A coal machine equipment dynamic flexibility and uniformity management and control method and system based on multi-source data fusion

Abstract

The application provides a coal machine equipment dynamic flexibility and complete set management and control method and system based on multi-source data fusion. The method comprises the following steps: collecting data of multiple heterogeneous production management systems, standardizing and spatiotemporal aligning the data, and obtaining fused multi-source data; determining a dynamic complete set index of coal machine equipment based on the fused multi-source data; constructing a material-process-capacity three-dimensional flexible constraint space based on material substitution relationship, process route elasticity relationship and capacity elasticity relationship; when the dynamic complete set index is lower than a preset threshold, relaxing the constraints in the three-dimensional flexible constraint space according to priorities, searching for an optimal complete set scheme, and monitoring the change of the dynamic complete set index in real time to form a closed-loop control. The technical scheme provided by the application realizes real-time quantitative evaluation and systematic flexible adjustment of the complete set state, and significantly improves the complete set rate and delivery punctuality rate of coal machine equipment production.

Description

Technical Field

[0001] This application relates to the field of coal mining equipment manufacturing technology, and in particular to a dynamic flexible completeness control method and system for coal mining equipment based on multi-source data fusion. Background Technology

[0002] Coal mining equipment, as core equipment in coal mining, has a complex manufacturing process characterized by diverse varieties, small batches, high degree of customization, and long production cycles. Among related technologies, a completeness management system based on fixed BOMs and static inventory has been constructed through the collaborative operation of systems such as ERP, MES, and SCM. Specifically, this system covers the entire process from material requirements planning to assembly execution, including key aspects such as BOM decomposition, inventory verification, and in-transit tracking.

[0003] However, existing kitting management methods directly rely on fixed BOMs and static inventory for determination, without integrating multi-source heterogeneous data. This can lead to a 3-7 day lag in kitting determination or an inability to quantify the intermediate state of "near kitting," thus impacting production efficiency and on-time delivery rates. Specifically, traditional methods depend solely on a single ERP data source, creating information silos and resulting in biased decision-making. Furthermore, kitting determination is a binary 0 / 1 result, failing to distinguish between "fully kitted" and "near kitted." Additionally, manual handling after anomalies occur results in slow response times and missed adjustment windows. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first aspect of this application proposes a method for dynamic flexible completeness control of coal mining equipment based on multi-source data fusion, the method comprising:

[0006] Data from multiple heterogeneous production management systems is collected, and the data is standardized and spatiotemporally aligned to obtain fused multi-source data. The dynamic completeness index of coal mining equipment is determined based on the fused multi-source data, wherein the dynamic completeness index is composed of a weighted average of material availability, time matching degree, quality compliance degree and flexibility adjustment degree. Based on material substitution relationships, process route flexibility relationships, and capacity flexibility relationships, a three-dimensional flexible constraint space of material-process-capacity is constructed. When the dynamic fitting index is lower than a preset threshold, the constraints in the three-dimensional flexible constraint space are relaxed step by step according to priority, the optimal fitting scheme is searched, and the change of the dynamic fitting index is monitored in real time to form a closed-loop control.

[0007] Preferably, the collection of data from multiple heterogeneous production management systems includes: Collect the number of material types, the required quantity of each material, the total required quantity of each material, and the current inventory of each material from the bill of materials in the ERP system. Collect the substitution relationships and compatibility scores among various materials in the PLM system, as well as the material substitution flexibility determined based on the substitution relationships; Collect the work-in-process quantity, estimated completion time of work-in-process, demand time of each material, and alternative process routes and their compatibility scores for each process in the MES system, and determine the flexibility of the process route based on the alternative process routes. Collect the in-transit quantity and estimated arrival time of each material in the SCM system; Collect the capacity elasticity coefficient of the devices in the IoT device, and determine the capacity flexibility based on the capacity elasticity coefficient; Collect historical defect rates of various materials and quality inspection results of each batch of materials from the QMS system.

[0008] Furthermore, the formula for calculating the dynamic completeness index of the coal mining equipment is as follows:

[0009] In the formula, It is a dynamic homogeneity index. For material availability, For time matching degree, For quality compliance, For flexible adjustment, This refers to the weighting coefficient corresponding to material availability. The weighting coefficients corresponding to the time matching degree. These are the weighting coefficients corresponding to the quality compliance. This represents the weighting coefficient corresponding to the degree of flexibility.

[0010] Furthermore, the formula for calculating the availability of the materials is as follows:

[0011] In the formula, For the current inventory level, The quantity of work-in-process. This refers to the amount of materials in transit. This represents the total demand.

[0012] The formula for calculating the time matching degree is as follows:

[0013] In the formula, For the estimated arrival time of materials, For material demand time points, This is a time buffer period.

[0014] The formula for calculating the quality compliance is as follows:

[0015] In the formula, For the number of material types, For the first Historical defect rate of similar materials For the first The required quantity of this type of material.

[0016] The formula for calculating the degree of flexibility is as follows:

[0017] In the formula, For material substitution flexibility, For the sake of process flexibility, For production capacity flexibility, Weighting coefficients for material substitution flexibility Weighting coefficients for process route flexibility This is the weighting coefficient for capacity flexibility.

[0018] Furthermore, the weighting coefficients α, β, γ, and δ are adaptively determined by combining the entropy weighting method with the analytic hierarchy process, and α+β+γ+δ=1; The dynamic homogeneity index ranges from [0,1]. When DKI ≥ 0.85, it is determined to be high homogeneity; when 0.70 ≤ DKI < 0.85, it is determined to be medium homogeneity; and when DKI < 0.70, it is determined to be low homogeneity.

[0019] Furthermore, the stepwise relaxation of constraints in the three-dimensional flexible constraint space according to priority includes: The dynamic homogeneity index is compared with a preset threshold. When the dynamic completeness index is lower than the preset threshold, the constraints are relaxed step by step in the priority order of standard scheme, material substitution relaxation, process route relaxation, capacity flexibility relaxation and comprehensive relaxation. After each relaxation, the dynamic completeness index is recalculated. If the dynamic completeness index reaches or exceeds the preset threshold, the relaxation is stopped and the current scheme is output. When the dynamic homogeneity index is greater than or equal to a preset threshold, the enabled flexible resources are released.

[0020] Furthermore, the stepwise relaxation constraint follows the principle of minimum cost priority, giving priority to relaxation schemes with high compatibility scores and small cost increments; The activation condition for material substitution relaxation is: the compatibility score of the substitute material is greater than or equal to the first preset value. The condition for enabling process route relaxation is that the compatibility score of the alternative process route is greater than or equal to the second preset value.

[0021] Preferably, the search for the optimal homogeneous solution includes: The NSGA-II multi-objective optimization algorithm is adopted to search for the Pareto optimal set of homogenization schemes in the flexible constraint space with the optimization objectives of maximizing the dynamic homogenization index, minimizing the cost increment, and minimizing the time delay.

[0022] Preferably, the closed-loop control includes: Set a fluctuation threshold and monitor the changes in the dynamic homogeneity index in real time; When the fluctuation of the dynamic homogeneity index exceeds the fluctuation threshold, a reassessment is automatically triggered, and the current dynamic homogeneity index is recalculated. Based on the comparison between the recalculated dynamic homogeneity index and the preset threshold, the system adaptively selects to initiate constraint relaxation or release the enabled flexible resources. The results of the optimization plan will be fed back to update the material status and process progress.

[0023] Furthermore, the quality compliance is integrated with the QMS system. When the QMS system detects that any batch of materials fails inspection, it automatically updates the historical defect rate of that batch of materials to 1.0 and recalculates the dynamic kitting index.

