An intelligent engineering soil material allocation system and method based on digital information

Abstract

The application discloses an engineering soil material intelligent allocation system and method based on digital information, relates to the technical field of soil material intelligent allocation, and collects and standardizes multiple source engineering parameters, constructs a data resource pool, builds a digital twin material yard model, extracts related parameters of material extraction partitions and filling working faces, and sequentially calculates actual moisture contents, actual fine particle contents and actual dry densities of the soil materials in the material extraction partitions after transportation; the material extraction partitions meeting quality target parameters are screened, a multi-target optimization function is constructed and solved in combination with a transportation distance, an initial optimal allocation scheme is obtained, construction process parameters are collected in real time to update soil material states and dynamically correct the allocation scheme, unique digital labels are generated to associate with all process parameters, and a traceable quality traceability chain is constructed.

Description

Technical Field

[0001] This invention relates to the field of intelligent soil material allocation technology, specifically an intelligent engineering soil material allocation system and method based on digital information. Background Technology

[0002] In the construction of civil engineering projects such as water conservancy and roads, soil allocation is a key link in controlling construction quality, transportation costs, and construction period. Most existing soil allocation plans rely solely on initial survey data of material yards, failing to comprehensively integrate spatial information of the entire area, including material yards, transportation roads, and filling work surfaces. This makes it impossible to achieve refined material extraction zoning and synchronous, accurate extraction of multi-dimensional parameters. Parameter management is scattered, static, and lagging, making it difficult to support high-precision allocation decisions.

[0003] Meanwhile, traditional construction methods generally ignore the impact of transportation on soil quality. They cannot quantify the changes in key indicators such as moisture content, fine particle content, and dry density during transportation. They can only test the quality after the soil arrives at the filling site, making it difficult to predict the soil condition in advance. This easily leads to problems such as the soil arriving at the site not meeting the filling requirements, high rework rates, and uncontrollable construction quality.

[0004] In addition, existing allocation methods mostly rely on experience to formulate fixed plans, simply satisfying the supply and demand of earthwork without taking filling quality as a prerequisite for multi-objective optimization, and cannot be dynamically adjusted in conjunction with real-time construction data, resulting in high transportation costs and unreasonable utilization of material sources; moreover, the whole process lacks a quality traceability mechanism, and when abnormal parameters, insufficient reserves, or supply and demand mismatch occur, it is impossible to quickly locate the source of the problem, resulting in poor overall intelligence and controllability. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent engineering soil material allocation system and method based on digital information to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for intelligent allocation of engineering soil materials based on digital information, comprising: Collect and standardize parameters from multiple sources to build a data resource pool; build a digital twin material yard model based on geographic information system and building information model; Extract parameters of each material extraction zone and filling face from the digital twin material yard model and data resource pool; For each material extraction zone, based on the parameters in the data resource pool, the change in soil moisture content during transportation is calculated to obtain the actual moisture content after transportation; based on the actual moisture content after transportation, the change in fine particle content of soil during transportation is calculated to obtain the actual fine particle content after transportation. Based on the actual moisture content and actual fine particle content after transportation, the change in dry density of soil during transportation is calculated to obtain the actual dry density after transportation. Material sampling zones that meet the quality target parameters after transportation are selected based on their actual moisture content, actual fine particle content, and actual dry density. The transportation distance is analyzed, a multi-objective optimization function is constructed and solved to generate an initial optimal allocation scheme. Construction process parameters are collected in real time, the actual soil parameters after transportation are updated, the initial optimal allocation scheme is adjusted, a unique digital tag is generated and associated with all process parameters, and a quality traceability chain is constructed.

