A method for predicting the fluctuation of railway construction material demand based on delivery time limit

Abstract

The present invention provides a method for predicting the volatility of railway construction material demand based on delivery time. The method includes: predicting the probability distribution of cargo delivery time along different routes based on vehicle driving trajectories, material source distribution, transportation routes, weather and road conditions; considering delivery time, transportation costs, supply guarantee requirements, vehicle ownership, and storage capacity, constructing a material supply demand prediction model and solving it to obtain the material supply demand in different time periods; combining the delivery time and material supply demand prediction, deducing the material inventory based on the Monte Carlo simulation method, and outputting the engineering material ordering strategy. Integrate the material demand volatility prediction method and develop a demand prediction simulation system. The method of the present invention proposes a material supply demand prediction technology that first predicts the probability distribution of cargo delivery time and then predicts the construction material supply demand in different time periods, thereby achieving accurate prediction of engineering material demand and providing data support for scientifically formulating procurement plans and reasonably adjusting construction organizations.

Description

Technical Field

[0001] The present invention relates to the technical field of material demand forecasting, and in particular to a method for forecasting the volatility of railway engineering construction material demand based on delivery time limits. Background Art

[0002] Ultra-large railway construction projects in complex and hazardous areas are often located in mountainous areas. These projects face difficulties in material supply for most sections, often hindering normal construction due to factors such as a shortage of construction materials, uneven resource distribution, fragile transportation infrastructure, an incomplete road network, frequent geological disasters, and severe traffic congestion. On-site material supply plans are primarily based on the experience of section material department staff, with insufficient consideration given to factors such as weather, road conditions, and fluctuating demand. Consequently, material supply plan management is extensive and lacks scientific and accurate forecasting methods. Therefore, studying the spatiotemporal evolution of diverse transportation demands, continuously fluctuating supply, and emergency reserve support under the influence of multiple factors, and developing a material supply demand forecasting simulation system based on this research, is crucial for accurately understanding the supply and demand of construction materials and ensuring their scientific supply.

[0003] Existing related technologies include:

[0004] 1. Probability distribution prediction of delivery time limit

[0005] Existing methods for predicting arrival times are mainly based on statistical models or machine learning algorithms, which predict arrival times using historical data. These methods can often only provide deterministic single-point prediction results, that is, predict a specific arrival time point. They cannot fully consider the impact of factors such as traffic conditions, road condition changes, weather influences, and individual differences of drivers on transportation time. They cannot provide an interval distribution of arrival times, cannot effectively describe the uncertainty of the prediction results, and lack comprehensive consideration of the uncertainty of the prediction results.

[0006] 2. Supply and demand forecast

[0007] In terms of material demand forecasting methods, research in this area began relatively early abroad, and generally can be divided into classic and modern forecasting methods. Classic forecasting methods primarily include time series forecasting and regression analysis. Modern forecasting methods primarily include neural network forecasting, gray system theory, and fuzzy forecasting theory. Meanwhile, domestic scholars have primarily focused on logistics demand forecasting methods and systems, as well as railway transportation demand planning schemes. Existing research has been applied to urban logistics demand forecasting and emergency material demand forecasting under special circumstances. In terms of engineering practice, my country's telecommunications and petroleum industries are conducting research on the forecasting and management of material demand plans based on big data technology to ensure the timely and accurate supply of materials. However, in the railway industry, there is a lack of research on the refinement, accuracy and timeliness of the preparation of material demand plans for engineering construction. Since most of my country's super-large construction projects are located in the eastern and central regions with relatively good social and economic foundations, good processing and manufacturing capabilities, and relatively complete material storage and transportation supporting conditions, most materials can be supplied nearby and in a timely manner. The existing material demand plans are mostly prepared on an annual or monthly basis. For example, the existing Wuhan-Guangzhou High-Speed ​​Railway, Beijing-Shanghai High-Speed ​​Railway and other construction projects all prepare annual and monthly material demand plans based on the construction organization plan.

[0008] 3. Implementation of Demand Forecasting Simulation System

[0009] The implementation of a demand forecasting simulation system can provide a basis for planning, determining operational policies, and making management decisions. However, traditional forecasting systems are typically developed based on static forecasting models, which rarely reflect real-time changes in material demand and make it difficult to accurately predict supply and demand. Currently, two main approaches to addressing this issue are improving model accuracy and integrating multiple technologies.

[0010] Currently, the shortcomings of existing methods for predicting the volatility of railway construction material demand include:

[0011] (1) Insufficient refinement in material demand forecasting: Existing material demand plans are mostly compiled on an annual or monthly basis. There is no precedent for accurately forecasting railway project material demand plans and conducting theoretical research and practical application of precise project logistics distribution. However, super-large railway construction projects in complex and dangerous areas are characterized by long construction cycles, large material supply and demand, shortage of construction materials along the line, and uneven resource distribution. The traffic conditions they face are extremely fragile. Existing construction material demand forecasting methods are difficult to adapt to the complexity of the changing laws of super-large construction materials.

[0012] (2) Material demand forecasts lack consideration for supply security: Since most of my country's super-large construction projects are located in the eastern and central regions with relatively good social and economic foundations, good processing and manufacturing capabilities, and relatively complete material storage and transportation supporting conditions, most materials can be supplied nearby and in a timely manner. The on-site material supply plan mainly relies on manual experience and lacks consideration of factors such as transportation conditions and demand changes. Summary of the Invention

[0013] The embodiment of the present invention provides a method for predicting the fluctuation of railway engineering construction material demand based on delivery time limit, so as to effectively predict the supply demand of railway engineering construction materials.

[0014] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions.

[0015] A method for predicting the volatility of railway construction material demand based on delivery time limits includes:

[0016] Based on vehicle trajectories, cargo source distribution, transportation routes, weather and road conditions, a Gaussian process regression-based forecasting method is used to predict the probability distribution of cargo arrival times along different routes.

[0017] Combined with the study of the probability distribution of cargo arrival time, a material supply demand forecasting model for railway construction is constructed by considering factors such as arrival time limit, transportation cost, supply guarantee requirements, vehicle ownership and storage capacity, and the material supply demand in different time periods is predicted;

[0018] Based on the forecast of material delivery time and material supply demand, combined with the actual project progress and material consumption, the Monte Carlo simulation algorithm is used to deduce the temporal changes of material inventory, and the material ordering strategy, namely the material ordering quantity and the planned material release time, is obtained;

[0019] A demand forecasting simulation system is developed by integrating the cargo arrival time probability distribution prediction method, the material supply demand prediction method and the material ordering strategy prediction method with geographic information data processing. The demand forecasting simulation system uses a railway engineering construction material demand volatility prediction method based on the arrival time limit to predict the supply demand of various types of materials in different sections and work sites and the time to issue the supply plan.

[0020] Preferably, the prediction method based on Gaussian process regression based on vehicle driving trajectory, cargo source distribution, transportation route, weather and road conditions predicts the probability distribution of cargo arrival time along different routes, including:

[0021] Taking into account road conditions, transportation environment, transportation vehicles, and date characteristics, historical freight transportation trajectory data and historical weather data are obtained. Based on the historical freight transportation trajectory data and historical weather data, a Gaussian process regression algorithm is used to construct a freight transportation travel time prediction model by mining vehicle transportation trajectories;

[0022] Before a cargo transport begins, the route, transport start date and transport vehicle information of the cargo transport process are obtained according to the cargo transport plan. The route, transport start date and transport vehicle information are processed and input into the cargo transport travel time prediction model. The cargo transport travel time prediction model outputs a predicted value of the arrival time of the cargo transport process.

