Electric power construction engineering material tracking and management system

Abstract

The invention relates to the technical field of tracking management, in particular to an electric power building engineering material tracking and management system, which comprises a material identification module, a material management module, a material management module and a material management module, and is characterized in that all materials entering and exiting from a warehouse are identified and tracked in real time by using an RFID technology to ensure the traceability of material flows; the database management module is used for storing material related information; the material scheduling module is used for automatically analyzing project requirements and optimizing material distribution and scheduling strategies based on a scheduling algorithm; the environmental adaptability analysis module is used for evaluating the influence of different environmental factors on the material performance; the data analysis and prediction module is used for evaluating a material demand trend and price fluctuation by adopting a big data analysis technology; and the material compatibility and life cycle evaluation module is used for evaluating compatibility among different materials. According to the method, the high traceability of the material flow is ensured, the material distribution and scheduling strategy is optimized through the artificial intelligence algorithm, the resource waste is remarkably reduced, and the material use efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tracking management, and in particular to a material tracking and management system for electric power construction projects. Background Art

[0002] In the current field of power construction engineering, material management is one of the key factors for project success. With the increasing scale and complexity of engineering projects, the demand for materials is large, the variety is large, and the use environment is complex. Traditional material management methods have been unable to meet the needs of efficient and accurate project execution. Traditional methods usually rely on manual recording and management, which is not only time-consuming and labor-intensive, but also prone to errors, especially in large projects with a wide variety of materials and frequent circulation. In addition, factors such as the impact of environmental factors on material performance, compatibility issues between materials, and the impact on the environment also need to be fully considered, all of which put higher demands on the material management system.

[0003] Although some automated material management solutions on the market have improved efficiency to a certain extent, most of them are still based on traditional inventory management and logistics tracking, lacking in-depth analysis and optimization of the entire material use cycle. In particular, existing technologies often fail to provide effective support in terms of environmental adaptability, material compatibility assessment, and life cycle cost analysis. In addition, with the popularization of the concept of sustainable construction, there is a growing demand for environmental impact assessment of building materials and optimization of building materials to reduce environmental footprint, and existing management systems often cannot meet these emerging needs. Summary of the invention

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

[0005] The present invention provides the following technical solution: a material tracking and management system for electric power construction projects, comprising:

[0006] Material identification module: Use RFID technology to identify and track all materials entering and leaving the warehouse in real time to ensure the traceability of material flow;

[0007] Database management module: used to store material-related information, including material type, specification, quantity, supplier information and usage items, and supports data retrieval and update;

[0008] Material scheduling module: Based on the scheduling algorithm, it automatically analyzes project requirements, optimizes material allocation and scheduling strategies, reduces waste, and improves utilization efficiency;

[0009] Environmental adaptability analysis module: evaluates the impact of different environmental factors on material performance to ensure the suitability and durability of materials;

[0010] Data analysis and forecasting module: Use big data analysis technology to evaluate material demand trends and price fluctuations to provide support for purchasing decisions;

[0011] Material compatibility and life cycle assessment module: Evaluate the compatibility between different materials, as well as the expected life and environmental impact in the specific project environment, and recommend the optimal material combination based on the specific environment and usage conditions of the project.

[0012] Furthermore, the material identification module specifically includes:

[0013] Identification stage: When materials are put into storage, a unique RFID tag is attached to each material. The RFID tag contains relevant information of the material, including the type, specification, batch number, supplier information and scheduled use items of the material, and is also entered into the database management module;

[0014] Tracking stage: When materials are stored in the warehouse and when they are taken out of the warehouse for use, the RFID readers installed at the warehouse entrance and exit continuously scan the passing materials. Every time an RFID tag passes through the reader, the movement of the materials is automatically recorded, including time, location and flow direction, and updated to the database management module in real time.

