Digital method and system for material parameters for full-cycle management

Abstract

The present invention discloses a material parameter digitization method and system for full-cycle management, which relates to the field of digital processing technology, including: determining multiple material management nodes, performing periodic monitoring, and obtaining a full-cycle parameter set of materials; associating and matching multiple material management nodes with multi-stage material parameters to generate a material parameter management network; traversing the material parameter management network to perform digital calculations and generate multiple digital coefficients; performing digital compensation analysis on the material parameter management network and formulating a digital compensation plan; digitally verifying the material parameter management network, performing regular feedback on multiple material management nodes based on the verification results, generating digital feedback information, and performing full-cycle digital management of multi-stage material parameters. The present invention solves the technical problem that the existing technology cannot effectively perform accurate digital management of material parameters, and achieves the technical effect of accurate digital management of material parameters.

Description

Technical Field

[0001] The present invention relates to the field of digital processing technology, and in particular to a material parameter digitization method and system for full-cycle management. Background Art

[0002] With the increasing complexity of modern industrial production and supply chain management, materials management is becoming increasingly important across various industries. Materials management not only involves procurement, inventory, usage, and maintenance, but also requires precise monitoring of materials' status, location, and lifecycle. However, most current materials management technologies remain at a traditional stage, unable to achieve precise digital management of material parameters. Summary of the Invention

[0003] This application provides a material parameter digitization method and system for full-cycle management, which is used to solve the technical problem that existing technologies cannot effectively and accurately digitize material parameters.

[0004] In view of the above problems, this application provides a material parameter digitization method and system for full-cycle management.

[0005] The first aspect of the present application provides a material parameter digitization method for full-cycle management, the method comprising:

[0006] Traverse the material supply chain to perform material correlation analysis, determine multiple material management nodes, perform periodic monitoring according to the multiple material management nodes through the sensor equipment group, and obtain a full-cycle parameter set of the material, wherein the full-cycle parameter set of the material includes multi-stage material parameters; associate and match the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, wherein the material parameter management network includes multiple node material association parameters; traverse the material parameter management network to perform digital calculations to generate multiple digital coefficients, wherein the multiple digital coefficients have a corresponding relationship with the multiple node material association parameters; perform digital compensation analysis on the material parameter management network based on the multiple digital coefficients combined with the multiple node material association parameters, and formulate a digital compensation plan; execute the digital compensation plan to digitally verify the material parameter management network, perform regular feedback on the multiple material management nodes based on the verification results, generate digital feedback information, and perform full-cycle digital management of the multi-stage material parameters based on the digital feedback information.

[0007] The second aspect of the present application provides a material parameter digitization system for full-cycle management, the system comprising:

[0008] a cycle monitoring module, which traverses the material supply chain to perform material correlation analysis, determines multiple material management nodes, and performs periodic monitoring according to the multiple material management nodes through a sensor device group to obtain a full-cycle parameter set for the material, wherein the full-cycle parameter set for the material includes multi-stage material parameters; an association matching module, which associates and matches the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, wherein the material parameter management network includes multiple node material association parameters; a digital calculation module, which traverses the material parameter management network to perform digital calculations to generate multiple digital coefficients, wherein the multiple digital coefficients have a corresponding relationship with the multiple node material association parameters; a digital compensation analysis module, which performs digital compensation analysis on the material parameter management network based on the multiple digital coefficients and the multiple node material association parameters to formulate a digital compensation plan; and a digital management module, which executes the digital compensation plan to digitally verify the material parameter management network, provides regular feedback to the multiple material management nodes based on the verification results, generates digital feedback information, and performs full-cycle digital management of the multi-stage material parameters based on the digital feedback information.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The present application traverses the material supply chain to perform material correlation analysis, determines multiple material management nodes, performs periodic monitoring according to the multiple material management nodes through a sensor equipment group, obtains a full-cycle parameter set of the material, and the full-cycle parameter set of the material includes multi-stage material parameters; associates and matches the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, and the material parameter management network includes multiple node material association parameters; traverses the material parameter management network to perform digital calculations to generate multiple digital coefficients, and the multiple digital coefficients have a corresponding relationship with the multiple node material association parameters; performs digital compensation analysis on the material parameter management network based on the multiple digital coefficients combined with the multiple node material association parameters, and formulates a digital compensation plan; executes the digital compensation plan to digitally verify the material parameter management network, and performs regular feedback on the multiple material management nodes based on the verification results to generate digital feedback information, and performs full-cycle digital management of the multi-stage material parameters based on the digital feedback information. The present invention solves the technical problem that the existing technology cannot effectively carry out accurate digital management of material parameters. It traverses the material supply chain and performs material correlation analysis, determines multiple material management nodes, uses sensor equipment to monitor the full-cycle parameters of materials, associates the nodes with multi-stage parameters to generate a material parameter management network, calculates the digital coefficient and performs compensation analysis, formulates a compensation plan, and after performing digital verification, conducts full-cycle digital management of material parameters based on feedback information, thereby achieving the technical effect of accurate digital management of material parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of the process flow of a material parameter digitization method for full-cycle management provided in an embodiment of the present application;

[0013] Figure 2 Schematic diagram of the structure of the material parameter digitization system for full-cycle management provided in the embodiment of the present application.

