm, determine the risk coordination degree of each data node according to the urgency and the risk probability, and generate a node coordination sequence of the two-way risk monitoring tree according to the risk coordination degree;

[0067] Generate a coordination factor for each data coordination node in the node coordination sequence according to the risk coordination degree, generate a coordination strategy for the data coordination node according to the coordination factor, and perform risk coordination on the data coordination node according to the coordination strategy.

[0068] In the embodiments of the present invention, by identifying the data nodes of the entire chain and their associated relationships, the complex manufacturing process is structured, which is conducive to discovering the mutual influences between nodes; by setting cycle time nodes, monitoring data can be collected in real time, risk changes can be quickly identified, and the risk factors of each data node are calculated to provide data support for subsequent risk management; by calculating the risk tolerance based on the target data of each node, the risks of different nodes can be evaluated and managed specifically, avoiding excessive or insufficient risk control; by calculating the urgency level, the team can prioritize the most urgent issues, improve the efficiency of resource allocation, and the calculation of the risk coordination degree enables the team to flexibly adjust the response strategy to ensure a rapid response when risks occur, reduce the impact, and according to the coordination strategy generated by the coordination factor, each data node has a clear risk response measure, enhancing the effectiveness of risk management. Therefore, the full-chain risk monitoring and coordination system and method for garment manufacturing proposed by the present invention can solve the problem of low accuracy in full-chain risk monitoring and coordination for garment manufacturing. Brief Description of the Drawings

[0069] Figure 1 It is a functional module diagram of the full-chain risk monitoring and coordination system for garment manufacturing provided by an embodiment of the present invention;

[0070] Figure 2 It is a schematic flowchart of the operation method of the full-chain risk monitoring and coordination system for garment manufacturing provided by an embodiment of the present invention.

[0071] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0074] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. "Plural" generally includes at least two.

[0075] Depending on the context, as used herein, the words "if" and "when" may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0076] In addition, the step timings in the following method embodiments are only examples and not strictly limited.

[0077] In fact, the server device deployed for the full-chain risk monitoring and coordination system for clothing manufacturing may be composed of one or more devices. The above-mentioned full-chain risk monitoring and coordination system for clothing manufacturing can be implemented as: a business instance, a virtual machine, or a hardware device. For example, the full-chain risk monitoring and coordination system for clothing manufacturing can be implemented as a business instance deployed on one or more devices in a cloud node. Briefly, the full-chain risk monitoring and coordination system for clothing manufacturing can be understood as a software deployed on a cloud node for providing the full-chain risk monitoring and coordination system for clothing manufacturing to each client. Or, the full-chain risk monitoring and coordination system for clothing manufacturing can also be implemented as a virtual machine deployed on one or more devices in a cloud node. An application software for managing each client is installed in the virtual machine. Or, the full-chain risk monitoring and coordination system for clothing manufacturing can also be implemented as a server composed of many identical or different types of hardware devices, with one or more hardware devices set to provide the full-chain risk monitoring and coordination system for clothing manufacturing to each client.

[0078] In terms of implementation form, the full-chain risk monitoring and coordination system for clothing manufacturing and the client adapt to each other. That is, if the full-chain risk monitoring and coordination system for clothing manufacturing is an application installed on a cloud service platform, then the client is a client that establishes a communication connection with the application; or if the full-chain risk monitoring and coordination system for clothing manufacturing is implemented as a website, then the client is implemented as a web page; or if the full-chain risk monitoring and coordination system for clothing manufacturing is implemented as a cloud service platform, then the client is implemented as a mini-program in an instant messaging application.

[0079] Refer to Figure 1 As shown, it is a functional module diagram of the full-chain risk monitoring and coordination system for clothing manufacturing provided by an embodiment of the present invention.

[0080] The full-chain risk monitoring and coordination system 100 for garment manufacturing according to the present invention can be set in a cloud server. In terms of implementation form, it can be used as one or more service devices, or can be installed as an application on the cloud (such as the server of a mobile service operator, a server cluster, etc.), or can also be developed into a website. According to the functions to be realized, the full-chain risk monitoring and coordination system 100 for garment manufacturing may include a two-way risk monitoring tree construction module 101, a risk factor calculation module 102, a risk probability calculation module 103, a node coordination sequence generation module 104, and a risk coordination module 105. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a device processor and can complete fixed functions, and are stored in the memory of the device.

[0081] In an embodiment of the present invention, in the full-chain risk monitoring and coordination system for garment manufacturing, each of the above modules can be independently implemented and called by other modules. Here, the call can be understood as that a certain module can be connected to multiple modules of another type and provide corresponding services for the multiple modules it is connected to. For example, the risk factor calculation module can call the two-way risk monitoring tree construction module to obtain the information collected by the two-way risk monitoring tree construction module. Based on the above characteristics, in the full-chain risk monitoring and coordination system for garment manufacturing provided by the embodiment of the present invention, without modifying the program code, the applicable range of the full-chain risk monitoring and coordination system architecture for garment manufacturing can be adjusted by adding modules and directly calling them, so as to achieve cluster-level horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the full-chain risk monitoring and coordination system for garment manufacturing. In practical applications, the above modules can be set in the same device or different devices, or can also be set in virtual devices, such as service instances in a cloud server.

[0082] The following will describe each component and the specific working process of the full-chain risk monitoring and coordination system for garment manufacturing in detail with reference to specific embodiments:

[0083] The two-way risk monitoring tree construction module 101 is used to obtain the data nodes of the full chain of garment manufacturing and the association relationships between the nodes, and construct a two-way risk monitoring tree for the full chain of garment manufacturing according to the data nodes and the association relationships.

[0084] In the embodiments of the present invention, the data node refers to a clothing manufacturing node that may generate risks in the entire clothing manufacturing chain. For example, in the entire clothing manufacturing chain, the top-level risk nodes include operation risk, financial risk, and market risk. The first-level risks include supply chain risk, production risk, and logistics risk in operation risk; cost control risk and cash flow risk in financial risk; demand change risk and price risk in market risk. The second-level risks involve supplier late delivery, raw material quality problems, and price fluctuations in supply chain risk; equipment failure, personnel loss, unstable production process, and production scheduling in production risk; transportation delay, goods damage, seasonal fluctuations, and goods shipping link in logistics risk; raw material cost increase and low production efficiency in cost control risk; delayed accounts receivable recovery and bad debts in cash flow risk; consumer preference change and competitor strategy in demand change risk; market price fluctuation and industry depression in price risk. The association relationship refers to the relationship between the top-level risk nodes and the first-level risk nodes, and the relationship between the first-level risk nodes and the second-level risk nodes.

