Collaborative optimization method for power grid infrastructure project material flow

Abstract

The invention provides a power grid infrastructure project material flow collaborative optimization method, which comprises the steps of collecting historical material use data, market quotation data and project progress data, and constructing and analyzing a data set; training the data set by using an improved meta-learning technology, and generating material demand prediction; based on demand prediction, historical data and market conditions of suppliers are analyzed, and suppliers and an optimal purchasing scheme are recommended; a transportation path is optimized through an improved Dijkstra algorithm; an intelligent warehouse management system is adopted to manage warehousing, ex-warehouse and checking processes, and real-time monitoring of material positions and states is realized through the Internet of Things; a self-adaptive optimization algorithm based on federal learning is utilized to process and analyze material flow data in real time, and data sharing and collaborative optimization of a plurality of distributed nodes are realized. According to the method, data analysis, machine learning and intelligent system technologies are combined, and the efficiency and accuracy of the power grid infrastructure project material flow are improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid infrastructure project management, and in particular to a method for collaborative optimization of material flows for power grid infrastructure projects. Background Art

[0002] As the demand for electricity in modern society continues to grow, the scale and complexity of power grid infrastructure projects are also increasing. In this context, the management of materials for power grid infrastructure projects has become particularly important. Traditional material management methods usually rely on manual operations and empirical judgments, which are inefficient and prone to problems such as material shortages or surpluses, supply chain disruptions, and transportation delays, which can seriously affect the progress and quality of the project.

[0003] The existing material management technology for power grid infrastructure projects has many defects, which are specifically reflected in the following aspects. First, the data is scattered and cannot be effectively used. In power grid infrastructure projects, the sources of information such as material usage data, market data and project progress data are diverse and scattered, and there is a lack of effective data integration and analysis methods, which makes it impossible to fully and accurately obtain and use these data. The isolation and incompatibility between data make it difficult to realize the potential value of data, affecting the accurate prediction of material demand and the scientific nature of management decisions. Secondly, the accuracy of material demand prediction is insufficient. Existing demand forecasting methods are usually based on simple statistical analysis of historical data, which makes it difficult to capture complex time series and nonlinear relationships. These methods often ignore market fluctuations, changes in project progress and other influencing factors, resulting in inaccurate prediction results and difficulty in meeting the dynamic changes in actual demand. This inaccuracy in prediction may lead to shortages or surpluses of materials, affecting the smooth progress of the project and cost control.

[0004] In terms of supplier selection and procurement decisions, existing technologies are inefficient, usually rely on manual experience, and lack systematic and intelligent support. Existing technologies lack an effective supplier evaluation and selection mechanism, and cannot dynamically analyze suppliers' historical data and market conditions to make scientific and reasonable procurement decisions. The procurement process is inefficient, costly, and prone to procurement risks. In addition, the transportation path is not optimized enough. In the process of material transportation, how to plan the optimal transportation path and adjust it in real time to cope with changes in traffic conditions is a complex problem. Traditional transportation management systems are often based on static path planning and cannot dynamically adjust transportation routes to cope with real-time traffic conditions, weather changes and other factors, resulting in long transportation time and high transportation costs, affecting the timely supply of materials and the overall project progress.

[0005] In terms of warehouse management, traditional warehouse management relies on manual operation, making it difficult to achieve real-time monitoring and management of materials. This management model is prone to problems such as inaccurate material positioning and untimely inventory information, which affects the efficiency of material entry and exit and inventory counting. Inefficient warehouse management not only increases operating costs, but may also lead to shortages or surpluses of materials, further affecting the normal progress of the project. Finally, data sharing and collaborative optimization are difficult. In each link of the material flow, data is scattered at different nodes, lacking an effective data sharing and collaborative optimization mechanism. Existing technologies make it difficult to achieve the circulation and integration of data between different links, resulting in information asymmetry between various links and inability to perform global optimization and management. This data island phenomenon seriously limits the efficiency and overall coordination of material management.

[0006] These defects have led to problems such as low efficiency, high cost, and frequent errors in the material management of existing power grid infrastructure projects, which has seriously affected the progress and quality of the project. Therefore, it is urgent to improve and optimize through new technical means to improve the efficiency and accuracy of material management, reduce operating costs, and ensure the smooth progress of the project. Summary of the invention

[0007] One object of the present invention is to propose a method for collaborative optimization of material flow for power grid infrastructure projects. The present invention makes full use of data analysis, machine learning, intelligent transportation and intelligent warehouse management technologies, accurately predicts material demand through improved meta-learning technology, combines intelligent supplier selection and procurement optimization, uses intelligent transportation systems to optimize transportation routes, and uses intelligent warehouse management systems to achieve real-time monitoring and management of materials. In addition, through an adaptive optimization algorithm based on federated learning, data in the entire material flow is processed and analyzed in real time, realizing data sharing and collaborative optimization among multiple distributed nodes. The present invention has the advantages of improving material management efficiency, reducing operating costs and ensuring the smooth progress of the project.

[0008] A method for collaborative optimization of material flow for power grid infrastructure projects according to an embodiment of the present invention includes the following steps:

[0009] S1. Collect historical material usage data, market data, and project progress data from different data sources, build a data set, and store, process, and analyze the data set;

[0010] S2. Use the improved meta-learning technology to train the constructed data set, generate material demand forecasts by optimizing the initial model parameters and introducing a dynamic task weight adjustment mechanism;

[0011] S3. Based on the predicted material demand, analyze the supplier's historical data and market conditions, recommend suppliers and the best procurement plan, and optimize procurement decisions;

[0012] S4. Use the improved Dijkstra algorithm to plan and optimize the transportation route and dynamically adjust the transportation route;

[0013] S5. Use intelligent warehouse management system to manage warehouse processes, including warehousing, outbound and inventory, and use the Internet of Things to achieve real-time monitoring of material location and status;

[0014] S6. Through the adaptive optimization algorithm based on federated learning, the data in the entire material flow is processed and analyzed in real time, and the data is shared and collaboratively optimized among multiple distributed nodes.