[0014] Description of the accompanying drawings: period monitoring module 11, association matching module 12, digital calculation module 13, digital compensation analysis module 14, digital management module 15. DETAILED DESCRIPTION

[0015] This application provides a material parameter digitization method and system for full-cycle management, aiming to solve the technical problem that existing technologies cannot effectively carry out accurate digital management of material parameters. By traversing the material supply chain and performing material correlation analysis, multiple material management nodes are determined, and sensor equipment is used to monitor the material full-cycle parameters. The nodes are associated with multi-stage parameters to generate a material parameter management network, the digitization coefficients are calculated and compensation analysis is performed, a compensation plan is formulated, and after digital verification, the material parameters are digitally managed throughout the entire cycle based on the feedback information, thereby achieving the technical effect of accurate digital management of material parameters.

[0016] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0018] Example 1, as Figure 1 As shown, the present application provides a material parameter digitization method for full-cycle management, the method comprising:

[0019] Step S100: traverse the material supply chain to perform material correlation analysis, determine multiple material management nodes, perform periodic monitoring according to the multiple material management nodes through the sensor equipment group, and obtain a full-cycle parameter set of the material, which includes multi-stage material parameters.

[0020] In the embodiment of the present application, the material supply chain is first traversed through material correlation analysis to identify and analyze the key characteristics and influencing factors of materials at each life cycle node. The material supply chain includes the entire process from procurement, transportation, storage, use, maintenance to scrapping. In this process, data mining methods are used to analyze the correlation between different nodes based on historical records and existing material data. For example, the relationship between the quality of materials in the procurement stage and their performance in subsequent use, or the potential impact of environmental conditions in the transportation stage on the storage and use of materials, is identified.

[0021] Next, based on the results of the material dependency analysis, multiple material management nodes are identified and confirmed. These nodes cover all important stages of the material life cycle, including procurement node, transportation node, storage node, use node, maintenance node, and scrap node.

[0022] Subsequently, periodic monitoring is performed according to multiple material management nodes through a preset sensor device group, which includes temperature and humidity sensors, load sensors, position sensors, and vibration sensors. Temperature and humidity sensors monitor the warehouse environment during the storage phase, position sensors help track the location and status of materials during transportation, load sensors monitor the equipment's usage load, and vibration sensors can record vibrations during transportation or during equipment use. Through monitoring, a full-cycle parameter set for materials is obtained, which includes multi-stage material parameters. For example, during the procurement phase, the batch and supplier information of the materials are recorded; during the transportation phase, the transportation conditions, temperature and humidity of the transportation environment, and other information of the materials are recorded; during the storage phase, the temperature and humidity, storage method, inventory quantity, etc. are recorded; during the use phase, the load, frequency of use, and operating time of the equipment are recorded; during the maintenance phase, the maintenance, repair cycle, and repair records are recorded; and during the scrapping phase, the scrapping time and disposal method of the materials are recorded.

[0023] Step S200: Associating and matching the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, wherein the material parameter management network includes multiple node material association parameters.

[0024] Furthermore, in the method provided in the embodiment of the application, the plurality of material management nodes are associated and matched with the multi-stage material parameters to generate a material parameter management network, further comprising:

[0025] The multi-stage material parameters are divided according to the multiple material management nodes to generate first-stage material parameters, second-stage material parameters...N-stage material parameters, where N is an integer greater than 1; the multiple material management nodes are reversely analyzed according to the first-stage material parameters, the second-stage material parameters...the N-stage material parameters to determine node hierarchy information; the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are weighted based on the node hierarchy information to generate multiple weight coefficients; multiple connection edges are constructed between the multiple material management nodes and the first-stage material parameters, the second-stage material parameters...the N-stage material parameters according to the multiple weight coefficients; the multiple material management nodes are matched and connected with the first-stage material parameters, the second-stage material parameters...the N-stage material parameters according to the multiple connection edges based on the node hierarchy information to generate the multiple node material association parameters; the multiple node material association parameters are integrated to construct the material parameter management network.

[0026] In this embodiment, the multi-stage material parameters of multiple material management nodes are first divided to generate first-stage material parameters, second-stage material parameters, and finally N-stage material parameters, where N represents the multiple stages in the material lifecycle, including parameters related to procurement, transportation, storage, use, maintenance, and disposal. These stage parameters involve multiple aspects such as quantity, quality, cost, and utilization efficiency. They are key data for material management and affect the entire lifecycle management of materials from procurement to disposal.

[0027] Next, based on the material parameters for each stage, a reverse analysis method is used to determine the node hierarchy. The goal of this reverse analysis is to analyze the influence of each material management node and clarify the degree of impact each node has on the material at different stages. For example, the procurement node directly affects the inventory and transportation stages of the material, the usage node affects the efficiency and lifespan of the material, and the maintenance node plays a significant role in the status of the material during the usage phase. Through reverse analysis, the role and impact of material management nodes in the life cycle are identified, thereby determining the node hierarchy.

[0028] Based on the determined node hierarchy information, weights are then assigned to material parameters at each stage. This is accomplished using the Analytic Hierarchy Process (AHP). The AHP method constructs a judgment matrix to quantify the relative importance of each node to material parameters at different stages. During this process, experts rate the importance of each pair of material management nodes. For example, a score of 1 indicates that both nodes are equally important, a score of 3 indicates that the first node is more important than the second, and a score of 5 indicates that the first node is significantly more important than the second. Through matrix operations and the eigenvalue method, a weight coefficient is calculated for each node, and a weight value is assigned to each node based on the calculated results. The weight coefficient reflects the degree of influence of the node on each stage of the material life cycle and serves as a quantitative indicator of the node's relative importance.

