Self-adaptive subpackage optimization method and system for waste and old materials, terminal and medium
By using an adaptive subcontracting optimization method, the problems of material difference identification and economic constraints in the disposal of composite material equipment were solved, achieving efficient and accurate subcontracting of waste materials and improving the transparency and digitalization of enterprise management.
Patent Information
- Application Number
- CN202511766279.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-04-14
AI Technical Summary
Existing technologies struggle to differentiate the material differences and disposal value of composite material equipment, and lack a dynamic balance mechanism between economic constraints and classification consistency, making it difficult for subcontracting results to balance disposal efficiency and profit optimization.
An adaptive subcontracting optimization method is adopted. By obtaining detailed data of the disposal plan, a subcontracting strategy model is established. Merging operations are carried out based on material consistency and economic constraints, the estimated total amount is calculated, and the subcontracting structure is optimized through adaptive learning feedback.
It enables precise classification and resource integration of composite material equipment, improves the accuracy and economy of the subcontracting process, and enhances disposal efficiency and revenue optimization capabilities.
Smart Images
Figure CN121860113A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of material subcontracting technology, specifically relating to an adaptive subcontracting optimization method, system, terminal, and medium for waste materials. Background Technology
[0002] In recent years, with the increasing frequency of industrial equipment upgrades and infrastructure renovations, the scale and complexity of waste material disposal have continued to rise. Various enterprises generally need to classify, combine, and subcontract waste materials according to planned lists in order to conduct centralized bidding or entrusted disposal in the processes of equipment scrapping, infrastructure decommissioning, and disposal of residual engineering materials.
[0003] Traditional material handling primarily relies on manual review and experience-based judgment to make subcontracting decisions. Related information is often exported from ERP or ECP systems and then manually processed. With the expansion of data volume, this manual approach is no longer adequate to meet current requirements for efficiency, economy, and compliance. Therefore, some existing technologies have attempted to achieve semi-automatic subcontracting through material coding rules, classification dictionaries, or templated grouping algorithms.
[0004] However, most of these methods are still limited to the static logic level, relying solely on fixed parameters and manually set thresholds to perform subcontracting operations. For complex materials (especially composite materials such as motors and transformers), existing systems often struggle to distinguish the material differences and disposal value of each component; at the same time, they lack a dynamic balancing mechanism between economic constraints and classification consistency, making it difficult for subcontracting results to simultaneously achieve disposal efficiency and profit optimization. Summary of the Invention
[0005] This invention addresses the problems in the prior art by providing an adaptive subcontracting optimization method, system, terminal, and medium for waste materials. This solves the problem in the prior art that it is difficult to distinguish the material differences and disposal value of each component. At the same time, it also solves the problem that the lack of a dynamic balance mechanism between economic constraints and classification consistency makes it difficult to balance disposal efficiency and profit optimization in the subcontracting results.
[0006] The technical solution adopted in this invention is as follows: Firstly, this application provides an adaptive subcontracting optimization method for waste materials, which includes the following steps: Step S1: Obtain detailed disposal plan data, parse the material code, quantity, unit price and material information of each material from the detailed disposal plan data, perform structured pre-grouping of the detailed disposal plan according to the preset material coding rules, and mark materials containing multiple materials as composite material objects. Step S2: Establish a subcontracting strategy model based on the overall objectives of the disposal plan. The subcontracting strategy model aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontracting amounts. It sets economic constraints, classification consistency constraints, and resource integration constraints. Step S3: Under the constraints of the subcontracting strategy model, perform material consistency judgment on the disposal plan details and virtual sub-details, and merge materials with the same material or high similarity into the same subcontract according to the material consistency rules to realize resource integration across material packages; Step S4: Based on the material consistency processing results, calculate the estimated total amount of each subcontract under the economic constraints of the subcontracting strategy model; The subcontracting structure is optimized based on the amount threshold calculated by the subcontracting strategy model: when the subcontracting amount is lower than the amount threshold, the subcontracting is merged with adjacent or similar subcontracting to form an optimized subcontracting set that meets the global economic constraints. Step S5: Output the optimized subcontracting details and strategy evaluation data to complete the adaptive subcontracting optimization of the waste material disposal plan.
[0007] Furthermore, in step S2, the subcontracting strategy model includes an optimization objective function and a constraint function. The optimization objective function aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontract amounts. It is expressed as:
[0008] Where F is the comprehensive optimization objective function; R is the disposal revenue function, representing the total revenue after the disposal plan is executed; and N is the subcontracting quantity function, representing the total number of subcontracts generated. Let be the subcontract amount balance function, representing the variance of each subcontract amount; This is the profit balance coefficient; These are the weighting coefficients for a balanced distribution of revenue, quantity, and amount. The subcontracting strategy model is solved under the following constraints:
[0009] in, This is an economic constraint function, representing the deviation between the amount of each subcontract and the overall disposal budget; A monetary threshold is used to limit economic constraints. The minimum profit condition; For classification consistency constraint function, it represents the degree to which similar materials are dispersed; This is a material similarity threshold used to limit classification consistency constraints. The scope of the combined package judgment; Let be the resource integration constraint function, representing the concentration rate of resources of the same material; This is the maximum number of subcontractors; These are the threshold parameters for the corresponding constraints; By solving for the optimal solution of the objective function F under constraints, we obtain the parameters for dynamic calculation. The strategy model.