[0023] Preferably, the processing of the route, transportation start date and transportation vehicle information and inputting them into the freight transportation travel time prediction model, the freight transportation travel time prediction model outputting a predicted value of the arrival time of the freight transportation process, includes:

[0024] The original data of the route, transportation start date and transportation vehicle are processed to obtain characteristic values ​​related to road conditions, transportation environment, vehicle type and date. The processing process of the original data is as follows:

[0025] Transportation environment: If you want to obtain weather information, the required information includes date and location. The time is determined by the data returned by the vehicle track query interface. For the location, the longitude and latitude returned by the vehicle track query interface are matched to the specific location through the Baidu map coordinate picking system to obtain weather information for the specific time and location;

[0026] Date feature and transport vehicle: The date feature refers to whether the transport process is on a holiday. The time in the trajectory data can be used to determine whether the transport process is on a holiday. The type of transport vehicle can be obtained by reading the trajectory data.

[0027] Road conditions: Obtain the road conditions corresponding to the trajectory by matching the read trajectory data to the specific road section;

[0028] The freight transport travel time prediction model finds a potential function to express the relationship between feature data and target vector by learning the data in the training set, which will affect the feature data x of freight transport travel time. i Defined as:

[0029] x i =(d i ,t i ,q i ,c i )

[0030] Where, d i =(d 1i ,d 2i ,…,d 6i ) is the road condition characteristic vector; t i is the date feature vector; q iis the meteorological information feature vector, c i is the vehicle type feature vector;

[0031] The travel time of the goods on each road is defined as y i , the freight transport travel time prediction model is expressed by the following formula:

[0032] f(x)~GP(m(x),k(x,x′))

[0033] Where m(x) is the mean function, which is assumed to be constant, and k(x,x′) is the covariance function (or kernel function) used to measure the similarity between the input data x and x′;

[0034] The training process of the freight transport travel time prediction model is defined as follows:

[0035] f=[f(x1),f(x2),……,f(x n )] T

[0036] Where f is the predicted value vector of all training data points, n is the number of training data, and assuming that the observation value is y = [y1,y2,……,y n ] T ;

[0037] The prediction process of the freight transport travel time prediction model is expressed as follows:

[0038] p(y * ∣x * ,X,y)=N(μ * ,σ *2 )

[0039] where y * is the output value of the new data point to be predicted, x * The features of the new data point to be predicted, X is the feature of the training data, y is the output of the training data, μ * is the mean of the predicted values, σ *2 is the variance of the predicted value, which is calculated as follows:

[0040]

[0041] where k * is the covariance vector between the predicted data point and all training data points, and K is the covariance matrix between the training data points.

[0042] Preferably, the above-mentioned combination of the study on the probability distribution of cargo arrival time and the factors of arrival time limit, transportation cost, supply guarantee requirements, vehicle ownership and storage capacity are considered to construct a material supply demand forecasting model for railway engineering construction, and the forecast of material supply demand in different time periods includes:

[0043] Based on the above-mentioned study on the probability distribution of cargo arrival time, taking into account factors such as arrival time limit, transportation costs, supply guarantee requirements, vehicle ownership and storage capacity, and with the goal of minimizing costs, a material supply and demand forecasting model for railway engineering construction is constructed within the framework of storage theory. Constraints of the material supply and demand forecasting model are set based on the temporal balance of material inventory, material supply guarantee, storage capacity, vehicle ownership and actual material consumption factors.

[0044] The actual on-site construction progress of the railway project and the actual material consumption, delivery time limit, transportation costs, supply guarantee requirements, vehicle ownership and storage capacity data are processed and input into the material supply demand forecasting model. The heuristic algorithm is used to solve the material supply demand forecasting model, calculate the optimal material arrival quantity for each day in the forecast period, and obtain the construction material supply demand by time period.

[0045] Preferably, the material supply demand forecasting model is based on the actual construction organization plan and actual material consumption at the project site, with inventory capacity, supply guarantee and transportation capacity as constraints, and the minimum average cost as the objective function. The meanings of relevant symbols in the material demand forecasting model are shown in Table 1;

[0046] Table 1 Symbol definitions of the material supply demand forecasting model

[0047]

[0048]

[0049] The objective function of the material supply demand forecasting model is composed of the following formula:

[0050] Taking the minimum average ordering cost, storage cost and shipping cost within a period as the goal, the objective function is constructed as follows:

[0051]

[0052] The constraints of the material demand forecasting model are composed of the following formula:

[0053] Material inventory constraint: The material inventory on day t is equal to the material inventory on day t-1 plus the material arrival on day t minus the material consumption on day t;

[0054] S(t)=S(t-1)+A(t)-C(t)

[0055] Inventory capacity constraint: indicates that the inventory of materials on day t should meet the material demand for the next Δt days and be less than the inventory capacity;

[0056] D(t+Δt)≤S(t)≤N

[0057] Material arrival time constraint: means that the material arrival quantity on day t is equal to the order quantity σ days before the delivery time;

[0058] A(t)=O(t-σ)

[0059] Material arrival quantity constraint: The material arrival quantity on day t is equal to the number of material arrival vehicles on day t multiplied by the transport capacity of a single vehicle;

[0060] A(t)=M*E(t)

[0061] Material delivery time probability distribution constraint: indicates that the material delivery time σ follows the probability distribution P(σ);

[0062] σ~P(σ)

[0063] Material consumption constraint: indicates that the material consumption on day t is determined by the actual project progress and the actual quota consumption;

[0064] C(t)=f(AP,AC)

[0065] Material demand constraint: indicates that the material demand in the next Δt days is determined by the planned project progress and the estimated quota consumption;

[0066] D(t+Δt)=g(PP,PC)

[0067] The solution process of the heuristic algorithm includes:

[0068] Step 1: Initialize the population

[0069] Coding rules: Assume that the number of vehicles that arrive on day t is A(t), and the number of vehicles arriving daily in the forecast period T is composed of an array A = [A(1), A(2), …, A(T)]. Convert the decimal array into a binary array, A′ = [A′(1), A′(2), …, A′(T)].

[0070] Set the evolution generation counter n=0, set the maximum evolution generation N, and randomly generate M individuals as the initial population P(0);

[0071] Step 2: Individual Evaluation

[0072] Calculate the fitness F[P(m)] of each individual in the population P(m), substitute the number of vehicles that deliver goods represented by different individuals into the objective function, calculate the average cost, and based on this, derive the fitness of the individual:

[0073] F[P(m)]=1 / K[P(m)]

[0074] Step 3: Select the operation

[0075] Apply the selection operator to the population, and pass the optimized individuals directly to the next generation or generate new individuals through pairing and crossover, which are then passed to the next generation. The selection operation is based on the fitness evaluation of individuals in the population. Based on the calculated fitness of each individual in the population, each individual is given a probability of being selected, and the next generation base population is formed through multiple sampling.

[0076] Step 4: Crossover

[0077] Apply the crossover operator to the population. Crossover refers to the operation of replacing and recombining part of the structure of two parent individuals to generate new individuals. With a certain probability p, some parent individuals are selected from the next generation base population for crossover, and individuals are reconstructed by cutting off at a certain position.