[0007] In conjunction with the first aspect, in the first implementation of the first aspect of this application, the step of collecting and standardizing multi-source parameters to construct a data resource pool includes: The multi-source parameters include intrinsic soil parameters, environmental parameters, transportation parameters, filling requirement parameters, and construction process parameters. Intrinsic soil parameters include initial moisture content, initial dry density, initial fine particle content, optimum moisture content, maximum dry density, and soil type coefficient. Environmental parameters include relative humidity, wind speed, and rainfall intensity. Transportation parameters include transportation time, cover condition coefficient, path slope, path bumpiness, stockpile height, rated stockpile height, and transportation distance, where the transportation distance is the distance from the extraction zone to the filling face. Filling requirement parameters include the required earthwork volume and quality target parameters for the filling face. Construction process parameters include the remaining reserves in the extraction zone, transportation status data, and compaction quality data of the filling face. The multi-source parameters are cleaned and deduplicated, unified with timestamps, spatial coordinates aligned and format standardized to form a data resource pool.

[0008] In conjunction with the first aspect, in the second implementation of the first aspect of this application, the step of building a digital twin material yard model based on a geographic information system and a building information model includes: Based on UAV aerial survey data and building information model, a three-dimensional geometric model of the material yard, transportation road and filling working face is constructed; the intrinsic parameters of soil in the data resource pool are mapped to the three-dimensional mesh cells of the three-dimensional geometric model through co-kriging interpolation. Based on real-time collected soil testing data, the parameters within the three-dimensional grid cells are dynamically corrected using the Bayesian update method to form a digital twin material yard model.

[0009] In conjunction with the first aspect, in the third implementation of the first aspect of this application, the step of extracting parameters of each material extraction zone and parameters of the filling working face from the digital twin material yard model and data resource pool includes: The material extraction zones are divided from the digital twin material yard model, and the initial moisture content, initial dry density, initial fine particle content and estimated exploitable reserves of each zone are extracted. The transportation distance, the required earthwork volume of the filling face and the quality target parameters are extracted from the data resource pool, including the target moisture content, target dry density and target fine particle content.

[0010] In conjunction with the first aspect, in the fourth implementation of the first aspect of this application, the step of calculating the change in soil moisture content during transportation based on parameters in the data resource pool for each material extraction zone, and obtaining the actual moisture content after transportation, includes: The initial moisture content, optimum moisture content, relative humidity, wind speed, rainfall intensity, transportation time, cover state coefficient, and soil type coefficient of the collected material sampling area were used as calculation parameters. Multiply the initial moisture content, relative humidity, wind speed, and transportation time to obtain the water evaporation rate; multiply the ratio of the initial moisture content to the optimum moisture content, rainfall intensity, and transportation time to calculate the rainfall absorption rate. Subtract the water evaporation from the calculated rainfall absorption, multiply by the cover state coefficient and soil type coefficient to obtain the change in moisture content; add the initial moisture content to the change in moisture content to obtain the actual moisture content after transportation.

[0011] In conjunction with the first aspect, in the fifth implementation of the first aspect of this application, the step of calculating the change in the fine particle content of the soil during transportation based on the actual moisture content after transportation, and obtaining the actual fine particle content after transportation, includes: The initial moisture content, initial fine particle content, actual moisture content after transportation, wind speed, relative humidity, transportation time, cover state coefficient, path bumpiness, path slope, stacking height, rated stacking height, soil type coefficient, and optimum moisture content of the collected material collection area are used as calculation parameters. The initial fine particle content, wind speed, relative humidity, transportation time, and moisture content ratio are multiplied to calculate the dust loss; the initial fine particle content, path bumpiness, path slope, stacking height, rated stacking height, and transportation time are multiplied to calculate the particle breakage. Subtract the dust loss from the calculated particle breakage amount, multiply it by the cover state coefficient and soil type coefficient to obtain the change in fine particle content; add the initial fine particle content to the change in fine particle content to obtain the actual fine particle content after transportation.