[0010] Furthermore, the optimization parameters Adaptive updates are performed based on historical disposal plan data and model iteration results, satisfying the following update function:
[0011] in, Let i be the i-th optimization parameter; This is the learning rate coefficient; For the objective function F with respect to parameters The partial derivative; t is the iteration number; By iteratively solving the problem, the objective function F converges to its optimal value, resulting in a dynamically adjusted set of parameters. The sub-package strategy model is adaptively optimized and real-time parameter correction is achieved based on the dynamically adjusted parameter set.
[0012] Furthermore, in step S3, the classification consistency constraint is based on the subcontracting strategy model. Calculate the material similarity between the detailed disposal plan and the virtual sub-details. The similarity calculation formula is as follows:
[0013] in, The overall material similarity between item i and item j; Indicates the consistency of the material categories of the two materials; Indicates the similarity of chemical components; Indicates physical similarity; The weighting coefficients for the three similarities satisfy the following conditions: ; when When this happens, the i-th item and the j-th item are grouped into the same sub-package; when At the same time, the i-th item and the j-th item remain separate.
[0014] Furthermore, in step S4, the economic constraints of the subcontracting strategy model are... Below, calculate the estimated total amount for each subcontractor after material consistency processing. And based on the profit balance coefficient With amount threshold Perform a dynamic economic evaluation, whereby the economic optimization objective function is:
[0015] Where E is the overall economic evaluation value; This represents the estimated total amount for the k-th subcontract; For dynamic amount thresholds; This is the revenue balancing coefficient, used to adjust the degree of impact of deviation in subcontract amounts; n is the number of subcontracts. when At that time, the system calculates the economic benefit deviation ratio. ;when When the time is right, a merge operation is triggered, merging the k-th sub-package with adjacent or similar sub-packages and updating the new amount set. and recalculate ; By iteratively adjusting the loop, E is made to fit the constraints. The process converges to obtain an optimized set of sub-packages that meets global economic constraints.
[0016] Furthermore, step S5 includes: Output the optimized subcontract details, which include subcontract number, subcontract material list, subcontract amount and material type.
[0017] Furthermore, for the composite material object marked in step S1, a virtual disassembly process is performed, which includes: Based on a pre-set disassembly template or actual measurement data, determine the material proportion of each component. With corresponding weight coefficients Calculate the classification and disposal value after virtual dismantling. Value of the whole machine ,in:
[0018]
[0019] in, The mass percentage of material of type k in the composite material; This is the material recycling correction factor; Q represents the unit price of the corresponding material; Q represents the total weight of the machine. is the unit price for the entire machine; m represents the type and quantity of composite materials; Calculate the profit-gain ratio of virtual dismantling :
[0020] in To reduce disassembly and sorting costs; when At that time, the composite material object is split into multiple virtual sub-details according to its material; when When this happens, the composite material object is assigned to the corresponding group according to its primary material. This is the threshold for the profit / gain ratio.
[0021] Secondly, this application provides an adaptive subcontracting optimization system for waste materials, used to implement the adaptive subcontracting optimization method for waste materials as described in the first aspect. The system includes... The data acquisition and preprocessing module is used to acquire detailed disposal plan data, and parse the material code, quantity, unit price and material information of each material from the detailed disposal plan data. The disposal plan details are pre-grouped in a structured manner according to the preset material coding rules, and materials containing multiple materials are marked as composite material objects. The strategy modeling module is used to establish a subcontracting strategy model based on the overall objectives of the disposal plan. The subcontracting strategy model aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontracting amounts. It sets economic constraints, classification consistency constraints, and resource integration constraints. The material consistency processing module is used to perform material consistency judgment on the disposal plan details and virtual sub-details under the constraints of the subcontracting strategy model, and merge materials with the same material or high similarity into the same subcontract according to the material consistency rules, so as to realize resource integration across material packages. The economic optimization module is used to calculate the estimated total amount of each subcontract based on the material consistency processing results and under the economic constraints of the subcontracting strategy model, and to optimize the subcontracting structure according to the amount threshold calculated by the subcontracting strategy model. When the subcontract amount is lower than the amount threshold, the subcontract is merged with adjacent or similar subcontracts to form an optimized subcontracting set that meets the global economic constraints. The results output module is used to output the optimized subcontracting details and strategy evaluation data to complete the adaptive subcontracting optimization of the waste material disposal plan.