[0015] Optionally, the S1 includes the following steps:

[0016] S11. Collect historical material usage data, market data and project progress data, and remove duplicates, fill missing values ​​and process outliers in the historical material usage data, market data and project progress data;

[0017] S12, unifying the format of the preprocessed data, integrating the historical material usage data, market data and project progress data into a comprehensive data set, and storing the comprehensive data set in a comprehensive database;

[0018] S13. Use data mining techniques to process and analyze comprehensive data sets;

[0019] S14. Use data analysis results to provide support for demand forecasting, procurement decisions and logistics optimization, warehouse management, and adaptive optimization.

[0020] Optionally, S2 includes the following steps:

[0021] S21. Use the improved method based on the MAML algorithm to train the data set and optimize the initial model parameters:

[0022]

[0023] Among them, θ is the initial model parameter, α is the learning rate, and L train is the loss function of the training set;

[0024] S22. Introduce a dynamic task weight adjustment mechanism during the training process to adjust the task weight in real time according to the importance and difficulty of the task:

[0025]

[0026] Among them, w i (t) represents the weight of the i-th task at time t, α i represents the initial weight of task i;

[0027] S23. Update task weights using task difficulty scoring mechanism:

[0028]

[0029] Among them, γ is the adjustment factor, δ i Score the difficulty of task i;

[0030] S24: Meta-train the optimized model parameters to adjust them to new tasks

[0031]

[0032] Among them, θ′ is the adjusted model parameter, β is the learning rate, and L i is the loss function of the i-th task;

[0033] S25. Comprehensive optimization using weighted loss function:

[0034]

[0035] Among them, L meta is the loss function of meta-training, w i (t) is the weight of the i-th task at time t, L i is the loss function of the i-th task;

[0036] S26. Generate material demand forecast using the adjusted model:

[0037] Y pred (t) = f θ′ (X(t));

[0038] Among them, Y pred (t) represents the forecast value of material demand at time t, and X(t) is the data input at time t;

[0039] S27. The generated material demand forecast results will be used for subsequent procurement decisions, logistics optimization and warehouse management.

[0040] Optionally, S3 includes the following steps:

[0041] S31, based on the material demand forecast data Y generated in step S2 pred (t) Analyze suppliers’ historical data, including suppliers’ delivery capabilities, delivery times, quality records, and price changes;

[0042] S32. Collect and analyze market data, including current market price P(t), market supply and market demand;

[0043] S33. Integrate supplier historical data and market data with predicted material demand to build a supplier scoring model:

[0044] S i =αQ i +βT i +γC i +δP i ;

[0045] Among them, S i represents the comprehensive score of the ith supplier, Q i represents the supplier’s delivery capability, T i Indicates the delivery time, C i Indicates quality record, P i represents price, α, β, γ, δ are weight coefficients;

[0046] S34. Use hybrid differential evolution adaptive genetic algorithm to optimize the supplier rating model by dynamically adjusting the weight coefficients α, β, γ, δ:

[0047]

[0048] Among them, λ1, λ2, λ3 are the corresponding weight coefficients,

[0049] R1(t) is the cost return, which represents the cost-effectiveness of the supplier providing materials at time t:

[0050]

[0051] Among them, C baseline is the base cost, C actual (t) is the actual purchase cost at time t; R2(t) is the delivery time return, which represents the benefit of the supplier's delivery time at time t:

[0052]

[0053] Among them, T baseline is the benchmark delivery time, T actual (t) is the actual delivery time at time t; R3(t) is the quality return, which represents the quality benefit of the materials provided by the supplier at time t:

[0054]

[0055] Among them, Q baseline is the benchmark quality score, Q actual (t) is the actual quality score at time t;

[0056] S35. Based on the optimized supplier rating model and combined with market data, determine the procurement plan:

[0057]

[0058] Among them, P opt represents the total cost of the optimal procurement solution;

[0059] S36. Optimize purchasing decisions by using recommended suppliers and best purchasing solutions.

[0060] Optionally, S4 includes the following steps:

[0061] S41, based on the material demand forecast data Y generated in step S2 pred (t) and current transportation needs, collect traffic data related to power grid infrastructure projects, collect traffic data through sensors and IoT devices, and transmit the data to a central database;

[0062] S42. Use the intelligent transportation system to process and analyze the collected traffic data, build a traffic flow model, and reflect the real-time situation of the transportation of materials for power grid infrastructure projects:

[0063] T(t)=αT f (t)+βT e (t)+γT r (t);

[0064] Among them, T(t) represents the comprehensive traffic status at time t, T f (t) represents the traffic flow, T e (t) represents traffic incident, T r (t) represents the road condition, α, β, γ are adaptive weight coefficients, and the adaptive weight coefficients are dynamically adjusted according to historical data and current traffic conditions:

[0065]

[0066] Where η is the adjustment factor;

[0067] S43. Based on the traffic flow model, the improved Dijkstra algorithm is used to plan the transportation route, combined with the special needs of the transportation of power grid infrastructure materials, including the transportation of large equipment, safety requirements, accuracy of transportation time and reliability of transportation routes:

[0068] D ′ (i,j)=min(D(i,k)+D(k,j)+T(k));

[0069] Where D′(i,j) represents the shortest path distance from node i to node j, k is the intermediate node, and T(k) is the traffic status of the intermediate node k. In each node selection process, the algorithm dynamically updates the priority of the node to adapt to the real-time traffic conditions;