[0015] Furthermore, the material scheduling module specifically includes:

[0016] Demand analysis phase: Use scheduling algorithms to conduct in-depth analysis of demand data, including project scale, design specifications, estimated construction period, specific environmental conditions, and the material demand characteristics of the project, including type, quantity, and usage time nodes;

[0017] Inventory assessment phase: synchronously assess the current material inventory status, including the type, quantity, and storage location information of existing materials. The assessment process takes into account the shelf life and storage conditions of the materials to ensure that the scheduled materials meet the usage conditions;

[0018] Scheduling strategy optimization phase: Based on the results of project demand analysis and inventory assessment, the scheduling algorithm generates and optimizes material allocation and scheduling strategies.

[0019] Furthermore, the scheduling algorithm is based on linear programming to optimize the allocation and scheduling of materials, aiming to minimize the total cost while meeting project requirements and inventory constraints. Suppose there are N materials, M project requirements, and the inventory of each material i is S i , the demand for material i by project j is D ij , the goal is to determine the quantity X of each material to be assigned to each item ij , to minimize the total cost, the total cost function is:

[0020] Among them, C is the total cost; C ij is the cost per unit of material i assigned to project j, H i is the storage cost per unit of material i, X ij is a decision variable, indicating the quantity of material i allocated to project j;

[0021] The constraints include:

[0022] Project requirements met: For all

[0023] Stock Limit: For all

[0024] Non-negative constraint: for all i,j,X ij ≥0.

[0025] Furthermore, the environmental adaptability analysis module specifically includes:

[0026] Environmental Factor Identification: Identify key environmental factors that affect material performance, including humidity, temperature, chemical corrosion, and ultraviolet radiation, by collecting and analyzing historical data, project reports, and real-time environmental monitoring data;

[0027] Performance impact model: For each material, a performance impact model is established to describe and predict how environmental factors affect the physical and chemical properties of the material, including strength loss, color fading, and increased corrosion rate;

[0028] Suitability and durability assessment: Using performance impact models, the suitability and expected life of the required materials under the specific environmental conditions of each project are assessed. The assessment process comprehensively considers the climate characteristics, environmental conditions and building structural characteristics of the project location to ensure that the selected materials not only meet the performance requirements at the beginning, but also maintain the necessary performance indicators during the expected service life;

[0029] Optimization recommendations: Based on the evaluation results, optimization recommendations for material use are automatically generated, including the selection of materials that are more suitable for specific environmental conditions, recommendations for protective measures, and guidance on maintenance and replacement cycles.

[0030] Furthermore, the data analysis and prediction module specifically includes:

[0031] Collect historical procurement data, project plan and progress information, market price change data and economic indicators;

[0032] Use data cleaning techniques to process the collected data and remove errors, and use data integration techniques to unify the format of data from different sources and conduct correlation analysis to form a comprehensive data set;

[0033] Use time series to build forecast models of demand trends and price fluctuations on comprehensive data sets, identify key influencing factors and patterns, and provide forecasts of future material demand and prices;

[0034] Execute forecasting models regularly or on demand to generate forecast reports on material demand and prices, analyze forecast results, and identify potential supply and demand conflicts, price peaks or troughs.

[0035] Furthermore, the prediction model is based on the ARIMA model, which includes autoregression AR, difference I and moving average MA, and is expressed as:

[0036] ARIMA(p, d, q) = AR(p) + I(d) + MA(q), where p is the order of the autoregressive term, which represents the relationship between the current value and its previous p values; d is the number of differences, which is the minimum number of differences required to make the time series a stable series; and q is the order of the moving average term, which represents the relationship between the forecast error and its previous q values.

[0037] Specific calculation formula: Among them, y t ' is the sequence after d times of difference, c is the constant term, are the coefficients of the autoregressive term, θ1,θ2,…,θ q is the coefficient of the moving average term, ε t is a random error term.

