Turnover material management system and method

Abstract

The invention discloses a turnover material management system and method. The turnover material management system comprises a user login module, a main interface navigation module, a material plan management module, an entry and exit and warehouse-in and warehouse-out management module, a data statistics and analysis module, a multi-stage dictionary management module and a user authority management module. The method has the beneficial effects that through digital machine account recording, multi-dimensional statistical analysis and visual presentation technologies, full-process tracking of materials from planning, warehousing, ex-warehouse to retreating is realized. According to the system, a building-floor-part three-level linkage selection mechanism and a warehouse four-level positioning system are specially designed, and the problem that material position information is fuzzy in traditional management is solved. And a real-time loss rate algorithm and a dynamic early warning function are innovatively integrated, so that the material waste can be reduced by more than 15%, and the inventory turnover efficiency is improved by 30%.

Description

Technical Field

[0001] The present invention relates to the technical field of construction project management, and in particular to a turnover material management system and method. Background Art

[0002] During the construction process, the management of turnover materials is of vital importance. Traditional turnover material management methods have problems such as low efficiency, inaccurate data, and difficulty in multi-dimensional analysis, and cannot meet the requirements of modern construction for refined management of turnover materials. For example, in aspects such as material plan formulation, incoming and outgoing yard management, loss control, and scheduling decision-making, there is a lack of effective information technology means to support, resulting in problems such as resource waste and cost increase.

[0003] Traditional turnover material management has the following defects: manual recording leads to lagging and error-prone data; lack of dynamic statistical analysis, unable to effectively calculate material requirements, losses, and remainders; low efficiency in multi-warehouse collaboration, difficult to track the dynamic flow path of materials and the location of material retention; extensive permission management, with data security risks.

[0004] Therefore, in view of the above technical problems, it is necessary to propose a turnover material management system and method. Summary of the Invention

[0005] The purpose of the present invention is to provide a turnover material management system and method.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A turnover material management system, comprising:

[0008] A user login module, used for multi-role permission authentication and supporting password input composed of a combination of uppercase and lowercase letters, numbers, and symbols;

[0009] A main interface navigation module, including a multi-level menu function, specifically including material plan entry, incoming and outgoing yard ledger management, incoming and outgoing warehouse ledger management, statistical analysis dashboard, multi-level dictionary management, and user management;

[0010] A material plan management module, supporting the entry of material locations, quantities, and usage dates through three-level linkage selection of building - floor - part, and associating multi-level dictionary data;

[0011] An incoming and outgoing yard and warehouse management module, supporting four-level warehouse location linkage entry, batch deletion, and multi-dimensional screening functions;

[0012] A data statistics and analysis module, performing multi-dimensional comparative analysis on key indicators such as material inventory, loss rate, and planned usage through visualization tools such as bar charts and line charts;

[0013] Multi-level dictionary management module, supporting the addition, deletion of hierarchical categories and keyword search functions;

[0014] User permission management module, realizing the dynamic configuration of administrators for user roles and function access permissions.

[0015] Among them, the multi-level dictionary management module stores material categories through a tree structure, supports dynamic expansion of levels and associates with the drop-down selection function for material plan entry.

[0016] Among them, the data statistics and analysis module builds a real-time loss rate calculation algorithm and generates dynamic warnings according to the floor-location dimension.

[0017] The steps of the real-time loss rate calculation algorithm are as follows:

[0018] S1. Multi-source data collection, obtaining the basic attributes of turnover materials, Internet of Things sensor data and business records;

[0019] S2. Dynamic model calculation, calculating the loss rate in real time based on the four-dimensional loss model;

[0020] S3. Online correction, dynamically adjusting model parameters through reinforcement learning.