[0029] After the weight coefficients are calculated, they are used to construct edges connecting nodes to material parameters at each stage. The weight of each edge is determined by the node's influence on the material parameters at that stage. Using a weighted graph, the relationship between nodes and material parameters is represented by edges, with the weight coefficients representing the edge weights. This connects each material management node to parameters at each stage in the material lifecycle. For example, a procurement node has a greater impact on material costs during the procurement phase, so the weight of that edge is higher; whereas a usage node may have less influence on the scrapping phase, so the weight of that edge is lower.

[0030] Finally, based on the node hierarchy information, a graph theory algorithm is used to match and connect the nodes with the material parameters of each stage, generating multiple node-material association parameters. This process establishes specific associations between each node and each material parameter by properly pairing material management nodes with the corresponding material parameter stages. For example, corresponding associations are established between procurement nodes and procurement quantities, transportation nodes and transportation time, and storage nodes and storage conditions. Finally, the multiple node-material association parameters generated above are integrated to construct a material parameter management network.

[0031] Step S300: traverse the material parameter management network to perform digital calculations and generate a plurality of digital coefficients, wherein the plurality of digital coefficients correspond to the plurality of node material associated parameters.

[0032] In an embodiment of the present application, when traversing the material parameter management network, the material association parameters of each node are first identified. The identification results include quantitative parameters (such as inventory, purchase volume, etc.) and qualitative parameters (such as material status, usage frequency, etc.). Then, based on these quantitative and qualitative parameters, correlation analysis is performed to generate quantitative correlation coefficients and qualitative correlation coefficients, respectively, to represent the relationship between different material management stages. Next, these correlation coefficients are digitally calculated to generate multiple digital coefficients. Each digital coefficient is closely related to the corresponding node material association parameter, reflecting the quantitative relationship between different material management nodes and the material parameters of each stage.

[0033] Furthermore, in the method provided in the embodiment of the application, traversing the material parameter management network to perform digital calculations and generate multiple digital coefficients also includes:

[0034] The material parameter management network is traversed to perform parameter identification on the material association parameters of the multiple nodes to obtain a material management parameter identification result, wherein the material management parameter identification result includes multiple quantitative parameters and multiple qualitative parameters; according to the multiple quantitative parameters, the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are associated with each other to generate multiple quantitative association coefficients; according to the multiple qualitative parameters, the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are associated with each other to generate multiple qualitative association coefficients; according to the multiple quantitative association coefficients and the multiple qualitative association coefficients, the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are digitally calculated to generate the multiple digital coefficients.

[0035] In the embodiments of this application, the material management network is first traversed to identify the material-related parameters at each node. This process typically utilizes sensor technology and IoT devices to collect data. For example, sensors and RFID technology can be used to collect parameters such as material inventory and usage frequency in real time during warehousing and transportation. This allows the determination of both quantitative parameters (such as inventory level and purchase volume) and qualitative parameters (such as material status and usage frequency) of the materials.

[0036] Next, correlation analysis is performed on the material parameters for the first phase, the second phase, and so on, based on multiple quantitative parameters. During this process, historical or real-time data is collected and organized to analyze the interrelationships between different phases. For example, the purchase volume (a quantitative parameter) in the first phase may have a direct impact on the inventory level in the second phase, and changes in inventory levels will be reflected in subsequent usage frequency or maintenance cycles. Correlations between these quantitative parameters are quantified using correlation coefficients (such as the Pearson correlation coefficient) to generate multiple quantitative correlation coefficients. For example, there may be a strong correlation between purchase volume and inventory level. By calculating the correlation coefficient, the strength of the relationship between these parameters can be quantified. This method generates a quantitative correlation coefficient for each pair of material phases.

[0037] At the same time, correlation analysis is conducted on material parameters at different stages based on qualitative parameters, such as material condition and usage frequency. Since qualitative parameters cannot be directly numerically calculated, fuzzy logic reasoning is used to address the fuzzy relationships between these parameters. Fuzzy logic effectively handles the uncertainty between qualitative parameters, such as the relationship between material condition (e.g., good, bad, under repair) and usage frequency. The specific fuzzy reasoning process involves first quantifying qualitative parameters. For example, "material condition is good" is converted into a fuzzy set membership value, such as 0.8 for "good," 0.2 for "bad," and 0.5 for "under repair." Technical experts then define fuzzy rules, such as "material condition is good" may lead to "high usage frequency." Fuzzy reasoning algorithms (such as the Mamdani model) are used for reasoning and calculation, ultimately deriving a qualitative correlation coefficient. For example, the relationship between good material condition and high usage frequency may be 0.7.

[0038] After obtaining the quantitative and qualitative correlation coefficients, these coefficients are further digitized. Specifically, technical experts first assign different weights to the quantitative and qualitative correlation coefficients based on the multiple quantitative and qualitative correlation coefficients. For example, quantitative parameters such as inventory and procurement volume may have a more significant impact and therefore be assigned a higher weight, while qualitative parameters such as material status and frequency of use may be assigned a lower weight. Next, a weighted average method is used to combine and calculate the multiple quantitative and qualitative correlation coefficients, ultimately resulting in multiple digitized coefficients.

