A turnover material management system and method

Abstract

The application discloses a kind of turnover material management system and method, including user login module, main interface navigation module, material plan management module, in and out of the scene and warehouse management module, data statistics and analysis module, multilevel dictionary management module, user authority management module.The application has beneficial effects: the application realizes the whole process tracking of material from plan, warehousing, warehousing to the scene by digitalization account record, multidimensional statistical analysis and visualization presentation technology.System specially designed building-floor-part three-level linkage selection mechanism and warehouse four-level positioning system, solve the problem of material location information fuzzy in traditional management.Innovative integration real-time loss rate algorithm and dynamic early warning function, can reduce material waste by more than 15%, improve inventory turnover efficiency by 30%.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building construction management, in particular to a turnover material management system and method. BACKGROUND

[0002] In the process of building construction, the management of turnover materials is crucial. The traditional turnover material management method has the problems of low efficiency, inaccurate data, and difficulty in multi-dimensional analysis, and cannot meet the needs of modern building construction for fine management of turnover materials. For example, in the aspects of material planning, entering and leaving the site and warehouse management, loss control and scheduling decision, there is a lack of effective information means support, leading to problems such as resource waste and cost increase.

[0003] The traditional turnover material management has the following defects: manual recording leads to data lag and error; lack of dynamic statistical analysis, unable to effectively estimate material demand, loss and surplus; multi-warehouse coordination efficiency is low, it is difficult to track the dynamic flow path of materials and the location of material retention; extensive permission management, data security risk exists.

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

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

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] A turnover material management system, comprising:

[0008] A user login module for supporting password input of uppercase and lowercase letters, numbers and symbol combinations through multi-role permission authentication;

[0009] A main interface navigation module including multi-level menu functions, specifically including material plan input, entering and leaving the site account management, warehouse account management, statistical analysis board, multi-level dictionary management and user management;

[0010] A material plan management module supporting the input of material parts, quantity and use date through three-level linkage selection of building-floor-part, and associating multi-level dictionary data;

[0011] An entering and leaving the site and warehouse management module supporting four-level warehouse position linkage input, batch deletion and multi-dimensional filtering function;

[0012] A data statistics and analysis module for multi-dimensional comparative analysis of key indicators such as material inventory, loss rate and planned quantity through column chart, line chart and other visualization tools;

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

[0014] User permission management module realizes the dynamic configuration of user roles and function access permissions by administrators.

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

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

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

[0018] S1, multi-source data acquisition, obtain the basic attributes of turnover materials, Internet of Things sensor data and business records;

[0019] S2, dynamic model calculation, real-time calculation of loss rate based on four-dimensional loss model;

[0020] S3, online correction, dynamically adjust model parameters through reinforcement learning.

[0021] The formula for real-time calculation of loss rate in step S2 is:

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

[0023] LR(t), represents the real-time loss rate of turnover materials at the current time t, which is the result of comprehensive multi-dimensional factor calculation;

[0024] α, β, γ, δ are the weight coefficients of each dimension, which are dynamically optimized through algorithms such as reinforcement learning and 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, which reflects the influence of environmental factors such as temperature, humidity, vibration, and light on the loss of turnover materials, for example, humid environment accelerates metal corrosion, and frequent vibration leads to structural loosening and loss;

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

[0028] LR abn is an abnormal event loss weight, a loss correction value set for abnormal situations such as loss, violation of operation, and overuse, for example, when the turnover material is lost or used incorrectly, the value will be significantly increased.

[0029] A method of a turnover material management system, the method steps are:

[0030] S21, full-scene data acquisition, deploying Internet of Things terminals to acquire turnover material position, state, and environmental parameters in real time; integrating external data sources such as construction progress, weather, and equipment utilization rate;

[0031] S22, dynamic priority evaluation, constructing a mathematical model containing dimensions such as emergency degree, use 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, generating an optimal deployment path using an improved ant colony algorithm, and dynamically adjusting the scheduling strategy combined with reinforcement learning to adapt to real-time changes.

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

[0034] Wherein the formula based on the analytic hierarchy process is P = α × E + β × F + γ × C + δ × S;

[0035] Wherein E is the emergency degree, F is the use frequency, C is the maintenance cost, and S is the remaining life.

