Waste and old material disassembly management method and system
By automatically generating disassembly plans and Internet of Things monitoring, and optimizing resource allocation with linear planning models, the problem of inefficiency in traditional waste material processing and management is solved, and efficient disassembly of waste materials and maximum resource utilization is achieved.
Patent Information
- Application Number
- CN202510619710.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-14
- Publication Date
- 2025-08-26
AI Technical Summary
Traditional waste materials processing and management rely on manual records and paper documents, resulting in inefficiency and untimely information, making it difficult to achieve transparent and effective supervision and control.
The disassembly plan generation subsystem is used to automatically generate a disassembly plan, the disassembly process is monitored using the Internet of Things sensor, and the resource allocation is optimized through a linear planning model, combining the disassembly priority and historical data to generate a disassembly plan.
It improves the efficiency of formulating dismantling plans and the effectiveness of process monitoring, optimizes resource allocation, and realizes efficient dismantling and maximizing utilization of waste materials.
Smart Images
Figure CN120542684A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of material recycling technology, and specifically relates to a method and system for dismantling and managing waste materials. Background Art
[0002] As society's emphasis on environmental protection and sustainable resource utilization continues to grow, the effective treatment and reuse of waste materials has become a crucial issue. Waste materials contain a large amount of recyclable resources, such as metals and plastics. Proper disassembly and processing of these materials not only reduces resource waste and lowers reliance on natural resources, but also mitigates the environmental pollution caused by waste. Therefore, establishing a scientific and efficient waste material treatment process is imperative.
[0003] Traditional waste management often relies on manual record-keeping and paper documentation to develop dismantling plans and monitor the dismantling process. This approach is not only inefficient and prone to human error, but also lacks timely information transmission and sharing, resulting in opaque management processes and difficulties in effectively monitoring and controlling the entire waste management process.
[0004] With the rapid development of information technology, various industries are actively promoting information construction to improve management efficiency and scientific decision-making. In the field of waste material processing, the introduction of information management systems and the use of advanced technologies to achieve automated and intelligent management of the processing process have become an inevitable trend in the industry's development. Summary of the Invention
[0005] The present application provides a waste material dismantling management method and system to solve or partially solve the problems raised in the above background technology.
[0006] This application provides a waste material dismantling management method, including the following steps:
[0007] S1: The disassembly plan generation subsystem generates the disassembly priority and disassembly time of each type of disassembly material based on the material information of the materials to be disassembled and the historical disassembly information, and automatically generates a disassembly plan based on the disassembly priority and disassembly time;
[0008] S2: The disassembly process monitoring subsystem uses IoT sensors to collect disassembly process and result data, compares it with the pre-calculated process and result data in the disassembly plan, and outputs abnormal warnings based on the data differences;
[0009] S3: Post-disassembly resource evaluation and allocation subsystem, based on the post-disassembly resource information, calculates the optimal resource allocation path through a linear programming model.
[0010] Preferably, the material information of the materials to be dismantled includes at least the material type and quantity, dismantling difficulty coefficient, recycling value, and environmental compliance level.
[0011] Preferably, in step S1, the specific method for the disassembly plan generation subsystem to determine the disassembly time is as follows:
[0012] S11: Obtain the historical disassembly time Th of the type of disassembly material from the database n and the historical dismantling quantity Nh n , get the average disassembly time Tavg of unit quantity materials n =Th n / Nh n , n is the number of the material to be dismantled;
[0013] S12: Introducing the scale adjustment factor k n , k n Associate the current planned material quantity N n And the average value of the material quantity in the historical dismantling plan Navg n , when N n Less than or equal to Navg n When k n ≥1, when N n Greater than Navg n When k n <1;
[0014] S13: Calculate the disassembly time T for each type of material n =Tavg n ×N n ×k n .
[0015] Preferably, in step S12, k n The value formula is as follows:
[0016]
[0017] Preferably, in step S1, the specific method for the disassembly plan generation subsystem to determine the disassembly priority of each type of material is as follows:
[0018] S21: For each type of dismantling material, the dismantling difficulty coefficient D n , recycling value R n , Environmental Compliance E n Evaluation and assignment are carried out in three aspects;
[0019] S22: Disassembly Difficulty Coefficient D n , recycling value R n , Environmental Compliance E n Perform normalization processing, the processing formula is as follows:
[0020]
[0021] Among them, X is the actual value of the variable, X min is the minimum value of the variable, X max is the maximum value of the variable;
[0022] S23: The priority scoring rules for each type of material are as follows:
[0023] P n =ω1×D norm-n +ω2×R norm-n +ω3×E norm-n
[0024] Among them, ω1, ω2, ω3 are preset weights, and ω1+ω2+ω3=1, D norm-n 、R norm-n 、E norm-n D n 、R n 、E n The normalized value, P n The smaller the value, the higher the dismantling priority of the corresponding dismantling material.