[0024] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application proposes a method and system for dynamic flexible completeness control of coal mining equipment based on multi-source data fusion. The method includes: collecting data from multiple heterogeneous production management systems, standardizing and spatiotemporally aligning the data to obtain fused multi-source data; determining a dynamic completeness index for coal mining equipment based on the fused multi-source data, wherein the dynamic completeness index is weighted by material availability, time matching degree, quality compliance, and flexibility adjustment degree; constructing a three-dimensional flexible constraint space of material-process-capacity based on material substitution relationships, process route elasticity relationships, and capacity elasticity relationships; when the dynamic completeness index is lower than a preset threshold, relaxing the constraints in the three-dimensional flexible constraint space step by step according to priority, searching for the optimal completeness solution, and monitoring the changes in the dynamic completeness index in real time to form a closed-loop control. The technical solution proposed in this application realizes real-time quantitative evaluation and systematic flexible adjustment of the completeness status, significantly improving the completeness rate and on-time delivery rate of coal mining equipment production.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a dynamic and flexible completeness control method for coal mining equipment based on multi-source data fusion, according to an embodiment of this application. Figure 2 This is a structural diagram of a dynamic flexible completeness control system for coal mining equipment based on multi-source data fusion, according to an embodiment of this application. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] This application proposes a method and system for dynamic flexible completeness control of coal mining equipment based on multi-source data fusion. The method includes: collecting data from multiple heterogeneous production management systems, standardizing and spatiotemporally aligning the data to obtain fused multi-source data; determining a dynamic completeness index for coal mining equipment based on the fused multi-source data, wherein the dynamic completeness index is weighted by material availability, time matching degree, quality compliance, and flexibility adjustment degree; constructing a three-dimensional flexible constraint space of material-process-capacity based on material substitution relationships, process route elasticity relationships, and capacity elasticity relationships; when the dynamic completeness index is lower than a preset threshold, relaxing the constraints in the three-dimensional flexible constraint space step by step according to priority, searching for the optimal completeness solution, and monitoring the changes in the dynamic completeness index in real time to form a closed-loop control. The technical solution proposed in this application realizes real-time quantitative evaluation and systematic flexible adjustment of the completeness status, significantly improving the completeness rate and on-time delivery rate of coal mining equipment production.

[0029] The following description, with reference to the accompanying drawings, illustrates a method and system for dynamic flexible completeness control of coal mining equipment based on multi-source data fusion, according to an embodiment of this application.

[0030] Example 1 Figure 1 This is a flowchart illustrating a dynamic and flexible completeness control method for coal mining equipment based on multi-source data fusion, according to an embodiment of this application. Figure 1 As shown, the method includes: Step 1: Collect data from multiple heterogeneous production management systems, standardize and align the data in time and space to obtain fused multi-source data; In this embodiment of the disclosure, the collection of data from multiple heterogeneous production management systems includes: Collect the number of material types, the required quantity of each material, the total required quantity of each material, and the current inventory of each material from the bill of materials in the ERP system. Collect the substitution relationships and compatibility scores among various materials in the PLM system, as well as the material substitution flexibility determined based on the substitution relationships; Collect the work-in-process quantity, estimated completion time of work-in-process, demand time of each material, and alternative process routes and their compatibility scores for each process in the MES system, and determine the flexibility of the process route based on the alternative process routes. Collect the in-transit quantity and estimated arrival time of each material in the SCM system; Collect the capacity elasticity coefficient of the devices in the IoT device, and determine the capacity flexibility based on the capacity elasticity coefficient; Collect historical defect rates of various materials and quality inspection results of each batch of materials from the QMS system.

[0031] It should be noted that data was collected from multiple heterogeneous production management systems. These systems each carry business information from different dimensions of the production process, and their data inherently differ in format, semantics, time base, and spatial scale. By standardizing the collected heterogeneous data, the differences in data format and encoding rule conflicts between different systems are eliminated, enabling all types of data to be expressed under a unified standard. Furthermore, spatiotemporal alignment is implemented to synchronize the data recorded by different systems on the time axis and spatial coordinates, ensuring that the data is comparable and consistent in both time and spatial dimensions.

[0032] Specifically, raw data is obtained from six core systems. First, BOM structure and inventory data are collected from the ERP system. BOM data includes the bill of materials required for product assembly and its hierarchical relationships, while inventory data reflects the actual stock of various materials in the current warehouse. Second, design BOM and substitution relationship data are collected from the PLM system. The design BOM provides the complete material composition of the product design, while substitution relationship data records the substitutability information between materials, including substitute material codes, compatibility scores, and applicable conditions. Third, work-in-process and process progress data are collected from the MES system. Work-in-process data reflects the quantity and status of materials currently being processed or in transit, while process progress data records the completion status of each production process, estimated completion time, and actual progress deviation. Finally, in-transit materials and supplier data are collected from the SCM system. In-transit materials data includes the quantity of materials that have been shipped but not yet arrived, their estimated arrival time, and logistics tracking information, while supplier data provides the supplier's supply capacity, delivery history, and credit rating. Next, equipment status and capacity data are collected from IoT devices. Equipment status data includes real-time status such as equipment operation, shutdown, and maintenance, while capacity data reflects the current processing capacity, load rate, and available working hours. Finally, quality inspection and defect rate data are collected from the QMS system. Quality inspection data includes the inspection results, qualified quantity, and reasons for non-conformity for each batch of materials, while defect rate data provides quality reliability indicators for various materials based on historical statistics. After the above six types of data are collected, they are used as input for subsequent standardization processing. Through unified data format conversion and timestamp alignment, a consistent data foundation is provided for the subsequent calculation of the dynamic kitting index.

[0033] Through the above-mentioned multi-source data collection and fusion, this invention realizes real-time perception and unified management of data across the entire coal mining equipment production chain, significantly improving data consistency and availability, and laying a solid data foundation for subsequent continuous quantitative evaluation and flexible path optimization.

[0034] Step 2: Determine the dynamic completeness index of coal mining equipment based on the fused multi-source data, wherein the dynamic completeness index is composed of a weighted average of material availability, time matching degree, quality compliance degree and flexibility adjustment degree. In this embodiment of the disclosure, the dynamic completeness index of the coal mining equipment is calculated as follows:

[0035] In the formula, It is a dynamic homogeneity index. For material availability, For time matching degree, For quality compliance, For flexible adjustment, This refers to the weighting coefficient corresponding to material availability. The weighting coefficients corresponding to the time matching degree. These are the weighting coefficients corresponding to the quality compliance. This represents the weighting coefficient corresponding to the degree of flexibility.

[0036] It should be noted that the weighting coefficients α, β, γ, and δ are adaptively determined by combining the entropy weighting method with the analytic hierarchy process, and α+β+γ+δ=1; The dynamic homogeneity index ranges from [0,1]. When DKI ≥ 0.85, it is determined to be high homogeneity; when 0.70 ≤ DKI < 0.85, it is determined to be medium homogeneity; and when DKI < 0.70, it is determined to be low homogeneity.

[0037] Furthermore, the formula for calculating the availability of the materials is as follows:

[0038] In the formula, For the current inventory level, The quantity of work-in-process. This refers to the amount of materials in transit. This represents the total demand.

[0039] The formula for calculating the time matching degree is as follows:

[0040] In the formula, For the estimated arrival time of materials, For material demand time points, This is a time buffer period.

[0041] The formula for calculating the quality compliance is as follows:

[0042] In the formula, For the number of material types, For the first Historical defect rate of similar materials For the first The required quantity of this type of material.

[0043] The formula for calculating the degree of flexibility is as follows:

[0044] In the formula, For material substitution flexibility, For the sake of process flexibility, For production capacity flexibility, Weighting coefficients for material substitution flexibility Weighting coefficients for process route flexibility This is the weighting coefficient for capacity flexibility.

[0045] In this embodiment of the disclosure, the quality compliance is integrated with the QMS system. When the QMS system detects that any batch of materials fails inspection, it automatically updates the historical defect rate of that batch of materials to 1.0 and recalculates the dynamic kitting index.

[0046] It should be noted that this method upgrades the traditional binary kitting determination to a continuous quantitative assessment. By integrating multi-source heterogeneous data, it comprehensively measures kitting status from four dimensions: material quantity, time matching, quality reliability, and flexibility. Material availability reflects the coverage of existing inventory, work-in-process, and in-transit materials to the total demand; time matching measures the deviation between the expected arrival time of materials and the demand time; quality compliance is based on the historical defect rate of each material, using the probability multiplication principle to assess overall quality reliability; and flexibility quantifies the ability to compensate for missing parts through flexible means such as material substitution, process route adjustments, and capacity expansion. The four dimensions are weighted and summed to obtain a dynamic kitting index, and the weights can be adaptively adjusted according to actual needs. As one implementation method, the dynamic kitting index can be calculated using the formula... The calculation is α+β+γ+δ=1, and each dimension sub-formula can be quantified based on the specific definitions of material availability, time matching degree, quality compliance degree and flexibility adjustment degree, thereby realizing continuous, real-time and multi-dimensional evaluation of the alignment status.