[0039] Step S400: performing digital compensation analysis on the material parameter management network according to the multiple digital coefficients combined with the multiple node material associated parameters, and formulating a digital compensation plan.

[0040] In this embodiment of the present application, a digital compensation analysis is performed on the material parameter management network based on multiple digital coefficients combined with multiple node material-related parameters. Specifically, digital deviations are first calculated to identify nodes whose deviation values ​​do not meet the desired digital threshold. The system then identifies these nodes as those to be compensated. Next, based on the deviation values, reinforcement learning methods are used to dynamically compensate the nodes to be compensated, ultimately developing a digital compensation plan.

[0041] Furthermore, in the method provided in the embodiment of the application, performing digital compensation analysis on the material parameter management network based on the multiple digital coefficients combined with the multiple node material-related parameters and formulating a digital compensation plan also includes:

[0042] Based on the multiple digital coefficients, digital deviation calculation is performed on the material-related parameters of the multiple nodes to obtain multiple deviation values; an expected digital threshold is set, and the material parameter management network is judged based on the multiple deviation values ​​to determine whether the multiple deviation values ​​of the multiple material management nodes meet the expected digital threshold; the material management nodes corresponding to the deviation values ​​that do not meet the expected digital threshold are identified, and multiple nodes to be compensated are determined; reinforcement learning is performed according to the multiple deviation values ​​to dynamically compensate the multiple nodes to be compensated, and the digital compensation plan is formulated.

[0043] In an embodiment of the present application, first, a digital deviation calculation is performed on the material-related parameters of each material management node. The material-related parameters of each node have a preset digital coefficient, which represents the target digital level set by the expert. The multiple digital coefficients generated by the previous steps are the values ​​actually calculated, which are used to represent the digital status of the current material management node. Then, these actually calculated digital coefficients are compared with the preset digital coefficients of the corresponding nodes to calculate the deviation value of each node. The deviation value reflects the difference between the actual digital level of each node and the preset target. The larger the deviation value, the greater the gap in digital level.

[0044] Next, technical experts set a desired digitization threshold and compare the obtained deviation values ​​with the desired threshold to determine whether the digitization level of each node meets the preset requirements. Specifically, if the deviation value exceeds the desired threshold, the node's digitization level does not meet expectations. Nodes that do not meet the desired threshold are then identified as "nodes to be compensated" and require further adjustment and optimization.

[0045] Finally, reinforcement learning is performed based on the multiple deviation values ​​to dynamically compensate the multiple nodes to be compensated, and the digital compensation plan is formulated.

[0046] Furthermore, in the method provided in the embodiment of the application, dynamic compensation is performed on the multiple nodes to be compensated by performing reinforcement learning based on the multiple deviation values, and a digital compensation plan is formulated, which also includes:

[0047] Based on the multiple deviation values, the multiple material management nodes are traversed to perform status assessment and determine the material management status information; a compensation solution space is constructed, the material management status information is mapped to the compensation solution space for search, multiple compensation action parameters are determined to introduce a reward function, the multiple compensation action parameters are executed in combination with the reward function for calculation, and multiple reward values ​​are generated; compensation analysis is performed based on the multiple reward values ​​to obtain a digital compensation effect, and the digital compensation effect includes multiple compensation scores; the digital compensation scheme is constructed based on the multiple deviation values ​​in combination with the multiple compensation scores.

[0048] In an embodiment of the present application, the status of the material management nodes is first traversed based on multiple deviation values ​​to perform a status assessment. Specifically, a regression analysis method is used to perform a detailed analysis of the deviation values ​​of each node. This process uses linear regression or polynomial regression analysis to identify the relationship between the deviation value and other material management parameters (such as inventory, frequency of use, maintenance cycle, etc.). Through the regression model, the specific management status of each node is estimated based on existing historical data or real-time data to ensure that the status assessment is not limited to a single dimension, but comprehensively considers the influence of multiple factors. Ultimately, the regression analysis results generate material management status information, which includes the specific digital management status of each node.

[0049] Next, a compensation solution space is constructed. Based on this space, the material management status information is mapped to the compensation solution space and then searched using a heuristic search algorithm (such as the A-algorithm). The A-algorithm is a path search method that combines heuristic search and cost calculation to help find the most appropriate compensation action in the compensation solution space. By evaluating the material management status of each node, the A-algorithm searches for the compensation action most likely to lead to optimization based on the current state and determines multiple compensation action parameters.

[0050] Next, based on the reward function, multiple compensatory actions are executed and the reward value for each compensatory action is calculated. The reward function is designed using the Q-learning algorithm in reinforcement learning. Q-learning represents the effect of the compensatory action by evaluating the Q-value of each state-action pair. The reward function is usually defined based on the contribution of the compensatory action to inventory changes, improved usage efficiency, or reduced failure rates. Formally, the reward function combines changes in multiple factors, such as inventory changes, changes in usage efficiency, and changes in failure rates, and assigns a corresponding reward value to each compensatory action, ultimately generating multiple reward values ​​to measure the effectiveness of the compensatory action.

[0051] After generating multiple reward values, compensation analysis is performed using a weighted average method. This method weights the reward values ​​according to the weights of different compensation actions, resulting in a comprehensive compensation effect score. The weights of different compensation actions are pre-set, and this score quantifies the impact of each compensation action on the node management status, providing a basis for further decision-making. Through compensation analysis, a digital compensation effect is derived, which includes the specific scores of each compensation action and provides data support for the subsequent construction of compensation plans.