[0036] Wherein the step S23 improved ant colony algorithm step is:

[0037] S31, problem modeling and parameter initialization;

[0038] S32, ant path construction;

[0039] S33, local search optimization;

[0040] S34, dynamic pheromone update.

[0041] Wherein step S31 takes the turnover material yard, construction project point, etc. as the graph node, and the road distance between nodes, transportation cost, etc. as the edge weight value, forming a weighted directed graph (G(V,E); setting the number of ants m, the maximum iteration number Tmax, the initial pheromone τ ij (0), the heuristic factor η ij = 1 / d ij , wherein d ij is the distance from node i to j), ρ is the pheromone evaporation coefficient, L bestFor global optimal path.

[0042] Wherein step S32 initializes the tabu table Tabu for each ant k k Randomly select the starting point to join the tabu table, and select the next node in the selectable node according to the transition probability formula:

[0043] Wherein alpha and beta are the weights of pheromone and heuristic information, allowed k The selectable node set of the ant k, the selected node is added to the tabu table, and the repetition is performed until all target nodes are traversed, and the total length L of the path of each ant is calculated k =∑d ij Update the global optimal path L best .

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

[0045] 1. The function visibility is controlled through the role permission matrix (as shown in the following table); information entry and processing: through the entry of material turnover information, the data is integrated and processed to calculate the floor balance, loss amount, loss amount, and material use workday; visual board: through the generation of real-time interactive charts, the plan amount, use amount, loss amount, loss rate, balance analysis, and display are supported.

[0046] 2. Through multi-level menu navigation, linkage entry and other functions, manual operation is reduced, data entry and query efficiency is improved, multi-dimensional analysis is performed on turnover material data by using visual tools and real-time loss rate calculation algorithm, and accurate basis is provided for decision-making.

[0047] 3. Improved ant colony algorithm and reinforcement learning are adopted to generate the optimal deployment path, dynamically adjust the scheduling strategy, reduce transportation cost and resource waste; an abnormal closed-loop management mechanism is established, abnormal events are predicted through a Bayesian network, problems are found and handled in a timely manner, and normal use of turnover materials is ensured; the multi-level dictionary management module supports dynamic expansion of levels and adapts to changing needs of different projects and businesses. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 Is the system composition block diagram of the present application;

[0049] Figure 2 Is the system architecture diagram of the present application

[0050] Figure 3 Is the data flow diagram between modules of the present application

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

[0052] Figure 5 is a material plan entry interface schematic diagram of the present application

[0053] Figure 6 is a material in-out entry interface schematic diagram of the present application

[0054] Figure 7 is a material in-out warehouse entry interface schematic diagram of the present application

[0055] Figure 8 is a material in-out data display interface schematic diagram of the present application

[0056] Figure 9 is a material usage analysis display interface schematic diagram of the present application

[0057] Figure 10 is a material usage and loss analysis display interface schematic diagram of the present application. DETAILED DESCRIPTION

[0058] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0059] The embodiments of the present application will be described in detail below in combination with the drawings, but the present application can be implemented in various different ways limited and covered by the claims.

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

[0061] A user login module is configured to support password input of uppercase and lowercase letters, numbers and symbol combinations through multi-role permission authentication.

[0062] A main interface navigation module comprises a multi-level menu function, specifically including material plan entry, in-out account management, in-out warehouse account management, statistical analysis board, multi-level dictionary management and user management.

[0063] A material plan management module supports entry of material position, quantity and use date through three-level linkage selection of building-floor-position, and is associated with multi-level dictionary data.

[0064] An in-out and in-out warehouse management module supports four-level warehouse location linkage entry, batch deletion and multi-dimensional filtering function.

[0065] A data statistics and analysis module performs multi-dimensional comparative analysis on key indicators such as material inventory, loss rate and planned usage through visualization tools such as column chart and line chart.