[0070] S44. During the transportation process, traffic data is monitored in real time, and the transportation route is dynamically adjusted according to the latest traffic status T(t):

[0071]

[0072] Among them, R(t) represents the optimal transportation route at time t, D'(i,j) represents the distance from node i to node j, and T(j) represents the traffic status of node j;

[0073] S45. Generate a transportation plan and optimize transportation decisions based on the dynamically adjusted transportation routes. Adjust the transportation vehicle scheduling plan based on the real-time updated routes and use genetic algorithms to optimize the transportation plan:

[0074]

[0075] Among them, Fitness(P) represents the fitness of the transportation plan P, D'(i,j) represents the shortest path distance, T(j) represents the traffic status, Q(i) represents the transportation volume, and λ1, λ2, λ3 are the weight coefficients of the objective function.

[0076] Optionally, the S43 includes the following steps:

[0077] S431. According to the traffic flow model T(t), a transportation path planning graph is constructed, and a set of nodes N and edges E is defined, where each node i∈N represents a position on the transportation path, and each edge (i,j)∈E represents a path from node i to node j, and the weight of the path is determined by the traffic status and other factors;

[0078] S432, initialize the distance D(i) of node i to infinity, the distance D(start) of the starting node to 0, and add all nodes to the unprocessed node set U;

[0079] S433. Use quantum annealing technology to optimize the path search process and construct the Hamiltonian H for path optimization:

[0080] H=A(t)H B +B(t)H P ;

[0081] Among them, H B is the initial Hamiltonian, which represents a simple driving Hamiltonian, H Pis the problem Hamiltonian, representing the path problem to be optimized, A(t) and B(t) are time-dependent coefficients, satisfying A(0)>>B(0) and A(T)<<B(T);

[0082] S434. In each step, the Hamiltonian H is minimized through the quantum annealing process to obtain the optimal path weight w(u,v):

[0083] D(v)=min(D(v),D(u)+w(u,v));

[0084] Among them, w(u,v) represents the path weight from node u to node v, including the basic distance D(u,v) and the comprehensive traffic status T(v);

[0085] S435, dynamically adjust the path weight calculation according to real-time traffic data and prediction error:

[0086] S436, repeat steps S433 and S434 until all nodes have been processed, that is, the unprocessed node set U is empty;

[0087] S437. According to the updated distance D(i), backtrack from the end node to find the optimal transportation path R.

[0088] Optionally, S5 includes the following steps:

[0089] S51. Obtain the location information and status data of materials through IoT devices, and match and update these data with the data in the central database;

[0090] S52. Use the intelligent warehouse management system to manage the warehousing, outbound and inventory process, monitor the warehousing, outbound and inventory process of materials in real time, process and enter the warehousing data in (t), outbound data W out (t) and inventory data W inv (t);

[0091] S53. Optimize and adjust the warehousing process based on real-time monitoring data flow and processing results:

[0092] W opt (t) = g opt (W in (t),W out (t),W inv (t));

[0093] Among them, g opt Optimize processing functions for warehousing;

[0094] S54. Dynamically adjust the configuration and monitoring strategy of IoT devices to obtain real-time monitoring of material location and status based on actual needs and data analysis results.

[0095] Optionally, the S6 includes the following steps:

[0096] S61. Deploy the federated learning model among multiple distributed nodes. Each node N i Local storage and processing of material flow data i ;

[0097] S62. At each node N i Perform local model training on it and calculate the local model parameters θ i ;

[0098] S63, through the dynamic weighted aggregation method, each node N i The local model parameters θ i Sent to the central server for aggregation, the aggregated global model parameters θ global Calculated by dynamic weighted average;

[0099] S64, the aggregated global model parameter θ global Distribute back to each node N i , update local model parameters;

[0100] S65. At each node N i On the graph, use the updated model parameters θ i Predict and analyze new material flow data to generate optimization suggestions and decision support;

[0101] S66. Regularly retrain and aggregate the federated learning model, dynamically adjust the model parameters θ, and adapt to changes in the material flow:

[0102]

[0103] Where t represents the time step;

[0104] S67. Realize data sharing and collaborative optimization among multiple distributed nodes through federated learning.

[0105] The beneficial effects of the present invention are:

[0106] (1) The method of the present invention solves many problems in the existing management of materials for power grid infrastructure projects and achieves significant beneficial effects. First, historical material usage data, market data, and project progress data from different sources are integrated to construct a comprehensive data set for processing and analysis, overcoming the defects of data dispersion and ineffective utilization. The improved meta-learning technology is used to train the data set to generate a more accurate material demand forecasting model, improve the forecasting accuracy and flexibility, and reduce the risk of material shortage or surplus.

[0107] (2) Based on the predicted material demand, the supplier scoring model and procurement decision are optimized. By analyzing the supplier's historical data and market conditions, the best supplier and procurement plan are recommended, which reduces procurement costs and improves procurement efficiency. The intelligent transportation system is used for transportation route planning and optimization, and the improved Dijkstra algorithm is used to dynamically adjust the path weight to ensure the efficiency and accuracy of transportation. This overcomes the defect that the traditional transportation management system cannot dynamically adjust the transportation route, and ensures the timely supply of materials.

[0108] (3) Adopting intelligent warehouse management system for warehousing, outbound and inventory, and realizing real-time monitoring of material location and status through IoT devices, the efficiency and accuracy of warehouse management are improved, and the problems of inaccurate positioning and information lag in traditional warehouse management are solved. Through the adaptive optimization algorithm based on federated learning, the material flow data is processed and analyzed in real time, and data sharing and collaborative optimization between multiple distributed nodes are realized, ensuring data privacy and security, and overcoming the data island phenomenon.