[0022] Thirdly, this application provides a terminal, including: Memory, used to store the adaptive subcontracting optimization program for waste materials; The processor is used to implement the steps of the adaptive subcontracting optimization method for waste materials as described in the first aspect when executing the waste material adaptive subcontracting optimization device.
[0023] Fourthly, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the adaptive subcontracting optimization method for waste materials as described in the first aspect.
[0024] As can be seen from the above technical solutions, the advantages of the present invention are: The adaptive subcontracting optimization method for waste materials proposed in this application introduces algorithmic and dynamic constraint mechanisms in multiple stages, including the parsing of detailed disposal plan data, strategy modeling, material consistency judgment, economic optimization, and adaptive learning feedback. This achieves a shift from manual experience-based decision-making to data-driven optimization. Compared to existing subcontracting methods that rely on manual or static rules, this method achieves significant improvements in operability, intelligence, and economy.
[0025] By automatically parsing material codes, quantities, unit prices, and material information from the disposal plan details and completing structured pre-grouping according to coding rules, the process of manual item-by-item screening and sorting is replaced. This achieves automatic modeling and standardized input of disposal data, providing a unified and reliable data foundation for subsequent subcontracting strategies and improving the accuracy and consistency of information in the subcontracting process.
[0026] The established subcontracting strategy model integrates disposal revenue, subcontracting quantity, and monetary balance into a unified optimization objective system. It solves the problem under three constraints: economic efficiency, classification consistency, and resource integration, forming an intelligent decision-making model with multi-objective trade-off capabilities. This strategy model can dynamically adjust weight parameters based on disposal plan characteristics and historical data, solving the problems of rigid rules and poor adaptability in existing technologies, and enabling the subcontracting strategy to possess self-learning and adaptive capabilities.
[0027] By calculating the material similarity between materials and performing package consolidation operations based on thresholds, cross-material type resource integration is achieved, effectively avoiding the problem of similar materials being dispersed into different packages or dissimilar materials being incorrectly merged. This process introduces multi-dimensional feature comparison of material category, chemical composition, and physical properties at the algorithm level, making the package consolidation results more consistent with physical properties and recycling logic, significantly improving classification accuracy and traceability.
[0028] An economic dynamic optimization mechanism based on a threshold amount is introduced. Under the constraints of the strategy model, the estimated total amount of each subcontract is calculated in real time. When the subcontract amount is lower than the threshold, a consolidation operation is automatically performed, realizing adaptive adjustment of the subcontract structure. Unlike manually set fixed amount limits, the threshold of this method is dynamically generated by the model based on the revenue balance coefficient and disposal plan characteristics, thereby ensuring the optimal balance between the number of subcontracts, the amount scale, and the expected revenue at the global level.
[0029] By outputting optimized subcontracting details and strategy evaluation data, the system can intuitively display the amount, revenue, and material structure of each subcontract, providing data support for subsequent bidding and resource recycling, realizing a closed loop from subcontracting decision-making to plan execution, and improving the transparency and digitalization level of enterprise waste material management.
[0030] The virtual dismantling mechanism for composite materials automatically identifies the optimal disposal path for composite materials by establishing a profit-gain ratio judgment model. When the percentage of the dismantled, categorized disposal value minus the dismantling cost exceeds a threshold for the overall disposal value of the equipment, the system automatically performs virtual dismantling; otherwise, it assigns the material based on its primary composition, avoiding profit deviations caused by human experience. This mechanism makes the disposal value of complex equipment quantifiable and controllable, balancing profit maximization with operational feasibility. Attached Figure Description
[0031] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0032] Figure 1 This is a flowchart illustrating the steps of the adaptive subcontracting optimization method for waste materials in this embodiment. Detailed Implementation
[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0034] Please see Figure 1 As shown, this invention provides an adaptive subcontracting optimization method for waste materials, comprising the following steps: Step S1: Obtain detailed disposal plan data, parse the material code, quantity, unit price and material information of each material from the detailed disposal plan data, perform structured pre-grouping of the detailed disposal plan according to the preset material coding rules, and mark materials containing multiple materials as composite material objects. In practical implementation, the system can connect to the enterprise's ERP or ECP platform via an information management system to automatically extract detailed disposal plan documents. The system parses the data based on the structural characteristics of the material codes to identify material types and specifications. To achieve structured pre-grouping, classification rules for material codes can be preset in the system database. For example, the prefix or median of the code can be used to identify the material category, purpose, or material type, thereby achieving automatic aggregation by category. For materials with multiple components or material properties detected in the material field (such as motors, transformers, composite pipe fittings, etc.), the system automatically marks them as composite material objects for subsequent virtual dismantling processing. In a specific embodiment, a batch of disposal plans includes metal components, insulating parts, and electronic equipment. The system identifies motor components containing copper, steel, and rubber through coding and automatically marks them as composite material objects for subsequent dismantling value assessment.