[0047] This step overcomes the shortcomings of traditional binary judgment in distinguishing intermediate states by constructing a continuously quantified dynamic completeness index, significantly improving the accuracy and timeliness of completeness assessment, and providing a refined quantitative basis for subsequent flexible adjustment and optimization decisions, thereby effectively supporting proactive control and rapid response in the coal mining equipment production process.

[0048] Specifically, firstly, when calculating material availability, this value reflects the degree to which available materials across the entire supply chain meet demand. When the numerator exceeds the denominator, it is truncated to 1 to avoid the impact of excessive inventory. Secondly, when calculating time matching, this value is set to 1 when materials arrive on time, decreasing linearly with increasing deviation, while penalizing both early and late arrivals to reflect the just-in-time principle. Thirdly, when calculating quality compliance, this value uses the probability multiplication principle to reflect the reliability of the series system. If the defect rate of a certain type of material is updated to 1.0 due to batch inspection failure, the overall quality compliance decreases significantly. Then, when calculating flexibility, this value quantifies the ability to compensate for shortages through material substitution, process route adjustments, and capacity expansion. Finally, the calculation results of the above four dimensions are used as input, weighted according to preset parameters. , , , (satisfy We perform a weighted summation to obtain the dynamic homogeneity index. The index has a value range of [0,1], which upgrades the traditional binary matching judgment to a continuous quantitative assessment, providing a precise quantitative basis for subsequent matching judgment and flexible adjustment.

[0049] In this embodiment, the weight coefficients of the Dynamic Homogenization Index (DKI) are adaptively determined by combining the entropy weight method and the analytic hierarchy process (AHP) to achieve dynamic adaptation to different production scenarios and material characteristics. Specifically, historical data sequences of each dimension indicator are extracted, including observations of material availability, time matching degree, quality compliance, and flexibility adjustment degree within multiple time windows, as input for the entropy weight method. The entropy weight method automatically calculates objective weights based on the dispersion of each indicator's data: for indicators with large data fluctuations, their information entropy is smaller, and their entropy weight is larger, indicating that the indicator has a stronger ability to distinguish homogenization assessment at the current stage; conversely, indicators with smaller data fluctuations are assigned smaller weights. This process requires no manual intervention and can reflect changes in production status in real time. Simultaneously, the analytic hierarchy process (AHP) is introduced to incorporate expert experience and construct a judgment matrix based on the technological characteristics of different coal mining equipment products. For example, for critical safety components such as hydraulic support columns, the comparison scale of quality compliance relative to other dimensions is increased in the judgment matrix, thus assigning a higher subjective weight to the quality dimension in subsequent weight synthesis; for orders with urgent delivery dates, the comparison scale of time matching is increased; and for cost-sensitive orders, the comparison scale of flexibility is increased. AHP obtains the subjective weights of each dimension by calculating the normalized eigenvector corresponding to the largest eigenvalue of the judgment matrix. Subsequently, the objective weights obtained by the entropy weight method are linearly combined with the subjective weights obtained by AHP. The combination coefficient can be dynamically adjusted according to actual needs, for example, increasing the proportion of subjective weights in the initial stage or when data is insufficient, and increasing the proportion of objective weights after sufficient data accumulation. The final output weights of the four dimensions α, β, γ, and δ satisfy the normalization constraint α+β+γ+δ=1, and each weight is within the interval [0,1]. This adaptive weight determination method enables DKI calculation to automatically adjust the contribution of each dimension based on real-time changes in production data and the domain knowledge of process experts, thereby more accurately reflecting the completeness status under different scenarios.

[0050] By using the adaptive weight determination method described above, this embodiment can flexibly adjust the importance of each dimension in DKI according to the dynamic changes in production data and expert experience, avoiding the evaluation bias caused by fixed weights, and significantly improving the accuracy and adaptability of completeness assessment. It is especially suitable for scenarios where material properties and production rhythms change frequently under the multi-variety, small-batch, and customized production mode of coal mining equipment.

[0051] In this embodiment, the calculation result of the Dynamic Fit Index (DKI) is used for refined fit status determination. Specifically, based on the DKI value calculated in step 2, the system compares it with a preset determination threshold. The DKI value is a continuous quantitative index with a value range of [0,1], and its value directly reflects the fit degree of materials or components. When the calculated DKI value is greater than or equal to 0.85, the system determines the current fit status as high fit, meaning that the materials are in good condition in terms of quantity, time, quality, and flexibility, and can proceed to the final assembly stage. When the DKI value is greater than or equal to 0.70 and less than 0.85, the system determines it as medium fit, indicating that there is a certain risk in the fit status, and a flexibility adjustment process needs to be initiated. When the DKI value is less than 0.70, the system determines it as low fit, indicating that the fit status is seriously insufficient, and immediate emergency intervention is required. This determination process is the trigger condition for constraint relaxation and flexible path optimization in step 4. Specifically, when the DKI value is below 0.85, the system automatically initiates the constraint relaxation process and selects the corresponding relaxation strategy and optimization objective based on the determination result (medium or low homogeneity). Through this hierarchical determination mechanism, this invention upgrades the traditional binary homogeneity determination to continuous quantitative evaluation, providing a precise decision-making basis for subsequent flexible adjustments.

[0052] This specific implementation compares the continuous values ​​of the Dynamic Homologousness Index (DKI) with preset thresholds in a hierarchical manner, thereby achieving a refined and quantitative assessment of the homologousness status. This avoids the ambiguity of traditional binary judgment (0 / 1) and provides clear and quantitative triggering conditions and target guidance for subsequent flexible adjustments and optimization decisions, significantly improving the accuracy and initiative of homologousness control.

[0053] The weights of the Dynamic Kitting Index (DKI) are not fixed but adaptively adjusted based on material type and order priority. This adaptive adjustment process is achieved by combining the entropy weight method and the Analytic Hierarchy Process (AHP). First, real-time data for each material in four dimensions—material availability, time matching, quality compliance, and flexibility—are extracted and used as input for the entropy weight method. The entropy weight method automatically calculates objective weights based on the dispersion of data for each dimension. The greater the dispersion, the stronger the dimension's ability to distinguish the evaluation results, and the higher its weight. Simultaneously, an AHP judgment matrix is ​​constructed. This matrix, based on the experience of coal mining equipment process experts, compares the importance of dimensions pairwise under different material types and order priorities. For example, for critical safety components such as hydraulic support columns, the importance of the quality compliance dimension is significantly higher than other dimensions; for orders with urgent delivery dates, the importance of the time matching dimension is increased; and for cost-sensitive orders, the importance of the flexibility dimension is enhanced. By linearly combining the objective weights obtained from the entropy weight method with the subjective weights obtained from the AHP, the final dimension weight coefficients, namely α, β, γ, and δ in the main formula, are obtained. This combination process ensures that the weights reflect both the statistical characteristics of the data itself and incorporate prior knowledge from domain experts. In one possible implementation, for critical safety components, the quality weight γ is increased to 0.35, and the time weight β is decreased to 0.20; for urgent delivery orders, the time weight β is increased to 0.40, and the quality weight γ is decreased to 0.20. The adjusted weight coefficients satisfy the constraint α+β+γ+δ=1 and are used as input for subsequent DKI calculations. This adaptive adjustment mechanism enables DKI assessments to dynamically respond to changes in material properties and production priorities, outputting quantitative results that better align with actual control needs.

[0054] This specific implementation introduces a weight adaptive adjustment mechanism that combines the entropy weight method with the analytic hierarchy process (AHP), enabling the evaluation of the dynamic kitting index (DKI) to be dynamically optimized based on material type and order priority. This significantly improves the accuracy and adaptability of the evaluation results, providing a more precise decision-making basis for subsequent flexible path optimization.