[0012] In conjunction with the first aspect, in the sixth implementation of the first aspect of this application, the step of calculating the change in soil dry density during transportation based on the actual moisture content and the actual fine particle content after transportation, to obtain the actual dry density after transportation, includes: The initial dry density, maximum dry density, actual moisture content after transportation, actual fine particle content after transportation, path bumpiness, path slope, stacking height, rated stacking height, transportation time, soil type coefficient, and optimum moisture content of the collected material sampling area are used as calculation parameters. Calculate the mechanical compaction amount by multiplying the initial dry density, path bumpiness, path slope, stacking height, rated stacking height, and transportation time; calculate the fine particle correlation amount based on the ratio of initial dry density to fine particle content change; and calculate the moisture content adaptation amount based on the ratio of initial dry density to moisture content deviation. Add the calculated mechanical compaction amount, fine particle correlation amount, and moisture content adaptation amount together, and multiply by the soil type coefficient to obtain the change in dry density; add the initial dry density and the change in dry density together to obtain the actual dry density after transportation.

[0013] In conjunction with the first aspect, in the seventh implementation of the first aspect of this application, the step of selecting the material sampling zones whose actual moisture content, actual fine particle content, and actual dry density after transportation meet the quality target parameters, analyzing the transportation distance, constructing and solving a multi-objective optimization function, and generating an initial optimal allocation scheme includes: By iterating through all material extraction zones, the calculated actual moisture content, actual fine particle content, and actual dry density after transportation are compared with the quality target parameters of the filling working face, and a set of material extraction zones that meet the quality requirements is selected. With the goal of minimizing transportation distance, a multi-objective optimization function is constructed, imposing reserves and demand constraints. The reserves constraint is that the amount of earth removed from the material extraction zone is less than or equal to the estimated recoverable reserves; the demand constraint is that the amount of earth moved into the filling face is greater than or equal to the required earthwork volume. An improved particle swarm optimization algorithm is used to solve the multi-objective optimization function and obtain the initial optimal allocation scheme.

[0014] In conjunction with the first aspect, in the eighth implementation of the first aspect of this application, the real-time collection of construction process parameters, updating of actual soil parameters after transportation, adjustment of the initial optimal allocation plan, generation of a unique digital tag and association with all process parameters, and construction of a quality traceability chain include: Real-time data collection of remaining reserves, transportation status, and compaction quality of filling surfaces in each material intake zone; when the post-transportation parameters of the material intake zone are not up to standard, the reserves are insufficient, or the demand changes, the optimization function is re-solved to correct the initial optimal allocation scheme; A unique digital tag is generated using RFID coding; the estimated exploitable reserves of the material extraction zone, initial soil parameters, transportation process parameters, actual soil parameters after transportation, transportation distance, earthwork volume required for the filling face, and compaction quality parameters are associated with the digital tag; the actual soil parameters after transportation include the actual moisture content after transportation, the actual fine particle content after transportation, and the actual dry density after transportation. By spatiotemporally binding tag data with digital twin material yard models, a queryable and traceable quality traceability chain is formed; the source of problems such as abnormal parameters, insufficient reserves, or mismatch between supply and demand is located through the quality traceability chain, and rectification is carried out and recorded.