[0038] Furthermore, the material compatibility and life cycle assessment module specifically includes:

[0039] Compatibility assessment: Collect and analyze the physical and chemical property data of various materials, including thermal expansion coefficient, corrosion resistance, and compressive strength. By setting compatibility assessment standards, compare the interactions and compatibility between different materials and identify material combinations that lead to reduced performance or durability.

[0040] Life cycle and environmental impact assessment: The life cycle assessment (LCA) method is used to evaluate the environmental impact of the entire process of material production, transportation, use and disposal, including carbon footprint, energy consumption and waste generation indicators. At the same time, the impact of the project environment on material performance and life is considered, and the expected life of various materials under actual use conditions is evaluated.

[0041] Furthermore, the material compatibility and life cycle assessment module also includes recommending the optimal material combination based on the results of compatibility assessment and life cycle and environmental impact assessment, using a multi-objective optimization algorithm, and comprehensively considering cost, performance, life and environmental impact.

[0042] Beneficial effects of the present invention:

[0043] Through the integrated application of the material identification module and the material scheduling module, the system can achieve real-time tracking and intelligent scheduling of the entire life cycle of building materials. This not only ensures the high traceability of the material flow, but also optimizes the material allocation and scheduling strategy through artificial intelligence algorithms, significantly reducing resource waste and improving material utilization efficiency.

[0044] The present invention uses big data analysis technology to predict material demand trends and price fluctuations, and provide data support for procurement decisions. This predictive capability helps companies grasp the best procurement time, avoid cost increases caused by price peaks, and effectively manage potential risks in the supply chain.

[0045] The present invention can evaluate the compatibility between different materials, their expected lifespan and environmental impact through the material compatibility and life cycle assessment module, and recommend the optimal material combination, which not only improves the quality and durability of construction projects, but also promotes sustainable environmental development, and is in line with the long-term goals of green buildings and sustainable construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 Schematic diagram of system function modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0049] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0050] like Figure 1 As shown, the power construction project material tracking and management system includes:

[0051] Material identification module: Use RFID technology to identify and track all materials entering and leaving the warehouse in real time to ensure the traceability of material flow;

[0052] Database management module: used to store material-related information, including material type, specification, quantity, supplier information and usage items, and supports data retrieval and update;

[0053] Material scheduling module: Based on the scheduling algorithm, it automatically analyzes project requirements, optimizes material allocation and scheduling strategies, reduces waste, and improves utilization efficiency;

[0054] Environmental adaptability analysis module: evaluates the impact of different environmental factors (such as humidity, temperature, chemical corrosion, etc.) on material performance to ensure the suitability and durability of the material;

[0055] Data analysis and forecasting module: Use big data analysis technology to evaluate material demand trends and price fluctuations to provide support for purchasing decisions;

[0056] Material compatibility and life cycle assessment module: Evaluate the compatibility between different materials, as well as the expected life and environmental impact in the specific project environment, and recommend the optimal material combination based on the specific environment and usage conditions of the project.

[0057] The material identification module specifically includes:

[0058] Identification stage: When materials are put into storage, a unique RFID tag is attached to each material. The RFID tag contains relevant information of the material, including the type, specification, batch number, supplier information and scheduled use items of the material, and is also entered into the database management module;

[0059] Tracking stage: When materials are stored in the warehouse and when they are taken out of the warehouse for use, the RFID readers installed at the warehouse entrance and exit continuously scan the passing materials. Every time the RFID tag passes through the reader, the movement of the materials is automatically recorded, including time, location and flow direction, and updated to the database management module in real time;

[0060] When it is necessary to query the flow or historical records of materials, users can enter relevant information of the materials through the system interface or directly scan the RFID tag of the materials. The system will then retrieve and display the complete flow history of the materials from the database management module, including detailed records of entry, storage, exit and use.

[0061] The method also includes integrating the real-time tracking data obtained through RFID technology with the data of the material scheduling module, environmental adaptability analysis module, data analysis and prediction module, and material compatibility and life cycle assessment module. Through cross-analysis and utilization of data, the efficiency and accuracy of material management can be further improved, while the accuracy of suitability assessment and prediction of material use can be enhanced.