[0035] Step S2: Establish a subcontracting strategy model based on the overall objectives of the disposal plan. The subcontracting strategy model aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontracting amounts. It sets economic constraints, classification consistency constraints, and resource integration constraints. In practice, the system automatically constructs a strategy model based on the overall scale of the disposal plan, material distribution, and historical transaction data. It generates optimization target weights by statistically analyzing transaction amounts, subcontracting quantities, and bidding success rates in historical subcontracting records. Economic constraints prevent subcontracting amounts from being too low, leading to failed bids; classification consistency constraints limit the dispersion of similar materials; and resource integration constraints ensure the concentration of the same type of materials in subcontracting. After the model is established, the system dynamically adjusts the optimization direction based on the characteristics of the disposal plan. For example, it increases the weight of subcontracting balance in scenarios involving large quantities of homogeneous materials and increases the weight of classification consistency in scenarios with mixed categories. In one embodiment, when the total disposal plan amount is large and there are many categories, the system automatically determines the appropriate range of subcontracting quantities and the target amount range through the strategy model, thereby achieving automated generation of subcontracting schemes.
[0036] Step S3: Under the constraints of the subcontracting strategy model, perform material consistency judgment on the disposal plan details and virtual sub-details, and merge materials with the same material or high similarity into the same subcontract according to the material consistency rules to realize resource integration across material packages; During implementation, the system retrieves material attribute parameters stored in the material database, including features such as chemical composition, density, hardness, and electrical conductivity, and generates a material similarity matrix through comparison. If the similarity exceeds a set threshold, the system automatically merges the related materials. This process is applicable not only to materials with the same code but also to materials with similar physical properties but different codes, thereby achieving a higher level of resource integration. In one specific embodiment, the scrapping plan includes steel supports and iron channel steel. After material analysis shows that their similarity reaches the threshold requirement, the system automatically merges them into the same subcontract, improving resource utilization and reducing the number of subcontracts.
[0037] Step S4: Based on the material consistency processing results, calculate the estimated total amount of each subcontract under the economic constraints of the subcontracting strategy model; The subcontracting structure is optimized based on the amount threshold calculated by the subcontracting strategy model: when the subcontracting amount is lower than the amount threshold, the subcontracting is merged with adjacent or similar subcontracting to form an optimized subcontracting set that meets the global economic constraints. In practice, the system calculates the estimated amount for each subcontract based on the aforementioned material consistency processing results. The total subcontract amount is obtained by considering the unit price and quantity of materials, as well as market fluctuation factors. If the subcontract amount does not reach a dynamic threshold, the system will automatically merge adjacent subcontracts based on material similarity or price similarity. This optimization process can be performed in multiple iterations until the amounts of all subcontracts meet the economic constraints. For example, in a disposal project, the system found in the first calculation that two subcontract amounts were both below the set threshold and automatically merged them into a more competitive subcontract, resulting in an overall increase in revenue of approximately 8% after optimization.
[0038] Step S5: Output the optimized subcontracting details and strategy evaluation data to complete the adaptive subcontracting optimization of the waste material disposal plan.
[0039] During implementation, the system generates a structured output file from the optimized subcontracting plan, including information such as subcontract number, material name and code, quantity, amount, and material category, and simultaneously generates strategy evaluation data. This strategy evaluation data displays the amount distribution, quantity, balance index, and revenue assessment results for each subcontract, and can be exported as a report for management review. In one embodiment, after completing the subcontracting optimization, the system generates a PDF report file, which managers can directly use for approval and bidding preparation, saving approximately 70% of the work time compared to manual methods.
[0040] In some embodiments, in step S2, the subcontracting strategy model includes an optimization objective function and a constraint function. The optimization objective function aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontract amounts. It is expressed as:
[0041] Where F is the comprehensive optimization objective function; R is the disposal revenue function, representing the total revenue after the disposal plan is executed; and N is the subcontracting quantity function, representing the total number of subcontracts generated. Let be the subcontract amount balance function, representing the variance of each subcontract amount; This is the profit balance coefficient; These are the weighting coefficients for a balanced distribution of revenue, quantity, and amount. The subcontracting strategy model is solved under the following constraints:
[0042] in, This is an economic constraint function, representing the deviation between the amount of each subcontract and the overall disposal budget; A monetary threshold is used to limit economic constraints. The minimum profit condition; For classification consistency constraint function, it represents the degree to which similar materials are dispersed; This is a material similarity threshold used to limit classification consistency constraints. The scope of the combined package judgment; Let be the resource integration constraint function, representing the concentration rate of resources of the same material; This is the maximum number of subcontractors; These are the threshold parameters for the corresponding constraints; By solving for the optimal solution of the objective function F under constraints, we obtain the parameters for dynamic calculation. The strategy model.