[0078] Step 5: Mutation operation

[0079] Applying the mutation operator to the population means changing the gene values ​​at certain loci of the individual strings in the population. Using binary numbers to construct individuals, the value at a certain locus in the individual is transformed from 0 to 1 with a certain probability p. After the population P(n) undergoes selection, crossover, and mutation operations, the next generation population P(n+1) is obtained.

[0080] Step 6: Termination condition judgment

[0081] If n=N, the individual with the maximum fitness obtained in the evolution process is output as the optimal solution, the calculation is terminated, and the daily material arrival volume within the prediction period is output to obtain the material supply demand in different time periods.

[0082] Preferably, the material delivery time and material supply demand forecast is combined with the actual project progress and material consumption, and a Monte Carlo simulation algorithm is used to deduce the temporal changes of material inventory to obtain a material ordering strategy, i.e., the material ordering quantity and the planned material release time, including:

[0083] The material inventory simulation process based on Monte Carlo simulation is as follows:

[0084] Input: number of simulations M; simulation duration T; initial material inventory D(0); material supply days requirement Δt; material delivery time σ probability distribution P(σ); material storage capacity N; actual project progress AP; actual quota consumption AC; planned project progress PP, estimated quota consumption PC;

[0085] process:

[0086] Step 1: Start the simulation of material demand volatility forecasting for the i-th time;

[0087] Step 2: Calculate the inventory of materials S(t) on day t:

[0088] Step 2.1: Calculate the material consumption C(t) on day t based on the project progress AP and the actual quota consumption AC;

[0089] Step 2.2: Obtain the material delivery volume A(t) on day t based on the output of the material supply demand forecasting model;

[0090] Step 2.3: Calculate the material inventory S(t) on day t based on the material inventory S(t-1) on day t-1, the material consumption C(t) on day t, and the material delivery volume A(t) on day t.

[0091] Step 3: Calculate the material demand D(t+Δt) in the next Δt days based on the planned project progress PP and the estimated quota consumption PC;

[0092] Step 4: Sample from the delivery time probability distribution P(σ) to determine the delivery time σ of the materials when scheduled on day t t ;

[0093] Step 5: Determine the order time t-σ for materials arriving on day t t ;

[0094] Step 6: If t = T, end the i-th simulation; otherwise, t = t + 1, return to step 2;

[0095] Step 7: If i=M, end the simulation; otherwise, i=i+1, return to step 1;

[0096] Output: Material ordering strategy, which includes the material ordering quantity by time period and the planned material release time.

[0097] Preferably, the integrated cargo arrival time probability distribution prediction method, material supply demand prediction method, and material ordering strategy prediction method are combined with geographic information data processing to develop a demand prediction simulation system. The demand prediction simulation system uses a railway construction material demand volatility prediction method based on arrival time limits to predict the supply demand of various materials at different sections and work sites and the time when the supply plan is issued, including:

[0098] The forecasting process of the probability distribution of cargo arrival time, the forecasting process of the construction material supply demand by time period, and the material inventory deduction process based on Monte Carlo simulation are integrated, and a demand forecasting simulation system is developed in combination with geographic information data processing. The demand forecasting simulation system uses a method for predicting the volatility of railway construction material demand based on arrival time, taking into account multiple factors such as the actual demand for construction materials, the material transportation network and route selection, the probability distribution of the arrival time of different types of materials, and the material storage capacity, to achieve the forecast of the supply demand of various types of materials and the time of issuing supply plans for different sections and work sites;

[0099] The calculation process of the demand forecast simulation system includes:

[0100] Step 1: Set basic parameters, including selecting the forecast starting time, forecast days, guaranteed supply days, bidding section, warehouse, and supply source, and obtaining information on environmental parameters, warehouse storage capacity, storage costs, and actual material consumption;

[0101] Step 2: Calculate the probability distribution of delivery time;

[0102] Step 2.1: Obtain weather conditions, road conditions, transportation environment, and vehicle type parameters for each road segment;

[0103] Step 2.2: Load the delivery time probability distribution prediction model, input the route information, including the mileage, road grade, and average speed of the road width, into the time probability distribution prediction model, and calculate the fluctuating delivery time probability distribution prediction result;

[0104] Step 3: Calculate material supply demand forecast;

[0105] Step 3.1: Combined with the calculated delivery time probability distribution results, input parameters include the forecast period, actual consumption, delivery time, number of vehicles, single-vehicle transportation, capacity freight rate, storage cost, ordering cost, initial inventory, inventory capacity, transportation distance, and guaranteed supply days, as well as basic parameters including population size and mutation probability;

[0106] Step 3.2: Load the material supply demand forecasting model and input the forecast period, actual consumption, delivery time, number of vehicles, single-vehicle transportation, capacity freight rate, storage cost, ordering cost, initial inventory, inventory capacity, transportation distance, and guaranteed supply days into the model to calculate the material supply demand forecast results for each time period.

[0107] Step 4: Calculation of material ordering strategy;

[0108] Step 4.1: Combining the delivery time probability distribution and the material supply demand forecast calculation results, input parameters include simulation time, initial material inventory, material supply guarantee days, material delivery time, material storage capacity, actual project progress, and actual material consumption;

[0109] Step 4.2: Load the material inventory simulation algorithm based on Monte Carlo simulation and output the corresponding material ordering strategy;

[0110] Step 5: Display the demand forecast results and output the material supply demand and material ordering strategy, that is, output the supply demand of various materials for different sections and work sites and the time when the supply plan is issued.

[0111] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the present invention proposes a material supply demand forecasting technology that first predicts the probability distribution of cargo arrival time along different routes and then predicts the supply demand of construction materials in different time periods. A material supply demand forecasting model for super-large railway engineering construction is constructed under the framework of storage theory to achieve accurate forecasting of engineering material demand, providing data support for scientifically formulating procurement plans and reasonably adjusting construction organization.

[0112] Additional aspects and advantages of the present invention will be set forth in part in the following description, will become apparent from the following description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0114] Figure 1 A schematic diagram illustrating a method for predicting the volatility of railway construction material demand based on delivery time limits, provided in an embodiment of the present invention;

[0115] Figure 2 A diagram illustrating the implementation principle of a method for predicting freight transport travel time provided by an embodiment of the present invention;

[0116] Figure 3 A schematic diagram of the data processing portion of a cargo travel time prediction model provided by an embodiment of the present invention;

[0117] Figure 4 A schematic diagram illustrating a time-divided forecast of construction material supply demand provided by an embodiment of the present invention;

[0118] Figure 5 A schematic diagram of an implementation scheme of a demand forecasting simulation system integrating a geographic information system and a demand forecasting model provided by an embodiment of the present invention;

[0119] Figure 6 A schematic diagram of an implementation scheme for constructing a material demand database for a demand forecasting simulation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0120] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0121] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or couplings. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0122] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such herein, will not be interpreted in an idealized or overly formal sense.

[0123] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0124] This embodiment of the present invention combines the characteristics of on-site material supply with a method for predicting the volatility of railway construction material demand based on delivery timelines. The method involves predicting the probability distribution of material delivery timelines, forecasting material supply and demand, simulating material inventory, and developing a demand forecasting simulation system. This method, combined with research on the probability distribution of freight travel time, constructs a Monte Carlo simulation-based material supply and demand forecasting method, enabling refined forecasting of material demand.