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

[0067] User permission management module, realize the dynamic configuration of administrator to user role and function access permission. Through multi-role permission authentication to ensure system security, support complex password to improve account protection ability; multi-level menu navigation realizes the rapid positioning of function module, improves the operation efficiency; three-level linkage entry ensures the accurate matching of material plan to construction part, reduces human error; four-level warehouse location linkage realizes the whole process tracking of turnover material, improves the precision of inventory management; visual data analysis module supports data-driven decision-making, improves resource allocation efficiency; tree dictionary structure supports dynamic expansion of material classification, adapts to diversified management needs; dynamic permission configuration realizes fine-grained function control, ensures data security.

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

[0069] Among them, the data statistics and analysis module has a built-in real-time loss rate calculation algorithm, and generates dynamic early warning according to the floor-part dimension.

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

[0071] S1, multi-source data acquisition, obtain the basic attributes of turnover material, Internet of Things sensor data and business records;

[0072] S2, dynamic model calculation, real-time calculation of loss rate based on four-dimensional loss model;

[0073] S3, online correction, dynamically adjust model parameters through reinforcement learning.

[0074] Real-time loss rate calculation provides immediate warning to avoid the accumulation of hidden loss; floor-part dimension warning realizes accurate positioning of problem areas, improves response speed; multi-dimensional data fusion analysis excavates loss rules, optimizes maintenance strategy. Multi-source data acquisition builds a comprehensive loss evaluation model, covering physical properties, environment, use scenarios and other dimensions; four-dimensional loss model realizes loss reason traceability, supports targeted improvement measures Online correction mechanism ensures continuous optimization of the model, adapts to different construction stage needs.

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

[0076] LR(t) = a.LR base + b.LR enυ + g.LR use + d.LR abn

[0077] Where LR(t) represents the real-time loss rate of the turnover material at the current time t, which is the result calculated by considering multiple factors.

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

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

[0080] LR enυ For example, the environmental factor loss coefficient reflects the impact 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 leads to loosening and loss of structural components.

[0081] LR use The strength loss index is related to the actual usage of the reusable material, such as usage time, load intensity, and operating frequency. For example, frequent disassembly of formwork and prolonged equipment operation will increase this value.

[0082] LR abn The abnormal event loss weighting is a loss correction value set for abnormal situations such as loss, improper operation, and overdue use. For example, this value will be significantly increased when turnover materials are lost or misused. The weighting formula enables quantitative assessment of different influencing factors, improving the scientific nature of loss calculation; the environmental factor coefficient reflects the impact of external conditions, supporting environmentally adaptable maintenance; the intensity index is used to quantify equipment load status, optimizing equipment scheduling strategies; and the abnormal event weighting strengthens the handling of special situations, reducing losses due to human error.

[0083] A method for managing turnover materials, the steps of which are as follows:

[0084] S21. Full-scenario data collection: Deploy IoT terminals to obtain real-time information on the location, status, and environmental parameters of reusable materials; integrate external data sources such as construction progress, weather, and equipment utilization.

[0085] S22. Dynamic priority assessment: Construct a mathematical model that includes dimensions such as urgency, frequency of use, and maintenance cost, and calculate the priority coefficient of each turnover material based on the analytic hierarchy process (AHP).

[0086] S23. Intelligent scheduling decision-making: The improved ant colony algorithm is used to generate the optimal allocation path, and the scheduling strategy is dynamically adjusted by combining reinforcement learning to adapt to real-time changes.

[0087] S23. Abnormal closed-loop management: Establish a closed-loop mechanism of "early warning-response-handling-review" and predict the probability of abnormal events through Bayesian networks.

[0088] Full-scene data acquisition constructs a digital twin management system to support real-time state monitoring; analytic hierarchy process priority evaluation realizes resource hierarchical management to improve emergency response capability; improved ant colony algorithm optimizes path planning to reduce transportation cost by more than 30%; closed-loop management mechanism realizes abnormal event life cycle management and control to reduce repeated failures.

[0089] wherein the formula based on the analytic hierarchy process is P = a x E + b x F + g x C + d x S;

[0090] wherein E is the urgency, F is the frequency of use, C is the maintenance cost, and S is the remaining life.

[0091] The remaining life parameter is introduced to extend the service life of the equipment and reduce asset waste; the weight coefficient can be configured to support flexible switching of different management strategies.

[0092] wherein step S23 is improved ant colony algorithm step:

[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, shortens path planning time, improves algorithm convergence speed with pheromone update mechanism, and avoids path repetition with tabu table strategy to ensure the feasibility of the scheme.