[0043] During implementation, the system uses disposal revenue, subcontracting quantity, and monetary balance as three key optimization objectives. By retrieving historical bidding data and disposal results from the database, the system determines the weight of each element and adjusts the proportions according to different scenarios. In centralized enterprise waste disposal scenarios, the system prioritizes maximizing revenue, while in routine, scattered material disposal scenarios, it emphasizes minimizing the number of subcontractors and maintaining monetary balance. In one embodiment, the system uses a weighted solution method to automatically generate the optimal solution, reducing the number of subcontractors by approximately 15%, increasing total revenue by approximately 10%, and controlling the difference in subcontracting amounts within a set range.
[0044] In some embodiments, the optimization parameters Adaptive updates are performed based on historical disposal plan data and model iteration results, satisfying the following update function:
[0045] in, Let i be the i-th optimization parameter; This is the learning rate coefficient; For the objective function F with respect to parameters The partial derivative; t is the iteration number; By iteratively solving the problem, the objective function F converges to its optimal value, resulting in a dynamically adjusted set of parameters. The sub-package strategy model is adaptively optimized and real-time parameter correction is achieved based on the dynamically adjusted parameter set.
[0046] During implementation, the system updates the optimization parameters to the database after each round of subcontracting results are generated. By analyzing the historical plan's revenue performance, subcontracting quantity, and failure rate, the system automatically calculates the parameter gradient and performs corrections, thus using the new parameter values in the next model solution. This self-learning mechanism enables the system to continuously improve prediction accuracy and strategy adaptability. For example, in a company's three-month continuous disposal project, the system automatically reduced the weight of the amount threshold during the third round of optimization, resulting in a more balanced distribution of subcontracting amounts and a significant improvement in disposal revenue.
[0047] In some embodiments, in step S3, the classification consistency constraint based on the sub-packaging strategy model is... Calculate the material similarity between the detailed disposal plan and the virtual sub-details. The similarity calculation formula is as follows:
[0048] in, The overall material similarity between item i and item j; Indicates the consistency of the material categories of the two materials; Indicates the similarity of chemical components; Indicates physical similarity; The weighting coefficients for the three similarities satisfy the following conditions: ; when When this happens, the i-th item and the j-th item are grouped into the same sub-package; when At the same time, the i-th item and the j-th item remain separate.
[0049] During implementation, the system calls a pre-set material parameter table in the database for feature comparison, calculating similarity based on material type, chemical composition, and physical properties. When the overall similarity reaches a threshold, the system determines the material to be bundled. This algorithm is applicable to various material types, including metals, plastics, and composite materials, effectively avoiding inaccuracies in manual classification. For example, in a construction equipment decommissioning project, the system detected an overall similarity of 0.87 between stainless steel pipes and galvanized steel pipes, exceeding the threshold of 0.8, and automatically bundled them together, reducing the need for manual verification.
[0050] In some embodiments, in step S4, under the economic constraints of the subcontracting strategy model Below, calculate the estimated total amount for each subcontractor after material consistency processing. And based on the profit balance coefficient With amount threshold Perform a dynamic economic evaluation, whereby the economic optimization objective function is:
[0051] Where E is the overall economic evaluation value; This represents the estimated total amount for the k-th subcontract; For dynamic amount thresholds; This is the revenue balancing coefficient, used to adjust the degree of impact of deviation in subcontract amounts; n is the number of subcontracts. when At that time, the system calculates the economic benefit deviation ratio. ;when When the time is right, a merge operation is triggered, merging the k-th sub-package with adjacent or similar sub-packages and updating the new amount set. and recalculate ; By iteratively adjusting the loop, E is made to fit the constraints. The process converges to obtain an optimized set of sub-packages that meets global economic constraints.
[0052] During implementation, the system calculates the total amount of each subcontract based on the material consistency results and introduces a revenue balancing coefficient into the model to control fluctuations in subcontract amounts. If the amount of some subcontracts is significantly lower than the average, the system automatically triggers a consolidation operation, updates and recalculates the overall economic indicators. In one embodiment, the system reduces the number of subcontracts below the amount threshold by 50% through two rounds of iterative adjustments, and improves the optimized economic evaluation value by 20%, effectively improving the overall revenue balance.
[0053] In some embodiments, step S5 includes: Output the optimized subcontract details, which include subcontract number, subcontract material list, subcontract amount and material type.
[0054] During implementation, the system displays the subcontracting results through a visual interface and generates corresponding output files. These output files include details of materials, amounts, material categories, and optimization identifiers for each subcontract, facilitating review and filing. The system can also output Excel, PDF, or JSON files as needed to enable data integration between different business systems. For example, in the company's annual disposal summary, the system automatically generates a summary report containing the amounts, revenues, and optimization parameters for all subcontracting, for subsequent decision-making and performance analysis.