[0021] The formula for calculating the loss rate in real time in step S2 is as follows:

[0022] LR(t)=α·LR base +β·LR enυ +γ·LR use +δ·LR abn

[0023] Among them, LR(t) represents the real-time loss rate of turnover materials at the current moment t, which is the result calculated by integrating multi-dimensional factors;

[0024] α, β, γ, δ are the weight coefficients of each dimension respectively, dynamically optimized through algorithms (such as reinforcement learning, analytic hierarchy process), reflecting the influence degree of different factors on the loss rate.

[0025] LR base is the benchmark loss rate calculated based on historical data;

[0026] LR enυ is: the environmental factor loss coefficient, reflecting the influence of environmental factors such as temperature, humidity, vibration, and light on the loss of turnover materials. For example, a humid environment accelerates the corrosion of metals, and frequent vibration causes loosening and loss of structural parts;

[0027] LR use is the usage intensity loss index, related to the actual usage of turnover materials, such as usage duration, load intensity, operation frequency, etc. For example, high-frequency disassembly of formwork and long-term operation of equipment will increase this value;

[0028] LR abn The abnormal event loss weight is a loss correction value set for abnormal situations such as loss, illegal operation, and overdue use. For example, when turnover materials are lost or misused, this value will increase significantly.

[0029] A method for a turnover material management system, and its method steps are as follows:

[0030] S21. Full-scenario data collection, deploying Internet of Things terminals to obtain the location, status, and environmental parameters of turnover materials in real time; integrating external data sources such as construction progress, weather, and equipment utilization rate;

[0031] S22. Dynamic priority assessment, constructing a mathematical model including dimensions such as urgency, usage frequency, and maintenance cost, and calculating the priority coefficient of each turnover material based on the Analytic Hierarchy Process (AHP);

[0032] S23. Intelligent scheduling decision-making, using an improved ant colony algorithm to generate the optimal allocation path, and combining reinforcement learning to dynamically adjust the scheduling strategy to adapt to real-time changes.

[0033] S23. Abnormal closed-loop management, establishing a closed-loop mechanism of "early warning - response - disposal - review", and predicting the probability of abnormal events through a Bayesian network.

[0034] Among them, the formula based on the Analytic Hierarchy Process is P = α×E + β×F + γ×C + δ×S;

[0035] Among them, E is the urgency, F is the usage frequency, C is the maintenance cost, and S is the remaining life.

[0036] Among them, the steps of the improved ant colony algorithm in step S23 are as follows:

[0037] S31. Problem modeling and parameter initialization;

[0038] S32. Ant path construction;

[0039] S33. Local search optimization;

[0040] S34. Dynamic pheromone update.

[0041] Among them, in step S31, the turnover material yard, construction project points, etc. are used as graph nodes, and the road distance and transportation cost between nodes are used as edge weights to form a weighted directed graph (G(V,E)); set the number of ants m, the maximum number of iterations Tmax, the initial pheromone τ ij (0), heuristic factor η ij = 1 / d ij where d ij is the distance from node i to j), ρ is the pheromone evaporation coefficient, L bestIt is the globally optimal path.

[0042] Among them, in step S32, for each ant k, the taboo table Tabu is initialized k , randomly select the starting point and add it to the taboo table. Among the optional nodes, select the next node according to the transition probability formula:

[0043] Among them, α and β are the weights of pheromone and heuristic information, and allowed k is the set of optional nodes of ant k. Add the selected node to the taboo table and repeat until all target nodes are traversed. Calculate the total path length L of each ant k =∑d ij , and update the globally optimal path L best .

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] 1. Control the function visibility through the role permission matrix (as shown in the following table); Information entry and processing: Through the entry of material turnover information, integrate and process the data, calculate the floor surplus, loss amount, loss amount, and working days of material use from the planned data and turnover data; Visualization dashboard: Support the analysis and display of planned quantity, usage quantity, loss quantity, loss rate, surplus through generating real-time interactive charts.