[0015] Secondly, the present invention provides an intelligent engineering soil material allocation system based on digital information, comprising: Multi-source data acquisition and preprocessing module: includes a multi-source parameter acquisition unit and a data preprocessing unit; the multi-source parameter acquisition unit acquires intrinsic soil parameters, environmental parameters, transportation parameters, filling requirement parameters, and construction process parameters; the data preprocessing unit cleans and removes duplicates from the multi-source parameters, unifies timestamps, aligns spatial coordinates, and standardizes formats to build a data resource pool; The digital twin material yard construction module includes a 3D geometric modeling unit and a parameter mapping and correction unit. The 3D geometric modeling unit constructs a 3D geometric model of the material yard, transportation roads, and filling work face based on UAV aerial survey data and building information model. The parameter mapping and correction unit maps the intrinsic parameters of the soil to 3D mesh cells and dynamically corrects the parameters using the Bayesian update method to form a digital twin material yard model. The zoning and parameter extraction module includes a material extraction zone and soil parameter extraction unit and a filling and transportation parameter extraction unit. The material extraction zone and soil parameter extraction unit divides the material extraction zone in the digital twin material yard model and extracts the soil parameters and estimated exploitable reserves of each zone. The filling and transportation parameter extraction unit extracts the transportation distance, the amount of earthwork required for the filling face, and the quality target parameters from the data resource pool. The post-transport soil parameter calculation module includes three units: actual post-transport moisture content calculation unit, actual post-transport fine particle content calculation unit, and actual post-transport dry density calculation unit. The actual post-transport moisture content calculation unit calculates the moisture change based on environmental and transportation parameters to determine the actual post-transport moisture content of each extraction zone. The actual post-transport fine particle content calculation unit calculates the particle content change based on dust and crushing mechanisms to determine the actual post-transport fine particle content of each extraction zone. The actual post-transport dry density calculation unit calculates the actual post-transport dry density of each extraction zone by considering the effects of compaction, fine particles, and moisture content. The optimal allocation scheme generation module includes a qualified material extraction zone screening unit and a multi-objective optimization solution unit. The qualified material extraction zone screening unit compares the parameters of the soil after transportation with the quality target and selects the material extraction zones that meet the filling quality requirements. The multi-objective optimization solution unit constructs a constrained optimization model with the shortest transportation distance as the objective and obtains the initial optimal allocation scheme through an improved particle swarm optimization algorithm. Dynamic adjustment and quality traceability module: including construction data acquisition and scheme adjustment unit and quality traceability unit; among them, construction data acquisition and scheme adjustment unit collects data of each material picking zone in real time, and dynamically updates the initial optimal allocation scheme when parameters are not up to standard, storage is insufficient or demand changes; quality traceability unit generates RFID digital tags and binds them to the parameters of the whole process, forming a quality traceability chain with the digital twin material yard model.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention considers three aspects of parameters: soil material yard, transportation road and filling working face, to construct a digital twin material yard model, and completes the division of material extraction zones and the extraction of soil material parameters, transportation distance and filling requirements parameters for each zone.

[0017] 2. This invention calculates the changes in soil parameters during transportation to obtain the actual moisture content, actual fine particle content, and actual dry density after transportation, thereby enabling the prediction of soil quality parameters after transportation.

[0018] 3. This invention selects qualified material extraction zones based on quality objectives, constructs a multi-objective optimization model with the shortest transportation distance, solves the optimal allocation scheme, and dynamically corrects the scheme by combining real-time construction data. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of an intelligent engineering soil material allocation method based on digital information according to the present invention. Figure 2 This is a flowchart of a method for intelligent allocation of engineering soil materials based on digital information according to the present invention. Figure 3 This is a system structure diagram of an intelligent engineering soil material allocation system based on digital information according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example: Figures 1-3 As shown, the present invention provides a technical solution. like Figure 1 A schematic diagram illustrating the steps of an intelligent engineering soil material allocation method based on digital information is shown. This invention provides an intelligent engineering soil material allocation method based on digital information, comprising: Step S100: Collect multi-source parameters and standardize them to build a data resource pool; build a digital twin material yard model based on geographic information system and building information model; Specifically, the multi-source parameters include intrinsic soil parameters, environmental parameters, transportation parameters, filling requirement parameters, and construction process parameters. Intrinsic soil parameters include initial moisture content, initial dry density, initial fine particle content, optimum moisture content, maximum dry density, and soil type coefficient. Environmental parameters include relative humidity, wind speed, and rainfall intensity. Transportation parameters include transportation time, cover condition coefficient, path slope, path bumpiness, stockpile height, rated stockpile height, and transportation distance, where the transportation distance is the distance from the extraction zone to the filling face. Filling requirement parameters include the required earthwork volume and quality target parameters for the filling face. Construction process parameters include the remaining reserves in the extraction zone, transportation status data, and compaction quality data of the filling face. The multi-source parameters are cleaned and deduplicated, unified with timestamps, spatial coordinates aligned and format standardized to form a data resource pool.