[0085] Specifically, the data nodes of the entire clothing manufacturing chain and the association relationship between the nodes can be obtained from a pre-stored storage area through a computer function with data scraping capabilities (such as Java statements, Python statements, etc.), where the storage area includes but is not limited to databases and blockchains.

[0086] Furthermore, in order to simultaneously focus on risks in the upstream (such as raw material procurement) and downstream (such as sales and after-sales service), and be able to quickly transfer risk information from one node to relevant nodes, it is necessary to construct a two-way data flow to analyze node risks.

[0087] In the embodiments of the present invention, the two-way risk monitoring tree refers to a structured model for identifying and managing risks, which graphically shows the mutual influence and risk association between different nodes (such as suppliers, production, logistics, etc.), organizes risk factors hierarchically, and facilitates the analysis of risk relationships at different levels.

[0088] In the embodiments of the present invention, when the two-way risk monitoring tree construction module 101 constructs a two-way risk monitoring tree for the entire clothing manufacturing chain according to the data nodes and the association relationship, it is used for:

[0089] Determine the two-way hierarchical association and one-way hierarchical association of the data nodes according to the association relationship;

[0090] Determine the two-way cycle points of the entire clothing manufacturing chain through the two-way hierarchical association, and determine the one-way cycle points of the entire clothing manufacturing chain according to the one-way hierarchical association;

[0091] Generate a two-way association path between the two-way cycle points according to the described two-way hierarchical association;

[0092] Generate a one-way association path between the one-way cycle points according to the described one-way hierarchical association;

[0093] Connect the two-way cycle points according to the two-way association path to obtain a two-way cycle subtree, and connect the one-way cycle points according to the one-way association path to obtain a one-way cycle subtree;

[0094] Associate the two-way cycle subtree with the one-way cycle subtree to obtain a two-way risk monitoring tree for the entire clothing manufacturing chain.

[0095] Specifically, determine the nodes that can be two-way associated and one-way associated between data nodes according to the association relationship. For example, there is a one-way hierarchical association between the top-level risk node and the first-level risk node. For example, operational risks affect supply chain risks, and supply chain risks do not affect operational risks. That is, problems in operations will lead to a decline in supply chain efficiency or an increase in costs, and fluctuations in the supply chain will not directly change the risk status in the operation process; there is a two-way hierarchical association between the first-level risk node and the second-level risk node. For example, the supply of raw materials directly affects production. Conversely, production demand will also affect the procurement of raw materials. Then, determine the two-way cycle points according to the two-way hierarchical association. Then, regard the data nodes involved in the first-level risk node and the second-level risk node as two-way cycle points. Determine the one-way cycle points according to the one-way hierarchical association. Then, regard the data nodes involved in the top-level risk node and the first-level risk node as one-way cycle points, and regard the path between the first-level risk node and the second-level risk node as the two-way association path, indicating the two-way influence between nodes, and regard the path between the top-level risk node and the first-level risk node as the one-way association path, indicating the one-way influence between nodes.

[0096] Specifically, connect the two-way cycle points according to the two-way association path to form a subtree, representing the two-way risk association of each node. Connect the one-way cycle points according to the one-way association path to form another subtree, representing the one-way risk association of each node. Then, associate the two-way cycle subtree with the one-way cycle subtree to obtain a complete two-way risk monitoring tree for the entire clothing manufacturing chain. Then, the two-way risk monitoring tree can effectively display the risk relationships of each node in the entire manufacturing chain and provide a clear perspective for risk management, promoting information flow and the timeliness of decision-making.

[0097] Furthermore, by monitoring the data involved in each data node in the two-way risk monitoring tree, to identify how risks are transmitted between different nodes and timely capture changes in risks between nodes. Therefore, it is necessary to regularly monitor the data nodes in the two-way risk monitoring tree to master the risk dynamics.

[0098] The risk factor calculation module 102 is configured to monitor the loop monitoring data of each data node in the two-way risk monitoring tree according to a preset two-way loop time node, and calculate the risk factor corresponding to the leaf node in the two-way risk monitoring tree through the loop monitoring data.

[0099] In an embodiment of the present invention, the loop monitoring data refers to continuously collecting the detection data in the two-way risk monitoring tree at a preset time interval. The loop monitoring data is regularly collected at a fixed time interval to ensure the real-time and accuracy of information.

[0100] In an embodiment of the present invention, when the risk factor calculation module 102 monitors the loop monitoring data of each data node in the two-way risk monitoring tree according to a preset two-way loop time node, it is configured to:

[0101] Trigger the two-way loop time node according to a preset time sliding window;

[0102] Collect the real-time two-way monitoring data of the two-way loop points and the real-time one-way monitoring data of the one-way loop points in the two-way risk monitoring tree according to the time points corresponding to the triggered two-way loop time nodes;

[0103] Attribute the real-time two-way monitoring data and the real-time one-way monitoring data to obtain real-time two-way monitoring attribute data and real-time one-way monitoring attribute data;

[0104] Aggregate the real-time two-way monitoring attribute data and the real-time one-way monitoring attribute data into loop monitoring data.

[0105] Specifically, determine the two-way loop time node according to a preset time sliding window, that is, trigger the two-way loop time node at the start time of a time window. For example, if the time sliding window includes the first window from time A to time B and the second window from time B to time C, the two-way loop time nodes are time A corresponding to the first window and time B corresponding to the second window. That is, at the time points corresponding to time A and time B, collect the nodes in the two-way risk monitoring tree that can generate data, and the data corresponding to each time point can be collected. For example, collect the supply time of the supplier corresponding to the real-time two-way monitoring data, material quality data, price data, whether the equipment fails, the number of personnel, production process, etc.; collect the operation data, financial data, market data, etc. in the clothing manufacturing process corresponding to the real-time one-way monitoring data. Among them, the change of fields in the database can be monitored in real time through computer statements to collect the real-time two-way monitoring data and the real-time one-way monitoring data.