[0046] 2. Through functions such as multi-level menu navigation and linked entry, reduce manual operations, improve the efficiency of data entry and query, and use visualization tools and real-time loss rate calculation algorithms to perform multi-dimensional analysis on the turnover material data to provide accurate basis for decision-making.

[0047] 3. Adopt the improved ant colony algorithm and reinforcement learning to generate the optimal allocation path, dynamically adjust the scheduling strategy, reduce transportation costs and resource waste; Establish an abnormal closed-loop management mechanism, predict abnormal events through the Bayesian network, discover and handle problems in a timely manner to ensure the normal use of turnover materials; The multi-level dictionary management module supports dynamic expansion of levels to adapt to the changing needs of different projects and businesses. Brief Description of the Drawings

[0048] Figure 1 is the system composition block diagram of the present invention;

[0049] Figure 2 is the system architecture diagram of the present invention

[0050] Figure 3 is the data flow diagram between modules of the present invention

[0051] Figure 4 is the four-level linkage selection flowchart of warehouse-building-floor-part of the present invention

[0052] Figure 5 Schematic diagram of the material plan entry interface of the present invention

[0053] Figure 6 Schematic diagram of the material entry and exit record interface of the present invention

[0054] Figure 7 Schematic diagram of the material warehousing and outbound record interface of the present invention

[0055] Figure 8 Schematic diagram of the material entry and exit data display interface of the present invention

[0056] Figure 9 Schematic diagram of the material usage analysis display interface of the present invention

[0057] Figure 10 Schematic diagram of the analysis and display interface for the material usage and loss situation of the present invention Detailed implementation manners

[0058] It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention may be combined with each other

[0059] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. However, the present invention may be implemented in many different ways defined and covered by the claims

[0060] As shown in Figure 1 and in combination with Figures 2 to 10 shown, a turnover material management system includes

[0061] A user login module, which is used for multi-role permission authentication and supports password input combining uppercase and lowercase letters, numbers and symbols

[0062] A main interface navigation module, which includes a multi-level menu function, specifically including material plan entry, entry and exit ledger management, warehousing and outbound ledger management, statistical analysis dashboard, multi-level dictionary management and user management

[0063] A material plan management module, which supports the entry of material location, quantity and usage date through the three-level linkage selection of building - floor - part, and associates with multi-level dictionary data

[0064] An entry / exit and warehousing management module, which supports the four-level warehouse location linkage entry, batch deletion and multi-dimensional screening functions

[0065] A data statistics and analysis module, which conducts multi-dimensional comparative analysis of key indicators such as material inventory, loss rate, planned usage, etc. through visualization tools such as bar charts and line charts

[0066] A multi-level dictionary management module, which supports the functions of adding, deleting hierarchical categories and keyword search

[0067] User Permission Management Module, which realizes the dynamic configuration of user roles and functional access permissions by administrators. It ensures system security through multi-role permission authentication, supports complex passwords to enhance account protection capabilities; multi-level menu navigation realizes quick positioning of functional modules, improving operation efficiency; three-level linkage entry ensures accurate matching of material plans to construction locations, reducing human errors; four-level warehouse location linkage realizes full-process tracking of turnover materials, improving inventory management accuracy; Visual Data Analysis Module supports data-driven decision-making, improving resource allocation efficiency; tree-shaped dictionary structure supports dynamic expansion of material classification, adapting to diverse management requirements; dynamic permission configuration realizes fine-grained function control, ensuring data security.

[0068] Among them, the multi-level dictionary management module stores material categories through a tree structure, supports dynamic expansion of levels and associates them with the drop-down selection function for material plan entry. The tree structure storage realizes hierarchical management of material classification, supports infinite-level expansion; the dynamic association of the drop-down selection function reduces material entry time and improves data consistency; the visual classification navigation reduces the learning cost for new users and improves operation convenience.

[0069] Among them, the Data Statistics and Analysis Module incorporates a real-time loss rate calculation algorithm and generates dynamic warnings based on the floor-location dimension.