[0022] Based on UAV aerial survey data and building information modeling, a three-dimensional geometric model of the material yard, transportation roads, and filling working face is constructed. The intrinsic parameters of soil in the data resource pool are mapped to the three-dimensional grid cells of the three-dimensional geometric model through co-kriging interpolation. Specifically, the three-dimensional geometric model is divided into uniform three-dimensional grid cells, and the grid size is set according to the actual area of ​​the material yard. The measured values ​​of the intrinsic parameters of soil in each material extraction zone are used as sample points. The co-kriging interpolation method is used to interpolate each intrinsic parameter of soil into each three-dimensional grid cell to realize the spatial visualization distribution of soil parameters. Based on real-time collected soil testing data, a Bayesian update method is used to dynamically correct the parameters within the three-dimensional grid cells. Specifically, the intrinsic soil parameters obtained from the initial interpolation of the three-dimensional grid cells are used as prior information, and the real-time collected soil testing data are used as likelihood information. The posterior distribution of soil parameters in each grid cell is calculated using the Bayesian formula, and the intrinsic soil parameter values ​​within the grid cells are updated to eliminate the error of the initial interpolation. At the same time, parameter change data during the construction process are received in real time, and the grid cell parameters are continuously updated dynamically to form a digital twin material yard model.

[0023] Step S200: Extract the parameters of each material extraction zone and the parameters of the filling working face from the digital twin material yard model and data resource pool; Specifically, the material extraction zones are divided from the digital twin material yard model, and the initial moisture content, initial dry density, initial fine particle content, and estimated exploitable reserves of each zone are extracted. The transportation distance, the required earthwork volume for the filling face, and quality target parameters are extracted from the data resource pool, including the target moisture content, target dry density, and target fine particle content.

[0024] Step S300: For each material taking zone, based on the parameters in the data resource pool, calculate the change in soil moisture content during transportation to obtain the actual moisture content after transportation; based on the actual moisture content after transportation, calculate the change in fine particle content of soil during transportation to obtain the actual fine particle content after transportation. Specifically, the initial moisture content, optimum moisture content, relative humidity, wind speed, rainfall intensity, transportation time, cover state coefficient, and soil type coefficient of the collected material sampling area are used as calculation parameters; Initial moisture content W0, relative humidity RH, and wind speed V w Multiplying the evaporation time T by the transportation time gives the amount of water evaporated, E, using the following formula: ; Where, k v This is the conversion coefficient between wind speed and water evaporation rate; The initial moisture content W0 and the optimum moisture content W p The proportion, rainfall intensity R i Multiply the rainfall absorption amount R by the transportation time T, using the following formula: ; Where, k r This is the conversion coefficient between rainfall intensity and rainfall absorption. Subtract the water evaporation rate E from the calculated rainfall absorption rate R, and then add the cover state coefficient K. c and soil type coefficient K t Multiplying them together, we get the change in moisture content ΔW, using the following formula: ; Add the initial moisture content W0 to the moisture content change ΔW to obtain the actual moisture content W after transportation. a .

[0025] The initial moisture content, initial fine particle content, actual moisture content after transportation, wind speed, relative humidity, transportation time, cover state coefficient, path bumpiness, path slope, stacking height, rated stacking height, soil type coefficient, and optimum moisture content of the collected material collection area are used as calculation parameters. Initial fine particulate content P0, wind speed V w The dust loss L is calculated by multiplying the relative humidity (RH), transport time (T), and moisture content ratio. d The formula is: ; The initial fine particle content P0 and the path bumpiness K are used as parameters. b Path slope S p Stacking height H s Rated stacking height H rMultiply by the transportation time T to calculate the particle breakage amount B. p The formula is: ; Where, k t This is the conversion factor between transport time and changes in fine particle content; The calculated particle breakage amount B p Subtract dust loss L d , and the coverage state coefficient K c and soil type coefficient K t Multiplying these together yields the change in fine particle content, ΔP, using the following formula: ; Add the initial fine particle content P0 to the change in fine particle content ΔP to obtain the actual fine particle content P after transportation. a .