[0106] Specifically, the monitoring data is transformed into a dataset with analyzable attributes, forming two different attribute datasets. The real-time two-way monitoring attribute data is the attribute information related to the two-way monitoring data, such as supply time, material quality, price data, equipment failure, number of personnel, production process, transportation time, degree of goods damage, raw material cost, production efficiency, cash return, etc.; the real-time one-way monitoring attribute data is the attribute information related to the one-way monitoring data, such as production scheduling, finished product inspection, and shipping link. Production scheduling means that information is transmitted from the scheduling department to the production line, and the production line does not give feedback on the scheduling; the finished product inspection is transmitted unidirectionally to the quality management department without affecting the immediate adjustment of the original production line; the shipping link means that after the goods are shipped, the information is transmitted to the logistics department, and the logistics process does not involve feedback from the production side. Furthermore, all the real-time two-way monitoring attribute data and real-time one-way monitoring attribute data are aggregated into a comprehensive cyclic monitoring dataset, so that the overall monitoring status can be evaluated and analyzed.

[0107] Furthermore, as the monitoring data is continuously updated, it is necessary to analyze the dynamic risks of the two-way risk monitoring tree in real time. Therefore, providing real-time data based on cyclic monitoring can timely detect potential risks, thereby enhancing the agility of risk management.

[0108] In the embodiment of the present invention, the risk factor is used to evaluate whether there is a risk in each leaf node of the two-way risk monitoring tree. When there is a risk in the leaf node, its risk factor is determined to be 1; when there is no risk in the leaf node, its risk factor is determined to be 0.

[0109] In the embodiment of the present invention, when the risk factor calculation module 102 calculates the risk factor corresponding to the leaf node in the two-way risk monitoring tree through the cyclic monitoring data, it is used for:

[0110] Obtain the target attribute data of the leaf node at the time point;

[0111] Calculate the two-way risk factor corresponding to the two-way cyclic point in the leaf node according to the real-time two-way monitoring attribute data and the target attribute data in the cyclic monitoring data by using the following preset two-way time factor algorithm:

[0112]

[0113] where, f i is the two-way risk factor corresponding to the i-th two-way cyclic point, p it is the positive feedback attribute value of the real-time two-way monitoring attribute data corresponding to the i-th two-way cyclic point under the condition of time point t, q it is the target attribute value of the target attribute data corresponding to the i-th two-way cyclic point under the condition of time point t. is the real-time attribute value of the real-time two-way monitoring attribute data corresponding to the i-th two-way cycle point under the condition of time point t, is the reverse feedback attribute value corresponding to the i-th two-way cycle point under the condition of time point t;

[0114] Calculate the one-way risk factor corresponding to the one-way cycle point in the leaf node according to the real-time one-way monitoring attribute data and the target attribute data in the cyclic monitoring data by using the following preset one-way time factor algorithm:

[0115]

[0116] where g i is the one-way risk factor corresponding to the i-th one-way cycle point, a it is the real-time attribute value of the real-time one-way monitoring attribute data corresponding to the i-th one-way cycle point under the condition of time point t, b it is the target attribute value of the target attribute data corresponding to the i-th one-way cycle point under the condition of time point t.

[0117] Specifically, extract the target attribute data corresponding to different cycle time points, such as the original production plan and the original transportation plan for the entire clothing manufacturing process in clothing manufacturing. For example, in the original production plan, the raw material data is 100, the raw material quality is of the first grade, the supplier delivery time is time A, and the production scheduling is the first day after production ends; in the transportation plan, the transportation time is x days, the goods cannot be damaged, the goods delivery time is the 5th day after production ends, and the raw material cost data and the sales price data are used to compare the target attribute data as q ijt where the target attribute data is the data in the original production plan and the original transportation plan. Furthermore, for the real-time two-way monitoring attribute data collected at the two-way cycle node, such as including supplier late delivery, raw material quality problems, price fluctuations, transportation delays, goods damage, goods delivery link, production process, production scheduling in the leaf node, the leaf node is divided into two-way cycle points and one-way cycle points. The two-way cycle points include supplier late delivery, raw material quality problems, price fluctuations, transportation delays, goods damage, and the one-way cycle points include goods delivery link, production process, production scheduling.

[0118] Specifically, the two-way time factor algorithm is used to calculate the risk impact of the data collected at different cycle time points on each leaf node. Then, the real-time collected supplier delivery time, raw material quality grade, sales price, transportation time, and the number of damaged goods are compared with the original target attribute data in turn. In the two-way cycle point, the real-time data collected at the time point is first compared with the original data. The positive feedback attribute value refers to the real-time data collected at the time point. However, at the time point, the original data may change due to customer needs. Then, the change in the original data is determined as the reverse feedback attribute value. Furthermore, the reverse feedback attribute value is compared with the real-time collected data to more accurately determine the risk factor corresponding to the two-way cycle point. When the result of the data comparison is equal to 0, it indicates that the data between the two is balanced. At this time, the two-way cycle point is risk-free, and the risk factor is 0. When the comparison result is greater than 0, it means that the data between the two is unbalanced. At this time, the two-way cycle point is risky, and the risk factor is 1. Similarly, for the one-way cycle point, only the real-time data collected at the one-way cycle point is compared with the target data, and then the one-way risk factor corresponding to the one-way cycle point is determined according to the comparison result.

[0119] Furthermore, in order to clearly identify the specific risk levels of each leaf node, it is necessary to determine the risk tolerance of each leaf node. Under limited resources, high-risk and low-tolerance nodes can be preferentially processed to more flexibly respond to market changes.

[0120] The risk probability calculation module 103 is used to calculate the risk tolerance of the leaf node according to the target node data corresponding to the leaf node in the two-way risk monitoring tree, and calculate the risk probability of each data node according to the risk factor and the risk tolerance.

[0121] In the embodiment of the present invention, the risk tolerance refers to the risk level or loss range that a leaf node can accept, reflecting the degree of risk that a node is willing to bear when facing potential threats or uncertainties.