[0125] This embodiment of the present invention proposes a method for forecasting the supply and demand of railway engineering materials. This method combines the predicted probability distribution results of cargo arrival times along different routes, considers factors such as arrival time limits, transportation costs, supply requirements, vehicle inventory, and storage capacity, constructs and solves a corresponding material demand forecasting model, and then performs material inventory deduction based on Monte Carlo simulation, thereby achieving an accurate forecast of the supply and demand of engineering materials. By refining the road network, accurately matching truck trajectories, and extracting characteristics such as arrival time limits, transportation costs, supply requirements, and storage capacity, and then combining this with a storage theory framework to construct a material supply and demand forecasting model, this technical solution can comprehensively consider various influencing factors, accurately forecast the demand for various material categories, and achieve a scientifically guaranteed supply of materials at railway engineering sites.

[0126] Through field research, this paper discovered that the primary challenge facing construction sites for ultra-large railway projects lies in ensuring supply, despite the current lack of precise methods for calculating material supply. This paper aims to address practical challenges encountered during the construction of ultra-large railway projects in complex and hazardous areas, aiming to accurately predict construction material supply demand. The research follows a technical approach encompassing freight travel time prediction, time-phased construction material supply demand forecasting, material inventory simulation, and system platform development.

[0127] The implementation principle diagram of a method for predicting the volatility of railway construction material demand based on delivery time limit proposed in an embodiment of the present invention is as follows: Figure 1 As shown, the processing steps include the following:

[0128] Step S10: Predict the probability distribution of cargo arrival times for different routes. The vehicle's trajectory, cargo source distribution, transportation route, and weather and road conditions are input, and a Gaussian process regression-based prediction method is used to predict the probability distribution of cargo arrival times for different routes.

[0129] Step S20 predicts the demand for construction material supply by time period. Based on the study of the probability distribution of cargo arrival times, the project site's construction progress, actual material consumption, and construction material demand, and taking into account factors such as arrival time limits, transportation costs, supply requirements, vehicle ownership, and storage capacity, a corresponding material demand forecasting model is constructed and a heuristic algorithm is designed to solve the problem and determine the material supply demand.

[0130] Step S30: Calculate the time series of material inventory changes. Combining the results of cargo arrival time and material supply demand research with the actual project progress and material consumption, Monte Carlo simulation is used to deduce material inventory. This results in a corresponding project material ordering strategy, which includes the order quantities and planned material release times for different sections and work sites within the forecast period.

[0131] Step S40: Develop a material demand forecasting simulation system. This system integrates methods for predicting cargo arrival time probability distribution, material supply demand, and material ordering strategy, combined with geographic information data processing. This system uses a map as a platform to visualize system business data. Based on the aforementioned method for predicting material demand volatility for railway construction, it predicts the supply demand for various materials and the timing of supply plan issuance for different sections and work sites.

[0132] Specifically, the above step S10 includes: a method for predicting freight transport travel time based on Gaussian process regression: by mining vehicle transport trajectory and weather data, a freight transport travel time prediction model is constructed based on the Gaussian process regression algorithm. The model comprehensively considers road conditions, transportation environment, transportation vehicles and date characteristics to improve the prediction performance of the model. The implementation principle of the freight transport travel time prediction method provided by the embodiment of the present invention is as follows: Figure 2 As shown, from Figure 2 It can be seen that building a travel time prediction model mainly involves the following steps:

[0133] 1: Raw data acquisition: The model comprehensively considers road conditions, transportation environment, transportation vehicles and date characteristics, so the required raw data include historical cargo transportation trajectory data and historical weather data.

[0134] 2: Data processing: This part mainly uses data mining technology to obtain feature values ​​by performing a series of transformations on the original data, which are used as feature space data for training model parameters.

[0135] 3: Model construction: The present invention constructs a cargo transportation travel time prediction model based on the Gaussian process regression algorithm. The basic idea of ​​the Gaussian process regression prediction method is: by assuming the prior probability that the learning sample obeys the Gaussian process, combining the Bayesian theory to obtain the corresponding posterior probability, and using the maximum likelihood method to obtain the corresponding optimal hyperparameters.

[0136] 4: Model application: Apply the constructed model to the actual cargo transportation process. Before a cargo transportation begins, the information about the transportation route, transportation start date and transportation vehicle can be obtained according to the cargo transportation plan. The known information can be processed and input into the travel time prediction model to obtain the predicted value of the transportation time for this transportation process.

[0137] The specific steps are:

[0138] (1) Data processing

[0139] Figure 3This is a schematic diagram of the data processing portion of a freight travel time prediction model provided by an embodiment of the present invention. To obtain training data for model parameters, the raw data needs to be processed to obtain characteristic values ​​related to road conditions, transportation environment, vehicle type, and date. The raw data processing process is as follows:

[0140] ① Transportation Environment: To obtain weather information, the required information includes date and location. The time can be determined from the data returned by the vehicle trajectory query interface. As for the location, the longitude and latitude returned by the vehicle trajectory query interface are first matched to the specific location through the Baidu Maps coordinate picking system. Combining these two information, the China Meteorological Administration website is first obtained. The website is then parsed to obtain weather information for the specific time and location, including whether there is rain or snow and visibility.

[0141] ② Date characteristics and transport vehicles: The date characteristics refer to whether the transport process is on a holiday. The time in the trajectory data can be used to determine whether the transport process is on a holiday. The type of transport vehicle is also obtained by reading the trajectory data.

[0142] ③Road conditions: Road conditions are mainly obtained by matching the read trajectory data to a specific road section to obtain the road conditions corresponding to the trajectory. Taking a certain road as an example, the specific process of matching trajectory data to the road is to first determine the longitude and latitude coordinates of the starting point of this road: starting point

[0143] The starting point is [97.0967068, 30.6611156], and the end point is [96.5987455, 30.87088213], where the horizontal coordinate is longitude (lon) and the vertical coordinate is latitude (lat). The distances between different trajectory points and the starting point during a single transport are calculated and the smallest distance is retained. (As shown in the table below, the closest distances between the trajectory point in the first transport trajectory and the starting point of this road are 587982 and 622889, respectively, and those in the third are 21.035134 and 55.022815, respectively.) Trajectory points A and B in the single transport process that are within 200m of the road starting point are retained (the third transport trajectory is retained). The transport time of this transport trajectory on this road can be obtained by recording the time at points A and B. Road condition data, including road width, number of lanes, design speed, and line grade, can also be obtained from the road name. After each trajectory matching operation, if there is a transport track on the matched road, the average speed of the transport process (speed = distance / time) will be recorded at a specific location in the database. After all trajectory matching operations are completed, the arithmetic average method is used to calculate the historical average speed of all vehicles passing through each road.

[0144] Table 2 Trajectory data matching road process table

[0145]

[0146]

[0147] (2) Model construction

[0148] Gaussian Process Regression (GP) models the Gaussian distribution of a function and uses known data points to estimate the parameters of the distribution, thereby achieving prediction of new data points and providing uncertainty estimation of the prediction results.

[0149] This model finds a potential function by learning the data in the training set to express the relationship between the feature data and the target vector, which will affect the feature data x of the cargo transportation travel time. i Defined as:

[0150] x i =(d i ,t i ,q i ,c i )

[0151] Where, d i =(d 1i ,d 2i ,…,d 6i ) is the road condition characteristic vector; t i is the date feature vector; q i is the meteorological information feature vector, c i is the vehicle type feature vector.