[0052] Finally, the analytic hierarchy process (AHP) was used to construct a digital compensation plan based on multiple deviation values ​​and compensation scores. This method constructs a judgment matrix, comprehensively considers deviation values ​​and compensation scores, and assesses the priority and impact of each compensation action. Through the AHP method, the optimal compensation action is implemented at the material management node, resulting in the most effective digital compensation plan, ensuring optimal material management accuracy and efficiency.

[0053] Furthermore, in the method provided in the embodiment of the application, constructing the compensation solution space also includes:

[0054] The multiple material management nodes are traversed to perform compensation analysis, a compensation target is constructed, and compensation constraints are constructed based on the compensation target in combination with the multiple deviation value definitions; discretization processing is performed according to the multiple material management nodes in combination with the multi-stage material parameters to determine the compensation solution space dimensional information; based on the compensation solution space dimensional information, the compensation target is updated according to the compensation constraint conditions to construct the compensation solution space.

[0055] In an embodiment of the present application, a compensation analysis is first performed by traversing multiple material management nodes and combining the deviation values ​​of each node, and a compensation target is constructed. In this process, the digital differences reflected by the deviation values ​​are used to determine the compensation target required for each node, that is, to set the expected result of digital compensation for the node. This compensation target is set based on the digital deviation of each node, aiming to narrow the gap between the current digital level and the expected target. Through deviation analysis, the compensation target is finally determined, that is, the digital compensation expectation of each node.

[0056] Next, based on the compensation target, the compensation constraints are defined and constructed by combining the deviation value with constraints such as resources and time. In this step, constraint definition methods, such as linear programming or nonlinear optimization, are used to set the boundary conditions for the compensation operation by evaluating factors such as resources, time, and cost between nodes. These constraints ensure that the compensation plan can be implemented within resource, time, and budget constraints, thereby maintaining its feasibility and practicality. This process ultimately determines the compensation constraints—specific conditions such as resource limits, budget limits, and time limits that must be adhered to during the compensation process.

[0057] Then, discretization is performed based on multiple material management nodes and multi-stage material parameters to determine the dimensional information of the compensation solution space. Specifically, through discretization, the digital parameters of the nodes and material management parameters are converted into discrete levels or values ​​to facilitate subsequent optimization and calculation. During this process, methods such as piecewise functions or quantitative analysis are used to divide the digital level into multiple discrete levels or intervals to facilitate compensation decision-making. After discretization, clear dimensional information of the compensation solution space is obtained. This dimensional information indicates the possible adjustment range of the compensation solution, including the combination of different node and material parameters.

[0058] Finally, based on the compensation objective, compensation constraints, and the dimensionality of the solution space, the compensation solution space is updated and constructed. In this step, optimization methods (such as genetic algorithms and particle swarm optimization) are used to search and update the compensation solution space. Unlike directly searching for the optimal solution, the core of this process is to explore and identify a diverse solution space that encompasses all potential compensation solutions that meet the compensation objective and constraints. This method gradually updates the compensation solution space, ultimately forming a solution space that covers all possible compensation actions for subsequent compensation decision-making and optimization analysis. This step constructs the compensation solution space—the set of all possible compensation solutions under the compensation objective and constraints.

[0059] Step S500: Execute the digital compensation scheme to digitally verify the material parameter management network, provide regular feedback to the multiple material management nodes based on the verification results, generate digital feedback information, and perform full-cycle digital management of the multi-stage material parameters based on the digital feedback information.

[0060] In an embodiment of the present application, in the process of implementing the digital compensation scheme, the compensation scheme is first evaluated based on multiple compensation scores. After setting the verification indicators, the material parameter management network is verified by these indicators to generate a verification token. Then, based on the verification token, regression analysis is performed on multiple material management nodes to obtain the digital regression results of each node. By setting a self-inspection cycle, these regression results are regularly fed back to generate improvement suggestions. Based on these suggestions, the digital compensation scheme is dynamically adjusted, and finally digital feedback information is generated. Subsequently, the multi-stage material parameters are digitally managed throughout the entire cycle based on these digital feedback information to ensure continuous optimization and refined management of material management.

[0061] Furthermore, in the method provided in the embodiment of the application, executing the digital compensation scheme to digitally verify the material parameter management network, providing regular feedback to the multiple material management nodes based on the verification results, and generating digital feedback information, further comprising:

[0062] The digital compensation scheme is evaluated based on the multiple compensation scores, and verification indicators are set; the material parameter management network is verified according to the verification indicators, and a verification token is generated; regression analysis is performed on the multiple material management nodes according to the verification token to obtain node digital regression results; a self-inspection cycle is set, and the node digital regression results are regularly fed back to the multiple material management nodes according to the self-inspection cycle to generate improvement suggestions; the digital compensation scheme is dynamically adjusted according to the improvement suggestions to generate the digital feedback information.

[0063] In an embodiment of the present application, the digital compensation scheme is evaluated based on multiple compensation scores. First, it is necessary to collect relevant data of each material management node, such as inventory, frequency of use, etc., through statistical analysis. The compensation score of each node is calculated based on these data. The higher the score, the more complete the digital management, and the node with a lower score indicates that there is a demand for compensation. After the compensation score is obtained, predetermined verification indicators are set based on these scores. These indicators include the operating efficiency of the node, the accuracy of resource allocation, etc. Next, each material management node is verified according to the preset verification indicators, and the actual operation results of the node are compared with the set indicators using a standard verification method to ensure that the node meets the target standard. If a node does not meet the standard, a "verification token" is generated. The token is an important basis for the node's compensation needs and subsequent processing.