[0062] The material scheduling module specifically includes:

[0063] Demand analysis phase: Use scheduling algorithms to conduct in-depth analysis of demand data, including project scale, design specifications, estimated construction period, specific environmental conditions, and the material demand characteristics of the project, including type, quantity, and usage time nodes;

[0064] Inventory assessment phase: synchronously assess the current material inventory status, including the type, quantity, and storage location information of existing materials. The assessment process takes into account the shelf life and storage conditions of the materials to ensure that the scheduled materials meet the usage conditions;

[0065] Scheduling strategy optimization stage: Based on the results of project demand analysis and inventory evaluation, the scheduling algorithm generates and optimizes material allocation and scheduling strategies. This strategy aims to match project demand with inventory status, optimize the order and timing of material allocation, and reduce over-purchasing and storage costs.

[0066] During the material scheduling process, the material usage status and project progress are monitored in real time. In case of special circumstances or deviations, the scheduling algorithm can dynamically adjust the scheduling strategy based on the latest data. In addition, the system continuously collects scheduling execution data, feeds back learning and optimization algorithm models, and improves the accuracy and efficiency of future scheduling strategies.

[0067] Integration with data from other modules: The method also includes integrating the data from the material scheduling module with the data from the material identification module, database management module, environmental adaptability analysis module, and data analysis and prediction module. This integration not only improves the overall efficiency of material management, but also ensures the optimization of scheduling strategies and enhances the accuracy of forecasting future demand.

[0068] The scheduling algorithm is based on linear programming and optimizes the allocation and scheduling of materials, aiming to minimize total cost while meeting project requirements and inventory constraints;

[0069] Specifically, there are N kinds of materials, M project requirements, and the inventory of each material i is S i , the demand for material i by project j is D ij , the goal is to determine the quantity X of each material to be assigned to each item ij , in order to minimize the total cost (including procurement cost and storage cost), the total cost function is:

[0070] Among them, C is the total cost; C ij is the cost per unit of material i assigned to project j, H i is the storage cost per unit of material i, X ij is a decision variable, indicating the quantity of material i allocated to project j;

[0071] The constraints include:

[0072] Project requirements met: For all

[0073] Stock Limit: For all

[0074] Non-negative constraint: for all i,j,X ij ≥0.

[0075] In the material scheduling module of the present invention, the algorithm first analyzes the specific requirements of the project. ij and the existing material inventory S i , and then apply the above linear programming model to calculate. This can automatically determine how each material should be allocated to each project X ij , and whether more material needs to be purchased to meet demand while minimizing total cost.

[0076] During this process, the scheduling algorithm can dynamically adjust the cost parameter C based on historical data and current market conditions. ij and H i , to reflect the real-time changes in material costs and the optimization of storage costs. In this way, not only can the effective allocation and scheduling of materials be achieved, but also the cost can be minimized and the resource utilization rate can be improved while ensuring the smooth progress of the project.

[0077] The environmental adaptability analysis module specifically includes:

[0078] Environmental Factor Identification: Identify key environmental factors that affect material performance, including humidity, temperature, chemical corrosion, and ultraviolet radiation, by collecting and analyzing historical data, project reports, and real-time environmental monitoring data;

[0079] Performance impact model: For each material, a performance impact model is established to describe and predict how environmental factors affect the physical and chemical properties of the material, including strength loss, color fading, and increased corrosion rate;

[0080] The steps for establishing the performance impact model include:

[0081] Data collection includes experimental design and data acquisition;

[0082] Experimental Design: If new data are needed, design and perform experiments to measure the performance of the material under different environmental conditions. Make sure the experimental design adequately covers all combinations of conditions of interest.

[0083] Data Acquisition: In addition to laboratory testing, data can be obtained from published research reports, databases, or other sources.