[0109] (4) The present invention realizes efficient collaborative optimization of the material flow of power grid infrastructure projects. Compared with traditional methods, it has significant improvements in data integration, demand forecasting, procurement optimization, transportation management, warehouse management, data sharing and collaborative optimization, which improves the efficiency and accuracy of material management, reduces operating costs, and ensures the smooth progress of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0111] Figure 1 A flowchart of a method for collaborative optimization of material flow for power grid infrastructure projects proposed by the present invention;

[0112] Figure 2 Schematic diagram of using improved meta-learning technology to predict material demand; DETAILED DESCRIPTION

[0113] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0114] refer to Figure 1-2 , a method for collaborative optimization of material flow for power grid infrastructure projects, comprising the following steps:

[0115] S1. Collect historical material usage data, market data, and project progress data from different data sources, build a data set, and store, process, and analyze the data set;

[0116] S2. Use the improved meta-learning technology to train the constructed data set, generate material demand forecasts by optimizing the initial model parameters and introducing a dynamic task weight adjustment mechanism;

[0117] S3. Based on the predicted material demand, analyze the supplier's historical data and market conditions, recommend suppliers and the best procurement plan, and optimize procurement decisions;

[0118] S4. Use the improved Dijkstra algorithm to plan and optimize the transportation route and dynamically adjust the transportation route;

[0119] S5. Use intelligent warehouse management system to manage warehouse processes, including warehousing, outbound and inventory, and use the Internet of Things to achieve real-time monitoring of material location and status;

[0120] S6. Through the adaptive optimization algorithm based on federated learning, the data in the entire material flow is processed and analyzed in real time, and the data is shared and collaboratively optimized among multiple distributed nodes.

[0121] In this implementation, S1 includes the following steps:

[0122] S11. Collect historical material usage data, market data and project progress data, and remove duplicates, fill missing values ​​and process outliers in the historical material usage data, market data and project progress data;

[0123] S12, unifying the format of the preprocessed data, integrating the historical material usage data, market data and project progress data into a comprehensive data set, and storing the comprehensive data set in a comprehensive database;

[0124] S13. Use data mining techniques to process and analyze comprehensive data sets;

[0125] S14. Use data analysis results to provide support for demand forecasting, procurement decisions and logistics optimization, warehouse management, and adaptive optimization.

[0126] In this implementation, S2 includes the following steps:

[0127] S21. Use the improved method based on the MAML algorithm to train the data set and optimize the initial model parameters:

[0128]

[0129] Among them, α is the learning rate, L train is the loss function of the training set;

[0130] S22. Introduce a dynamic task weight adjustment mechanism during the training process to adjust the task weight in real time according to the importance and difficulty of the task:

[0131]

[0132] Among them, w i (t) represents the weight of the i-th task at time t, α i represents the initial weight of task i;

[0133] S23. Update task weights using task difficulty scoring mechanism:

[0134]

[0135] Among them, γ is the adjustment factor, δ i Score the difficulty of task i;

[0136] S24: Meta-train the optimized model parameters to adjust them to new tasks

[0137]

[0138] Among them, θ′ is the adjusted model parameter, β is the learning rate, and L i is the loss function of the i-th task;

[0139] S25. Comprehensive optimization using weighted loss function:

[0140]

[0141] Among them, L meta is the loss function of meta-training, w i (t) is the weight of the i-th task at time t, L i is the loss function of the i-th task;

[0142] S26. Generate material demand forecast using the adjusted model:

[0143] Y pred (t) = f θ′ (X(t));

[0144] Among them, Y pred (t) represents the forecast value of material demand at time t, and X(t) is the data input at time t;

[0145] S27. The generated material demand forecast results will be used for subsequent procurement decisions, logistics optimization and warehouse management.

[0146] In this implementation, S3 includes the following steps:

[0147] S31, based on the material demand forecast data Y generated in step S2 pred (t) Analyze suppliers’ historical data, including suppliers’ delivery capabilities, delivery times, quality records, and price changes;

[0148] S32. Collect and analyze market data, including current market price P(t), market supply and market demand;

[0149] S33. Integrate supplier historical data and market data with predicted material demand to build a supplier scoring model:

[0150] S i =αQ i +βT i +γC i +δP i ;

[0151] Among them, S i represents the comprehensive score of the ith supplier, Q i represents the supplier’s delivery capability, T i Indicates the delivery time, C i Indicates quality record, P i represents price, α, β, γ, δ are weight coefficients;

[0152] S34. Use hybrid differential evolution adaptive genetic algorithm to optimize the supplier rating model by dynamically adjusting the weight coefficients α, β, γ, δ:

[0153]

[0154] Among them, λ1, λ2, λ3 are the corresponding weight coefficients,

[0155] R1(t) is the cost return, which represents the cost-effectiveness of the supplier providing materials at time t:

[0156]

[0157] Among them, C baseline is the base cost, C actual(t) is the actual purchase cost at time t; R2(t) is the delivery time return, which represents the benefit of the supplier's delivery time at time t:

[0158]

[0159] Among them, T baseline is the benchmark delivery time, T actual (t) is the actual delivery time at time t; R3(t) is the quality return, which represents the quality benefit of the materials provided by the supplier at time t:

[0160]

[0161] Among them, Q baseline is the benchmark quality score, Q actual (t) is the actual quality score at time t;

[0162] S35. Based on the optimized supplier rating model and combined with market data, determine the procurement plan:

[0163]

[0164] Among them, P opt represents the total cost of the optimal procurement solution;

[0165] S36. Optimize purchasing decisions by using recommended suppliers and best purchasing solutions.