[0064] After obtaining a verification token, a regression analysis is performed on multiple material management nodes based on the token. A simple linear regression model is used to analyze historical data from these nodes to identify potential issues in digital management. Specifically, regression analysis calculates the regression coefficient for each node, analyzes the deviation between node performance and expected targets, predicts potential future digital management issues, and generates digital regression results for each node. Through regression analysis, it accurately assesses which nodes have management issues or require further compensation, and identifies each node's management performance and optimization direction.

[0065] Next, based on the digital regression results of the nodes, a self-inspection cycle is established and regular feedback is provided. This self-inspection cycle can be set to monthly, quarterly, or other time periods based on actual management needs. Within each cycle, the regression results of each node are checked and compared with the set targets. If the performance of certain nodes does not meet expectations, targeted improvement suggestions are generated. These suggestions may include increasing inventory accuracy, strengthening usage frequency monitoring, and so on, helping managers take timely measures to optimize the digital management of nodes.

[0066] Finally, the digital compensation plan is dynamically adjusted based on improvement suggestions generated through regular feedback. In this step, based on the self-check feedback, managers adjust key parameters of the compensation plan, such as compensation thresholds and node optimization targets, to ensure that the compensation plan consistently meets the actual needs of current material management. The adjusted compensation plan generates digital feedback information, which is provided to managers to evaluate the effectiveness of the adjusted plan and provide a basis for subsequent decision-making.

[0067] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0068] The present application traverses the material supply chain to perform material correlation analysis, determines multiple material management nodes, performs periodic monitoring according to the multiple material management nodes through a sensor equipment group, obtains a full-cycle parameter set of the material, and the full-cycle parameter set of the material includes multi-stage material parameters; associates and matches the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, and the material parameter management network includes multiple node material association parameters; traverses the material parameter management network to perform digital calculations to generate multiple digital coefficients, and the multiple digital coefficients have a corresponding relationship with the multiple node material association parameters; performs digital compensation analysis on the material parameter management network based on the multiple digital coefficients combined with the multiple node material association parameters, and formulates a digital compensation plan; executes the digital compensation plan to digitally verify the material parameter management network, and performs regular feedback on the multiple material management nodes based on the verification results to generate digital feedback information, and performs full-cycle digital management of the multi-stage material parameters based on the digital feedback information. The present invention solves the technical problem that the existing technology cannot effectively carry out accurate digital management of material parameters. It traverses the material supply chain and performs material correlation analysis, determines multiple material management nodes, uses sensor equipment to monitor the full-cycle parameters of materials, associates the nodes with multi-stage parameters to generate a material parameter management network, calculates the digital coefficient and performs compensation analysis, formulates a compensation plan, and after performing digital verification, conducts full-cycle digital management of material parameters based on feedback information, thereby achieving the technical effect of accurate digital management of material parameters.

[0069] Example 2 is based on the same inventive concept as the material parameter digitization method for full-cycle management in the previous embodiment. Figure 2 As shown, the present application provides a material parameter digitization system for full-cycle management. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0070] The cycle monitoring module 11 traverses the material supply chain to perform material correlation analysis, determines multiple material management nodes, performs cycle monitoring according to the multiple material management nodes through the sensor equipment group, and obtains the full cycle parameter set of the material, and the full cycle parameter set of the material includes multi-stage material parameters; the association matching module 12, the association matching module 12 associates and matches the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, and the material parameter management network includes multiple node material association parameters; the digital calculation module 13, the digital calculation module 13 traverses the material parameter management network to perform digital calculations, and generates multiple digital parameters. a digital coefficient, wherein the multiple digital coefficients correspond to the multiple node material-related parameters; a digital compensation analysis module 14, wherein the digital compensation analysis module 14 performs digital compensation analysis on the material parameter management network according to the multiple digital coefficients in combination with the multiple node material-related parameters, and formulates a digital compensation plan; a digital management module 15, wherein the digital management module 15 executes the digital compensation plan to digitally verify the material parameter management network, and performs regular feedback on the multiple material management nodes according to the verification results, generates digital feedback information, and performs full-cycle digital management of the multi-stage material parameters according to the digital feedback information.

[0071] Furthermore, the system is also used to implement the following functions:

[0072] The multi-stage material parameters are divided according to the multiple material management nodes to generate first-stage material parameters, second-stage material parameters...N-stage material parameters, where N is an integer greater than 1; the multiple material management nodes are reversely analyzed according to the first-stage material parameters, the second-stage material parameters...the N-stage material parameters to determine node hierarchy information; the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are weighted based on the node hierarchy information to generate multiple weight coefficients; multiple connection edges are constructed between the multiple material management nodes and the first-stage material parameters, the second-stage material parameters...the N-stage material parameters according to the multiple weight coefficients; the multiple material management nodes are matched and connected with the first-stage material parameters, the second-stage material parameters...the N-stage material parameters according to the multiple connection edges based on the node hierarchy information to generate the multiple node material association parameters; the multiple node material association parameters are integrated to construct the material parameter management network.