[0122] In the embodiment of the present invention, when the risk probability calculation module 103 calculates the risk tolerance of the leaf node according to the target node data corresponding to the leaf node in the two-way risk monitoring tree, it is used for:

[0123] Determine the data collection time period corresponding to the leaf node according to the time point corresponding to the two-way cycle node in the two-way risk monitoring tree and the preset time collection point;

[0124] Extract the historical critical data corresponding to the leaf node through the data collection time period;

[0125] Calculate the risk tolerance of the leaf node according to the target node data and the historical critical data, where the risk tolerance calculation formula is:

[0126]

[0127] where R kα is the risk tolerance corresponding to the k-th leaf node at the α-th time point, S kα is the target node data corresponding to the k-th leaf node at the α-th time point, L kα is the minimum value in the historical critical data corresponding to the k-th leaf node at the α-th time point, H kα is the maximum value in the historical critical data corresponding to the k-th leaf node at the α-th time point, and min is the minimum value function.

[0128] Specifically, determine the data collection time period of the leaf node according to the time point corresponding to each leaf node and the preset time collection point. For example, if the time point is t and the preset time collection point is t 1 , then the data collection time period is determined as (t 1 , t0), that is, taking the time point t as the end time, and collecting the historical critical data from before the time point t to the time point t 1 . Then the historical critical data includes the maximum value and the minimum value of the data corresponding to each leaf node within the data collection time period, and the historical critical data corresponding to the leaf node can be collected through a computer statement with a data scraping function.

[0129] Specifically, determine the risk tolerance of each leaf node according to the target node data corresponding to each leaf node and the historical critical data corresponding to each leaf node, that is, compare the target node data corresponding to each leaf node with its corresponding historical maximum value and historical minimum value respectively. Then |S kα -L kα | represents the gap between the current attribute value S kα and the historical minimum value L kα , reflecting the degree of deviation of the current value from the past low point. |S kα -H kα | represents the gap between the current attribute value S kα and the historical maximum value H kαThe gap reflects the deviation of the current value from the past high point. Furthermore, the deviation degree is operated with the range of historical values (the difference between the maximum value and the minimum value) to obtain the tolerance of the data collected at each leaf node under the range based on the time point t, where the value range of k is from 1 to the number of leaf nodes, and the value range of α is from 1 to the number of cyclic acquisitions at cyclic time points. Furthermore, based on the number of acquisitions at cyclic time points, the tolerance corresponding to each leaf node under multiple time ranges is calculated, and the minimum value is selected from the tolerances of all time ranges, that is, the data with the smallest tolerance is selected as the tolerance corresponding to each leaf node. By collecting data multiple times within different time ranges, the influence of accidental factors on the result can be reduced, the representativeness and reliability of the data can be improved, and the data-based multiple time ranges can reflect the change trend of the data over time, enabling a more accurate assessment of the risk of leaf nodes.

[0130] Furthermore, as new data is continuously collected, the tolerance of each leaf node can be dynamically updated to ensure that the decision-making is always based on the latest information, thereby enabling a more accurate determination of the risk probability of each data node in the two-way risk monitoring tree.

[0131] In the embodiment of the present invention, the risk probability refers to the probability of a specific risk event corresponding to the leaf occurring, measuring the impact of potential risks on the entire chain of clothing manufacturing.

[0132] In the embodiment of the present invention, when the risk probability calculation module 103 calculates the risk probability of each data node according to the risk factor and the risk tolerance, it is used for:

[0133] Determine the target risk probability corresponding to the leaf node according to the risk factor and the risk tolerance:

[0134]

[0135] where Y k is the target risk probability of the k-th leaf node, f k is the two-way risk factor corresponding to the two-way cyclic point in the k-th leaf node, g k is the one-way risk factor corresponding to the one-way cyclic point in the k-th leaf node, R k is the risk tolerance of the k-th leaf node, ∨ is the OR symbol, and ∧ is the AND symbol;

[0136] Extract the node hierarchy relationship corresponding to the leaf node through the two-way risk monitoring tree;

[0137] Determine the hierarchical node corresponding to the leaf node according to the node hierarchy relationship;

[0138] Statistically calculate the node risk probability corresponding to the hierarchical node through the target risk probability;

[0139] Determine the risk probability of each data node according to the node risk probability.

[0140] Specifically, when the risk factor of a leaf node is the value 1, it indicates a risk, or when the target risk tolerance is greater than 1, it indicates that the risk probability corresponding to this leaf node is 1; when the risk factor of a leaf node is the value 0 and the target risk tolerance is less than 1 and greater than 0, it indicates that the risk probability corresponding to this leaf node is 0; when the risk factor of a leaf node is 0 and the target risk tolerance is greater than 1, then the reciprocal of the target risk tolerance is used as the risk probability of the leaf node at this time, so as to obtain the target risk probability corresponding to each leaf node in the two-way risk monitoring tree, and further calculate the risk probability of all data nodes in the entire two-way risk monitoring tree based on the target risk probability.

[0141] Specifically, determine the node hierarchical relationship corresponding to the leaf node according to the two-way risk monitoring tree, and determine the hierarchical node corresponding to the leaf node according to the hierarchical relationship. For example, if the node hierarchical relationship corresponding to leaf node A has a parent node, then when the number of leaf nodes of the parent node corresponding to the leaf node is 3, and the risk probabilities of all 3 leaf nodes are 1 at this time, the risk probability corresponding to the parent node is 1. If only two leaf nodes have a risk probability of 1 at this time, the risk probability corresponding to the parent node is 1 / 3. If one leaf node has a risk probability of 1, one leaf node has a risk probability of 0, and one leaf node has a risk probability of 1 / 8, then the risk probability corresponding to the parent node is (1 + 1 / 8) / 3 = 3 / 8. Thus, based on all hierarchical nodes and the number of child nodes corresponding to the nodes, the risk probabilities corresponding to all data nodes in the two-way risk monitoring tree can be calculated.

[0142] Furthermore, in order to be able to allocate resources and time more effectively and concentrate efforts on dealing with the most urgent problems, adjust the nodes that need to be processed first based on the latest data and association relationships. Therefore, it is necessary to analyze the urgency of each data node.

[0143] The node coordination sequence generation module 104 is configured to calculate the urgency of each data node according to the association relationship through a preset dynamic hierarchical association algorithm, determine the risk coordination degree of each data node according to the urgency and the risk probability, and generate the node coordination sequence of the two-way risk monitoring tree according to the risk coordination degree.

[0144] In the embodiment of the present invention, the urgency is to evaluate which data node needs to be solved first, so as to optimize resource allocation and emergency response, and improve the overall efficiency and reliability of the entire chain of clothing manufacturing.