[0055] In this embodiment, the calculation of quality compliance in step 2 is deeply integrated with the QMS system to achieve real-time perception and dynamic adjustment of material quality status. Specifically, the calculation of quality compliance (Quality) is based on the probability multiplication principle, and its formula is as follows: ,in Represents the historical defect rate of material type i. This represents the quantity demanded for this type of material. The historical defect rate. This is not a static value, but rather dynamically maintained by the QMS system based on the inspection results of each batch. When the QMS system completes the inspection of a batch of materials and determines it to be non-conforming, the corresponding historical defect rate for that batch of materials is recorded. It was automatically updated to version 1.0. The input for this update was the quality inspection results data from the QMS system, and the processing involved the system automatically updating the defect rate parameter for the corresponding material category. Assigning a value of 1.0 outputs the updated defect rate, which will be immediately used in subsequent quality compliance calculations. Because... Updated to 1.0 in the formula In this process, the calculation result of this item will become 0, which in turn will cause the product of the overall quality compliance (Quality) to become zero. This mechanism reflects the reliability principle of a "serial system," that is, the failure of any critical material to meet quality standards will lead to the failure of the overall kitting. For example, for Class A critical safety components in coal mining equipment, such as hydraulic support columns, the defect rate is usually required to be ≤0.001 and full inspection is required. If the QMS system detects a fatal defect in a batch of columns, the system will automatically remove all but one batch of columns from that batch. The update to version 1.0 marks the batch of materials as unavailable in terms of quality, triggering subsequent alternative search or constraint relaxation processes. Through this integration, quality compliance calculations can reflect the inspection results of the QMS system in real time, ensuring that the assessment of the Dynamic Kitting Index (DKI) is always based on the latest quality status data, thereby improving the accuracy and timeliness of the assessment.

[0056] This specific implementation integrates the quality compliance index with the QMS system in real time, enabling immediate perception and quantitative feedback of quality defects. This significantly improves the response speed of the Dynamic Kitting Index (DKI) to quality risks, avoids assessment bias caused by the use of outdated quality data, and enhances the initiative and accuracy of kitting control.

[0057] Step 3: Based on material substitution relationships, process route flexibility relationships, and capacity flexibility relationships, construct a three-dimensional flexible constraint space of materials-process-capacity; It should be noted that in the process of completeness management of coal mining equipment, traditional rigid constraints are difficult to adapt to the dynamic changes in the production site. Therefore, this step constructs a three-dimensional flexible constraint space of materials, processes, and capacity to replace the traditional single, fixed constraint system. Specifically, this flexible constraint space is constructed based on the substitution relationships between materials, the flexible selection relationships of process routes, and the scalability relationships of capacity, transforming the originally discrete, either-or constraints into a continuous, adjustable feasible domain. In the material dimension, by defining the substitution compatibility between materials, the set of available materials can be expanded according to substitution relationships when standard materials are unavailable. In the process dimension, by defining alternative process routes and their compatibility for each process, alternative feasible process paths can be switched when the standard process is blocked. In the capacity dimension, by defining the flexible expansion range of capacity, capacity can be temporarily increased through overtime, outsourcing, etc., when the standard capacity is insufficient. These three dimensions are interconnected and work together to form a three-dimensional flexible constraint space. Each point in this space represents a feasible material-process-capacity combination scheme, thus expanding the single feasible solution under traditional rigid constraints into a continuous region containing multiple feasible solutions. As one implementation method, this flexible constraint space can be quantitatively characterized by a material substitution matrix, a process route elasticity matrix, and a capacity elasticity coefficient. The material substitution matrix records the substitution relationships and compatibility scores between different materials; the process route elasticity matrix records the alternative routes and compatibility scores for each process; and the capacity elasticity coefficient defines the scalability factor of capacity. Together, these three dimensions constitute the mathematical expression of the three-dimensional flexible constraint space, providing a computable feasible region basis for subsequent constraint relaxation and path optimization.

[0058] By constructing this three-dimensional flexible constraint space, this step transforms the rigid constraints in traditional homogeneity control into a flexible feasible domain, significantly improving the system's adaptability to production anomalies and resource fluctuations. This provides a structured spatial foundation for subsequent flexible path search under multi-objective optimization, thereby effectively expanding the search range of feasible solutions and reducing the risk that the overall solution may become infeasible due to the failure of a single constraint.

[0059] Specifically, the concrete implementation method for constructing the three-dimensional flexible constraint space of materials-process-capacity is as follows. First, a material substitution matrix is ​​constructed based on the material substitution relationship. The input to this matrix comes from the design BOM and material substitution relationship data stored in the PLM system, and the historical substitution compatibility assessment results recorded in the QMS system. The processing includes: for each material i and each substitute material j, a comprehensive score is generated based on dimensions such as design parameter compatibility, material performance matching degree, and historical usage pass rate. Simultaneously record the substitution cost coefficient. (This represents the ratio of the unit cost after substitution to the original material cost). When At that time, this substitution relationship was incorporated into the material substitution matrix. The rows of the matrix represent the original materials, the columns represent the substitute materials, and the elements are... A binary tuple. The output is a material substitution matrix. This matrix extends traditional rigid material constraints (i.e., the requirement to use a specified material) to flexible constraints, allowing alternative materials to be enabled when compatibility requirements are met.

[0060] Secondly, a process route flexibility matrix is ​​constructed based on the process route flexibility relationship. The input comes from standard process route data stored in the MES system, alternative process solutions recorded in the PLM system, and compatibility scores of the alternative routes by the process department. The processing actions include: for each process or component, listing all feasible alternative process routes; comparing each alternative route with the standard route; and calculating a compatibility score based on dimensions such as processing accuracy, equipment capability, time efficiency, and quality stability. Record the cost coefficient of the route. and time coefficient .when At that time, the alternative route was incorporated into the process route flexibility matrix. The rows of the matrix represent the standard route, and the columns represent alternative routes. The elements are... Triplet. The output is the process route flexibility matrix. This matrix extends the traditional single process route constraint into a flexible constraint with multiple optional routes.

[0061] Secondly, a capacity elasticity coefficient is constructed based on the capacity elasticity relationship. The inputs come from equipment operation data collected by IoT devices, capacity statistics from the MES system, and overtime and outsourcing rules set by the production planning department. Processing actions include: for each production resource (such as equipment, production line, or work group), calculating the capacity elasticity coefficient based on its historical maximum capacity utilization rate, available overtime hours, and available capacity from outsourcing suppliers. Its value range is ,in Typically, a value of 1.5 is used (meaning that production capacity can be expanded to 1.5 times the standard capacity through overtime and outsourcing). Simultaneously, the cost increment coefficient corresponding to different flexibility measures is recorded. and time increment coefficient The output is the capacity elasticity coefficient. With its associated cost-time parameter, this coefficient extends the traditional fixed capacity constraint into a flexible capacity constraint.

[0062] Finally, the above three outputs—the material substitution matrix—are combined. Flexibility matrix of process route Production capacity elasticity coefficient —The three dimensions of the three-dimensional flexible constraint space together constitute a multi-dimensional flexible feasible region. Within this feasible region, the constraints in each dimension are no longer rigid boundaries, but rather flexible boundaries with an adjustable range, providing a quantifiable relaxation space for constraint relaxation and path optimization in subsequent step 4. Specifically, the flexibility in the material dimension is determined by… The definition of substitution relationships in the text, and the flexibility in the process dimension are determined by... The definition of alternative routes in the text, and the flexibility in terms of production capacity are... Definition. The three dimensions are independent yet synergistic, jointly expanding the traditional rigid constraint space into a flexible constraint space, enabling the search for homogeneous solutions to be carried out in a wider feasible domain.

[0063] The three-dimensional flexible constraint space constructed in the above manner provides a structured input for subsequent constraint relaxation and multi-objective optimization, ensuring that alternative materials, alternative process routes and capacity flexibility measures can be systematically activated when standard solutions are not feasible, thereby significantly improving the feasibility and optimization space of the complete solution.

[0064] Step 4: When the dynamic fitting index is lower than the preset threshold, the constraints in the three-dimensional flexible constraint space are relaxed step by step according to priority, the optimal fitting scheme is searched, and the change of the dynamic fitting index is monitored in real time to form a closed-loop control.

[0065] In this embodiment of the disclosure, the step of relaxing the constraints in the three-dimensional flexible constraint space in order of priority includes: The dynamic homogeneity index is compared with a preset threshold. When the dynamic completeness index is lower than the preset threshold, the constraints are relaxed step by step in the priority order of standard scheme, material substitution relaxation, process route relaxation, capacity flexibility relaxation and comprehensive relaxation. After each relaxation, the dynamic completeness index is recalculated. If the dynamic completeness index reaches or exceeds the preset threshold, the relaxation is stopped and the current scheme is output. When the dynamic homogeneity index is greater than or equal to a preset threshold, the enabled flexible resources are released.