[0070] The steps of the real-time loss rate calculation algorithm are as follows:

[0071] S1. Multi-source data collection to obtain the basic attributes of turnover materials, IoT sensor data, and business records;

[0072] S2. Dynamic model calculation to calculate the loss rate in real time based on the four-dimensional loss model;

[0073] S3. Online correction to dynamically adjust model parameters through reinforcement learning.

[0074] The real-time loss rate calculation provides instant warnings to avoid the accumulation of hidden losses; the floor-location dimension warning realizes accurate positioning of problem areas, improving the response speed; multi-dimensional data fusion analysis mines loss patterns and optimizes maintenance strategies. Multi-source data collection constructs a comprehensive loss assessment model covering multiple dimensions such as physical attributes, environment, and usage scenarios; the four-dimensional loss model enables traceability of loss causes, supports targeted improvement measures, and the online correction mechanism ensures continuous optimization of the model to adapt to the needs of different construction stages.

[0075] The formula for real-time calculation of the loss rate in step S2 is as follows:

[0076] LR(t) = α·LR base +β·LR enυ +γ.LR use +δ·LR abn

[0077] Among them, LR(t) represents the real-time loss rate of turnover materials at the current moment t, which is the result calculated by integrating multi-dimensional factors;

[0078] α, β, γ, and δ are the weight coefficients of each dimension, which are dynamically optimized through algorithms (such as reinforcement learning and analytic hierarchy process) to reflect the influence degree of different factors on the loss rate.

[0079] LR base is the benchmark loss rate calculated based on historical data;

[0080] LR enυ is the environmental factor loss coefficient, which reflects the influence of environmental factors such as temperature, humidity, vibration, and light on the loss of turnover materials. For example, a humid environment accelerates metal corrosion, and frequent vibration causes loosening and loss of structural components;

[0081] LR use is the usage intensity loss index, which is related to the actual usage situation of turnover materials, such as usage duration, load intensity, operation frequency, etc. For example, high-frequency disassembly of templates and long-term operation of equipment will increase this value;

[0082] LR abn is the loss weight for abnormal events, which is a loss correction value set for abnormal situations such as loss, illegal operation, and overdue use. For example, when turnover materials are lost or misused, this value will increase significantly. The weighted formula realizes the quantitative evaluation of different influencing factors and improves the scientificity of loss calculation; the environmental factor coefficient reflects the influence of external conditions and supports environmental adaptability maintenance; the usage intensity index quantifies the equipment load status and optimizes the equipment scheduling strategy; the abnormal event weight strengthens the handling of special situations and reduces the losses caused by human errors.

[0083] A method for a turnover material management system, and its method steps are as follows:

[0084] S21. Full-scenario data collection, deploying Internet of Things terminals to obtain the location, status, and environmental parameters of turnover materials in real time; integrating external data sources such as construction progress, weather, and equipment utilization rate;

[0085] S22. Dynamic priority evaluation, constructing a mathematical model including dimensions such as urgency, usage frequency, and maintenance cost, and calculating the priority coefficient of each turnover material based on the analytic hierarchy process (AHP);

[0086] S23. Intelligent scheduling decision-making, using an improved ant colony algorithm to generate the optimal allocation path, and dynamically adjusting the scheduling strategy in combination with reinforcement learning to adapt to real-time changes.

[0087] S23. Abnormal closed-loop management, establishing a closed-loop mechanism of "early warning - response - disposal - review", and predicting the probability of abnormal events through a Bayesian network.

[0088] Full-scenario data collection to build a digital twin management system, supporting real-time status monitoring; priority evaluation by the analytic hierarchy process to achieve hierarchical resource management and improve emergency response capabilities; improvement of the ant colony algorithm to optimize path planning and reduce transportation costs by more than 30%; closed-loop management mechanism to achieve full-life cycle control of abnormal events and reduce repeated failures.