[0152] The target vector, i.e. the travel time of the cargo on each road segment, is defined as y i .

[0153] Therefore, the model of the present invention can be expressed by the following formula:

[0154] f(x)~GP(m(x),k(x,x′))

[0155] Where m(x) is the mean function, which is assumed to be constant, and k(x,x′) is the covariance function (or kernel function) used to measure the similarity between the input data x and x′. The model training process can be defined as follows:

[0156] f=[f(x1),f(x2),……,f(x n )] T

[0157] Where f is the predicted value vector of all training data points, n is the number of training data, and assuming that the observation value is y = [y1,y2,……,yn ] T The prediction process of the model can be expressed as follows:

[0158] p(y * ∣x * ,X,y)=N(μ * ,σ *2 )

[0159] where y * is the output value of the new data point to be predicted, x * The features of the new data point to be predicted, X is the feature of the training data, y is the output of the training data, μ * is the mean of the predicted values, σ *2 is the variance of the predicted value, which is calculated as follows:

[0160]

[0161] where k * is the covariance vector between the predicted data point and all training data points, and K is the covariance matrix between the training data points.

[0162] Specifically, the above step S20 includes: time-divided construction material supply and demand forecasting technology: constructing a railway engineering construction material supply and demand forecasting model under the framework of storage theory, forecasting the engineering material demand based on the actual construction organization plan and the actual quota consumption of materials at the engineering site, and taking inventory capacity, supply guarantee, transportation capacity, etc. as constraints, and taking the minimum average cost as the objective function. A solution algorithm for the engineering construction material supply and demand forecasting model based on a heuristic algorithm is designed to obtain the material supply demand in different time periods. The implementation principle diagram of a time-divided construction material supply and demand forecast provided by an embodiment of the present invention is as follows: Figure 4 As shown. Figure 4 It can be seen that the construction of a time-divided material supply demand forecasting method mainly includes the following steps:

[0163] 1: Model construction: A railway engineering construction material supply demand forecasting model is constructed within the framework of storage theory. This model is based on the actual construction organization plan and actual material demand at the project site, and is constrained by inventory capacity, supply guarantee, and transportation capacity, with the minimum average cost as the objective function.

[0164] 2: Algorithm design: According to the material supply demand forecasting model, an algorithm for solving the engineering construction material supply demand forecasting model based on a heuristic algorithm is designed. The material supply demand in different time periods is obtained through the algorithm solution.

[0165] The specific steps are:

[0166] (1) Construction of a time-based construction material supply demand forecasting model

[0167] The meanings of relevant symbols in the definition model are shown in Table 3.

[0168] Table 3 Symbol definitions of construction material supply demand forecasting model

[0169]

[0170] Taking the minimum average ordering cost, storage cost, and shipping cost within a period as the goal, the objective function is constructed as follows:

[0171]

[0172] The model constraints are composed of the following formula:

[0173] Material inventory constraint. This means that the material inventory on day t is equal to the material inventory on day t-1 plus the material arrival on day t minus the material consumption on day t.

[0174] S(t)=S(t-1)+t(t)-C(t)

[0175] Inventory capacity constraint. This means that the inventory quantity on day t should meet the demand for the next Δt days and be less than the inventory capacity.

[0176] D(t+Δt)≤S(t)≤N

[0177] Material arrival time constraint. This means that the material arrival quantity on day t is equal to the quantity ordered σ days before the delivery time.

[0178] A(t)=O(t-σ)

[0179] Material arrival quantity constraint. This means that the material arrival quantity on day t is equal to the number of material arrival vehicles on day t multiplied by the transport capacity of each vehicle.

[0180] A(t)=M*E(t)

[0181] The probability distribution constraint of the material delivery time. It means that the material delivery time σ follows the probability distribution P(σ).

[0182] σ~P(σ)

[0183] Material consumption constraint. This means that the material consumption on day t is determined by the actual project progress and the actual quota consumption.

[0184] C(t)=f(AP,AC)

[0185] Material demand constraint. This means that the material demand for the next Δt days is determined by the planned project progress and the estimated quota consumption.

[0186] D(t+Δt)=g(PP,PC)

[0187] (2) Heuristic algorithm design

[0188] The model is solved using a heuristic algorithm. The algorithm flow is as follows:

[0189] Step 1: Initialize the population

[0190] Coding rules: Assume that the number of vehicles that arrive on day t is A(t), and the number of vehicles arriving daily during the forecast period T is composed of an array A = [A(1), A(2), …, A(T)]. To improve the efficiency of the algorithm, the decimal array is converted into a binary array, A′ = [A′(1), A′(2), …, A′(T)].

[0191] Set the evolutionary generation counter n=0, set the maximum evolutionary generation N, and randomly generate M individuals as the initial population P(0).

[0192] Step 2: Individual Evaluation

[0193] Calculate the fitness F[P(m)] of each individual in the population P(m). Substitute the number of vehicles carrying goods represented by each individual into the objective function, calculate the average cost, and based on this, derive the individual fitness:

[0194] F[P(m)]=1 / K[P(m)]

[0195] Step 3: Select the operation

[0196] Apply a selection operator to the population. The goal of selection is to either directly pass optimized individuals to the next generation or to generate new individuals through pairing and crossover, which are then passed on to the next generation. The selection operation is based on the fitness evaluation of individuals in the population. Based on the calculated fitness of each individual in the population, a probability of selection is assigned to each individual, and multiple sampling is used to form the base population for the next generation.

[0197] Step 4: Crossover

[0198] Apply the crossover operator to the population. Crossover refers to the process of replacing and recombining parts of the structure of two parent individuals to generate new individuals. Crossover significantly improves the search capability of the genetic algorithm. With a certain probability p, some parent individuals are selected from the next generation base population for crossover, and individuals are reconstructed by cutting at a certain position.

[0199] Step 5: Mutation operation

[0200] Apply the mutation operator to the population. This means changing the gene values ​​at certain loci within the individual strings. Because this algorithm uses binary numbers to construct individuals, the value at a locus within an individual is transformed from 0 to 1 with a certain probability p. After selection, crossover, and mutation operations on the population P(n), the next generation population P(n+1) is obtained.

[0201] Step 6: Termination condition judgment

[0202] If n=N, the individual with the maximum fitness obtained in the evolution process is output as the optimal solution, the calculation is terminated, and the optimal material arrival quantity for each day in the prediction period is output to obtain the material supply demand in each time period.

[0203] Specifically, step S30 includes: simulation and deduction of material inventory time series changes: based on material transportation travel time and material supply demand forecast research, combined with actual project progress and material consumption, using a Monte Carlo simulation algorithm to deduce the time series changes of material inventory, and obtain the corresponding project material ordering strategy, namely, the material ordering quantity and the planned material release time;

[0204] The material inventory deduction process based on Monte Carlo simulation mainly consists of the following steps:

[0205] 1: Simulation and deduction: Combining the cargo arrival time and material supply demand forecast, the material inventory is deduced based on the Monte Carlo simulation method to obtain the corresponding engineering material ordering strategy.