[0098] wherein step S31 takes the turnover material yard, construction project point, etc. as the graph nodes, and the road distance between nodes, transportation cost, etc. as the edge weight value 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), the heuristic factor η ij =1 / d ij , wherein d ij is the distance from node i to j), p is the pheromone evaporation coefficient, and L best is the global optimal path.

[0099] Graph theory modeling realizes digital mapping of physical space to support complex scene path planning; multi-parameter edge weight value (distance / cost) provides multi-objective optimization options; initial parameter configuration standardization reduces the algorithm usage threshold.

[0100] wherein step S32 initializes the tabu table Tabu k, randomly select the starting point to join the tabu table, in the optional node, the next node is selected according to the transition probability formula:

[0101] Where alpha, beta are the weights of pheromone and heuristic information, allowed k The selected node is added to the tabu table, and the repetition is repeated until all target nodes are traversed, and the total length L of each ant path is calculated k = ∑d ij The global optimal path L is updated best .

[0102] The transition probability formula balances phermonone guidance and heuristic search, improves the quality of the solution; the dynamic updating of the tabu table guarantees the legality of the path and avoids the deadlock phenomenon; the global optimal path tracking mechanism continuously optimizes the historical scheme and forms knowledge accumulation.

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

[0104] 1. The function visibility is controlled by the role permission matrix (as shown in the following table); information input and processing: through the input of material turnover information, the data is integrated and processed to calculate the floor balance, loss, loss, and material use workday; visual board: through the generation of real-time interactive charts, the plan quantity, usage quantity, loss, loss rate, balance analysis and display are supported.

[0105] 2. Through the functions of multi-level menu navigation and linkage input, the manual operation is reduced, the data input and query efficiency is improved, the visual tool and real-time loss rate calculation algorithm are used to perform multi-dimensional analysis on the turnover material data, and accurate basis is provided for decision-making.

[0106] 3. Improved ant colony algorithm and reinforcement learning are adopted to generate the optimal allocation path, dynamically adjust the scheduling strategy, reduce the transportation cost and resource waste; an abnormal closed loop management mechanism is established, abnormal events are predicted through Bayesian network, problems are found and handled in time, and the normal use of turnover materials is ensured; the multi-level dictionary management module supports dynamic expansion of levels and adapts to the changing needs of different projects and businesses

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

[0108] Role Accessible module Administrator Full function Project manager Plan review, statistical analysis Material man Inbound / outbound record, current location inquiry

[0109] After the user inputs the account and password in the login interface, the system verifies the identity through encryption; the material staff inputs the in-out field, warehouse in-out record and associates to time, use location; the administrator checks the whole project material turnover rate report through the data board, optimizes the allocation strategy; generates daily report according to the data board, including warning list and to-be-processed task item.

[0110] (I) System deployment and user login

[0111] The turnover material management system is deployed on a server, and users access the system login page through a browser. Enter the username and password containing a combination of uppercase and lowercase letters, numbers, and symbols on the login page. The system performs multi-role permission authentication, and after authentication, it enters the main interface.

[0112] (II) Material plan development

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

[0114] (III) In and out of the scene and warehouse management

[0115] In the "In and Out of the Scene Account Management" and "In and Out of the Warehouse Account Management" functions, use four-level warehouse location linkage to enter the turnover material's in and out of the scene and warehouse information. Support batch deletion operation to facilitate the cleaning of invalid data. Users can quickly find the required turnover material information through multi-dimensional filtering 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 column charts and line charts to display analysis results, and generate dynamic warnings based on floor-part dimensions to remind managers to pay attention to abnormal situations.

[0118] (V) Multi-level dictionary management

[0119] Administrators can add, delete, and search keywords for material categories in the "Multi-level Dictionary Management" function. Store material categories through tree structure, support dynamic expansion of levels, and associate with the drop-down selection function of material plan entry to facilitate user management of 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 functions and data within their permission range.