[0145] In the embodiment of the present invention, when calculating the urgency of each data node according to the association relationship through a preset dynamic hierarchical association algorithm, the node coordination sequence generation module 104 is configured to:

[0146] Determine the node level corresponding to each data node according to the association relationship;

[0147] Determine the level urgency coefficient of each data node according to the node level;

[0148] Determine the dynamic demand data of each data node through the cycle time point corresponding to the cycle time node;

[0149] Calculate the urgency of each data node according to the occurrence times of the dynamic demand data through the following preset dynamic hierarchical association algorithm:

[0150]

[0151] where d σ is the urgency of the σ-th data node, δ σ is the level urgency coefficient of the σ-th data node, c σε is the occurrence times of the σ-th data node at the cycle time point ε, e is the exponential, λ is the time decay rate, T is the final time of the cycle time point, ε σ is the cycle time point corresponding to the σ-th data node, and m is the number of cycles of the cycle time point.

[0152] Specifically, according to the association relationship between nodes, determine the level of each data node, and then determine the level urgency coefficient of each data node according to the node level. For example, if data node A is in the first level, the corresponding level urgency coefficient is n; if data node B is in the second level, the corresponding level urgency coefficient is n - 1; if data node C is in the n-th level, the corresponding level urgency coefficient is 1. The level urgency coefficient reflects the importance of the node and the possible impact it may cause in the system. The higher the level, the greater the possible urgency coefficient, and the nodes at higher levels have a greater impact on the stability or safety of the entire clothing manufacturing chain.

[0153] Specifically, through the cycle time node, determine the dynamic demand data of each data node. The cycle time node refers to the data points within a specific time interval, which can be real-time data or demand data collected regularly. The occurrence times of the dynamic demand data is a key indicator, which represents the occurrence frequency of the demand or event of each data node within a specific time period. Then calculate the urgency of each data node according to the occurrence times of the dynamic demand data. In the dynamic hierarchical association algorithm, the time decay rate λ controls the decay speed of the node urgency over time. A larger λ value means that the node urgency will decrease rapidly. Considering the influence of time factors, as time goes by, the urgency of nodes gradually decreases, reflecting the concept that the earlier the processing, the more important it is. That is, count the number of occurrences in different cycles, that is, at different time points, count the dynamic occurrence times of dynamic demand data, and as time increases, attenuate the urgency of changes in each dynamic demand data, prioritize the evaluation of changes in subsequent dynamic demands, so as to obtain the urgency of each node. Among them, the dynamic demand data refers to the number of changes in external demands corresponding to each data node. For example, if a customer requests an additional order or a reduction in the order, then the order quantity is a change. Furthermore, the number of occurrences of the dynamic demand data corresponding to the supply chain node will increase by 1. If a certain node is requested more times at a certain time point, it indicates that its demand is more urgent and the impact is greater.

[0154] Furthermore, by analyzing the importance of nodes (hierarchical emergency coefficient) and demand frequency (number of occurrences), and over time, adjusting through the time decay rate, the entire chain of clothing manufacturing can flexibly adjust the attention and resource allocation to different data nodes according to real-time situations, so as to coordinate the nodes that need to be coordinated based on the urgency and risk probability of the nodes, and balance the risks of each data node in the entire chain of clothing manufacturing.

[0155] In the embodiments of the present invention, the risk coordination degree is an index that measures the contribution degree of each data node to the overall risk management in a specific situation, combining the urgency and risk probability, which is beneficial to identifying which nodes in the entire chain of clothing manufacturing are reducing risks, optimizing resource allocation, or improving response capabilities.

[0156] In the embodiments of the present invention, when the node coordination sequence generation module 104 determines the risk coordination degree of each data node according to the urgency and the risk probability, it is used for:

[0157] Extract the cycle urgency and cycle risk probability corresponding to each data node at the cycle time point corresponding to the cycle time node according to the urgency and the risk probability;

[0158] Select the best combination parameters corresponding to the cycle urgency and the cycle risk probability under the condition of the cycle time point;

[0159] Determine the risk coordination degree of each data node according to the best combination parameters.

[0160] Specifically, extract the cycle urgency and cycle risk probability corresponding to each cycle time point. The cycle urgency is screened from the urgency, and the cycle risk probability is screened from the risk probability. For example, if the cycle time points are t 1 、t 2 、t 3 ,then at the cycle time point t1 Extract the cyclic emergency level d 1 and the cyclic risk probability Y 1 at the cyclic time point t 2 Extract the cyclic emergency level d 2 and the cyclic risk probability Y 2 at the cyclic time point t 3 Extract the cyclic emergency level d 3 and the cyclic risk probability Y 3 and then determine the optimal combination between the cyclic emergency level and the cyclic risk probability under different cyclic time point conditions.

[0161] Specifically, select the cyclic risk probability corresponding to the time point with the maximum cyclic emergency level and multiply them to obtain the risk coordination degree. When the cyclic emergency levels are the same, select the risk probability corresponding to the time point with the minimum risk probability among the cyclic risk probabilities and multiply them to obtain the risk coordination degree. For example, if the cyclic emergency level corresponding to the cyclic time point t 3 is the maximum, then the cyclic emergency level and the cyclic risk probability corresponding to the cyclic time point t 3 of the data node A are used as the optimal combination parameters, and then multiply the two parameters in the optimal combination parameters to obtain the risk coordination degree of the data node A. If the cyclic emergency levels corresponding to the cyclic time points t 1 and t 3 of the data node A are the same, and if the risk probability corresponding to the cyclic time point t 1 is the minimum, then multiply the cyclic emergency level and the risk probability corresponding to the cyclic time point t 1 to obtain the risk coordination degree of the data node A.

[0162] Furthermore, generate a node coordination sequence for the two-way risk monitoring tree according to the risk coordination degree, that is, sort each data node in the two-way risk monitoring tree in descending order of the risk coordination degree, so as to obtain the node coordination sequence.

[0163] Even further, perform risk coordination for nodes with a high risk level based on the risk coordination degree, avoid risk diffusion, and ensure the effective use of resources and the minimization of risks.

[0164] The risk coordination module 105 is used to generate a coordination factor for each data coordination node in the node coordination sequence according to the risk coordination degree, generate a coordination strategy for the data coordination node according to the coordination factor, and perform risk coordination on the data coordination node according to the coordination strategy.