[0025] Preferably, in step S1, the disassembly plan generation subsystem matches disassembly resources based on the disassembly time and disassembly priority of each type of material, and then generates a disassembly plan in the following specific method:
[0026] S31: Each type of dismantling material corresponds to a dismantling process. Each step of the dismantling process corresponds to different dismantling resources. According to historical data, the proportion of each step in the dismantling process in the overall dismantling time is estimated. The dismantling time T is calculated based on the corresponding proportion. n Assign to each step and generate time set {T ni}, i is the step number;
[0027] S32: According to the number of dismantled materials of each type N n , each step corresponds to the time T ni , the processing capacity of unit disassembly resources generates the disassembly curve F n (t) = {S 1n (t),S 2n (t),…,S Jn (t)}={S jn (t)}, where S jn (t) is the number of dismantling resources numbered j used by dismantling materials of type n at time t, and J is the number of types of dismantling resources;
[0028] S33: Generate a disassembly plan based on the quantity constraints of each disassembly resource and the disassembly priority ranking.
[0029] Preferably, in step S33, the rules for dynamic superposition are as follows:
[0030] S331: Assume that n is 1, 2, ..., N, obtain the initial time point t0 of the disassembly, set the operation queues C1 and C2 and make them empty, and calculate each disassembly curve F n (t) Sort by the disassembly priority, add F1(t) to queue C1, and add F2(t) to F N (t) Join queue C2;
[0031] S332: Extract elements from queue C2 and elements in queue C1 with t0 as the starting time to form a combined curve F total (t) = {S j (t)}, where S j (t) = ∑S jn (t), judge ∑S jn (t)≤S jmax Is it established? jmax is the maximum number of disassembly resources numbered j,
[0032] If true, add the corresponding element to queue C1 and delete it from queue C2.
[0033] If not, put the element back into queue C2.
[0034] Traverse the elements in queue C2 and generate new queues C1 and C2;
[0035] S333: Get each element F in queue C1 n (t) time interval [t0, t-end-n], generate the disassembly plan set {[F n (t), (t-start-n, t-end-n)]}, where t-start-n = t0, is F n (t) corresponds to the starting point of the time interval, t-end-n is F n (t) At the end of the corresponding time interval, determine whether the queue C2 is empty.
[0036] If yes, jump to step S334,
[0037] If not, set t0 = min(t-end-n), delete the element corresponding to min(t-end-n) from queue C1, and jump to step S332;
[0038] S334: Output the final disassembly plan set {[F n (t),(t-start-n,t-end-n)]}, where t-start-n is F n (t) corresponds to the starting point of the time interval, t-end-n is F n (t) corresponds to the end point of the time interval.
[0039] Preferably, the method for determining the disassembly plan by the disassembly plan generating subsystem further includes obtaining the types and quantities of intermediate objects corresponding to each disassembly material in the current disassembly plan and the types and quantities of the final recycled resources based on the disassembly process data and disassembly result data of the same type of disassembly materials in the historical disassembly plans, i.e., the pre-calculation process and result data of the disassembly plan in step S2;
[0040] In step S2, the disassembly process monitoring subsystem selects RFID tags or weight sensors as monitoring sensors based on the structure of intermediate objects and final recycled resources, verifies their quantity and / or weight during the transportation or warehousing process, transmits the detection data to the backend server through the Internet of Things, compares the pre-calculated process and result data of the disassembly plan, sets an error threshold, and outputs an abnormal warning if the error threshold is exceeded.
[0041] Preferably, in step S3, the specific method of the post-disassembly resource evaluation and allocation subsystem calculating the optimal resource allocation path through a linear programming model based on the post-disassembly resource information is as follows:
[0042] For each type of recycled resources, three processing paths are set up: sales, reprocessing, and environmentally friendly treatment. Recycled resources are processed based on maximizing market value. The objective function is as follows:
[0043]
[0044] Among them, X mk is the number of recycled resources of type m processed through type k, where m is 1, 2, 3, ...M, and k is 1 (sales), 2 (reprocessing), 3 (environmentally friendly treatment), r mk is the purity ratio of the mth type of recycled resources after processing through the kth path, q mk is the market value (unit price) of the mth type of recycled resources after being processed through the kth path, c mk is the unit cost of processing the mth type of recycled resource through the kth path.