[0166] In this implementation, S4 includes the following steps:

[0167] S41, based on the material demand forecast data Y generated in step S2 pred (t) and current transportation needs, collect traffic data related to power grid infrastructure projects, collect traffic data through sensors and IoT devices, and transmit the data to a central database;

[0168] S42. Use the intelligent transportation system to process and analyze the collected traffic data, build a traffic flow model, and reflect the real-time situation of the transportation of materials for power grid infrastructure projects:

[0169] T(t)=αT f (t)+βT e (t)+γT r (t);

[0170] Among them, T(t) represents the comprehensive traffic status at time t, T f (t) represents the traffic flow, T e (t) represents traffic incident, T r(t) represents the road condition, α, β, γ are adaptive weight coefficients, and the adaptive weight coefficients are dynamically adjusted according to historical data and current traffic conditions:

[0171]

[0172] Where η is the adjustment factor;

[0173] S43. Based on the traffic flow model, the improved Dijkstra algorithm is used to plan the transportation route, combined with the special needs of the transportation of power grid infrastructure materials, including the transportation of large equipment, safety requirements, accuracy of transportation time and reliability of transportation routes:

[0174] D ′ (i,j)=min(D(i,k)+D(k,j)+T(k));

[0175] Where D′(i,j) represents the shortest path distance from node i to node j, k is the intermediate node, and T(k) is the traffic status of the intermediate node k. In each node selection process, the algorithm dynamically updates the priority of the node to adapt to the real-time traffic conditions;

[0176] S44. During the transportation process, traffic data is monitored in real time, and the transportation route is dynamically adjusted according to the latest traffic status T(t):

[0177]

[0178] Among them, R(t) represents the optimal transportation route at time t, D'(i,j) represents the distance from node i to node j, and T(j) represents the traffic status of node j;

[0179] S45. Generate a transportation plan and optimize transportation decisions based on the dynamically adjusted transportation routes. Adjust the transportation vehicle scheduling plan based on the real-time updated routes and use genetic algorithms to optimize the transportation plan:

[0180]

[0181] Among them, Fitness(P) represents the fitness of the transportation plan P, D'(i,j) represents the shortest path distance, T(j) represents the traffic status, Q(i) represents the transportation volume, and λ1, λ2, λ3 are the weight coefficients of the objective function.

[0182] In this implementation, S43 includes the following steps:

[0183] S431. According to the traffic flow model T(t), a transportation path planning graph is constructed, and a set of nodes N and edges E is defined, where each node i∈N represents a position on the transportation path, and each edge (i,j)∈E represents a path from node i to node j, and the weight of the path is determined by the traffic status and other factors;

[0184] S432, initialize the distance D(i) of node i to infinity, the distance D(start) of the starting node to 0, and add all nodes to the unprocessed node set U;

[0185] S433. Use quantum annealing technology to optimize the path search process and construct the Hamiltonian H for path optimization:

[0186] H=A(t)H B +B(t)H P ;

[0187] Among them, H B is the initial Hamiltonian, which represents a simple driving Hamiltonian, H P is the problem Hamiltonian, representing the path problem to be optimized, A(t) and B(t) are time-dependent coefficients, satisfying A(0)>>B(0) and A(T)<<B(T);

[0188] S434. In each step, the Hamiltonian H is minimized through the quantum annealing process to obtain the optimal path weight w(u,v):

[0189] D(v)=min(D(v),D(u)+w(u,v));

[0190] Among them, w(u,v) represents the path weight from node u to node v, including the basic distance D(u,v) and the comprehensive traffic status T(v);

[0191] S435, dynamically adjust the path weight calculation according to the real-time traffic data and prediction error;

[0192] S436, repeat steps S433 and S434 until all nodes have been processed, that is, the unprocessed node set U is empty;

[0193] S437. According to the updated distance D(i), backtrack from the terminal node to find the optimal transportation path R.

[0194] In this implementation, S5 includes the following steps:

[0195] S51. Obtain the location information and status data of materials through IoT devices, and match and update these data with the data in the central database;

[0196] S52. Use the intelligent warehouse management system to manage the warehousing, outbound and inventory process, monitor the warehousing, outbound and inventory process of materials in real time, process and enter the warehousing data in (t), outbound data W out (t) and inventory data W inv (t);

[0197] S53. Optimize and adjust the warehousing process based on real-time monitoring data flow and processing results:

[0198] W opt (t) = g opt (W in (t),W out (t),W inv (t));

[0199] Among them, g opt Optimize processing functions for warehousing;

[0200] S54. Dynamically adjust the configuration and monitoring strategy of IoT devices to obtain real-time monitoring of material location and status based on actual needs and data analysis results.

[0201] In this implementation, S6 includes the following steps:

[0202] S61. Deploy the federated learning model among multiple distributed nodes. Each node N i Local storage and processing of material flow data i ;

[0203] S62. At each node N i Perform local model training on it and calculate the local model parameters θ i ;

[0204] S63, through the dynamic weighted aggregation method, each node N i The local model parameters θ i Sent to the central server for aggregation, the aggregated global model parameters θ global Calculated by dynamic weighted average;

[0205] S64, the aggregated global model parameter θ global Distribute back to each node N i , update local model parameters;

[0206] S65. At each node N i On the graph, use the updated model parameters θ i Predict and analyze new material flow data to generate optimization suggestions and decision support;

[0207] S66. Regularly retrain and aggregate the federated learning model, dynamically adjust the model parameters θ, and adapt to changes in the material flow:

[0208]

[0209] Where t represents the time step;

[0210] S67. Realize data sharing and collaborative optimization among multiple distributed nodes through federated learning.