[0165] In the embodiment of the present invention, the coordination factor refers to an index type used to measure and optimize the degree of interaction and cooperation between different nodes or resources when adopting a certain measure during the risk coordination process.

[0166] In the embodiment of the present invention, when generating the coordination factor of each data coordination node in the node coordination sequence according to the risk coordination degree, the risk coordination module 105 is configured to:

[0167] Perform a priority identification on each data coordination node in the node coordination sequence according to the risk coordination degree to obtain the node priority corresponding to each data coordination node;

[0168] Determine the risk coordination type corresponding to each data coordination node according to the node priority;

[0169] Generate the coordination factor of each data coordination node according to the risk coordination type.

[0170] Specifically, perform a priority identification on each data coordination node according to the node coordination sequence. For example, if the node coordination sequence is {A 1 , A 2 , A 3 , A 4}, then perform a priority identification in the order of the data coordination nodes. For example, the priority identification corresponding to node A 1 is 1, the priority identification corresponding to node A 2 is 2, and the priority identification corresponding to node A n is n, so as to obtain the node priority corresponding to each data node in the node coordination sequence. Furthermore, determine the risk coordination type identification corresponding to each data node according to the node priority. Then, when the node priority is , the corresponding risk coordination type is the acceptance type; when the node priority is , the corresponding risk coordination type is the transfer type; when the node priority is , the corresponding risk coordination type is the mitigation type; when the node priority is , the corresponding risk coordination type is the avoidance type, where x represents the priority of the data node in the node coordination sequence.

[0171] Specifically, generate the coordination factor of each data coordination node according to the risk coordination type. Then, the coordination factor corresponding to the avoidance type is 0, the coordination factor corresponding to the mitigation type is 1, the coordination factor corresponding to the transfer type is 2, and the coordination factor corresponding to the acceptance type is 3. The acceptance type means accepting the occurrence of the risk. If it has been determined that there will be a delay, communicate with relevant stakeholders and set an acceptable delay range; the transfer type means introducing external help, such as signing an agreement with an external supplier to ensure the timely delivery of key components; the mitigation type means starting a backup plan. If the main supplier cannot deliver on time, an alternative supplier can be selected; the avoidance type means adjusting the project scope, such as reducing the delivery content to ensure timely completion.

[0172] Furthermore, to ensure data consistency among different nodes and avoid decision-making mistakes caused by inconsistent data, it is necessary to generate coordination strategies for different data coordination nodes. Through reasonable coordination, resources can be efficiently allocated and utilized, redundancy and waste can be reduced, thereby lowering operating costs.

[0173] In the embodiments of the present invention, the coordination strategy refers to the specific guidelines formulated among multiple data coordination nodes for realizing effective data management and collaborative work.

[0174] In the embodiments of the present invention, when the risk coordination module 105 generates the coordination strategy of the data coordination node according to the coordination factor, it is used for:

[0175] Determine the coordination content of the data coordination node according to the coordination factor;

[0176] Generate the coordination task of the data coordination node according to the coordination content;

[0177] Generate the coordination strategy of the data coordination node according to the coordination task.

[0178] Specifically, when the coordination factor is 0, the coordination content is to adjust the project scope, the coordination task is to re-evaluate the project objectives, identify and reduce unnecessary functions or requirements, ensure the effective utilization of project resources, and the coordination strategy is to re-define the scope of the clothing manufacturing project; when the coordination factor is 1, the coordination content is to start the backup plan, the coordination task is to retrieve and prepare the existing backup plan, allocate relevant resources and personnel, ensure that the clothing manufacturing project can seamlessly switch to the backup plan, and the coordination strategy is to generate the implementation process of the backup plan; when the coordination factor is 2, the coordination content is to introduce external help, the coordination task is to determine the areas that require external support, screen suitable partners or experts, formulate a plan for introducing external help, and the coordination strategy is to select suitable external support, sign a contract and set the cooperation period and clothing manufacturing goals; when the coordination factor is 3, the coordination content is to accept the occurrence of risks, the coordination task is to evaluate the current risk situation, formulate risk response measures, and the coordination strategy is to establish a regular risk assessment mechanism, use a risk matrix analysis tool, ensure that all potential risks are identified and recorded, and formulate corresponding emergency plans for clothing manufacturing.

[0179] Exemplarily, during the project progress, it was found that some design functions were unnecessary. The team decided to adjust the project scope, re-evaluate the goals, delete the complex embroidery designs, and focus on a minimalist style to ensure the effective utilization of resources. Eventually, a simple and easy-to-wear clothing series was launched. During the production process, equipment failures occurred. The team activated the backup plan, retrieved the previously developed backup plan, allocated spare equipment and personnel, and ensured that the production line could quickly switch to other machines to avoid production delays and ensure on-time delivery. The project team realized the lack of expertise in sustainable materials and decided to introduce external help. They screened several environmentally friendly material suppliers, developed a cooperation plan, and after signing the contract, cooperated with these experts to optimize the material selection and launched an environmentally friendly clothing series. During the market research, it was found that the competition was fierce. The team evaluated the potential risks, developed countermeasures, established a regular risk assessment mechanism, and used a risk matrix to identify the risks of market changes to ensure that the team could quickly adjust the production strategy, such as increasing online marketing and responding promptly to consumer demands.

[0180] Furthermore, risk coordination is carried out on the data coordination nodes according to the coordination strategy, that is, corresponding risk coordination is carried out on the data coordination nodes according to the generated coordination strategy, which can effectively reduce the uncertainty and potential losses in the implementation of the clothing manufacturing project, can quickly respond when risks occur, and ensure the smooth progress of the clothing manufacturing project.

[0181] In the embodiment of the present invention, by identifying the data nodes and their association relationships in the whole chain, the complex manufacturing process is structured, which is conducive to discovering the mutual influence between nodes; by setting the cycle time nodes, monitoring data can be collected in real time, risk changes can be quickly identified, and the risk factors of each data node are calculated to provide data support for subsequent risk management; by calculating the risk tolerance according to the target data of each node, the risks of different nodes can be evaluated and managed specifically, avoiding excessive or insufficient risk control; by calculating the urgency, the team can give priority to dealing with the most urgent problems, improve the efficiency of resource allocation, and the calculation of the risk coordination degree enables the team to flexibly adjust the response strategy to ensure a rapid response when risks occur, reduce the impact, and the coordination strategy generated according to the coordination factor enables each data node to have clear risk response measures, enhancing the effectiveness of risk management. Therefore, the whole-chain risk monitoring and coordination system and method for clothing manufacturing proposed by the present invention can solve the problem of low accuracy in the whole-chain risk monitoring and coordination of clothing manufacturing.