[0089] Among them, the formula based on the analytic hierarchy process is P = α×E + β×F + γ×C + δ×S;

[0090] Where E is the urgency, F is the usage frequency, C is the maintenance cost, and S is the remaining life.

[0091] The introduction of the remaining life parameter extends the service life of the equipment and reduces asset waste; the weight coefficient is configurable to support flexible switching of different management strategies.

[0092] Among them, the steps to improve the ant colony algorithm in step S23 are as follows:

[0093] S31. Problem modeling and parameter initialization;

[0094] S32. Ant path construction;

[0095] S33. Local search optimization;

[0096] S34. Dynamic pheromone update.

[0097] The four-step improved ant colony algorithm balances global search and local optimization, shortening the path planning time; the pheromone update mechanism improves the algorithm convergence speed and adapts to the dynamic construction environment; the taboo list strategy avoids path repetition and ensures the feasibility of the solution.

[0098] Among them, in step S31, the turnover material yard, construction project points, etc. are used as graph nodes, and the road distance, transportation cost, etc. between nodes are used as edge weights to form a weighted directed graph (G(V,E)); set the number of ants m, the maximum number of iterations Tmax, the initial pheromone τ ij (0), heuristic factor η ij = 1 / d ij where d ij is the distance from node i to j), ρ is the pheromone evaporation coefficient, and L best is the global optimal path.

[0099] Graph theory modeling realizes the digital mapping of the physical space, supporting path planning in complex scenarios; multi-parameter edge weights (distance / cost) provide multi-objective optimization options; standardized initial parameter configuration reduces the threshold for algorithm use.

[0100] Among them, in step S32, for each ant k, initialize the taboo list Tabu k, randomly select a starting point and add it to the taboo list. Among the optional nodes, select the next node according to the transfer probability formula:

[0101] where α and β are the weights of pheromone and heuristic information, and allowed k is the set of optional nodes for ant k. Add the selected node to the taboo list and repeat until all target nodes are traversed. Calculate the total path length L of each ant k = ∑d ij , and update the global optimal path L best .

[0102] The transfer probability formula balances pheromone guidance and heuristic search to improve the quality of the solution; the dynamic update of the taboo list ensures path legality and avoids deadlock phenomena; the global optimal path tracking mechanism continuously optimizes historical solutions to form knowledge accumulation.

[0103] Compared with the prior art, the beneficial effects of the present invention are:

[0104] 1. Control function visibility through the role permission matrix (as shown in the following table); Information entry and processing: Integrate and process data by entering material turnover information, calculate floor surplus, loss, loss, and working days of material use from planned data and turnover data; Visualization dashboard: Support the analysis and display of planned quantity, usage quantity, loss quantity, loss rate, and surplus by generating real-time interactive charts.

[0105] 2. Reduce manual operations and improve data entry and query efficiency through functions such as multi-level menu navigation and linked entry. Use visualization tools and real-time loss rate calculation algorithms to perform multi-dimensional analysis on turnover material data and provide accurate basis for decision-making.

[0106] 3. Adopt an improved ant colony algorithm and reinforcement learning to generate the optimal allocation path, dynamically adjust the scheduling strategy, reduce transportation costs and resource waste; Establish an abnormal closed-loop management mechanism, predict abnormal events through Bayesian networks, discover and handle problems in a timely manner to ensure the normal use of turnover materials; The multi-level dictionary management module supports dynamic expansion of levels to adapt to the changing needs of different projects and businesses

[0107] Multi-level permission system: Control function visibility through the role permission matrix (as shown in the following table):

[0108] Role Accessible module Administrator Full function Project manager Plan review, statistical analysis Material clerk In-out record, current location query

[0109] After the user enters the account password on the login interface, the system verifies the identity through encryption; The material keeper enters the in-out and storage records and associates them with time and usage location; The administrator views the material turnover rate report of the entire project through the data dashboard and optimizes the allocation strategy; Generate a daily report based on the data dashboard, including a warning list and pending task items.