[0211] Embodiment 1:

[0212] In a new power grid infrastructure project in a certain city, the project covers multiple areas and involves a large amount of material dispatching and management. Traditional material management methods rely on manual operations and experience judgment, which often lead to problems such as insufficient or excessive material supply, transportation delays, and chaotic warehousing, which affect the smooth progress of the project and cost control. In order to improve the efficiency and accuracy of material management, the project team decided to introduce the collaborative optimization method of the material flow of power grid infrastructure projects proposed in this invention.

[0213] At the project start-up stage, the project team first collected a large amount of historical material usage data, market data, and project progress data. These data came from multiple data sources, including material procurement records over the past three years, market price fluctuations, and detailed project plan schedules. By building a comprehensive data set, these data were stored, processed, and analyzed to provide basic data support for subsequent material demand forecasts and decision-making.

[0214] The constructed data set was trained using improved meta-learning techniques. The team optimized the initial model parameters and introduced a dynamic task weight adjustment mechanism to generate a material demand forecasting model. The model can capture complex time series and nonlinear relationships, greatly improving the accuracy of material demand forecasting. The forecast results show that in the next three quarters, the demand for various materials in different regions will be: 100 kilometers of cables, 50 transformers, 200 towers, and several other accessories.

[0215] Based on the predicted material demand, the team further analyzed the supplier's historical data and market conditions, optimized the supplier scoring model, and recommended the best supplier and procurement plan. Through the hybrid differential evolution adaptive genetic algorithm, the weight coefficient of supplier selection was dynamically adjusted, and the optimal procurement decision was finally determined. The specific results are as follows: the cable is provided by supplier A at a total price of 1 million yuan; the transformer is provided by supplier B at a total price of 2 million yuan; the tower is provided by supplier C at a total price of 1.5 million yuan. Compared with traditional manual decision-making, this process not only improves procurement efficiency, but also reduces procurement costs by about 10%.

[0216] In the material transportation link, the team introduced an intelligent transportation system to plan and optimize the transportation route through the improved Dijkstra algorithm. Combined with real-time traffic conditions and road conditions, the transportation route was dynamically adjusted to ensure the efficiency and accuracy of material transportation. Specific data shows that through this method, the average transportation time was reduced by 15% and the transportation cost was reduced by 20%.

[0217] In terms of warehouse management, the project team adopted an intelligent warehouse management system, including warehousing, outbound and inventory, and achieved real-time monitoring of the location and status of materials through IoT devices. The application of the intelligent warehouse system has greatly improved the efficiency and accuracy of warehouse management. For example, in the first three months of the project implementation, the time for warehousing and outbound of materials was shortened by 30% and 25% respectively, and the accuracy of inventory counting was improved to 99%.

[0218] Finally, through the adaptive optimization algorithm based on federated learning, the team achieved data sharing and collaborative optimization among multiple distributed nodes. The material demand and supply data of each region were uploaded and analyzed in real time, ensuring the privacy and security of the data, while improving the efficiency of data processing and overall collaborative capabilities. Specific data showed that through this method, the coordination of material scheduling in each region was improved by 20%, the overall project progress was completed two weeks ahead of schedule, and the cost was saved by about 150,000 yuan.

[0219] Through the application of the present invention, the project team significantly improved the management efficiency and accuracy of the material flow of power grid infrastructure projects, which are specifically manifested as follows: data utilization increased by 40%, the accuracy of material demand forecasting increased by 35%, procurement costs reduced by 10%, procurement efficiency increased by 20%, transportation time reduced by 15%, transportation costs reduced by 20%, warehousing management’s warehousing time shortened by 30%, and outbound time shortened by 25%, inventory count accuracy increased to 99%, material scheduling coordination improved by 20%, the overall project progress was advanced by two weeks, and costs saved by approximately RMB 150,000.

[0220] Table 1 Comparison data before and after improvement of material management

[0221] project Before improvement After Improvement Improvement effect Data utilization 60％ 100％ 40% improvement Accuracy of material demand forecast 65％ 100％ 35% improvement Procurement costs 1 million yuan 900,000 yuan 10% reduction Procurement efficiency 80％ 100％ 20% improvement Average shipping time 10 hours 8.5 hours 15% reduction Shipping costs 100,000 yuan 80,000 yuan 20% reduction Warehouse time 5 hours 3.5 hours 30% shorter Shipping time 4 hours 3 hours 25% shorter Inventory count accuracy 70％ 99％ 29% improvement Material dispatch coordination 80％ 100％ 20% improvement Project progress Delayed 1 week 2 weeks in advance 3 weeks in advance Cost savings 0 150,000 yuan Save 150,000 yuan

[0222] By implementing the method of the present invention, the material management of power grid infrastructure projects has been significantly improved, specifically, data utilization has been increased by 40%, material demand forecast accuracy has been increased by 35%, procurement costs have been reduced by 10%, procurement efficiency has been increased by 20%, transportation time has been reduced by 15%, transportation costs have been reduced by 20%, storage time has been shortened by 30%, storage time has been shortened by 25%, inventory count accuracy has been increased by 29%, material scheduling coordination has been improved by 20%, the overall project progress has been advanced by 3 weeks, and 150,000 yuan in costs have been saved. These improvements have effectively solved many problems in traditional material management, significantly improved management efficiency and accuracy, and ensured the smooth progress of the project.