[0045] Real-time mk ,q mk 、c mk to update.
[0046] This application also provides a waste material dismantling management system, including: front-end UI module, back-end functional module, and database. The functions of each module are as follows:
[0047] Front-end UI module, providing user interaction interface and obtaining input data;
[0048] Back-end functional modules include disassembly plan generation subsystem, disassembly process monitoring subsystem, and post-disassembly resource evaluation and allocation subsystem;
[0049] The disassembly plan generation subsystem generates the disassembly priority and disassembly time of various types of disassembly materials based on the material information of the materials to be disassembled and the historical disassembly information, and automatically generates the disassembly plan based on the disassembly priority and disassembly time;
[0050] The disassembly process monitoring subsystem uses IoT sensors to collect data from the disassembly process, compares it with the data in the disassembly plan, and outputs abnormal warnings based on the data differences;
[0051] The post-disassembly resource evaluation and allocation subsystem calculates the optimal resource allocation path based on the post-disassembly resource information through a linear programming model;
[0052] Database, used to store data.
[0053] Compared with the prior art, this application has the following beneficial effects:
[0054] (1) This application automatically generates a disassembly plan through a disassembly plan generation subsystem, thereby avoiding human errors when manually formulating plans and improving the efficiency of plan formulation. The disassembly process is monitored by a disassembly process monitoring subsystem, thereby improving the effectiveness of monitoring the disassembly process. The resource allocation path is reasonably planned through the post-dismantling resource evaluation and allocation subsystem, which is conducive to maximizing the utilization of recycled resources.
[0055] (2) This application generates the disassembly priority and disassembly time of various types of disassembly materials based on historical data, which is conducive to avoiding the non-quantitative parameters of the disassembly site that affect the disassembly time, and then generates matching disassembly resources based on the disassembly priority and disassembly time. Through the quantity constraint of disassembly resources, the disassembly curves of various types of disassembly materials are dynamically combined to generate an overall disassembly plan, thereby ensuring the effective utilization of site disassembly resources on the basis of disassembly efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0056] The present application is further described below with reference to the accompanying drawings and examples.
[0057] Figure 1 This is a schematic diagram of the system composition of this application.
[0058] Figure 2 Schematic diagram of the method flow of this application. DETAILED DESCRIPTION
[0059] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of the components as the criteria for distinction. For example, "including" mentioned throughout the specification and claims is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect.
[0060] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "horizontal", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only used to facilitate the description of the present application and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limiting the present application.
[0061] In this application, unless otherwise specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they can refer to fixed connection, detachable connection, or integral connection; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.
[0062] Example 1
[0063] like Figure 2 As shown, this application provides a waste material dismantling management method, which specifically includes the following steps:
[0064] S1: The disassembly plan generation subsystem generates the disassembly priority and disassembly time of each type of disassembly material based on the material information of the materials to be disassembled and the historical disassembly information, and automatically generates a disassembly plan based on the disassembly priority and disassembly time;
[0065] S2: The disassembly process monitoring subsystem uses IoT sensors to collect disassembly process and result data, compares it with the pre-calculated process and result data in the disassembly plan, and outputs abnormal warnings based on the data differences;
[0066] S3: Post-disassembly resource evaluation and allocation subsystem, based on the post-disassembly resource information, calculates the optimal resource allocation path through a linear programming model.
[0067] Specifically, the material information of the materials to be dismantled includes at least the material type and quantity, dismantling difficulty coefficient, recycling value, and environmental compliance level; the historical dismantling information includes historical dismantling plans and separate historical dismantling data for each type of material.
[0068] Specifically, in step S1, the disassembly plan generation subsystem determines the disassembly time in the following manner:
[0069] S11: Obtain the historical disassembly time Th of the type of disassembly material from the database n and the historical dismantling quantity Nh n , get the average disassembly time Tavg of unit quantity materials n =Th n / Nh n , n is the number of the material to be dismantled;
[0070] S12: Introducing the scale adjustment factor k n , k n Associate the current planned material quantity N n And the average value of the material quantity in the historical dismantling plan Navg n , when N n Less than or equal to Navg n When k n ≥1, when N n Greater than Navg n When k n <1;
[0071] S13: Calculate the disassembly time T for each type of material n =Tavg n ×N n ×k n .