[0206] 2: Technical application: The constructed material supply and demand forecasting method is applied to actual material ordering. Based on the actual material demand points and material supply point distribution on site, the corresponding material supply and demand OD pairs are formed. Combined with the research on the probability distribution of the arrival time of the goods, the arrival time forecast value of each supply and demand OD pair can be obtained. The initial date, forecast time, supply guarantee days, initial inventory and other parameters as well as the probability forecast results of the arrival time limit are input into the material supply and demand forecasting model to obtain the material supply demand in different time periods. Further, through Monte Carlo simulation deduction, the material inventory, arrival and ordering status within the deduction period can be obtained.

[0207] The specific steps are as follows:

[0208] Input: number of simulations M; simulation duration T; initial material inventory D(0); material supply days requirement Δt; material arrival time σ probability distribution P(σ); material storage capacity N; actual project progress AP; actual quota consumption AC; planned project progress PP, estimated quota consumption PC.

[0209] process:

[0210] Step 1: Start the simulation of material demand volatility forecasting for the i-th time;

[0211] Step 2: Calculate the inventory of materials S(t) on day t:

[0212] Step 2.1: Calculate the material consumption C(t) on day t based on the project progress AP and the actual quota consumption AC;

[0213] Step 2.2: Obtain the material delivery volume A(t) on day t based on the output of the material supply demand forecasting model;

[0214] Step 2.3: Calculate the material inventory S(t) on day t based on the material inventory S(t-1) on day t-1, the material consumption C(t) on day t, and the material delivery volume A(t) on day t.

[0215] Step 3: Calculate the material demand D(t+Δt) within the next Δt days based on the planned project progress PP and the estimated quota consumption PC.

[0216] Step 4: Sample from the delivery time probability distribution P(σ) to determine the delivery time σ of the materials when scheduled on day t t ;

[0217] Step 5: Determine the order time t-σ for materials arriving on day t t ;

[0218] Step 6: If t = T, end the i-th simulation; otherwise, t = t + 1, return to step 2;

[0219] Step 7: If i=M, end the simulation; otherwise, i=i+1, return to step 1.

[0220] Output: Material time sequence order quantity range.

[0221] Specifically, the above step S40 includes: an embodiment of the present invention provides a demand forecasting simulation system implementation scheme that integrates geographic information data and the railway engineering construction material demand volatility forecasting method based on delivery time limit. Figure 5 shown.

[0222] The system uses a method to predict the volatility of railway construction material demand based on delivery time limits. This method takes into account multiple factors, including the actual demand for construction materials, the material transportation network and route selection, the probability distribution of delivery time limits for different types of materials, and material storage capacity. It predicts the supply demand for various materials in different sections and work sites, as well as the time when supply plans will be issued. The system digitally reconstructs material demand data, builds a multi-dimensional database of material demand, and designs functional modules such as map display, material demand forecasting, and material supply early warning. This provides auxiliary decision-making support for on-site understanding of the material supply situation in each section and for each construction unit to issue supply plans. The specific implementation steps are as follows:

[0223] (1) Data processing.

[0224] The material demand data is digitally reconstructed to achieve standardization, systematization, visualization and display of material demand data. The data contained in the material demand database is mainly divided into multiple dimensions such as geographic data, business data, and calculation data. Figure 6 As shown. Among them, geographic data includes basic geographic data such as provinces, cities, counties, and water systems, and professional geographic data such as railway networks and highway network construction roads. It is the carrier for displaying business and computing data, and is also the entrance to system spatial query and user interaction; business data includes entity data such as bidding sections, work sites, work surfaces, warehouses, contracts, and sources of goods. It is the physical carrier for realizing functions such as demand forecasting and material supply guarantee; computing data includes vehicle trajectories, weather conditions, road information, material reporting requirements, forecast parameters and other data. It is the parameter data required for model calculation, and the calculation results can be visualized. The data processing process includes:

[0225] Step 1: Create a geographic information grid, dividing the space into multiple grids by city, obtain real-time weather conditions within the grid, and establish the topology of the public and railway networks in the study area to calculate the optimal path between the supply source and the warehouse;

[0226] Step 2: Digitally reconstruct the material demand data to achieve standardized, systematized, and visualized operation and display of the material demand data, which is used to calculate the probability distribution of delivery time and material supply demand.

[0227] (2) Prediction method integration

[0228] The demand forecasting simulation system is developed by integrating the cargo arrival time probability distribution prediction method, the time-segment construction material supply demand prediction method, and the material inventory deduction method based on Monte Carlo simulation, combined with geographic information data processing. The demand forecasting simulation system uses a railway construction material demand volatility prediction method based on arrival time, taking into account multiple factors such as the actual demand for construction materials, the material transportation network and route selection, the probability distribution of the arrival time of different types of materials, and the material storage capacity, to achieve the prediction of the supply demand of various materials and the time of issuing supply plans for different sections and work sites. The system calculation process includes:

[0229] Step 1: Set basic parameters, including selecting the initial forecast time, forecast days, guaranteed supply days, bidding section, warehouse, source of goods, etc., and obtain information such as environmental parameters, warehouse storage capacity, storage cost, actual material consumption, etc.

[0230] Step 2: Calculate the probability distribution of delivery time;

[0231] Step 2.1: Obtain parameters such as weather conditions, road conditions, transportation environment, and vehicle type for each road section.

[0232] Step 2.2: Load the delivery time probability distribution prediction model, input the route section information, including section mileage, road grade, road width and average speed, into the model, and calculate the volatility time probability distribution prediction result.

[0233] Step 3: Calculate material supply demand forecast;

[0234] Step 3.1: Combined with the calculated results of the delivery time probability distribution, the input parameters include the forecast period, actual consumption, delivery time, number of vehicles, single-vehicle transportation, capacity freight rate, storage cost, ordering cost, initial inventory, inventory capacity, transportation distance, guaranteed supply days, and basic parameters including population size and mutation probability.

[0235] Step 3.2: Load the material supply demand forecasting model, enter the forecast period, actual consumption, delivery time, number of vehicles, single-vehicle transportation, capacity freight rate, storage cost, ordering cost, initial inventory, inventory capacity, transportation distance, and supply guarantee days into the model, and calculate the material supply demand forecast results for each time period.

[0236] Step 4: Calculation of material ordering strategy;

[0237] Step 4.1: Combining the delivery time probability distribution and the material supply demand forecast calculation results, input parameters include simulation time, initial material inventory, material supply guarantee days, material delivery time, material storage capacity, actual project progress, and actual material consumption;

[0238] Step 4.2: Load the material inventory simulation algorithm based on Monte Carlo simulation and output the corresponding engineering material ordering strategy;

[0239] Step 5: Display the demand forecast results and output the engineering material supply demand and ordering strategy, that is, the supply demand of various materials in different sections and work sites and the time when the supply plan is issued.

[0240] (3) Functional design

[0241] The main functional modules of the material demand forecasting simulation system include map display, demand forecasting, supply forecasting, and results export, as shown in Table 4.

[0242] Table 4 System function overview

[0243]

[0244]

[0245] In summary, in terms of delivery time prediction, the present invention effectively solves the problems of insufficient consideration of influencing factors and lack of uncertainty quantification in the existing technology by making full use of trajectory data and historical weather data, combining the road condition characteristics obtained by path matching mining, and using Gaussian process regression to output the predicted distribution and evaluate the distribution, providing a more accurate, comprehensive and reliable solution for cargo transportation time prediction.