[0066] Furthermore, the stepwise relaxation constraint follows the principle of minimum cost priority, giving priority to relaxation schemes with high compatibility scores and small cost increments; The activation condition for material substitution relaxation is: the compatibility score of the substitute material is greater than or equal to the first preset value. The condition for enabling process route relaxation is that the compatibility score of the alternative process route is greater than or equal to the second preset value.

[0067] In this embodiment of the disclosure, the search for the optimal homogeneous scheme includes: The NSGA-II multi-objective optimization algorithm is adopted to search for the Pareto optimal set of homogenization schemes in the flexible constraint space with the optimization objectives of maximizing the dynamic homogenization index, minimizing the cost increment, and minimizing the time delay.

[0068] In this embodiment of the disclosure, the closed-loop control includes: Set a fluctuation threshold and monitor the changes in the dynamic homogeneity index in real time; When the fluctuation of the dynamic homogeneity index exceeds the fluctuation threshold, a reassessment is automatically triggered, and the current dynamic homogeneity index is recalculated. Based on the comparison between the recalculated dynamic homogeneity index and the preset threshold, the system adaptively selects to initiate constraint relaxation or release the enabled flexible resources. The results of the optimization plan will be fed back to update the material status and process progress.

[0069] It should be noted that when the dynamic fitability index is lower than a preset threshold, it indicates that the current fitability state cannot meet the assembly requirements. In this case, within the constructed three-dimensional flexible constraint space of materials, processes, and capacity, the constraints of each dimension are relaxed step-by-step according to a preset priority order to search for the optimal solution that meets the fitability requirements. Specifically, this method first evaluates the standard solution (i.e., the initial constraint without any relaxation). If its dynamic fitability index is still lower than the threshold, it sequentially attempts to relax material substitution constraints, process route constraints, and capacity elasticity constraints until a feasible solution is found that makes the dynamic fitability index reach or exceed the threshold. After each level of relaxation, the dynamic fitability index is recalculated, and the principle of minimum cost priority is followed, prioritizing relaxation measures with high compatibility and small cost increments. Simultaneously, this method monitors changes in the dynamic fitability index in real time. When a significant fluctuation in the index is detected or it falls below the threshold again, a new round of constraint relaxation and solution search is automatically triggered, thus forming a closed-loop control process of "monitoring-evaluation-optimization-execution". As one implementation method, a multi-objective optimization algorithm (such as NSGA) can be used in the flexible constraint space. II) Solve for the Pareto optimal solution set to simultaneously consider multiple objectives such as homogeneity, cost increment, and time delay.

[0070] This step, by introducing a priority-driven, stepwise constraint relaxation mechanism, extends traditional rigid constraints into a flexible feasible domain, realizing a shift from passive response to proactive optimization, and significantly improving the feasibility and adaptability of kitting solutions. At the same time, real-time monitoring and closed-loop feedback ensure the continuous controllability of kitting status, enabling rapid response to dynamic changes in the production process, thereby effectively improving kitting rate and on-time delivery rate.

[0071] Specifically, firstly, the standard solution is implemented as the first level of relaxation. This standard solution directly uses the standard materials, standard process routes, and rated capacity configuration specified in the design BOM, without enabling any substitution or flexibility measures. The system calculates the Dynamic Homogeneity Index (DKI) based on this solution. If the DKI value reaches or exceeds a preset threshold (e.g., 0.85), it is determined to have high homogeneity, and the standard solution is directly output as the optimal homogeneity solution. If the DKI value is lower than this threshold, the second level of relaxation, namely material substitution relaxation, is triggered. At this time, the material substitution matrix is ​​retrieved, which predefines the substitution relationships and compatibility scores between various materials. The system prioritizes selecting substitute materials with a compatibility score not lower than a first preset value (e.g., 0.85) to replace missing or low-availability materials, and recalculates the DKI value based on the new material combination. If the DKI value is still lower than the threshold after material substitution, the third level of relaxation, namely process route relaxation, is entered. The process route flexibility matrix is ​​retrieved, which defines the alternative process routes for each process and their compatibility scores. The system activates alternative process routes with a compatibility score no lower than the second preset value (e.g., 0.70). This could involve adjusting robotic welding to manual welding and adding non-destructive testing, or adopting outsourced processing routes to increase work-in-process inventory or shorten processing cycles. After applying the new process route, the system recalculates the DKI value. If the threshold is still not met, it enters the fourth level of relaxation, namely capacity elasticity relaxation. Based on the capacity elasticity coefficient, the system expands capacity by increasing overtime hours and enabling outsourced capacity, for example, increasing the capacity elasticity coefficient to 1.5 times to increase the amount of available materials or shorten the production cycle, and then recalculates the DKI value. If none of the above four levels of relaxation achieve the DKI value target, it enters the fifth level of comprehensive relaxation. At this level, the system combines various flexible methods such as material substitution, process route adjustment, and capacity elasticity expansion to search for the optimal solution in a three-dimensional flexible constraint space. Each level of relaxation follows the principle of minimum cost priority, prioritizing relaxation schemes with high compatibility, small cost increments, and short time delays. The DKI value is recalculated after each relaxation, and relaxation stops once the DKI value reaches the threshold, outputting the current solution. This step-by-step relaxation process ensures that while meeting kitting requirements, the impact on product quality, production costs, and production cycle are kept to a minimum.

[0072] This specific implementation method achieves a smooth transition from standard solutions to comprehensive flexible solutions through a five-level progressive constraint relaxation mechanism. It avoids the drawbacks of directly adopting high-cost, high-risk alternative solutions. While ensuring the completeness rate, it effectively controls cost increments and time delays, and improves the flexibility and economy of completeness management.

[0073] In this embodiment, when the dynamic homogeneity index is lower than a preset threshold, the specific implementation of searching for the optimal homogeneous solution is as follows: based on the three-dimensional flexible constraint space of material-process-capacity constructed in step 3, the NSGA-II multi-objective optimization algorithm is used for solving. Specifically, the input of this algorithm comes from the dynamic homogeneity index of each material or component calculated in step 2 and its four-dimensional component values, as well as the feasible solution domain defined by the flexible constraint space constructed in step 3. This feasible solution domain is jointly defined by the material substitution matrix, the process route elasticity matrix, and the capacity elasticity coefficient. The processing steps include: First, initializing a population containing multiple individuals (each representing a homogeneous solution) within a flexible constraint space, with the three optimization objectives of maximizing the dynamic homogeneity index, minimizing cost increment, and minimizing time delay; second, stratifying and ranking the individuals in the population through non-dominated sorting and crowding calculation to identify non-dominated solutions on the Pareto front; then, generating a progeny population through selection, crossover, and mutation operations, merging the parent and progeny generations, and performing non-dominated sorting and crowding calculation again to retain elite individuals; repeating the above iterative process until a preset number of generations or convergence condition is reached. The output is a Pareto optimal homogeneous solution set, where each solution corresponds to a specific set of material substitutions, process route adjustments, and capacity flexibility configurations. Within this set, it is impossible to improve one optimization objective without lowering another. Decision-makers can select the most suitable homogeneous solution from this Pareto front to implement based on actual production needs, such as prioritizing on-time delivery or cost control. This multi-objective optimization process ensures that cost and time are systematically balanced while meeting the homogeneity requirement.

[0074] This specific implementation introduces the NSGA-II multi-objective optimization algorithm, which can efficiently search for a set of Pareto optimal solutions that take into account homogeneity, cost, and delivery time in a complex flexible constraint space. This provides decision-makers with a quantitative basis for trade-offs and significantly improves the scientificity and flexibility of homogeneous solution optimization.