[0072] In step S12, k n The value of can be obtained by regression analysis of historical data. Preferably, k n The value formula is as follows:
[0073]
[0074] Specifically, in step S1, the disassembly plan generation subsystem determines the disassembly priority of each type of material in the following manner:
[0075] S21: For each type of dismantling material, the dismantling difficulty coefficient D n , recycling value R n , Environmental Compliance E n Evaluation and assignment are carried out in three aspects;
[0076] S22: Disassembly Difficulty Coefficient D n , recycling value R n , Environmental Compliance E n Perform normalization processing, the processing formula is as follows:
[0077]
[0078] Among them, X is the actual value of the variable, X min is the minimum value of the variable, X max is the maximum value of the variable;
[0079] S23: The priority scoring rules for each type of material are as follows:
[0080] P n =ω1×D norm-n +ω2×R norm-n +ω3×E norm-n
[0081] Among them, ω1, ω2, ω3 are preset weights, and ω1+ω2+ω3=1, D norm-n 、R norm-n 、E norm-n D n 、R n 、E n The normalized value, P n The smaller the value, the higher the dismantling priority of the corresponding dismantling material.
[0082] Specifically, in step S1, the disassembly plan generation subsystem matches disassembly resources based on the disassembly time and disassembly priority of each type of material, and then generates a disassembly plan in the following specific method:
[0083] S31: Each type of dismantling material corresponds to a dismantling process. Each step of the dismantling process corresponds to different dismantling resources. According to historical data, the proportion of each step in the dismantling process in the overall dismantling time is estimated. The dismantling time T is calculated based on the corresponding proportion. n Assign to each step and generate time set {T ni}, i is the step number;
[0084] S32: According to the number of dismantled materials of each type N n , each step corresponds to the time T ni , the processing capacity of unit disassembly resources generates the disassembly curve F n (t) = {S 1n (t),S 2n (t),…,S Jn (t)}={S jn (t)}, where S jn(t) is the number of dismantling resources numbered j used by dismantling materials of type n at time t, and J is the number of types of dismantling resources;
[0085] S33: Generate a disassembly plan based on the quantity constraints of each disassembly resource and the disassembly priority ranking.
[0086] In step S31, for example, the material disassembly process of type A is divided into three steps a1, a2, and a3. Each step corresponds to different disassembly resources, and the disassembly resources include disassembly equipment and / or manpower. For example, step a1 corresponds to equipment B1, and B1 is the unit quantity of the disassembly resource corresponding to a1. Step a2 requires the use of equipment B2, but B2 cannot be fully automatic. Each B2 needs to be configured with a worker to operate, so the unit quantity of the disassembly resource corresponding to a2 is one B2 plus one manpower. Step a3 requires manpower processing, so the unit quantity of the disassembly resource corresponding to a3 is one manpower. Assuming that in historical data, for the same amount of material processing time, a1, a2, and a3 take the same time, then in the newly created disassembly plan, steps a1, a2, and a3 each take T n / 3 of the time.
[0087] In step S32, it is assumed that the number of type n materials processed by a certain unit of dismantling resources per unit time is L n , then for N n / (L n ×T ni ) rounded up to get the time period T corresponding to the disassembled resource ni The number of steps corresponds to the time T ni The corresponding resource combination can be generated in any time length T n The curve F of the period n (t).
[0088] In step S33, the rules for dynamic superposition are as follows:
[0089] S331: Assume that n is 1, 2, ..., N, obtain the initial time point t0 of the disassembly, set the operation queues C1 and C2 and make them empty, and calculate each disassembly curve F n (t) Sort by the disassembly priority, add F1(t) to queue C1, and add F2(t) to F N (t) Join queue C2;
[0090] S332: Extract elements from queue C2 and elements in queue C1 with t0 as the starting time to form a combined curve F total (t) = {S j (t)}, where S j (t) = ∑S jn (t), judge ∑Sjn (t)≤S jmax Is it established? jmax is the maximum number of disassembly resources numbered j,
[0091] If true, add the corresponding element to queue C1 and delete it from queue C2.
[0092] If not, put the element back into queue C2.