[0055] In some embodiments, a virtual disassembly process is performed on the composite material object marked in step S1, the virtual disassembly process including: Based on a pre-set disassembly template or actual measurement data, determine the material proportion of each component. With corresponding weight coefficients Calculate the classification and disposal value after virtual dismantling. Value of the whole machine ,in:
[0056]
[0057] in, The mass percentage of material of type k in the composite material; This is the material recycling correction factor; Q represents the unit price of the corresponding material; Q represents the total weight of the machine. is the unit price for the entire machine; m represents the type and quantity of composite materials; Calculate the profit-gain ratio of virtual dismantling :
[0058] in To reduce disassembly and sorting costs; when At that time, the composite material object is split into multiple virtual sub-details according to its material; when When this happens, the composite material object is assigned to the corresponding group according to its primary material. This is the threshold for the profit / gain ratio.
[0059] During implementation, the system calls the dismantling template library to determine the composition ratio of the composite equipment and the unit price of each material, and calculates the classification and disposal value after dismantling and the value of the whole machine. If the revenue after dismantling is higher than the proportion threshold of the revenue from the disposal of the whole machine, the system will dismantle the equipment into multiple virtual sub-items according to material to increase the overall recycling value; if the proportion does not reach the threshold, the whole machine ownership will be maintained. In one embodiment, for motor-type equipment, the system calculates that the revenue ratio after virtual dismantling is increased by about 12%, automatically selects a dismantling scheme and incorporates it into the subcontracting optimization process to maximize revenue.
[0060] In some embodiments, this application provides an adaptive subcontracting optimization system for waste materials, used to implement a method for adaptive subcontracting optimization of waste materials, the system comprising: The data acquisition and preprocessing module is used to acquire detailed disposal plan data, and parse the material code, quantity, unit price and material information of each material from the detailed disposal plan data. The disposal plan details are pre-grouped in a structured manner according to the preset material coding rules, and materials containing multiple materials are marked as composite material objects. In practical implementation, the data acquisition and preprocessing module establishes a data interface with the enterprise's ERP, EAM, or ECP systems to automatically read plan details from the disposal plan database. Based on material coding segment rules and field mapping tables, the system parses the material category, quantity, amount, and material attributes. To improve classification accuracy, the module has a field standardization mechanism that uniformly converts data from different sources into the system's standard format. For equipment or parts containing multiple material fields (such as copper, iron, and plastic), the module automatically identifies and marks them as composite material objects, ensuring that the subsequent subcontracting strategy modeling process can correctly handle such mixed materials. In a practical application, when the system detects keywords containing multiple materials such as "motor" or "transformer," it automatically marks and generates composite material labels, providing a data foundation for subsequent virtual dismantling.
[0061] The strategy modeling module is used to establish a subcontracting strategy model based on the overall objectives of the disposal plan. The subcontracting strategy model aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontracting amounts. It sets economic constraints, classification consistency constraints, and resource integration constraints. During implementation, the strategy modeling module utilizes historical disposal plans and bidding results stored in the database to construct a multi-objective optimization model. By analyzing historical revenue distribution and subcontracting scale trends, the module automatically generates weight parameters and establishes the optimal subcontracting strategy function. This module also features parameter self-learning capabilities, adjusting economic weights and consistency thresholds based on recent disposal task execution feedback, enabling the model to adaptively adjust to different material categories and disposal environments. Taking a large equipment dismantling plan as an example, after the initial run, the model automatically reduces the weight of subcontracting quantity and strengthens the weight of revenue based on historical data, resulting in a final subcontracting scheme that significantly increases revenue while reducing quantity.
[0062] The material consistency processing module is used to perform material consistency judgment on the disposal plan details and virtual sub-details under the constraints of the subcontracting strategy model, and merge materials with the same material or high similarity into the same subcontract according to the material consistency rules, so as to realize resource integration across material packages. During implementation, the module calls upon a material attribute library to compare material characteristics between the disposal plan details and the virtual sub-details. The system identifies the similarity relationships between different materials by calculating the consistency coefficients of material category, chemical composition, and physical properties, and automatically consolidates them when a set threshold is exceeded. This approach not only ensures centralized recycling of similar materials but also reduces logistics and disposal costs caused by dispersed packaging. In one embodiment, for steel and cast iron materials, the module automatically calculates the similarity value; when the similarity exceeds the threshold of 0.85, the system includes both in the same sub-package, achieving centralized resource collection and packaged disposal.