[0223] Through the method of the present invention, multiple key problems in the existing power grid infrastructure project material management are solved, and significant beneficial effects are achieved. By collecting historical material usage data, market data and project progress data from different data sources, a comprehensive data set is constructed and processed and analyzed, and effective integration and utilization of data are achieved, overcoming the defects of data dispersion and ineffective utilization. The improved meta-learning technology is used to train the data set to generate a more accurate material demand forecasting model, improve the forecasting accuracy and flexibility, and reduce the risk of material shortage or surplus. Based on the predicted material demand, the supplier scoring model and procurement decision are optimized, and the best supplier and procurement plan are recommended by analyzing the supplier's historical data and market conditions, which reduces the procurement cost and improves the procurement efficiency. The intelligent transportation system is used for transportation path planning and optimization, and the improved Dijkstra algorithm is combined to dynamically adjust the path weight to ensure the efficiency and accuracy of transportation. The intelligent warehouse management system is used for warehousing, outbound and inventory, and the real-time monitoring of the location and status of materials is realized through the Internet of Things equipment, which improves the efficiency and accuracy of warehouse management. Through the adaptive optimization algorithm based on federated learning, the material flow data is processed and analyzed in real time, and data sharing and collaborative optimization between multiple distributed nodes are realized.

[0224] Through the application of the present invention, the project team effectively solved many problems in traditional material management, significantly improved the management efficiency and accuracy of the material flow of power grid infrastructure projects, reduced operating costs, and ensured the smooth progress of the project. In summary, the present invention has demonstrated strong practicality and innovation in actual engineering applications, and achieved the expected beneficial effects.

[0225] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for collaborative optimization of material flow for power grid infrastructure projects, characterized in that: The following steps are involved: S1. Collect historical material usage data, market data, and project progress data from different data sources, build a data set, and store, process, and analyze the data set; S2. Use the improved meta-learning technology to train the constructed data set, generate material demand forecasts by optimizing the initial model parameters and introducing a dynamic task weight adjustment mechanism; S3. Based on the predicted material demand, analyze the supplier's historical data and market conditions, recommend suppliers and the best procurement plan, and optimize procurement decisions; S4. Use the improved Dijkstra algorithm to plan and optimize the transportation route and dynamically adjust the transportation route; S5. Use intelligent warehouse management system to manage warehouse processes, including warehousing, outbound and inventory, and use the Internet of Things to achieve real-time monitoring of material location and status; S6. Through the adaptive optimization algorithm based on federated learning, the data in the entire material flow is processed and analyzed in real time, and the data is shared and collaboratively optimized among multiple distributed nodes.

2. A method for collaborative optimization of material flow for power grid infrastructure projects according to claim 1, characterized in that: The step S1 specifically includes: S11. Collect historical material usage data, market data and project progress data, and remove duplicates, fill in missing values ​​and process outliers for the historical material usage data, market data and project progress data; S12, performing format unification processing on the pre-processed data, integrating the historical material usage data, market data and project progress data into a comprehensive data set, and storing the comprehensive data set in a comprehensive database; S13. Use data mining techniques to process and analyze comprehensive data sets; S14. Use data analysis results to provide support for demand forecasting, procurement decisions and logistics optimization, warehouse management, and adaptive optimization.

3. A method for collaborative optimization of material flow for power grid infrastructure projects according to claim 1, characterized in that: The step S2 specifically includes: S21. Use the improved method based on the MAML algorithm to train the data set and optimize the initial model parameters: Among them, θ is the initial model parameter, α is the learning rate, L train is the loss function of the training set; S22. Introduce a dynamic task weight adjustment mechanism during the training process to adjust the task weight in real time according to the importance and difficulty of the task: Among them, w i (t) represents the weight of the i-th task at time t, α i represents the initial weight of task i; S23. Update task weights using task difficulty scoring mechanism: Among them, γ is the adjustment factor, δ i Score the difficulty of task i; S24: Meta-train the optimized model parameters to adjust them to new tasks Among them, θ′ is the adjusted model parameter, β is the learning rate, and L i is the loss function of the i-th task; S25. Comprehensive optimization using weighted loss function: Among them, L meta is the loss function of meta-training, w i (t) is the weight of the i-th task at time t, L i is the loss function of the i-th task; S26. Generate material demand forecast using the adjusted model: Y pred (t)=f θ’ (X(t)); Among them, Y pred (t) represents the forecast value of material demand at time t, and X(t) is the data input at time t; S27. The generated material demand forecast results will be used for subsequent procurement decisions, logistics optimization and warehouse management.

4. A method for collaborative optimization of material flow for power grid infrastructure projects according to claim 1, characterized in that: The step S3 specifically includes: S31, based on the material demand forecast data Y generated in step S2 pred (t) Analyze suppliers’ historical data, including suppliers’ delivery capabilities, delivery times, quality records, and price changes; S32. Collect and analyze market data, including current market price P(t), market supply and market demand; S33. Integrate supplier historical data and market data with predicted material demand to build a supplier scoring model: S i =αQ i +βT i +γC i +δP i ; Among them, S i represents the comprehensive score of the ith supplier, Q i represents the supplier’s delivery capability, T i Indicates the delivery time, C i Indicates quality record, P i represents price, α, β, γ, δ are weight coefficients; S34. Use hybrid differential evolution adaptive genetic algorithm to optimize the supplier rating model by dynamically adjusting the weight coefficients α, β, γ, δ: Among them, λ1, λ2, λ3 are the corresponding weight coefficients, R1(t) is the cost return, which represents the cost-effectiveness of the supplier providing materials at time t: Among them, C baseline is the base cost, C actual (t) is the actual purchase cost at time t; R2(t) is the delivery time return, which represents the benefit of the supplier's delivery time at time t: Among them, T baseline is the benchmark delivery time, T actual (t) is the actual delivery time at time t; R3(t) is the quality return, which represents the quality benefit of the materials provided by the supplier at time t: Among them, Q baseline is the benchmark quality score, Q actual (t) is the actual quality score at time t; S35. Based on the optimized supplier rating model and combined with market data, determine the procurement plan: Among them, P opt represents the total cost of the optimal procurement solution; S36. Optimize purchasing decisions by using recommended suppliers and best purchasing solutions.