[0122] (VII) Turnover material scheduling decision

[0123] The system obtains the position, state, environmental parameters and other information of the turnover materials through full-scene data acquisition, and integrates external data sources such as construction progress, weather, and equipment utilization. Based on the analytic hierarchy process, the priority coefficient of each turnover material is calculated, the improved ant colony algorithm is used to generate the optimal deployment path, and the scheduling strategy is dynamically adjusted combined with reinforcement learning. At the same time, an abnormal closed-loop management mechanism is established, the abnormal event occurrence probability is predicted through the Bayesian network, and the abnormal situation is handled in time.

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

[0125] Although embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A turnover material management system, comprising: a user login module for supporting password input of combinations of uppercase and lowercase letters, numbers and symbols through multi-role permission authentication; a main interface navigation module including multi-level menu functions, specifically including material plan input, in-out yard account management, warehouse in-out account management, statistical analysis board, multi-level dictionary management and user management; a material plan management module supporting the input of material position, quantity and use date through three-level linkage selection of building-floor-position, and associating multi-level dictionary data; an in-out yard and warehouse management module supporting four-level warehouse location linkage input, batch deletion and multi-dimensional filtering functions; a data statistics and analysis module for multi-dimensional comparative analysis of key indicators of material inventory, loss rate and planned consumption through column chart and line chart visualization tools; a multi-level dictionary management module supporting the addition, deletion and keyword search functions of hierarchical categories; a user permission management module for dynamic configuration of user roles and function access permissions by administrators; wherein the multi-level dictionary management module stores material categories through a tree structure, supports dynamic expansion of levels and is associated with the drop-down selection function of material plan input; wherein the data statistics and analysis module has a built-in real-time loss rate calculation algorithm and generates dynamic warnings according to the floor-position dimension; wherein the steps of the real-time loss rate calculation algorithm are: S1, multi-source data collection to obtain turnover material basic attributes, Internet of Things sensor data and business records; S2, dynamic model calculation to calculate the loss rate in real time based on a four-dimensional loss model; S3, online correction to dynamically adjust model parameters through reinforcement learning; wherein the formula for real-time calculation of loss rate in step S2 is: ； wherein represents the real-time loss rate of the turnover material at the current time t, which is a result calculated by comprehensively considering multiple factors. a reference loss rate calculated based on historical data; is an environmental factor loss coefficient, reflecting the influence of temperature, humidity, vibration, and light on the loss of the turnover material; Using the intensity loss index, the actual use of the turnover material is related, including the use time, the load intensity, the operation frequency; Abnormal event loss weight, loss correction value set for loss, violation of operation, overuse of abnormal situation; The turnover material management method further comprises the following steps: S21, full-scene data collection to deploy Internet of Things terminals to obtain turnover material position, state and environmental parameters in real time; integrate construction progress, weather, equipment utilization rate and external data sources; S22, dynamic priority evaluation to build a mathematical model including emergency level, usage frequency and maintenance cost dimensions, and calculate the priority coefficient of each turnover material based on the analytic hierarchy process; S23, intelligent scheduling decision to generate an optimal deployment path using an improved ant colony algorithm, and dynamically adjust the scheduling strategy using reinforcement learning to adapt to real-time changes; S23, abnormal closed-loop management to establish a closed-loop mechanism of "warning-response-disposal-review" to predict the probability of abnormal events through Bayesian networks; wherein the formula based on the analytic hierarchy process is ； wherein E is the emergency level, F is the usage frequency, C is the maintenance cost, and S is the remaining life; wherein the steps of the improved ant colony algorithm in step S23 are: S31, problem modeling and parameter initialization; S32, ant path construction; S33, local search optimization; S34, dynamic pheromone update; In step S31, the turnover material yard and the construction project site are taken as graph nodes, and the road distance and transportation cost between nodes are taken as edge weights to form a weighted directed graph ; Set the number of ants m, the maximum number of iterations , pheromone initial , heuristic factor , wherein is the distance from node i to j, is the pheromone evaporation coefficient, is the global optimal path; wherein step S32 initializes the tabu list for each ant k , randomly selects a starting point to join the tabu list, and in the selectable nodes, selects the next node according to the transition probability formula: ; wherein , is the weight of pheromone and heuristic information, is the set of selectable nodes for ant k, the selected node is added to the tabu list, and the process is repeated until all target nodes are traversed, the total length of the path for each ant is calculated , and the global optimal path is updated .

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