[0063] The economic optimization module is used to calculate the estimated total amount of each subcontract based on the material consistency processing results and under the economic constraints of the subcontracting strategy model, and to optimize the subcontracting structure according to the amount threshold calculated by the subcontracting strategy model. When the subcontract amount is lower than the amount threshold, the subcontract is merged with adjacent or similar subcontracts to form an optimized subcontracting set that meets the global economic constraints. In practice, the economic optimization module analyzes the relationship between the amount and revenue of each sub-package based on the dynamic amount threshold calculated by the strategy model. If the amount of a sub-package is lower than the threshold, the module automatically searches for adjacent or similar sub-packages and performs a merging operation to form a new sub-package structure. This module has a multi-round iteration and convergence judgment mechanism, and can repeatedly optimize according to the revenue balance coefficient and overall budget constraints until the global economic indicators reach the optimal level. Taking an actual disposal plan as an example, after the first round of optimization, the module merged three sub-packages with low amounts into two, increasing the overall disposal revenue by about 12% and reducing the number of packages in the bidding stage.
[0064] The results output module is used to output the optimized subcontracting details and strategy evaluation data to complete the adaptive subcontracting optimization of the waste material disposal plan.
[0065] During implementation, the results output module outputs the optimized subcontracting results in structured data format, generating detailed tables including subcontracting numbers, material lists, amounts, and material types. The module also outputs strategy evaluation data, displaying indicators such as subcontracting quantity, amount balance, and disposal revenue, which can be exported as reports for subsequent bidding and management review. For easy enterprise integration, the module supports synchronizing results to internal asset management or bidding system interfaces, achieving seamless data integration. In one embodiment, the system automatically generates a subcontracting result report, allowing managers to directly view subcontracting details and optimization evaluation data through the system terminal, eliminating the need for manual statistics and significantly improving the efficiency of plan generation and approval.
[0066] In some embodiments, this application provides a terminal, including: Memory, used to store the adaptive subcontracting optimization program for waste materials; A processor is used to execute the steps of the adaptive subcontracting optimization method for waste materials when implementing the adaptive subcontracting optimization system for waste materials.
[0067] In some embodiments, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the adaptive subcontracting optimization method for waste materials.
[0068] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.
Claims
1. An adaptive subcontracting optimization method for waste materials, characterized in that, Includes the following steps: Step S1: Obtain detailed disposal plan data, parse the material code, quantity, unit price and material information of each material from the detailed disposal plan data, perform structured pre-grouping of the detailed disposal plan according to the preset material coding rules, and mark materials containing multiple materials as composite material objects. Step S2: Establish a subcontracting strategy model based on the overall objectives of the disposal plan. The subcontracting strategy model aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontracting amounts. It sets economic constraints, classification consistency constraints, and resource integration constraints. Step S3: Under the constraints of the subcontracting strategy model, perform material consistency judgment on the disposal plan details and virtual sub-details, and merge materials with the same material or high similarity into the same subcontract according to the material consistency rules to realize resource integration across material packages; Step S4: Based on the material consistency processing results, calculate the estimated total amount of each subcontract under the economic constraints of the subcontracting strategy model; The subcontracting structure is optimized based on the amount threshold calculated by the subcontracting strategy model: when the subcontracting amount is lower than the amount threshold, the subcontracting is merged with adjacent or similar subcontracting to form an optimized subcontracting set that meets the global economic constraints. Step S5: Output the optimized subcontracting details and strategy evaluation data to complete the adaptive subcontracting optimization of the waste material disposal plan.
2. The adaptive subcontracting optimization method for waste materials according to claim 1, characterized in that, In step S2, the subcontracting strategy model includes an optimization objective function and a constraint function. The optimization objective function aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontract amounts. It is expressed as: Where F is the comprehensive optimization objective function; R is the disposal revenue function, representing the total revenue after the disposal plan is executed; and N is the subcontracting quantity function, representing the total number of subcontracts generated. Let be the subcontract amount balance function, representing the variance of each subcontract amount; This is the profit balance coefficient; These are the weighting coefficients for a balanced distribution of revenue, quantity, and amount. The subcontracting strategy model is solved under the following constraints: in, This is an economic constraint function, representing the deviation between the amount of each subcontract and the overall disposal budget; A monetary threshold is used to limit economic constraints. The minimum profit condition; The classification consistency constraint function represents the degree to which similar materials are dispersed; This is a material similarity threshold used to limit classification consistency constraints. The scope of the combined package judgment; Let be the resource integration constraint function, representing the concentration rate of resources of the same material; This is the maximum number of subcontractors; These are the threshold parameters for the corresponding constraints; By solving for the optimal solution of the objective function F under constraints, we obtain the parameters for dynamic calculation. The strategy model.