[0084] Feature engineering includes feature selection, data preprocessing, and feature conversion;

[0085] Feature selection: Based on the problem definition and the results of the literature review, the most relevant features are selected.

[0086] Data preprocessing: clean data, handle missing values ​​and outliers, and perform standardization or normalization operations.

[0087] Feature transformation: creating new features or transforming existing features to improve the explanatory power of the model, such as dimensionality reduction through principal component analysis (PCA).

[0088] Model selection and training includes model selection and model training;

[0089] Model selection: Choose an appropriate statistical model or machine learning model based on the characteristics of the problem. For linear relationships, linear regression can be used, while for nonlinear relationships, support vector machines (SVM), neural networks, etc. can be considered.

[0090] Model training: Use the selected dataset to train the model. During the training process, hyperparameters can be adjusted through methods such as cross-validation to optimize model performance.

[0091] Model evaluation includes evaluation indicators, verification and testing;

[0092] Evaluation metrics: Choose appropriate evaluation metrics to measure the performance of the model, such as mean square error (MSE), R-squared (R^2), etc.

[0093] Verification and testing: Use an independent data set (validation set or test set) to test the generalization ability of the model.

[0094] The model that meets the verification and testing requirements is set as the performance impact model;

[0095] Suitability and durability assessment: Using performance impact models, the suitability and expected life of the required materials under the specific environmental conditions of each project are assessed. The assessment process comprehensively considers the climate characteristics, environmental conditions and building structural characteristics of the project location to ensure that the selected materials not only meet the performance requirements at the beginning, but also maintain the necessary performance indicators during the expected service life;

[0096] Optimization recommendations: Based on the evaluation results, optimization recommendations for material use are automatically generated, including the selection of materials that are more suitable for specific environmental conditions, recommendations for protective measures, and guidance on maintenance and replacement cycles.

[0097] The data analysis and prediction module specifically includes:

[0098] Collect historical procurement data, project plans and progress information, market price change data, and economic indicators. These data come from internal records, public databases, and market research reports, providing rich basic data for analysis;

[0099] Use data cleaning techniques to process the collected data and remove errors, and use data integration techniques to unify the format of data from different sources and conduct correlation analysis to form a comprehensive data set;

[0100] Use time series to build forecast models of demand trends and price fluctuations on comprehensive data sets, identify key influencing factors and patterns, and provide forecasts of future material demand and prices;

[0101] Execute forecasting models regularly or on demand to generate forecast reports on material demand and prices, analyze forecast results, and identify potential supply and demand conflicts, price peaks or troughs;

[0102] Based on the forecast results, the system provides procurement recommendations, including the best procurement time, quantity adjustment recommendations, and potential supplier selection, etc. These recommendations take into account cost-effectiveness, risk management, and inventory strategies, aiming to help procurement departments make more scientific and economical decisions.

[0103] The forecasting model is based on the ARIMA model, which includes autoregression AR, difference I and moving average MA, expressed as:

[0104] ARIMA(p, d, q) = AR(p) + I(d) + MA(q), where p is the order of the autoregressive term, which represents the relationship between the current value and its previous p values; d is the number of differences, which is the minimum number of differences required to make the time series a stable series; and q is the order of the moving average term, which represents the relationship between the forecast error and its previous q values.

[0105] Specific calculation formula: Among them, y t ' is the sequence after d times of difference, c is the constant term, are the coefficients of the autoregressive term, θ1,θ2,…,θ q is the coefficient of the moving average term, ε t is a random error term.

[0106] The material compatibility and life cycle assessment module specifically includes:

[0107] Compatibility assessment: Collect and analyze the physical and chemical property data of various materials, including thermal expansion coefficient, corrosion resistance, and compressive strength. By setting compatibility assessment standards, compare the interactions and compatibility between different materials and identify material combinations that lead to reduced performance or durability.