[0110] (I) System Deployment and User Login

[0111] Deploy the turnover material management system on the server. Users access the system login page through a browser. Enter the username and password (a combination of uppercase and lowercase letters, numbers, and symbols) on the login page. The system performs multi-role permission authentication, and after successful authentication, enter the main interface.

[0112] (II) Material Plan Formulation

[0113] In the main interface navigation module, select the "Material Plan Entry" function. Through the three-level linkage of building - floor - location, select the usage location of the material, and enter the quantity and usage date of the material. The system automatically associates multi-level dictionary data to ensure the accuracy of material information.

[0114] (III) In-and-Out and Warehouse Management

[0115] In the "In-and-Out Ledger Management" and "Warehouse In-and-Out Ledger Management" functions, use the four-level warehouse location linkage to enter the in-and-out and warehouse in-and-out information of the turnover materials. Support batch deletion operations to facilitate cleaning up invalid data. Users can quickly find the required turnover material information through the multi-dimensional screening function.

[0116] (IV) Data Statistics and Analysis

[0117] The system regularly collects the basic attributes of turnover materials, Internet of Things sensor data, and business records, and performs real-time loss rate calculation and multi-dimensional comparative analysis through the data statistics and analysis module. Use visualization tools such as bar charts and line charts to display the analysis results, and generate dynamic warnings according to the floor - location dimension to remind managers to pay attention to abnormal situations.

[0118] (V) Multi-Level Dictionary Management

[0119] Administrators can perform operations such as adding, deleting, and keyword searching on material categories in the "Multi-Level Dictionary Management" function. Store material categories in a tree structure, support dynamic expansion of levels, and associate them with the drop-down selection function in material plan entry to facilitate users to manage material information.

[0120] (VI) User Permission Management

[0121] Administrators can dynamically configure user roles and function access permissions through the user permission management module in the "User Management" function to ensure that different users can only access the functions and data within their permission scope.

[0122] (VII) Turnover Material Scheduling Decision

[0123] The system obtains information such as the location, status, and environmental parameters of turnover materials through full-scenario data collection, and integrates external data sources such as construction progress, weather, and equipment utilization rate. Based on the analytic hierarchy process, the priority coefficient of each turnover material is calculated, and an improved ant colony algorithm is used to generate the optimal allocation path, and the scheduling strategy is dynamically adjusted in combination with reinforcement learning. At the same time, an abnormal closed-loop management mechanism is established, and the probability of abnormal events is predicted through a Bayesian network to handle abnormal situations in a timely manner.

[0124] The above specific implementation manners are only examples, and appropriate adjustments and optimizations can be made according to the specific project requirements and business processes in actual applications.

[0125] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A turnover material management system, characterized in that: Including: A user login module for multi-role permission authentication, supporting password input consisting of a combination of uppercase and lowercase letters, numbers, and symbols; A main interface navigation module with a multi-level menu function, specifically including material plan entry, incoming and outgoing inventory management, warehouse in and out inventory management, statistical analysis dashboard, multi-level dictionary management, and user management; A material plan management module that supports the entry of material locations, quantities, and usage dates through three-level linkage selection of building - floor - part, and associates with multi-level dictionary data; An incoming and outgoing and warehouse in and out management module that supports four-level warehouse location linkage entry, batch deletion, and multi-dimensional screening functions; A data statistics and analysis module that conducts multi-dimensional comparative analysis of key indicators such as material stock, loss rate, and planned usage through bar chart and line chart visualization tools; A multi-level dictionary management module that supports functions such as adding, deleting hierarchical categories, and keyword search; A user permission management module that enables administrators to dynamically configure user roles and function access permissions.

2. The turnover material management system according to claim 1, characterized in that: Among them, the multi-level dictionary management module stores material categories through a tree structure, supports dynamic expansion of levels, and associates with the drop-down selection function for material plan entry.