3. The adaptive subcontracting optimization method for waste materials according to claim 2, characterized in that, The optimization parameters Adaptive updates are performed based on historical disposal plan data and model iteration results, satisfying the following update function: in, Let i be the i-th optimization parameter; This is the learning rate coefficient; For the objective function F with respect to parameters The partial derivative; t is the iteration number; By iteratively solving the problem, the objective function F converges to its optimal value, resulting in a dynamically adjusted set of parameters. The sub-package strategy model is adaptively optimized and real-time parameter correction is achieved based on the dynamically adjusted parameter set.
4. The adaptive subcontracting optimization method for waste materials according to claim 2 or 3, characterized in that, In step S3, the classification consistency constraint based on the subcontracting strategy model is applied. Calculate the material similarity between the detailed disposal plan and the virtual sub-details. The similarity calculation formula is as follows: in, The overall material similarity between item i and item j; Indicates the consistency of the material categories of the two materials; Indicates the similarity of chemical components; Indicates physical similarity; The weighting coefficients for the three similarities satisfy the following conditions: ; when When this happens, the i-th item and the j-th item are grouped into the same sub-package; when At the same time, the i-th item and the j-th item remain separate.
5. The adaptive subcontracting optimization method for waste materials according to claim 2, characterized in that, In step S4, the economic constraints of the subcontracting strategy model are considered. Below, calculate the estimated total amount for each subcontractor after material consistency processing. And based on the profit balance coefficient With amount threshold Perform a dynamic economic evaluation, whereby the economic optimization objective function is: Where E is the overall economic evaluation value; This represents the estimated total amount for the k-th subcontract; For dynamic amount thresholds; This is the revenue balancing coefficient, used to adjust the degree of impact of deviation in subcontract amounts; n is the number of subcontracts. when At that time, the system calculates the economic benefit deviation ratio. ;when When the time is right, a merge operation is triggered, merging the k-th sub-package with adjacent or similar sub-packages and updating the new amount set. And recalculate ; By iteratively adjusting the loop, E is made to fit the constraints. The process converges to obtain an optimized set of sub-packages that meets global economic constraints.
6. The adaptive subcontracting optimization method for waste materials according to claim 1, characterized in that, Step S5 includes: Output the optimized subcontract details, which include subcontract number, subcontract material list, subcontract amount and material type.
7. The adaptive subcontracting optimization method for waste materials according to claim 1, characterized in that, For the composite material object marked in step S1, a virtual disassembly process is performed, which includes: Based on a pre-set disassembly template or actual measurement data, determine the material proportion of each component. With corresponding weight coefficients Calculate the classification and disposal value after virtual dismantling. Value of the whole machine ,in: in, The mass percentage of material of type k in the composite material; This is the material recycling correction factor; Q represents the unit price of the corresponding material; Q represents the total weight of the machine. is the unit price for the entire machine; m represents the type and quantity of composite materials; Calculate the profit-gain ratio of virtual dismantling : in To reduce disassembly and sorting costs; when At that time, the composite material object is split into multiple virtual sub-details according to its material; when When this happens, the composite material object is assigned to the corresponding group based on its primary material. This is the threshold for the profit / gain ratio.
8. A waste material adaptive subcontracting optimization system, used to implement the waste material adaptive subcontracting optimization method as described in claim 1, characterized in that, The system includes: The data acquisition and preprocessing module is used to acquire detailed disposal plan data, and parse the material code, quantity, unit price and material information of each material from the detailed disposal plan data. According to the preset material coding rules, the detailed disposal plan is pre-grouped in a structured manner, and materials containing multiple materials are marked as composite material objects. The strategy modeling module is used to establish a subcontracting strategy model based on the overall objectives of the disposal plan. The subcontracting strategy model aims to maximize disposal revenue, minimize the number of subcontractors, and maintain a balance in subcontracting amounts. It sets economic constraints, classification consistency constraints, and resource integration constraints. The material consistency processing module is used to perform material consistency judgment on the disposal plan details and virtual sub-details under the constraints of the subcontracting strategy model, and merge materials with the same material or high similarity into the same subcontract according to the material consistency rules, so as to realize resource integration across material packages. The economic optimization module is used to calculate the estimated total amount of each subcontract based on the material consistency processing results and under the economic constraints of the subcontracting strategy model, and to optimize the subcontracting structure according to the amount threshold calculated by the subcontracting strategy model. When the subcontract amount is lower than the amount threshold, the subcontract is merged with adjacent or similar subcontracts to form an optimized subcontracting set that meets the global economic constraints. The results output module is used to output the optimized subcontracting details and strategy evaluation data to complete the adaptive subcontracting optimization of the waste material disposal plan.
9. A terminal, characterized in that, include: Memory, used to store the adaptive subcontracting optimization program for waste materials; The processor is used to implement the steps of the adaptive subcontracting optimization method for waste materials as described in claim 1 when executing the adaptive subcontracting optimization device for waste materials.
10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the adaptive subcontracting optimization method for waste materials as described in claim 1.