[0246] In terms of material supply demand forecasting, existing railway construction projects typically prepare annual and monthly material demand plans based on construction organization plans under favorable material transportation conditions. However, for ultra-large railway construction projects in complex and hazardous areas, material transportation conditions are complex, and the key to material management lies in ensuring the supply of materials. To ensure adequate material supply during railway construction, this technology aims to accurately predict construction material supply demand. Combined with research on the probability distribution of arrival times, it proposes a time-based construction material supply demand forecasting method. This method accurately grasps on-site material demand and supply status, ensuring that there is neither a shortage of materials nor an oversupply of materials in warehouses.

[0247] In implementing the demand forecasting simulation system, geographic information technology was introduced to provide precise geographic grid information. Information such as road divisions and real-time weather conditions in each region was incorporated into the model calculations. This comprehensive consideration of various supply and demand factors enabled a refined forecast of construction material supply demand. A combination of time forecasting and demand forecasting models was constructed, improving the accuracy and reliability of the model calculations. The forecasting simulation results were intuitively displayed through geographic information visualization.

[0248] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0249] From the above description of the embodiments, it can be seen that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus the necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0250] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. A person of ordinary skill in the art can understand and implement it without making any creative efforts.

[0251] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for predicting the volatility of railway construction material demand based on delivery time limit, characterized by: include: Based on vehicle trajectories, cargo source distribution, transportation routes, weather and road conditions, a Gaussian process regression-based forecasting method is used to predict the probability distribution of cargo arrival times along different routes. Combined with the study of the probability distribution of cargo arrival time, a material supply demand forecasting model for railway construction is constructed by considering factors such as arrival time limit, transportation cost, supply guarantee requirements, vehicle ownership and storage capacity, and the material supply demand in different time periods is predicted; Based on the forecast of material delivery time and material supply demand, combined with the actual project progress and material consumption, the Monte Carlo simulation algorithm is used to deduce the temporal changes of material inventory, and the material ordering strategy, namely the material ordering quantity and the planned material release time, is obtained; A demand forecasting simulation system was developed by integrating a method for predicting the probability distribution of cargo arrival time, a method for predicting material supply demand, and a method for predicting material ordering strategies, combined with geographic information data processing. The system uses a method for predicting the volatility of demand for railway construction materials based on arrival time limits to predict the supply demand for various materials at different sections and work sites, as well as the time to issue supply plans. The above-mentioned combination of the research on the probability distribution of cargo arrival time and the factors of arrival time limit, transportation cost, supply guarantee requirements, vehicle ownership and storage capacity are considered to construct a material supply demand forecasting model for railway engineering construction, and forecast the material supply demand in different time periods, including: Based on the above-mentioned research on the probability distribution of cargo arrival time, taking into account factors such as arrival time limit, transportation costs, supply guarantee requirements, vehicle ownership and storage capacity, and with the goal of minimizing costs, a material supply and demand forecasting model for railway engineering construction is constructed within the framework of storage theory. Constraints for the material supply and demand forecasting model are set based on factors such as material inventory time series balance, material supply guarantee, storage capacity, vehicle ownership and actual material consumption. The actual on-site construction progress of the railway project and the actual material consumption, delivery time limit, transportation costs, supply guarantee requirements, vehicle ownership and storage capacity data are processed and input into the material supply demand forecasting model. The heuristic algorithm is used to solve the material supply demand forecasting model, calculate the optimal material arrival quantity for each day in the forecast period, and obtain the construction material supply demand by time period.

2. The method according to claim 1, characterized in that The prediction method based on Gaussian process regression, which predicts the probability distribution of cargo arrival time along different routes based on vehicle driving trajectories, cargo source distribution, transportation routes, weather and road conditions, includes: Taking into account road conditions, transportation environment, transportation vehicles, and date characteristics, historical freight transportation trajectory data and historical weather data are obtained. Based on the historical freight transportation trajectory data and historical weather data, a Gaussian process regression algorithm is used to construct a freight transportation travel time prediction model by mining vehicle transportation trajectories; Before a cargo transport begins, the route, transport start date and transport vehicle information of the cargo transport process are obtained according to the cargo transport plan. The route, transport start date and transport vehicle information are processed and input into the cargo transport travel time prediction model. The cargo transport travel time prediction model outputs a predicted value of the arrival time of the cargo transport process.

3. The method according to claim 2, characterized in that The route, transportation start date and transportation vehicle information are processed and input into the freight transportation travel time prediction model, and the freight transportation travel time prediction model outputs a predicted value of the arrival time during the freight transportation process, including: The original data of the route, transportation start date and transportation vehicle are processed to obtain characteristic values ​​related to road conditions, transportation environment, vehicle type and date. The processing process of the original data is as follows: Transportation environment: If you want to obtain weather information, the required information includes date and location. The time is determined by the data returned by the vehicle track query interface. For the location, the longitude and latitude returned by the vehicle track query interface are matched to the specific location through the Baidu map coordinate picking system to obtain weather information for the specific time and location; Date feature and transport vehicle: The date feature refers to whether the transport process is on a holiday. The time in the trajectory data can be used to determine whether the transport process is on a holiday. The type of transport vehicle can be obtained by reading the trajectory data. Road conditions: Obtain the road conditions corresponding to the trajectory by matching the read trajectory data to the specific road section; The freight transport travel time prediction model finds a potential function to express the relationship between feature data and target vector by learning the data in the training set, which will affect the feature data x of freight transport travel time. i Defined as: x i =(d i ,t i ,q i ,c i ) Where, d i =(d 1i ,d 2i ,…,d 6i ) is the road condition characteristic vector; t i is the date feature vector; q i is the meteorological information feature vector, c i is the vehicle type feature vector; The travel time of the goods on each road is defined as y i , the freight transport travel time prediction model is expressed by the following formula: f(x)~GP(m(x),k(x,x′)) Where m(x) is the mean function, which is assumed to be constant, and k(x,x′) is the covariance function (or kernel function) used to measure the similarity between the input data x and x′; The training process of the freight transport travel time prediction model is defined as follows: f=[f(x1),f(x2),……,f(x n )] T Where f is the predicted value vector of all training data points, n is the number of training data, and assuming that the observation value is y = [y1,y2,……,y n ] T ; The prediction process of the freight transport travel time prediction model is expressed as follows: p(y * ∣x * ,X,y)=N(μ * ,s *2 ) where y * is the output value of the new data point to be predicted, x * The features of the new data point to be predicted, X is the feature of the training data, y is the output of the training data, μ * is the mean of the predicted values, σ *2 is the variance of the predicted value, which is calculated as follows: where k * is the covariance vector between the predicted data point and all training data points, and K is the covariance matrix between the training data points.