3. A turnover material management system according to claim 1, characterized in that: Among them, the data statistics and analysis module has a built-in real-time loss rate calculation algorithm and generates dynamic warnings based on the floor - part dimension.

4. A turnover material management system according to claim 1, characterized in that: The steps of the real-time loss rate calculation algorithm are as follows: S1. Multi-source data collection, obtaining the basic attributes of turnover materials, Internet of Things sensor data, and business records; S2. Dynamic model calculation, calculating the loss rate in real time based on a four-dimensional loss model; S3. Online correction, dynamically adjusting model parameters through reinforcement learning.

5. A turnover material management system according to claim 1, characterized in that: The formula for calculating the real-time loss rate in step S2 is as follows: LR(t) = α·LR base + β·LR env + ·LR use + δ·LR abn Among them, LR(t) represents the real-time loss rate of turnover materials at the current moment t, which is the result calculated by integrating multi-dimensional factors; α, β, γ, and δ are the weight coefficients of each dimension respectively, dynamically optimized through the algorithm, reflecting the influence degree of different factors on the loss rate; LR base is the benchmark loss rate calculated based on historical data; LRb env It is: the environmental factor loss coefficient, which reflects the influence of environmental factors such as temperature, humidity, vibration, and light on the loss of turnover materials; LR use Using the intensity loss index, which is related to the actual usage of the reusable materials, such as usage duration, load intensity, and operation frequency; LR abn It is the loss weight for abnormal events, which is the loss correction value set for abnormal situations such as loss, illegal operation, and overdue use.

6. A method for a turnover material management system according to any one of claims 1-5, characterized in that: The method steps are as follows: S21. Full-scenario data collection, deploying Internet of Things terminals to obtain the location, status, and environmental parameters of turnover materials in real time; integrating construction progress, weather, equipment utilization rate, and external data sources; S22. Dynamic priority evaluation, constructing a mathematical model including dimensions of urgency, usage frequency, and maintenance cost, and calculating the priority coefficient of each turnover material based on the analytic hierarchy process; S23. Intelligent scheduling decision-making, using an improved ant colony algorithm to generate the optimal allocation path, and dynamically adjusting the scheduling strategy through reinforcement learning to adapt to real-time changes; S23. Abnormal closed-loop management, establishing a closed-loop mechanism of "warning - response - disposal - review", and predicting the probability of abnormal events through a Bayesian network.

7. The turnover material management method according to claim 6, characterized in that: The formula based on the analytic hierarchy process is P = α×E + β×F + γ×C + δ×S; Among them, E is the urgency, F is the usage frequency, C is the maintenance cost, and S is the remaining life.

8. The turnover material management method according to claim 6, wherein: The steps of the improved ant colony algorithm in step S23 are as follows: S31. Problem modeling and parameter initialization; S32. Ant path construction; S33. Local search optimization; S34. Dynamic pheromone update.

9. A method for managing turnover materials according to claim 8, characterized in that: Among them, in step S31, the turnover material yard and the construction project site are used as graph nodes, and the road distance and transportation cost between nodes are used as edge weights to form a weighted directed graph (G(V, E)); the number of ants m and the maximum number of iterations T are set max , the initial pheromone τ ij (0), the heuristic factor η ij = 1 / d ij , where d ij is the distance from node i to j), ρ is the pheromone evaporation coefficient, and L best is the global optimal path.

10. A turnover material management method according to claim 8, characterized in that: Among them, in step S32, for each ant k, the taboo list Tabu is initialized k , randomly select a starting point and add it to the taboo list. Among the optional nodes, select the next node according to the transfer probability formula: where α and β are the weights of pheromone and heuristic information, and allowed k is the set of optional nodes for ant k. Add the selected node to the taboo list and repeat until all target nodes are traversed. Calculate the total path length L of each ant k = ∑d ij , and update the global optimal path L best .

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