[0093] Traverse the elements in queue C2 and generate new queues C1 and C2;
[0094] S333: Get each element F in queue C1 n (t) time interval [t0, t-end-n], generate the disassembly plan set {[F n (t), (t-start-n, t-end-n)]}, where t-start-n = t0, is F n (t) corresponds to the starting point of the time interval, t-end-n is F n (t) At the end of the corresponding time interval, determine whether the queue C2 is empty.
[0095] If yes, jump to step S334,
[0096] If not, set t0 = min(t-end-n), delete the element corresponding to min(t-end-n) from queue C1, and jump to step S332;
[0097] S334: Output the final disassembly plan set {[F n (t),(t-start-n,t-end-n)]}, where t-start-n is F n (t) corresponds to the starting point of the time interval, t-end-n is F n (t) corresponds to the end point of the time interval.
[0098] In the first traversal of queue C2 in step S332, initially, there is only F1(t) in queue C1. First, F2(t) is taken out from queue C2. At this time, S j (t)=S1(t)+S2(t), assuming that ∑S jn (t)≤S jmax If it is established, then F2(t) is added to queue C1 and deleted from queue C2, and then F3(t) is taken out from queue C2. At this time, S j (t)=S1(t)+S2(t)+S3(t), assuming that ∑S jn (t)≤S jmaxIf it is not true, put F3(t) back into queue C2, and then take F4(t) out from C2, and calculate S j (t)=S1(t)+S2(t)+S4(t)≤S jmax Is it true? This continues until all elements in queue C2 are traversed.
[0099] In step S333, it should be noted that each round of traversal of the queue C2 involves the disassembly plan set {[F n (t), (t-start-n, t-end-n)]} regeneration, repeated F n (t) The time intervals need to be merged, and the min() function is used to find the minimum value.
[0100] According to the disassembly plan set, the start time node and end time node of the disassembly time corresponding to each type of material can be obtained, corresponding to F n The utilization of various dismantling resources in (t) constitutes a complete dismantling plan.
[0101] The method for the disassembly plan generation subsystem to determine the disassembly plan also includes obtaining the types and quantities of intermediate objects corresponding to each disassembly material in the current disassembly plan and the types and quantities of final recycled resources based on the disassembly process data and disassembly result data of the same type of disassembly materials in the historical disassembly plan, that is, the pre-calculation process and result data of the disassembly plan in step S2.
[0102] Specifically, in step S2, the specific method in which the disassembly process monitoring subsystem uses the Internet of Things sensor to monitor the disassembly process is as follows:
[0103] RFID tags or weight sensors are selected as monitoring sensors based on the structure of intermediate objects and final recycled resources. Their quantity and / or weight are inspected during the transportation or warehousing process, and the inspection data is transmitted to the backend server through the Internet of Things. The pre-calculated process and result data of the disassembly plan are compared, and an error threshold is set. If the error threshold is exceeded, an abnormal warning is output.
[0104] The principles and setting methods of weight sensors and RFID tags are conventional technologies in this field and will not be described in detail in this application.
[0105] In step S3, the specific method for the post-disassembly resource evaluation and allocation subsystem to calculate the optimal resource allocation path through a linear programming model based on the post-disassembly resource information is as follows:
[0106] For each type of recycled resources, three processing paths are set up: sales, reprocessing, and environmentally friendly treatment. Recycled resources are processed based on maximizing market value. The objective function is as follows:
[0107]
[0108] Among them, X mk is the number of recycled resources of type m processed through type k, where m is 1, 2, 3, ...M, and k is 1 (sales), 2 (reprocessing), 3 (environmentally friendly treatment), r mk is the purity ratio of the mth type of recycled resources after processing through the kth path, q mk is the market value (unit price) of the mth type of recycled resources after being processed through the kth path, c mk is the unit cost of processing the mth type of recycled resource through the kth path.
[0109] Real-time mk ,q mk 、c mk Updates are made to ensure that the market value of each recycled resource is maximized.