[0075] In this embodiment, the specific implementation of real-time monitoring of dynamic uniformity index changes and the formation of closed-loop control in step 4 is as follows. First, the system sets a fluctuation threshold, which is used to measure the change amplitude of the dynamic uniformity index between adjacent time points. Specifically, this fluctuation threshold is denoted as δ, and its value can be configured according to the actual fluctuation characteristics of the coal mining equipment production process, for example, set to 0.05 or 0.1, to balance monitoring sensitivity and false alarm rate. During the monitoring process, the data fusion layer continuously obtains the latest production data from systems such as ERP, MES, SCM, and IoT, including inventory updates, work-in-process reporting, logistics status of materials in transit, equipment capacity changes, and quality inspection results. Based on these real-time updated data, the core calculation layer recalculates the dynamic uniformity index DKI(t) at the current time t according to a preset frequency (e.g., per shift or per hour). Subsequently, the anomaly detection link compares the current DKI(t) with the previous DKI(t-1) and calculates the absolute value of the difference |DKI(t)-DKI(t-1)|. When the absolute value of the difference exceeds the set fluctuation threshold δ, it is determined that the kitting status has changed significantly, and the system automatically triggers a reassessment process. This reassessment process includes: recalculating the DKI values ​​of all relevant materials or components based on the latest data, and determining whether constraint relaxation and flexible path optimization need to be initiated based on the recalculation results. Specifically, if the DKI value decreases after reassessment and falls below a preset threshold (e.g., 0.85), the constraint relaxation and flexible path optimization process in step 4 is automatically initiated, relaxing material, process, or capacity constraints level by level according to priority, and searching for new optimal kitting solutions; if the DKI value increases after reassessment and exceeds the preset threshold, the release of activated flexible resources is considered to reduce costs. The execution results of the optimization plan, such as the implementation status of material substitution or the progress of outsourcing, are fed back to the data fusion layer to update material status and process progress, thereby providing an accurate data foundation for the next round of monitoring, forming a complete closed-loop control process of "monitoring-assessment-optimization-execution".

[0076] This specific implementation achieves rapid response to changes in the alignment status by setting a fluctuation threshold and automatically triggering reassessment based on real-time data. It transforms the traditional passive post-event processing into proactive real-time control, significantly improving the system's adaptive adjustment capability to production anomalies and avoiding decision-making delays caused by information lag.

[0077] In this embodiment, the closed-loop control involved in step 4 is implemented in the following way. First, the system continuously acquires the latest production data from ERP, PLM, MES, SCM, IoT, and QMS systems through the data fusion layer, and recalculates the dynamic completeness index DKI based on this data at a set frequency. This calculation process is based on the master formula. The weighting coefficient , , , The DKI value is adaptively determined by combining the entropy weight method and the analytic hierarchy process. The system monitors changes in the DKI value in real time and sets a fluctuation threshold. When the absolute value of the difference between the current DKI value and the previous DKI value is detected to be greater than the fluctuation threshold, When the DKI value falls below a preset threshold (e.g., 0.85), the system automatically triggers a constraint relaxation and flexible path optimization process. This process relaxes constraints in the three-dimensional flexible constraint space step by step according to preset priorities, sequentially attempting material substitution relaxation, process route relaxation, capacity flexibility relaxation, and comprehensive relaxation. After each relaxation level, the DKI value is recalculated until the DKI value reaches or exceeds the preset threshold, thereby searching for the optimal kitting solution. Subsequently, the system feeds back the execution results of this optimization solution, such as the specific implementation details of material substitution, process route adjustment records, or actual data on capacity expansion, to the data fusion layer. The data fusion layer updates data such as material status, work-in-process progress, and process route based on this feedback information, providing the latest data foundation for the next round of DKI calculation and kitting assessment, thus forming a complete closed-loop control process of "monitoring-assessment-optimization-execution-feedback".

[0078] This specific implementation method, by establishing a closed-loop mechanism of real-time monitoring and automatic triggering, realizes the transformation from passive response to proactive control, significantly improves the dynamic response capability and automation level of completeness management, and ensures the continuous optimization of completeness solutions and the real-time consistency of production data.

[0079] In this embodiment, when the Dynamic Homogeneity Index (DKI) is lower than a preset threshold (e.g., 0.85), the system initiates a constraint relaxation and flexible path optimization process. This process strictly follows the principle of minimum cost priority, prioritizing relaxation schemes with high compatibility and small cost increments. Specifically, the system first obtains the DKI value under the current standard scheme. If the value is lower than 0.85, the standard scheme is deemed infeasible, and the system enters the first level of relaxation. In the first level of relaxation, the material substitution matrix M_sub is read, which defines the substitution relationships between materials and their compatibility scores. The system iterates through all missing materials, filters out substitute materials with a compatibility score greater than or equal to 0.85, and calculates the cost increment of each substitution scheme. For example, when replacing imported seals with domestic seals, the cost coefficient is 0.85, and the compatibility score is 0.85. The system prioritizes the substitution scheme with the highest compatibility score and the smallest cost increment, and recalculates the DKI value based on this scheme. If the recalculated DKI value reaches 0.85 or higher, the current scheme is output and relaxation stops; if it is still lower than 0.85, the system enters the second level of relaxation. In the second level of relaxation, the system reads the process route flexibility matrix R_route, which defines the alternative process routes for each process and their compatibility scores. The system filters out alternative process routes with a compatibility score greater than or equal to 0.70. For example, replacing robotic welding with manual welding plus UT inspection has a compatibility score of 0.75 and a cost coefficient of 0.70. The system also prioritizes alternative routes with high compatibility scores and small cost increments, and recalculates the DKI value accordingly. If the threshold is still not met, the system proceeds to the third level of relaxation. The system reads the capacity flexibility coefficient η_capacity, which defines the range of capacity expansion through overtime or outsourcing. For example, the capacity flexibility coefficient for 1-2 hours of overtime is 1.20, and the cost coefficient is 1.30. The system prioritizes the capacity expansion method with the smallest cost increment and recalculates the DKI value. If none of the single-level relaxations meet the threshold, the system proceeds to the fourth level of comprehensive relaxation. The system combines multiple flexibility measures mentioned above, such as simultaneously performing material substitution and process route adjustments, and re-evaluates the DKI value. Throughout the relaxation process, the system consistently adheres to the principle of minimizing cost, prioritizing relaxation schemes with high compatibility and minimal cost increments to ensure that the impact on product quality and production costs is kept to a minimum while meeting the kitting requirements. The DKI value is recalculated after each relaxation stage, and relaxation stops and the current optimal kitting scheme is output once the threshold is met.

[0080] This specific implementation method follows the principle of prioritizing minimum cost and systematically screens solutions with high compatibility and small cost increments during the constraint relaxation process. It avoids the waste of computing resources and decision delays caused by blindly trying all possible solutions, significantly improves the efficiency of the search for complete solutions and the economy of the results, and ensures that while meeting the completeness requirements, the cost and risk of production adjustments are controlled to the lowest level.

[0081] In this embodiment, when the dynamic homogeneity index falls below a preset threshold, the system initiates a constraint relaxation and flexible path optimization process. This process follows a preset priority order, relaxing constraints in the three-dimensional flexible constraint space level by level. Specifically, the system first starts with the standard solution, i.e., without any relaxation, directly calculating the dynamic homogeneity index based on the current data. If the index is still below the preset threshold, it enters the first level of relaxation, i.e., material substitution relaxation. At this time, the material substitution matrix is ​​invoked to filter out substitute materials with a compatibility score of not less than 0.85, and the bill of materials is updated accordingly. Then, the dynamic homogeneity index is recalculated. If the updated index still does not reach the threshold, it enters the second level of relaxation, i.e., process route relaxation. The system invokes the process route flexibility matrix, enables alternative process routes with a compatibility score of not less than 0.70, and updates the process progress and time parameters accordingly, recalculating the dynamic homogeneity index again. If the conditions are still not met, it enters the third level of relaxation, i.e., capacity flexibility relaxation. The system expands capacity to 1.5 times based on the capacity elasticity coefficient (e.g., through overtime or outsourcing), updates capacity constraints accordingly, and then recalculates the dynamic matching index. If all single-level relaxations fail to meet the index target, the system enters the fourth level of comprehensive relaxation, combining the aforementioned flexibility measures, and finally recalculates the dynamic matching index. After each level of relaxation is completed, the system immediately recalculates the index value of the current solution based on the updated constraints and data, according to the main formula of the dynamic matching index. This calculation process receives updated material availability, time matching, quality compliance, and flexibility adjustment as inputs from the previous level of relaxation, and obtains a new dynamic matching index through weighted summation. The system compares this new index with a preset threshold. If the new index is greater than or equal to the threshold, the system determines that the current solution meets the matching requirements, immediately stops all subsequent relaxation operations, and outputs the current solution as the optimal matching solution. If the new index is still lower than the threshold, the system automatically enters the next level of relaxation, repeating the above calculation and judgment process until a solution that meets the conditions is found or all relaxation levels have been tried. This iterative mechanism of gradual relaxation, real-time computation, and real-time decision-making ensures rapid convergence to a feasible solution with minimal cost.