[0182] Refer to Figure 2 As shown, it is a schematic flowchart of the operation method of the whole-chain risk monitoring and coordination system for clothing manufacturing provided by an embodiment of the present invention. In this embodiment, the operation method of the whole-chain risk monitoring and coordination system for clothing manufacturing includes:

[0183] S1. Obtain the data nodes of the entire clothing manufacturing chain and the association relationships between the nodes, and construct a two-way risk monitoring tree for the entire clothing manufacturing chain according to the data nodes and the association relationships;

[0184] S2. Monitor the cyclic monitoring data of each data node in the two-way risk monitoring tree according to the preset two-way cyclic time nodes, and calculate the risk factors corresponding to each data node through the cyclic monitoring data;

[0185] S3. Calculate the risk tolerance of each data node according to the target node data corresponding to each data node in the two-way risk monitoring tree, and calculate the risk probability of each data node according to the risk factors and the risk tolerance;

[0186] S4. Calculate the urgency of each data node according to the association relationship through the preset dynamic hierarchical association algorithm, determine the risk coordination degree of each data node according to the urgency and the risk probability, and generate a node coordination sequence of the two-way risk monitoring tree according to the risk coordination degree;

[0187] S5. Generate a coordination factor for each data coordination node in the node coordination sequence according to the risk coordination degree, generate a coordination strategy for the data coordination node according to the coordination factor, and perform risk coordination on the data coordination node according to the coordination strategy.

[0188] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0189] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0190] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is not limited only by the above description. Therefore, it is intended to include all changes within the meaning and scope of the equivalent elements falling within the protection scope in the present invention.

[0191] In addition, obviously, the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or systems stated in the system claims can also be implemented by one unit or system through software or hardware. The terms such as first and second are used to represent names and do not represent any specific order.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A full-chain risk monitoring and coordination system for clothing manufacturing, characterized in that: The system includes a bidirectional risk monitoring tree construction module, a risk factor calculation module, a risk probability calculation module, a node coordination sequence generation module and a risk coordination module, wherein: The bidirectional risk monitoring tree construction module is used to obtain data nodes of the entire chain of clothing manufacturing and the association relationship between the nodes, and to construct a bidirectional risk monitoring tree of the entire chain of clothing manufacturing according to the data nodes and the association relationship; The risk factor calculation module is used to monitor the cyclic monitoring data of each data node in the bidirectional risk monitoring tree according to a preset bidirectional cyclic time node, and calculate the risk factor corresponding to the leaf node in the bidirectional risk monitoring tree through the cyclic monitoring data; The risk probability calculation module is used to calculate the risk tolerance of the leaf node according to the target node data corresponding to the leaf node in the bidirectional risk monitoring tree, and is used to: determine the data collection time period corresponding to the leaf node according to the time point corresponding to the bidirectional loop node in the bidirectional risk monitoring tree and the preset time collection point; extract the historical critical data corresponding to the leaf node through the data collection time period; calculate the risk tolerance of the leaf node according to the target node data and the historical critical data, wherein the risk tolerance calculation formula is: Among them, R kα is the risk tolerance corresponding to the kth leaf node at the αth time point, S kα is the target node data corresponding to the kth leaf node at the αth time point, L kα is the minimum value of the historical critical data corresponding to the kth leaf node at the αth time point, H kα is the maximum value of the historical critical data corresponding to the kth leaf node at the αth time point, and min is the minimum value function; Calculate the risk probability of each data node according to the risk factor and the risk tolerance; The node coordination sequence generation module is used to calculate the urgency of each data node according to the association relationship through a preset dynamic hierarchical association algorithm, determine the risk coordination degree of each data node according to the urgency and the risk probability, and generate a node coordination sequence of the bidirectional risk monitoring tree according to the risk coordination degree; The risk coordination module is used to generate a coordination factor for each data coordination node in the node coordination sequence according to the risk coordination degree, generate a coordination strategy for the data coordination node according to the coordination factor, and perform risk coordination on the data coordination node according to the coordination strategy.

2. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 1, characterized in that: When constructing a bidirectional risk monitoring tree of the entire clothing manufacturing chain according to the data nodes and the association relationships, the bidirectional risk monitoring tree construction module is used to: Determine the bidirectional hierarchical association and the unidirectional hierarchical association of the data nodes according to the association relationship; Determine the bidirectional circulation points of the whole chain of garment manufacturing through the bidirectional hierarchical association, and determine the unidirectional circulation points of the whole chain of garment manufacturing according to the unidirectional hierarchical association; generating a bidirectional association path between the bidirectional cycle points according to the bidirectional hierarchical association; Generating a unidirectional association path between the unidirectional cycle points according to the unidirectional hierarchical association; Connecting the bidirectional loop points according to the bidirectional association path to obtain a bidirectional loop subtree, and connecting the unidirectional loop points according to the unidirectional association path to obtain a unidirectional loop subtree; The bidirectional cycle subtree is associated with the unidirectional cycle subtree to obtain a bidirectional risk monitoring tree for the entire clothing manufacturing chain.

3. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 1, characterized in that: When monitoring the cyclic monitoring data of each data node in the bidirectional risk monitoring tree according to a preset bidirectional cyclic time node, the risk factor calculation module is used to: Trigger the bidirectional cycle time node according to a preset time sliding window; Collect the real-time bidirectional monitoring data of the bidirectional circulation points and the real-time unidirectional monitoring data of the unidirectional circulation points in the bidirectional risk monitoring tree according to the time point corresponding to the bidirectional circulation time node after the triggering; Attribute the real-time two-way monitoring data and the real-time one-way monitoring data to obtain real-time two-way monitoring attribute data and real-time one-way monitoring attribute data; The real-time two-way monitoring attribute data and the real-time one-way monitoring attribute data are collected as cyclic monitoring data.

4. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 1, characterized in that: When the risk factor calculation module calculates the risk factor corresponding to the leaf node in the bidirectional risk monitoring tree through the cyclic monitoring data, it is used to: Obtain target attribute data of the leaf node at the time point; The bidirectional risk factor corresponding to the bidirectional circulation point in the leaf node is calculated according to the real-time bidirectional monitoring attribute data and the target attribute data in the circulation monitoring data using the following preset bidirectional time factor algorithm: Among them, f i is the bidirectional risk factor corresponding to the i-th bidirectional cycle point, p it is the positive feedback attribute value of the real-time bidirectional monitoring attribute data corresponding to the i-th bidirectional cycle point at time point t, q it is the target attribute value of the target attribute data corresponding to the i-th bidirectional loop point at time point t, is the real-time attribute value of the real-time bidirectional monitoring attribute data corresponding to the i-th bidirectional cycle point at time point t, is the reverse feedback attribute value corresponding to the i-th bidirectional loop point at time point t; The one-way risk factor corresponding to the one-way circulation point in the leaf node is calculated according to the real-time one-way monitoring attribute data and the target attribute data in the circulation monitoring data using the following preset one-way time factor algorithm: Among them, g i is the one-way risk factor corresponding to the i-th one-way cycle point, a it is the real-time attribute value of the real-time one-way monitoring attribute data corresponding to the i-th one-way cycle point at time point t, b it is the target attribute value of the target attribute data corresponding to the i-th one-way cycle point under the condition of time point t.

5. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 1, characterized in that: When calculating the risk probability of each data node according to the risk factor and the risk tolerance, the risk probability calculation module is used to: Determine the target risk probability corresponding to the leaf node according to the risk factor and the risk tolerance: Among them, Y k is the target risk probability of the kth leaf node, f k is the bidirectional risk factor corresponding to the bidirectional loop point in the kth leaf node, g k is the one-way risk factor corresponding to the one-way cycle point in the kth leaf node, R k is the risk tolerance of the kth leaf node, ∨ is the OR symbol, and ∧ is the AND symbol; Extracting the node hierarchical relationship corresponding to the leaf node through a bidirectional risk monitoring tree; Determine the level node corresponding to the leaf node according to the node level relationship; Counting the node risk probabilities corresponding to the hierarchical nodes through the target risk probability; The risk probability of each data node is determined according to the node risk probability.

6. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 3, characterized in that: When the node coordination sequence generation module calculates the urgency of each data node according to the association relationship by using a preset dynamic hierarchical association algorithm, it is used to: Determine the node level corresponding to each data node according to the association relationship; Determining a level emergency coefficient of each data node according to the node level; Determine the dynamic demand data of each data node through the cycle time point corresponding to the cycle time node; The urgency of each data node is calculated according to the number of occurrences of the dynamic demand data by using the following preset dynamic hierarchical association algorithm: Among them, d σ is the urgency of the σth data node, δ σ is the hierarchical emergency coefficient of the σth data node, c σε is the number of occurrences of the σth data node at the cycle time point ε, e is the exponent, λ is the time decay rate, T is the final time of the cycle time point, ε σ is the cycle time point corresponding to the σth data node, and m is the number of cycles of the cycle time point.

7. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 1, characterized in that: When determining the risk coordination degree of each data node according to the urgency and the risk probability, the node coordination sequence generation module is used to: Extracting the cycle urgency and the cycle risk probability corresponding to each data node at the cycle time point corresponding to the cycle time node according to the urgency and the risk probability; Selecting the best combination parameter corresponding to the cycle urgency and the cycle risk probability under the cycle time point condition; The risk coordination degree of each data node is determined according to the optimal combination parameters.

8. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 1, characterized in that: When the risk coordination module generates the coordination factor of each data coordination node in the node coordination sequence according to the risk coordination degree, it is used to: Priority identification is performed on each data coordination node in the node coordination sequence according to the risk coordination degree to obtain a node priority corresponding to each data coordination node; Determine the risk coordination type corresponding to each data coordination node according to the node priority; A coordination factor for each data coordination node is generated according to the risk coordination type.

9. The full-chain risk monitoring and coordination system for clothing manufacturing as claimed in claim 1, characterized in that: When the risk coordination module generates the coordination strategy of the data coordination node according to the coordination factor, it is used to: Determining the coordination content of the data coordination node according to the coordination factor; Generate a coordination task of the data coordination node according to the coordination content; A coordination strategy of the data coordination node is generated according to the coordination task.

10. A method for operating a full-chain risk monitoring and coordination system for clothing manufacturing, characterized in that: Used to implement the full-chain risk monitoring and coordination system for clothing manufacturing as described in any one of claims 1 to 9, the method comprising: Acquire data nodes of the entire chain of clothing manufacturing and the association relationships between the nodes, and construct a bidirectional risk monitoring tree of the entire chain of clothing manufacturing according to the data nodes and the association relationships; Monitoring the cyclic monitoring data of each data node in the bidirectional risk monitoring tree according to a preset bidirectional cyclic time node, and calculating the risk factor corresponding to each data node through the cyclic monitoring data; The risk tolerance of each data node is calculated according to the target node data corresponding to each data node in the bidirectional risk monitoring tree, and is used to: determine the data collection time period corresponding to the leaf node according to the time point corresponding to the bidirectional loop node in the bidirectional risk monitoring tree and the preset time collection point; extract the historical critical data corresponding to the leaf node through the data collection time period; calculate the risk tolerance of the leaf node according to the target node data and the historical critical data, wherein the risk tolerance calculation formula is: Among them, R kα is the risk tolerance corresponding to the kth leaf node at the αth time point, S kα is the target node data corresponding to the kth leaf node at the αth time point, L kα is the minimum value of the historical critical data corresponding to the kth leaf node at the αth time point, H kα is the maximum value of the historical critical data corresponding to the kth leaf node at the αth time point, and min is the minimum value function; Calculate the risk probability of each data node according to the risk factor and the risk tolerance; The urgency of each data node is calculated according to the association relationship by a preset dynamic hierarchical association algorithm, the risk coordination degree of each data node is determined according to the urgency and the risk probability, and a node coordination sequence of the bidirectional risk monitoring tree is generated according to the risk coordination degree; A coordination factor of each data coordination node in the node coordination sequence is generated according to the risk coordination degree, a coordination strategy of the data coordination node is generated according to the coordination factor, and risk coordination is performed on the data coordination node according to the coordination strategy.

Images