[0110] Example 2
[0111] like Figure 1 As shown, based on Example 1, this application provides a waste material dismantling and management system, including: a front-end UI module, a back-end functional module, and a database. The functions of each module are as follows:
[0112] Front-end UI module, providing user interaction interface and obtaining input data;
[0113] Back-end functional modules include disassembly plan generation subsystem, disassembly process monitoring subsystem, and post-disassembly resource evaluation and allocation subsystem;
[0114] The disassembly plan generation subsystem generates the disassembly priority and disassembly time of various types of disassembly materials based on the material information of the materials to be disassembled and the historical disassembly information, and automatically generates the disassembly plan based on the disassembly priority and disassembly time;
[0115] The disassembly process monitoring subsystem uses IoT sensors to collect data from the disassembly process, compares it with the data in the disassembly plan, and outputs abnormal warnings based on the data differences;
[0116] The post-disassembly resource evaluation and allocation subsystem calculates the optimal resource allocation path based on the post-disassembly resource information through a linear programming model;
[0117] Database, used to store data.
[0118] Specifically, the front-end UI module is a custom framework based on Layui, the back-end functional module is written in C# language, and the database uses MySQL database.
[0119] The above describes the implementation methods of the present application in detail in conjunction with the accompanying drawings, but the present application is not limited to the above implementation methods. Various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present application.
Claims
1. A method for dismantling and managing waste materials, characterized in that: The steps include: S1: The disassembly plan generation subsystem generates the disassembly priority and disassembly time of each type of disassembly material based on the material information of the materials to be disassembled and the historical disassembly information, and automatically generates a disassembly plan based on the disassembly priority and disassembly time; S2: The disassembly process monitoring subsystem uses IoT sensors to collect disassembly process and result data, compares it with the pre-calculated process and result data in the disassembly plan, and outputs abnormal warnings based on the data differences; S3: Post-disassembly resource evaluation and allocation subsystem, based on the post-disassembly resource information, calculates the optimal resource allocation path through a linear programming model.
2. The waste material dismantling management method according to claim 1, characterized in that: The material information to be dismantled includes at least the material type and quantity, dismantling difficulty coefficient, recycling value, and environmental compliance level.
3. The waste material dismantling management method according to claim 2, characterized in that: In step S1, the specific method for the disassembly plan generation subsystem to determine the disassembly time is as follows: S11: Obtain the historical disassembly time Th of the type of disassembly material from the database n and the historical dismantling quantity Nh n , get the average disassembly time Tavg of unit quantity materials n =Th n / Nh n , n is the number of the material to be dismantled; S12: Introducing the scale adjustment factor k n , k n Associate the current planned material quantity N n And the average value of the material quantity in the historical dismantling plan Navg n , when N n Less than or equal to Navg n When k n ≥1, when N n Greater than Navg n When k n <1; S13: Calculate the disassembly time T for each type of material n =Tavg n ×N n ×k n .
4. The waste material dismantling management method according to claim 3, characterized in that: In step S12, k n The value formula is as follows:
5. The waste material dismantling management method according to claim 3, characterized in that: In step S1, the specific method for the disassembly plan generation subsystem to determine the disassembly priority of each type of material is as follows: S21: For each type of dismantling material, the dismantling difficulty coefficient D n , recycling value R n , Environmental Compliance E n Evaluation and assignment are carried out in three aspects; S22: Disassembly Difficulty Coefficient D n , recycling value R n , Environmental Compliance E n Perform normalization processing, the processing formula is as follows: Among them, X is the actual value of the variable, X min is the minimum value of the variable, X max is the maximum value of the variable; S23: The priority scoring rules for each type of material are as follows: P n =ω1×D norm-n +ω2×R norm-n +ω3×E norm-n Among them, ω1, ω2, ω3 are preset weights, and ω1+ω2+ω3=1, D norm-n 、R norm-n 、E norm-n D n 、R n 、E n The normalized value, P n The smaller the value, the higher the dismantling priority of the corresponding dismantling material.
6. The waste material dismantling management method according to claim 5, characterized in that: In step S1, the disassembly plan generation subsystem matches disassembly resources based on the disassembly time and disassembly priority of each type of material, and then generates a disassembly plan in the following specific method: S31: Each type of dismantling material corresponds to a dismantling process. Each step of the dismantling process corresponds to different dismantling resources. According to historical data, the proportion of each step in the dismantling process in the overall dismantling time is estimated. The dismantling time T is calculated based on the corresponding proportion. n Assign to each step and generate time set {T ni }, i is the step number; S32: According to the number of dismantled materials of each type N n , each step corresponds to time T ni , the processing capacity of unit disassembly resources generates the disassembly curve F n (t) = {S 1n (t),S 2n (t),…,S Jn (t)}={S jn (t)}, where S jn (t) is the number of dismantling resources numbered j used by dismantling materials of type n at time t, and J is the number of types of dismantling resources; S33: Generate a disassembly plan based on the quantity constraints of each disassembly resource and the disassembly priority ranking.