[0082] This specific implementation introduces an immediate recalculation and threshold determination mechanism after each level of relaxation, which enables the automatic termination of the constraint relaxation process, avoids unnecessary consumption of computing resources, significantly improves the efficiency of solution search, and ensures that the final output solution meets the homogeneity requirement while keeping its cost increment within a minimum range.

[0083] The following uses the ZY12000 / 28 / 58 hydraulic support manufactured by a coal mining equipment company as an example to illustrate the specific implementation process of this invention. This type of hydraulic support contains more than 2,000 parts, involves more than 30 suppliers, and has a production cycle of 45-60 days.

[0084] 1. Multi-source data acquisition and fusion. Obtain BOM structure and inventory data from the ERP system; design BOM and material substitution relationships from the PLM system; work-in-process status and process progress from the MES system; in-transit materials and supplier information from the SCM system; equipment status and capacity data from IoT devices; and quality inspection results from the QMS system. Standardize the above data.

[0085] 2: DKI Calculation. Taking the hydraulic support column as an example, this component is a Class A critical safety part. Calculate Availability: Inventory S = 5 units, Work-in-Process W = 8 units (MES reports indicate heat treatment has been completed), In-Transit T = 3 units (GPS tracking shows estimated arrival in 2 days), Demand D = 20 units, Availability = min(1, (5+8+3) / 20) = 0.80. Calculate Timeliness: Estimated arrival time. =Day 25, Required Time =Day 28, buffer period Δt = 4 days, Timeliness = max(0, 1 - |25 - 28| / 4) = 0.25. Calculate Quality: Column defect rate q = 0.001, required quantity n = 20, Quality = (1 - 0.001)^20 ≈ 0.98. Calculate Flexibility: No substitute material for the column ( =0), but there are outsourcing processing routes ( =0.85), overtime production is possible ( =0.70) Flexibility = 0.2 × 0 + 0.4 × 0.85 + 0.4 × 0.70 = 0.62. Since the column is a critical safety component, the weights are adjusted to α = 0.35, β = 0.20, γ = 0.35, δ = 0.10, DKI = 0.35 × 0.80 + 0.20 × 0.25 + 0.35 × 0.98 + 0.10 × 0.62 = 0.28 + 0.05 + 0.343 + 0.062 = 0.735.

[0086] 3: Fit determination. DKI=0.735, which is in the range of 0.70≤DKI<0.85, and is determined to be "medium fit", requiring the initiation of flexible adjustment.

[0087] 4: Constraint Relaxation. Level 1 Standard Solution DKI=0.735<0.85, not satisfied. Level 2 Material Substitution Relaxation: No substitute material with compatibility ≥0.85 for the column, skipped. Level 3 Process Route Relaxation: Activate the outsourcing processing route (compatibility 0.85), increasing the number of in-process units (W) from 8 to 12, and improving Availability to min(1, (5+12+3) / 20)=1.0. Recalculate DKI=0.35×1.0+0.20×0.25+0.35×0.98+0.10×0.62=0.855≥0.85, meeting the requirements.

[0088] 5. Dynamic monitoring. The system continuously monitors the progress of outsourced processing of the columns and the status of materials in transit. If delays in outsourced processing cause a drop in DKI (Distribution Key Indicator), it automatically triggers a reassessment and adjustment of the plan.

[0089] The dynamic and flexible completeness control method for coal mining equipment based on multi-source data fusion proposed in this embodiment has the following advantages: (1) Real-time performance improvement: By integrating data from six heterogeneous systems, including ERP, PLM, MES, SCM, IoT, and QMS, real-time perception of the completeness status is achieved, reducing the time lag for completeness determination from 3-7 days to hours. (2) Improved quantification accuracy: By constructing the dynamic completeness index DKI, the traditional 0 / 1 binary judgment is upgraded to [0,1] continuous quantification evaluation, which can accurately distinguish different states such as "complete completeness", "near completeness", and "serious missing parts", providing a more refined basis for decision-making; (3) Flexible adjustment capability: By constructing a three-dimensional flexible constraint space of materials-process-capacity and a five-level constraint relaxation rule, when the standard solution is not feasible, it systematically provides alternative materials, alternative process routes and capacity flexibility solutions, which significantly improves the completeness rate; (4) Closed-loop management: Through dynamic monitoring and feedback closed-loop mechanism, continuous tracking and automatic optimization of the complete set status are realized, transforming from passive response to active management; (5) Industry adaptability: The weight coefficients, threshold parameters and substitution rules in the method can be parameterized according to the characteristics of different coal mining equipment products, which has good industry adaptability and scalability.

[0090] In summary, the dynamic and flexible completeness control method for coal mining equipment based on multi-source data fusion proposed in this embodiment realizes real-time quantitative assessment and systematic flexible adjustment of the completeness status, significantly improving the completeness rate and on-time delivery rate of coal mining equipment production.

[0091] Example 2 Figure 2This is a structural diagram of a dynamic flexible completeness control system for coal mining equipment based on multi-source data fusion, according to an embodiment of this application. Figure 2 As shown, the system includes: The data acquisition module 100 is used to acquire data from multiple heterogeneous production management systems, perform standardization processing and spatiotemporal alignment on the data, and obtain fused multi-source data. The collection of data from multiple heterogeneous production management systems includes: Collect the number of material types, the required quantity of each material, the total required quantity of each material, and the current inventory of each material from the bill of materials in the ERP system. Collect the substitution relationships and compatibility scores among various materials in the PLM system, as well as the material substitution flexibility determined based on the substitution relationships; Collect the work-in-process quantity, estimated completion time of work-in-process, demand time of each material, and alternative process routes and their compatibility scores for each process in the MES system, and determine the flexibility of the process route based on the alternative process routes. Collect the in-transit quantity and estimated arrival time of each material in the SCM system; Collect the capacity elasticity coefficient of the devices in the IoT device, and determine the capacity flexibility based on the capacity elasticity coefficient; Collect historical defect rates of various materials and quality inspection results of each batch of materials from the QMS system.

[0092] The determination module 200 is used to determine the dynamic completeness index of coal mining equipment based on the fused multi-source data, wherein the dynamic completeness index is composed of a weighted average of material availability, time matching degree, quality compliance degree and flexibility adjustment degree. The formula for calculating the dynamic completeness index of the coal mining equipment is as follows:

[0093] In the formula, It is a dynamic homogeneity index. For material availability, For time matching degree, For quality compliance, For flexible adjustment, This refers to the weighting coefficient corresponding to material availability. The weighting coefficients corresponding to the time matching degree. These are the weighting coefficients corresponding to the quality compliance. This represents the weighting coefficient corresponding to the degree of flexibility.

[0094] The formula for calculating the availability of the materials is as follows:

[0095] In the formula, For the current inventory level, The quantity of work-in-process. This refers to the amount of materials in transit. This represents the total demand.

[0096] The formula for calculating the time matching degree is as follows:

[0097] In the formula, For the estimated arrival time of materials, For material demand time points, This is a time buffer period.

[0098] The formula for calculating the quality compliance is as follows:

[0099] In the formula, For the number of material types, For the first Historical defect rate of similar materials For the first The required quantity of this type of material.

[0100] The formula for calculating the degree of flexibility is as follows:

[0101] In the formula, For material substitution flexibility, For the sake of process flexibility, For production capacity flexibility, Weighting coefficients for material substitution flexibility Weighting coefficients for process route flexibility This is the weighting coefficient for capacity flexibility.

[0102] The weighting coefficients α, β, γ, and δ are adaptively determined by combining the entropy weighting method with the analytic hierarchy process, and α+β+γ+δ=1; The dynamic homogeneity index ranges from [0,1]. When DKI ≥ 0.85, it is determined to be high homogeneity; when 0.70 ≤ DKI < 0.85, it is determined to be medium homogeneity; and when DKI < 0.70, it is determined to be low homogeneity.