[0073] Furthermore, the system is also used to implement the following functions:

[0074] The material parameter management network is traversed to perform parameter identification on the material association parameters of the multiple nodes to obtain a material management parameter identification result, wherein the material management parameter identification result includes multiple quantitative parameters and multiple qualitative parameters; according to the multiple quantitative parameters, the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are associated with each other to generate multiple quantitative association coefficients; according to the multiple qualitative parameters, the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are associated with each other to generate multiple qualitative association coefficients; according to the multiple quantitative association coefficients and the multiple qualitative association coefficients, the first-stage material parameters, the second-stage material parameters...the N-stage material parameters are digitally calculated to generate the multiple digital coefficients.

[0075] Furthermore, the system is also used to implement the following functions:

[0076] Based on the multiple digital coefficients, digital deviation calculation is performed on the material-related parameters of the multiple nodes to obtain multiple deviation values; an expected digital threshold is set, and the material parameter management network is judged based on the multiple deviation values ​​to determine whether the multiple deviation values ​​of the multiple material management nodes meet the expected digital threshold; the material management nodes corresponding to the deviation values ​​that do not meet the expected digital threshold are identified, and multiple nodes to be compensated are determined; reinforcement learning is performed according to the multiple deviation values ​​to dynamically compensate the multiple nodes to be compensated, and the digital compensation plan is formulated.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] Based on the multiple deviation values, the multiple material management nodes are traversed to perform status assessment and determine the material management status information; a compensation solution space is constructed, the material management status information is mapped to the compensation solution space for search, multiple compensation action parameters are determined to introduce a reward function, the multiple compensation action parameters are executed in combination with the reward function for calculation, and multiple reward values ​​are generated; compensation analysis is performed based on the multiple reward values ​​to obtain a digital compensation effect, and the digital compensation effect includes multiple compensation scores; the digital compensation scheme is constructed based on the multiple deviation values ​​in combination with the multiple compensation scores.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] The multiple material management nodes are traversed to perform compensation analysis, a compensation target is constructed, and compensation constraints are constructed based on the compensation target in combination with the multiple deviation value definitions; discretization processing is performed according to the multiple material management nodes in combination with the multi-stage material parameters to determine the compensation solution space dimensional information; based on the compensation solution space dimensional information, the compensation target is updated according to the compensation constraint conditions to construct the compensation solution space.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] The digital compensation scheme is evaluated based on the multiple compensation scores, and verification indicators are set; the material parameter management network is verified according to the verification indicators, and a verification token is generated; regression analysis is performed on the multiple material management nodes according to the verification token to obtain node digital regression results; a self-inspection cycle is set, and the node digital regression results are regularly fed back to the multiple material management nodes according to the self-inspection cycle to generate improvement suggestions; the digital compensation scheme is dynamically adjusted according to the improvement suggestions to generate the digital feedback information.

[0083] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0085] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A digital method for material parameters oriented to full-cycle management, characterized by: The method comprises: Traversing the material supply chain to perform material relevance analysis, determine multiple material management nodes, perform periodic monitoring according to the multiple material management nodes through a sensor device group, and obtain a full-cycle parameter set of the material, wherein the full-cycle parameter set of the material includes multi-stage material parameters; Associating and matching the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, wherein the material parameter management network includes multiple node material association parameters; Traversing the material parameter management network to perform digital calculations and generate a plurality of digital coefficients, wherein the plurality of digital coefficients correspond to the plurality of node material-related parameters; Performing digital compensation analysis on the material parameter management network according to the multiple digital coefficients combined with the multiple node material associated parameters, and formulating a digital compensation plan; Executing the digital compensation scheme to digitally verify the material parameter management network, providing regular feedback to the multiple material management nodes based on the verification results, generating digital feedback information, and performing full-cycle digital management of the multi-stage material parameters based on the digital feedback information; Performing digital compensation analysis on the material parameter management network based on the multiple digital coefficients combined with the multiple node material-related parameters, and formulating a digital compensation plan, the method includes: Performing digital deviation calculation on the plurality of node material associated parameters based on the plurality of digital coefficients to obtain a plurality of deviation values; Setting an expected digitization threshold, and judging the material parameter management network based on the multiple deviation values ​​to determine whether the multiple deviation values ​​of the multiple material management nodes meet the expected digitization threshold; Identify the material management nodes corresponding to the deviation values ​​that do not meet the expected digital threshold, and determine a plurality of nodes to be compensated; Performing reinforcement learning based on the multiple deviation values ​​to dynamically compensate the multiple nodes to be compensated, and formulating the digital compensation plan; The method of dynamically compensating the multiple nodes to be compensated by performing reinforcement learning based on the multiple deviation values ​​and formulating a digital compensation plan includes: traversing the plurality of material management nodes to perform status evaluation based on the plurality of deviation values, and determining material management status information; Constructing a compensation solution space, mapping the material management status information to the compensation solution space for searching, and determining a plurality of compensation action parameters; Introducing a reward function, executing the calculation of the plurality of compensation action parameters in combination with the reward function to generate a plurality of reward values; Perform compensation analysis based on the multiple reward values ​​to obtain a digital compensation effect, wherein the digital compensation effect includes multiple compensation scores; The digital compensation scheme is constructed according to the multiple deviation values ​​and the multiple compensation scores.