4. The method according to claim 1, wherein The material supply demand forecasting model is based on the actual construction organization plan and actual material consumption at the project site, with inventory capacity, supply guarantee and transportation capacity as constraints, and the minimum average cost as the objective function. The meanings of the relevant symbols in the material supply demand forecasting model are shown in Table 1; Table 1 Symbol definitions of the material supply demand forecasting model The objective function of the material supply demand forecasting model is composed of the following formula: Taking the minimum average ordering cost, storage cost and shipping cost within a period as the goal, the objective function is constructed as follows: The constraints of the material supply demand forecasting model are composed of the following formula: Material inventory constraint: The material inventory on day t is equal to the material inventory on day t-1 plus the material arrival on day t minus the material consumption on day t; S(t)=S(t-1)+A(t)-C(t) Inventory capacity constraint: indicates that the inventory quantity on day t should meet the material demand for the next Δt days and be less than the inventory capacity; D(t+Δt)≤S(t)≤N Material arrival time constraint: means that the material arrival quantity on day t is equal to the order quantity σ days before the delivery time; A(t)=O(t―σ) Material arrival quantity constraint: The material arrival quantity on day t is equal to the number of material arrival vehicles on day t multiplied by the transport capacity of a single vehicle; A(t)=M*E(t) Material delivery time probability distribution constraint: indicates that the material delivery time σ follows the probability distribution P(σ); σ~P(σ) Material consumption constraint: indicates that the material consumption on day t is determined by the actual project progress and the actual quota consumption; C(t)=f(AP,AC) Material demand constraint: indicates that the material demand in the next Δt days is determined by the planned project progress and the estimated quota consumption; D(t+Δt)=g(PP,PC) The solution process of the heuristic algorithm includes: Step 1: Initialize the population Coding rules: Assume that the number of vehicles that arrive on day t is A(t), and the number of vehicles arriving daily in the forecast period T is composed of an array A = [A(1), A(2), ..., A(T)]. Convert the decimal array into a binary array, A' = [A'(1), A'(2), ..., A'(T)]. Set the evolution generation counter n=0, set the maximum evolution generation N, and randomly generate M individuals as the initial population P(0); Step 2: Individual Evaluation Calculate the fitness F[P(m)] of each individual in the population P(m), substitute the number of vehicles that deliver goods represented by different individuals into the objective function, calculate the average cost, and based on this, derive the fitness of the individual: F[P(m)]=1 / K[P(m)] Step 3: Select the operation Apply the selection operator to the population, and pass the optimized individuals directly to the next generation or generate new individuals through pairing and crossover, which are then passed to the next generation. The selection operation is based on the fitness evaluation of individuals in the population. Based on the calculated fitness of each individual in the population, each individual is given a probability of being selected, and the next generation base population is formed through multiple sampling. Step 4: Crossover Apply the crossover operator to the population. Crossover refers to the operation of replacing and recombining part of the structure of two parent individuals to generate new individuals. With a certain probability p, some parent individuals are selected from the next generation base population for crossover, and individuals are reconstructed by cutting off at a certain position. Step 5: Mutation operation Applying the mutation operator to the population means changing the gene values ​​at certain loci of the individual strings in the population. Using binary numbers to construct individuals, the value at a certain locus in the individual is transformed from 0 to 1 with a certain probability p. After the population P(n) undergoes selection, crossover, and mutation operations, the next generation population P(n+1) is obtained. Step 6: Termination condition judgment If n=N, the individual with the maximum fitness obtained in the evolution process is output as the optimal solution, the calculation is terminated, and the daily material arrival volume within the prediction period is output to obtain the material supply demand in different time periods.

5. The method according to claim 4, characterized in that The aforementioned material delivery time and material supply demand forecast, combined with actual project progress and material consumption, uses a Monte Carlo simulation algorithm to deduce the temporal changes in material inventory to obtain a material ordering strategy, namely, the material ordering quantity and the planned material release time, including: The material inventory simulation process based on Monte Carlo simulation is as follows: Input: number of simulations M; simulation duration T; initial material inventory D(0); material supply days requirement Δt; material delivery time σ probability distribution P(σ); material storage capacity N; actual project progress AP; actual quota consumption AC; planned project progress PP, estimated quota consumption PC; process: Step 1: Start the simulation of material demand volatility forecasting for the i-th time; Step 2: Calculate the inventory of materials S(t) on day t: Step 2.1: Calculate the material consumption C(t) on day t based on the project progress AP and the actual quota consumption AC; Step 2.2: Obtain the material delivery volume A(t) on day t based on the output of the material supply demand forecasting model; Step 2.3: Calculate the material inventory S(t) on day t based on the material inventory S(t-1) on day t, the material consumption C(t) on day t, and the material delivery volume A(t) on day t. Step 3: Calculate the material demand D(t+Δt) in the next Δt days based on the planned project progress PP and the estimated quota consumption PC; Step 4: Sample from the delivery time probability distribution P(σ) to determine the delivery time σ of the materials when scheduled on day t t ; Step 5: Determine the order time t-σ for materials arriving on day t t ; Step 6: If t = T, end the i-th simulation; otherwise, t = t + 1, return to step 2; Step 7: If i=M, end the simulation; otherwise, i=i+1, return to step 1; Output: Material ordering strategy, which includes the material ordering quantity by time period and the planned material release time.

6. The method according to claim 5, characterized in that The integrated cargo arrival time probability distribution prediction method, material supply demand prediction method, and material ordering strategy prediction method are combined with geographic information data processing to develop a demand prediction simulation system. The demand prediction simulation system uses a railway construction material demand volatility prediction method based on arrival time limits to predict the supply demand of various materials at different bidding sections and construction sites, as well as the time when the supply plan is issued, including: The forecasting process of the probability distribution of cargo arrival time, the forecasting process of the construction material supply demand by time period, and the material inventory deduction process based on Monte Carlo simulation are integrated, and a demand forecasting simulation system is developed in combination with geographic information data processing. The demand forecasting simulation system uses a method for predicting the volatility of railway construction material demand based on arrival time, taking into account multiple factors such as the actual demand for construction materials, the material transportation network and route selection, the probability distribution of the arrival time of different types of materials, and the material storage capacity, to achieve the forecast of the supply demand of various types of materials and the time of issuing supply plans for different sections and work sites; The calculation process of the demand forecast simulation system includes: Step 1: Set basic parameters, including selecting the forecast starting time, forecast days, guaranteed supply days, bidding section, warehouse, and supply source, and obtaining information on environmental parameters, warehouse storage capacity, storage costs, and actual material consumption; Step 2: Calculate the probability distribution of delivery time; Step 2.1: Obtain weather conditions, road conditions, transportation environment, and vehicle type parameters for each road segment; Step 2.2: Load the delivery time probability distribution prediction model, input the route information, including the mileage, road grade, and average speed of the road width, into the time probability distribution prediction model, and calculate the fluctuating delivery time probability distribution prediction result; Step 3: Calculate material supply demand forecast; Step 3.1: Combined with the calculated delivery time probability distribution results, input parameters include the forecast period, actual consumption, delivery time, number of vehicles, single-vehicle transportation, capacity freight rate, storage cost, ordering cost, initial inventory, inventory capacity, transportation distance, and guaranteed supply days, as well as basic parameters including population size and mutation probability; Step 3.2: Load the material supply demand forecasting model and input the forecast period, actual consumption, delivery time, number of vehicles, single-vehicle transportation, capacity freight rate, storage cost, ordering cost, initial inventory, inventory capacity, transportation distance, and guaranteed supply days into the model to calculate the material supply demand forecast results for each time period. Step 4: Calculation of material ordering strategy; Step 4.1: Combining the delivery time probability distribution and the material supply demand forecast calculation results, input parameters include simulation time, initial material inventory, material supply guarantee days, material delivery time, material storage capacity, actual project progress, and actual material consumption; Step 4.2: Load the material inventory simulation algorithm based on Monte Carlo simulation and output the corresponding material ordering strategy; Step 5: Display the demand forecast results and output the material supply demand and material ordering strategy, that is, output the supply demand of various materials for different sections and work sites and the time when the supply plan is issued.

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