[0108] Life cycle and environmental impact assessment: The life cycle assessment (LCA) method is used to evaluate the environmental impact of the entire process of material production, transportation, use and disposal, including carbon footprint, energy consumption and waste generation indicators. At the same time, the impact of the project environment on material performance and life is considered, and the expected life of various materials under actual use conditions is evaluated.

[0109] Among them, Life Cycle Assessment (LCA) is a systematic environmental management tool used to evaluate the environmental impact of products, processes or services throughout their life cycle. The LCA method can help decision makers understand the impact of different options on the environment and provide a basis for improving design and reducing negative impacts; it includes impact assessment and interpretation;

[0110] Among them, impact assessment:

[0111] Classification: Classify the data in the inventory into different environmental impact categories, such as global warming potential, acidification, eutrophication, etc.

[0112] Characterization: Quantify each type of environmental impact into corresponding environmental impact indicators.

[0113] Normalization: If necessary, convert different types of environmental impacts into comparable forms to facilitate comprehensive assessment.

[0114] Weight allocation: Assign weight values ​​to different environmental impacts to reflect their relative importance based on social preferences or expert opinions.

[0115] explain:

[0116] Results summary: Summarize the results of the LCA and identify the main sources of environmental impacts.

[0117] Sensitivity analysis: Assess the impact of key assumptions and data uncertainties on the results.

[0118] Conclusions and recommendations: Provide improvement suggestions based on the LCA results, which may include recommendations on product design, process improvement, or supply chain management.

[0119] The material compatibility and life cycle assessment module also includes the use of multi-objective optimization algorithms based on the results of compatibility assessment and life cycle and environmental impact assessment, and recommends the optimal material combination by comprehensively considering cost, performance, life and environmental impact.

[0120] Provide decision makers with detailed assessment reports and recommendation reasons, including comparisons of the advantages and disadvantages of various material combinations, cost-benefit analysis, and expected performance and life predictions. The report also includes an assessment of potential risks and uncertainties to help decision makers make more informed choices.

[0121] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0122] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The material tracking and management system for power construction projects is characterized by: include: Material identification module: used to identify and track all materials entering and leaving the warehouse in real time using RFID technology; Database management module: used to store material-related information, including material type, specification, quantity, supplier information and usage items, and supports data retrieval and update; Material scheduling module: used to automatically analyze project requirements and optimize material allocation and scheduling strategies based on scheduling algorithms; Environmental adaptability analysis module: used to evaluate the impact of different environmental factors on material performance; Data analysis and forecasting module: used to evaluate material demand trends and price fluctuations using big data analysis technology; Material compatibility and life cycle assessment module: used to evaluate the compatibility between different materials, as well as the expected life and environmental impact in a specific project environment, and recommend the optimal material combination based on the specific environment and usage conditions of the project.

2. The electric power construction project material tracking and management system according to claim 1 is characterized in that: The material identification module comprises: Identification stage: When materials are put into storage, a unique RFID tag is attached to each material. The RFID tag contains relevant information of the material, including the type, specification, batch number, supplier information and scheduled use items of the material, and is also entered into the database management module; Tracking stage: When materials are stored in the warehouse and when they are taken out of the warehouse for use, the RFID readers installed at the warehouse entrance and exit continuously scan the passing materials. Every time an RFID tag passes through the reader, the movement of the materials is automatically recorded, including time, location and flow direction, and updated to the database management module in real time.

3. The electric power construction project material tracking and management system according to claim 2, characterized in that: The material scheduling module includes: Demand analysis phase: Use scheduling algorithms to conduct in-depth analysis of demand data, including project scale, design specifications, estimated construction period, specific environmental conditions, and the material demand characteristics of the project, including type, quantity, and usage time nodes; Inventory assessment phase: synchronously assess the current material inventory status, including the type, quantity, and storage location information of existing materials. The assessment process takes into account the shelf life and storage conditions of the materials to ensure that the scheduled materials meet the usage conditions; Scheduling strategy optimization phase: Based on the results of project demand analysis and inventory assessment, the scheduling algorithm generates and optimizes material allocation and scheduling strategies.