[0103] It should be noted that the quality compliance is integrated with the QMS system. When the QMS system detects that any batch of materials fails inspection, it automatically updates the historical defect rate of that batch of materials to 1.0 and recalculates the dynamic kitting index.

[0104] Module 300 is used to construct a three-dimensional flexible constraint space of material-process-capacity based on material substitution relationships, process route flexibility relationships, and capacity flexibility relationships. The control module 400 is used to relax the constraints in the three-dimensional flexible constraint space step by step according to priority when the dynamic homogeneity index is lower than a preset threshold, search for the optimal homogeneity scheme, and monitor the changes in the dynamic homogeneity index in real time to form a closed-loop control.

[0105] In this embodiment of the disclosure, the control module 400 is further configured to: The dynamic homogeneity index is compared with a preset threshold. When the dynamic completeness index is lower than the preset threshold, the constraints are relaxed step by step in the priority order of standard scheme, material substitution relaxation, process route relaxation, capacity flexibility relaxation and comprehensive relaxation. After each relaxation, the dynamic completeness index is recalculated. If the dynamic completeness index reaches or exceeds the preset threshold, the relaxation is stopped and the current scheme is output. When the dynamic homogeneity index is greater than or equal to a preset threshold, the enabled flexible resources are released.

[0106] The stepwise relaxation constraint follows the principle of minimum cost priority, giving priority to relaxation schemes with high compatibility scores and small cost increments; The activation condition for material substitution relaxation is: the compatibility score of the substitute material is greater than or equal to the first preset value. The condition for enabling process route relaxation is that the compatibility score of the alternative process route is greater than or equal to the second preset value.

[0107] Furthermore, the control module 400 is also used for: The NSGA-II multi-objective optimization algorithm is adopted to search for the Pareto optimal set of homogenization schemes in the flexible constraint space with the optimization objectives of maximizing the dynamic homogenization index, minimizing the cost increment, and minimizing the time delay.

[0108] Furthermore, the control module 400 is also used for: Set a fluctuation threshold and monitor the changes in the dynamic homogeneity index in real time; When the fluctuation of the dynamic homogeneity index exceeds the fluctuation threshold, a reassessment is automatically triggered, and the current dynamic homogeneity index is recalculated. Based on the comparison between the recalculated dynamic homogeneity index and the preset threshold, the system adaptively selects to initiate constraint relaxation or release the enabled flexible resources. The results of the optimization plan will be fed back to update the material status and process progress.

[0109] In summary, the dynamic flexible completeness control system for coal mining equipment based on multi-source data fusion proposed in this embodiment realizes real-time quantitative assessment and systematic flexible adjustment of the completeness status, significantly improving the completeness rate and on-time delivery rate of coal mining equipment production.

[0110] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0111] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0112] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for dynamic flexible completeness control of coal mining equipment based on multi-source data fusion, characterized in that, The method includes: Data from multiple heterogeneous production management systems is collected, and the data is standardized and spatiotemporally aligned to obtain fused multi-source data. The dynamic completeness index of coal mining equipment is determined based on the fused multi-source data, wherein the dynamic completeness index is composed of a weighted average of material availability, time matching degree, quality compliance degree and flexibility adjustment degree. Based on material substitution relationships, process route flexibility relationships, and capacity flexibility relationships, a three-dimensional flexible constraint space of material-process-capacity is constructed. When the dynamic fitting index is lower than a preset threshold, the constraints in the three-dimensional flexible constraint space are relaxed step by step according to priority, the optimal fitting scheme is searched, and the change of the dynamic fitting index is monitored in real time to form a closed-loop control.

2. The method as described in claim 1, characterized in that, The data collected from multiple heterogeneous production management systems includes: Collect the number of material types, the required quantity of each material, the total required quantity of each material, and the current inventory of each material from the bill of materials in the ERP system. Collect the substitution relationships and compatibility scores among various materials in the PLM system, as well as the material substitution flexibility determined based on the substitution relationships; Collect the work-in-process quantity, estimated completion time of work-in-process, demand time of each material, and alternative process routes and their compatibility scores for each process in the MES system, and determine the flexibility of the process route based on the alternative process routes. Collect the in-transit quantity and estimated arrival time of each material in the SCM system; Collect the capacity elasticity coefficient of the devices in the IoT device, and determine the capacity flexibility based on the capacity elasticity coefficient; Collect historical defect rates of various materials and quality inspection results of each batch of materials from the QMS system.

3. The method as described in claim 2, characterized in that, The formula for calculating the dynamic completeness index of the coal mining equipment is as follows: In the formula, It is a dynamic homogeneity index. For material availability, For time matching degree, For quality compliance, For flexible adjustment, This refers to the weighting coefficient corresponding to material availability. The weighting coefficients corresponding to the time matching degree. These are the weighting coefficients corresponding to the quality compliance. This represents the weighting coefficient corresponding to the degree of flexibility.

4. The method as described in claim 3, characterized in that, The formula for calculating the availability of the materials is as follows: In the formula, For the current inventory level, The quantity of work-in-process. This refers to the amount of materials in transit. This represents the total demand. The formula for calculating the time matching degree is as follows: In the formula, For the estimated arrival time of materials, For material demand time points, This is a time buffer period. The formula for calculating the quality compliance is as follows: In the formula, For the number of material types, For the first Historical defect rate of similar materials For the first The required quantity of this type of material. The formula for calculating the degree of flexibility is as follows: In the formula, For material substitution flexibility, For the sake of process flexibility, For production capacity flexibility, Weighting coefficients for material substitution flexibility Weighting coefficients for process route flexibility This is the weighting coefficient for capacity flexibility.

5. The method as described in claim 4, characterized in that, The weighting coefficients α, β, γ, and δ are adaptively determined by combining the entropy weighting method with the analytic hierarchy process, and α+β+γ+δ=1; The dynamic homogeneity index ranges from [0,1]. When DKI ≥ 0.85, it is determined to be high homogeneity; when 0.70 ≤ DKI < 0.85, it is determined to be medium homogeneity; and when DKI < 0.70, it is determined to be low homogeneity.

6. The method as described in claim 5, characterized in that, The stepwise relaxation of constraints in the three-dimensional flexible constraint space according to priority includes: The dynamic homogeneity index is compared with a preset threshold. When the dynamic completeness index is lower than the preset threshold, the constraints are relaxed step by step in the priority order of standard scheme, material substitution relaxation, process route relaxation, capacity flexibility relaxation and comprehensive relaxation. After each relaxation, the dynamic completeness index is recalculated. If the dynamic completeness index reaches or exceeds the preset threshold, the relaxation is stopped and the current scheme is output. When the dynamic homogeneity index is greater than or equal to a preset threshold, the enabled flexible resources are released.

7. The method as described in claim 6, characterized in that, The stepwise relaxation constraint follows the principle of minimum cost priority, and prioritizes relaxation schemes with high compatibility scores and small cost increments. The activation condition for material substitution relaxation is: the compatibility score of the substitute material is greater than or equal to the first preset value. The condition for enabling process route relaxation is that the compatibility score of the alternative process route is greater than or equal to the second preset value.

8. The method as described in claim 1, characterized in that, The search for the optimal homogeneous solution includes: The NSGA-II multi-objective optimization algorithm is adopted to search for the Pareto optimal set of homogenization schemes in the flexible constraint space with the optimization objectives of maximizing the dynamic homogenization index, minimizing the cost increment, and minimizing the time delay.

9. The method as described in claim 1, characterized in that, The closed-loop management includes: Set a fluctuation threshold and monitor the changes in the dynamic homogeneity index in real time; When the fluctuation of the dynamic homogeneity index exceeds the fluctuation threshold, a reassessment is automatically triggered, and the current dynamic homogeneity index is recalculated. Based on the comparison between the recalculated dynamic homogeneity index and the preset threshold, the system adaptively selects to initiate constraint relaxation or release the enabled flexible resources. The results of the optimization plan will be fed back to update the material status and process progress.

10. The method as described in claim 3, characterized in that, The quality compliance index is integrated with the QMS system. When the QMS system detects that any batch of materials fails inspection, it automatically updates the historical defect rate of that batch of materials to 1.0 and recalculates the dynamic kitting index.

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