7. The waste material dismantling management method according to claim 6, characterized in that: In step S33, the rules for dynamic superposition are as follows: S331: Assume that n is 1, 2, ..., N, obtain the initial time point t0 of the disassembly, set the operation queues C1 and C2 and make them empty, and calculate the disassembly curves F n (t) Sort by the disassembly priority, add F1(t) to queue C1, and add F2(t) to F N (t) Join queue C2; S332: Extract elements from queue C2 and elements in queue C1 with t0 as the starting time to form a combined curve F total (t) = {S j (t)}, where S j (t) = ∑S jn (t), judge ∑S jn (t)≤S jmax Is it established? jmax is the maximum number of disassembly resources numbered j, If true, add the corresponding element to queue C1 and delete it from queue C2. If not, put the element back into queue C2. Traverse the elements in queue C2 and generate new queues C1 and C2; S333: Get each element F in queue C1 n (t) time interval [t0, t-end-n], generate the disassembly plan set {[F n (t), (t-start-n, t-end-n)]}, where t-start-n = t0, is F n (t) corresponds to the starting point of the time interval, t-end-n is F n (t) At the end of the corresponding time interval, determine whether the queue C2 is empty. If yes, jump to step S334, If not, set t0 = min(t-end-n), delete the element corresponding to min(t-end-n) from queue C1, and jump to step S332; S334: Output the final disassembly plan set {[F n (t),(t-start-n,t-end-n)]}, where t-start-n is F n (t) corresponds to the starting point of the time interval, t-end-n is F n (t) corresponds to the end point of the time interval.
8. The waste material dismantling management method according to claim 1, characterized in that: The method for determining the disassembly plan by the disassembly plan generation subsystem further includes obtaining the types and quantities of intermediate objects corresponding to each disassembly material in the current disassembly plan and the types and quantities of the final recycled resources based on the disassembly process data and disassembly result data of the same type of disassembly materials in historical disassembly plans, i.e., the pre-calculation process and result data of the disassembly plan in step S2; In step S2, the disassembly process monitoring subsystem selects RFID tags or weight sensors as monitoring sensors based on the structure of intermediate objects and final recycled resources, verifies their quantity and / or weight during the transportation or warehousing process, transmits the detection data to the backend server through the Internet of Things, compares the pre-calculated process and result data of the disassembly plan, sets an error threshold, and outputs an abnormal warning if the error threshold is exceeded.
9. The waste material dismantling management method according to claim 1, characterized in that: In step S3, the specific method for the post-disassembly resource evaluation and allocation subsystem to calculate the optimal resource allocation path through a linear programming model based on the post-disassembly resource information is as follows: For each type of recycled resources, three processing paths are set up: sales, reprocessing, and environmentally friendly treatment. Recycled resources are processed based on maximizing market value. The objective function is as follows: Among them, X mk is the number of recycled resources of type m processed through type k, where m is 1, 2, 3, ...M, and k is 1 (sales), 2 (reprocessing), 3 (environmentally friendly treatment), r mk is the purity ratio of the mth type of recycled resources after processing through the kth path, q mk is the market value (unit price) of the mth type of recycled resources after being processed through the kth path, c mk is the unit cost of processing the mth type of recycled resource through the kth path. Real-time mk ,q mk 、c mk to update.
10. Waste material dismantling management system, characterized by: include: Front-end UI module, back-end functional module, database, the functions of each module are as follows: Front-end UI module, providing user interaction interface and obtaining input data; Back-end functional modules include disassembly plan generation subsystem, disassembly process monitoring subsystem, and post-disassembly resource evaluation and allocation subsystem; The disassembly plan generation subsystem generates the disassembly priority and disassembly time of various types of disassembly materials based on the material information of the materials to be disassembled and the historical disassembly information, and automatically generates the disassembly plan based on the disassembly priority and disassembly time; The disassembly process monitoring subsystem uses IoT sensors to collect data from the disassembly process, compares it with the data in the disassembly plan, and outputs abnormal warnings based on the data differences; The post-disassembly resource evaluation and allocation subsystem calculates the optimal resource allocation path based on the post-disassembly resource information through a linear programming model; Database, used to store data.