4. The electric power construction project material tracking and management system according to claim 3 is characterized in that: The scheduling algorithm is based on linear programming and optimizes the allocation and scheduling of materials, aiming to minimize total cost while meeting project requirements and inventory constraints. Suppose there are N materials, M project requirements, and the inventory of each material i is S i , the demand for material i by project j is D ij , the goal is to determine the quantity X of each material to be assigned to each item ij , to minimize the total cost.

5. The electric power construction project material tracking and management system according to claim 1, characterized in that: The environmental adaptability analysis module includes: Environmental Factor Identification: Identify key environmental factors that affect material performance, including humidity, temperature, chemical corrosion, and ultraviolet radiation, by collecting and analyzing historical data, project reports, and real-time environmental monitoring data; Performance impact model: For each material, a performance impact model is established to describe and predict how environmental factors affect the physical and chemical properties of the material, including strength loss, color fading, and increased corrosion rate; Suitability and durability assessment: Using performance impact models, the suitability and expected life of the required materials under the specific environmental conditions of each project are assessed. The assessment process comprehensively considers the climate characteristics, environmental conditions and building structural characteristics of the project location to ensure that the selected materials not only meet the performance requirements at the beginning, but also maintain the necessary performance indicators during the expected service life; Optimization recommendations: Based on the evaluation results, optimization recommendations for material use are automatically generated, including the selection of materials that are more suitable for specific environmental conditions, recommendations for protective measures, and guidance on maintenance and replacement cycles.

6. The electric power construction project material tracking and management system according to claim 1, characterized in that: The data analysis and prediction module includes: Collect historical procurement data, project plan and progress information, market price change data and economic indicators; Data cleaning techniques are used to process the collected data, and data integration techniques are used to unify the data from different sources into a unified format and conduct correlation analysis to form a comprehensive data set; Use time series to build forecast models of demand trends and price fluctuations on comprehensive data sets, identify key influencing factors and patterns, and provide forecasts of future material demand and prices; Execute forecasting models regularly or on demand to generate forecast reports on material demand and prices, analyze forecast results, and identify potential supply and demand conflicts, price peaks or troughs.

7. The electric power construction project material tracking and management system according to claim 6, characterized in that: The prediction model is based on the ARIMA model, which includes autoregression AR, difference I and moving average MA, expressed as: ARIMA(p, d, q) = AR(p) + I(d) + MA(q), where p is the order of the autoregressive term, which represents the relationship between the current value and its previous p values; d is the number of differences, which is the minimum number of differences required to make the time series a stable series; and q is the order of the moving average term, which represents the relationship between the forecast error and its previous q values. Specific calculation formula: Among them, y t ′ is the sequence after d times of difference, c is a constant term, are the coefficients of the autoregressive term, θ1,θ2,…,θ q is the coefficient of the moving average term, ε t is a random error term.

8. The electric power construction project material tracking and management system according to claim 7, characterized in that: The material compatibility and life cycle assessment module includes: Compatibility assessment: Collect and analyze the physical and chemical property data of various materials, including thermal expansion coefficient, corrosion resistance, and compressive strength. By setting compatibility assessment standards, compare the interactions and compatibility between different materials and identify material combinations that lead to reduced performance or durability. Life cycle and environmental impact assessment: The life cycle assessment (LCA) method is used to evaluate the environmental impact of the entire process of material production, transportation, use and disposal, including carbon footprint, energy consumption and waste generation indicators. At the same time, the impact of the project environment on material performance and life is considered, and the expected life of various materials under actual use conditions is evaluated.

9. The electric power construction project material tracking and management system according to claim 8, characterized in that: The material compatibility and life cycle assessment module also includes recommending the optimal material combination based on the results of compatibility assessment and life cycle and environmental impact assessment, using a multi-objective optimization algorithm, and comprehensively considering cost, performance, life and environmental impact.

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