5. The method for collaborative optimization of material flow for power grid infrastructure projects according to claim 1, characterized in that: The step S4 specifically includes: S41, based on the material demand forecast data Y generated in step S2 pred (t) and current transportation needs, collect traffic data related to power grid infrastructure projects, collect traffic data through sensors and IoT devices, and transmit the data to a central database; S42. Use the intelligent transportation system to process and analyze the collected traffic data, build a traffic flow model, and reflect the real-time situation of the transportation of materials for power grid infrastructure projects: T(t)=αT f (t)+βT e (t)+γT r (t); Among them, T(t) represents the comprehensive traffic status at time t, T f (t) represents the traffic flow, T e (t) represents traffic incident, T r (t) represents the road condition, α, β, γ are adaptive weight coefficients, and the adaptive weight coefficients are dynamically adjusted according to historical data and current traffic conditions: Where η is the adjustment factor; S43. Based on the traffic flow model, the improved Dijkstra algorithm is used to plan the transportation route, combined with the special needs of the transportation of power grid infrastructure materials, including the transportation of large equipment, safety requirements, accuracy of transportation time and reliability of transportation routes: D'(i,j)=min(D(i,k)+D(k,j)+T(k)); Where D′(i,j) represents the shortest path distance from node i to node j, k is the intermediate node, and T(k) is the traffic status of the intermediate node k. In each node selection process, the algorithm dynamically updates the priority of the node to adapt to the real-time traffic conditions; S44. During the transportation process, traffic data is monitored in real time, and the transportation route is dynamically adjusted according to the latest traffic status T(t): Among them, R(t) represents the optimal transportation route at time t, D'(i,j) represents the distance from node i to node j, and T(j) represents the traffic status of node j; S45. Generate a transportation plan and optimize transportation decisions based on the dynamically adjusted transportation routes. Adjust the transportation vehicle scheduling plan based on the real-time updated routes and use genetic algorithms to optimize the transportation plan: Among them, Fitness(P) represents the fitness of the transportation plan P, D'(i,j) represents the shortest path distance, T(j) represents the traffic status, Q(i) represents the transportation volume, and λ1, λ2, λ3 are the weight coefficients of the objective function.

6. A method for collaborative optimization of material flow for power grid infrastructure projects according to claim 5, characterized in that: The step S43 specifically includes: S431. According to the traffic flow model T(t), a transportation path planning graph is constructed, and a set of nodes N and edges E is defined, where each node i∈N represents a position on the transportation path, and each edge (i,j)∈E represents a path from node i to node j, and the weight of the path is determined by the traffic status and other factors; S432, initialize the distance D(i) of node i to infinity, the distance D(start) of the starting node to 0, and add all nodes to the unprocessed node set U; S433. Use quantum annealing technology to optimize the path search process and construct the Hamiltonian H for path optimization: H=A(t)H B +B(t)H P ; Among them, H B is the initial Hamiltonian, which represents a simple driving Hamiltonian, H P is the problem Hamiltonian, representing the path problem to be optimized, A(t) and B(t) are time-dependent coefficients, satisfying A(0)>>B(0) and A(T)<<B(T); S434. In each step, the Hamiltonian H is minimized through the quantum annealing process to obtain the optimal path weight w(u,v): D(v)=min(D(v),D(u)+w(u,v)); Among them, w(u,v) represents the path weight from node u to node v, including the basic distance D(u,v) and the comprehensive traffic status T(v); S435, dynamically adjust the path weight calculation according to the real-time traffic data and prediction error; S436, repeat steps S433 and S434 until all nodes have been processed, that is, the unprocessed node set U is empty; S437. According to the updated distance D(i), backtrack from the end node to find the optimal transportation path R.

7. A method for collaborative optimization of material flow for power grid infrastructure projects according to claim 1, characterized in that: The step S5 specifically includes: S51. Obtain the location information and status data of materials through IoT devices, and match and update these data with the data in the central database; S52. Use the intelligent warehouse management system to manage the warehousing, outbound and inventory process, monitor the warehousing, outbound and inventory process of materials in real time, process and enter the warehousing data in (t), outbound data W out (t) and inventory data W inv (t); S53. Optimize and adjust the warehousing process based on real-time monitoring data flow and processing results: W opt (t)=g opt (W in (t),W out (t),W inv (t)); Among them, g opt Optimize processing functions for warehousing; S54. Dynamically adjust the configuration and monitoring strategy of IoT devices to obtain real-time monitoring of material location and status based on actual needs and data analysis results.

8. The method for collaborative optimization of material flow for power grid infrastructure projects according to claim 1, characterized in that: The step S6 specifically includes: S61. Deploy the federated learning model among multiple distributed nodes. Each node N i Local storage and processing of material flow data i ; S62. At each node N i Perform local model training on it and calculate the local model parameters θ i ; S63, through the dynamic weighted aggregation method, each node N i The local model parameters θ i Sent to the central server for aggregation, the aggregated global model parameters θ global Calculated by dynamic weighted average; S64, the aggregated global model parameter θ global Distribute back to each node N i , update local model parameters; S65. At each node N i On the graph, use the updated model parameters θ i Predict and analyze new material flow data to generate optimization suggestions and decision support; S66. Regularly retrain and aggregate the federated learning model, dynamically adjust the model parameters θ, and adapt to changes in the material flow: Where t represents the time step; S67. Realize data sharing and collaborative optimization among multiple distributed nodes through federated learning.

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