2. The material parameter digitization method for full-cycle management according to claim 1, characterized in that: Associating and matching the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, the method comprising: Dividing the multi-stage material parameters according to the multiple material management nodes to generate first-stage material parameters, second-stage material parameters, ..., N-stage material parameters, where N is an integer greater than 1; Reversely analyze the plurality of material management nodes according to the first-stage material parameters, the second-stage material parameters, ..., the N-stage material parameters to determine node hierarchical information; Based on the node hierarchical information, weights are assigned to the first-stage material parameters, the second-stage material parameters, ..., the N-stage material parameters to generate a plurality of weight coefficients; Constructing a plurality of connection edges between the plurality of material management nodes and the first-stage material parameters, the second-stage material parameters, ..., the N-stage material parameters according to the plurality of weight coefficients; Matching and connecting the plurality of material management nodes with the first-stage material parameters, the second-stage material parameters, ..., the N-stage material parameters according to the plurality of connection edges based on the node hierarchical information to generate the plurality of node material association parameters; The material parameter management network is constructed based on the integration of the material associated parameters of the multiple nodes.

3. The material parameter digitization method for full-cycle management according to claim 2, characterized in that: Traversing the material parameter management network to perform digital calculations and generate multiple digital coefficients, the method includes: Traversing the material parameter management network to perform parameter identification on the plurality of node material associated parameters to obtain a material management parameter identification result, wherein the material management parameter identification result includes a plurality of quantitative parameters and a plurality of qualitative parameters; performing correlation analysis on the first-stage material parameters, the second-stage material parameters, ..., the N-stage material parameters according to the multiple quantitative parameters to generate multiple quantitative correlation coefficients; performing correlation analysis on the first-stage material parameters, the second-stage material parameters, ..., the N-stage material parameters according to the multiple qualitative parameters to generate multiple qualitative correlation coefficients; The first-stage material parameters, the second-stage material parameters, ..., the N-stage material parameters are digitally calculated according to the multiple quantitative correlation coefficients and the multiple qualitative correlation coefficients to generate the multiple digital coefficients.

4. The material parameter digitization method for full-cycle management according to claim 1, characterized in that: Constructing the compensation solution space, the methods include: Traversing the plurality of material management nodes to perform compensation analysis, constructing a compensation target, and constructing compensation constraint conditions based on the compensation target and the plurality of deviation values; Discretization processing is performed based on the multiple material management nodes and the multi-stage material parameters to determine compensation solution space dimension information; Based on the compensation solution space dimension information, the compensation target is updated according to the compensation constraint condition to construct the compensation solution space.

5. The material parameter digitization method for full-cycle management according to claim 1, characterized in that: The digital compensation scheme is executed to digitally verify the material parameter management network, and regular feedback is provided to the plurality of material management nodes based on the verification results to generate digital feedback information. The method includes: Evaluate the digital compensation scheme based on the multiple compensation scores and set verification indicators; Verifying the material parameter management network according to the verification indicators to generate a verification token, and performing regression analysis on the multiple material management nodes based on the verification token to obtain a node digital regression result; Setting a self-check cycle, regularly feeding back the digital regression results of the nodes to the multiple material management nodes according to the self-check cycle, and generating improvement suggestions; The digital compensation scheme is dynamically adjusted according to the improvement suggestion to generate the digital feedback information.

6. The material parameter digital system for full-cycle management is characterized by: The system comprises: A cycle monitoring module, which traverses the material supply chain to perform material correlation analysis, determines multiple material management nodes, and performs cycle monitoring according to the multiple material management nodes through a sensor device group to obtain a full-cycle parameter set of the material, which includes multi-stage material parameters; an association matching module, wherein the association matching module associates and matches the multiple material management nodes with the multi-stage material parameters to generate a material parameter management network, wherein the material parameter management network includes multiple node material association parameters; a digital calculation module, wherein the digital calculation module traverses the material parameter management network to perform digital calculations and generate a plurality of digital coefficients, wherein the plurality of digital coefficients correspond to the plurality of node material-related parameters; a digital compensation analysis module, which performs digital compensation analysis on the material parameter management network according to the multiple digital coefficients and the multiple node material-related parameters, and formulates a digital compensation plan; a digital management module, which executes the digital compensation scheme to digitally verify the material parameter management network, provides regular feedback to the multiple material management nodes based on the verification results, generates digital feedback information, and performs full-cycle digital management of the multi-stage material parameters based on the digital feedback information; The system is also used to implement the following functions: Performing digital deviation calculations on the material-related parameters of the multiple nodes based on the multiple digital coefficients to obtain multiple deviation values; setting an expected digital threshold, and judging the material parameter management network based on the multiple deviation values ​​to determine whether the multiple deviation values ​​of the multiple material management nodes meet the expected digital threshold; identifying the material management nodes corresponding to the deviation values ​​that do not meet the expected digital threshold, and determining multiple nodes to be compensated; performing reinforcement learning based on the multiple deviation values ​​to dynamically compensate the multiple nodes to be compensated, and formulating the digital compensation plan; Based on the multiple deviation values, the multiple material management nodes are traversed to perform status assessment and determine the material management status information; a compensation solution space is constructed, the material management status information is mapped to the compensation solution space for search, multiple compensation action parameters are determined to introduce a reward function, the multiple compensation action parameters are executed in combination with the reward function for calculation, and multiple reward values ​​are generated; compensation analysis is performed based on the multiple reward values ​​to obtain a digital compensation effect, and the digital compensation effect includes multiple compensation scores; the digital compensation scheme is constructed based on the multiple deviation values ​​in combination with the multiple compensation scores.

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