[0026] Step S400: Based on the actual moisture content and actual fine particle content after transportation, calculate the change in dry density of the soil during transportation to obtain the actual dry density after transportation; Specifically, the initial dry density, maximum dry density, actual moisture content after transportation, actual fine particle content after transportation, path bumpiness, path slope, stacking height, rated stacking height, transportation time, soil type coefficient, and optimum moisture content of the collected material sampling area are used as calculation parameters. Initial dry density ρ0, path bumpiness K b Path slope S p Stacking height H s Rated stacking height H r Multiply by the transportation time T to calculate the mechanical compaction amount C. m : ; Where, k n This is the time conversion coefficient for the mechanical compaction effect; Calculate the fine particle correlation quantity C based on the ratio of the initial dry density ρ0 to the change in fine particle content. p The formula is: ; Calculate the moisture content fit C based on the ratio of the initial dry density ρ0 to the moisture content deviation. w The formula is: ; The calculated mechanical compaction amount ρ and fine particle correlation amount C are used to... p Matching amount C with moisture content w Add them together and multiply by the soil type coefficient K. t The change in dry density Δρ is obtained using the following formula: ; Where, k ρ This is the conversion coefficient between mechanical compaction amount and dry density change; Add the initial dry density ρ0 to the change in dry density Δρ to obtain the actual dry density ρ after transportation. a .

[0027] Step S500: Select material sampling zones that meet the quality target parameters in terms of actual moisture content, actual fine particle content, and actual dry density after transportation; analyze the transportation distance; construct and solve a multi-objective optimization function to generate an initial optimal allocation scheme; collect construction process parameters in real time; update the actual soil parameters after transportation; adjust the initial optimal allocation scheme; generate a unique digital tag and associate it with all process parameters to build a quality traceability chain.

[0028] Specifically, all material taking zones are traversed, and the calculated actual moisture content, actual fine particle content, and actual dry density after transportation are compared with the quality target parameters of the filling working face to select a set of material taking zones that meet the quality requirements. To minimize the transportation distance, a multi-objective optimization function is constructed, specifically as follows: ; Where, x ij x is the decision variable. ij =1 indicates that soil is allocated from the i-th material intake zone to the j-th filling face, x ij =0 indicates no allocation; n is the total number of qualified material extraction zones; m is the total number of filling working faces; D ij This represents the transportation distance from the i-th material taking zone to the j-th filling face; Apply reserve constraints and demand constraints. The reserve constraint is that the amount of earth removed from the extraction zone is less than or equal to the estimated recoverable reserves. The demand constraint is that the amount of earth moved into the filling face is greater than or equal to the required earth volume. An improved particle swarm optimization algorithm is used to solve the multi-objective optimization function and obtain the initial optimal allocation scheme.

[0029] Real-time data collection of remaining reserves, transportation status, and compaction quality of filling surfaces in each material intake zone; when the post-transportation parameters of the material intake zone are not up to standard, the reserves are insufficient, or the demand changes, the optimization function is re-solved to correct the initial optimal allocation scheme; A unique digital tag is generated using RFID coding; the estimated exploitable reserves of the material extraction zone, initial soil parameters, transportation process parameters, actual soil parameters after transportation, transportation distance, earthwork volume required for the filling face, and compaction quality parameters are associated with the digital tag; the actual soil parameters after transportation include the actual moisture content after transportation, the actual fine particle content after transportation, and the actual dry density after transportation. By spatiotemporally binding tag data with digital twin material yard models, a queryable and traceable quality traceability chain is formed; the source of problems such as abnormal parameters, insufficient reserves, or mismatch between supply and demand is located through the quality traceability chain, and rectification is carried out and recorded.

[0030] like Figure 2 The flowchart of a method for intelligent allocation of engineering soil materials based on digital information is shown. This invention provides a method for intelligent allocation of engineering soil materials based on digital information, comprising: After the process is initiated, multi-source parameter collection and standardization are performed first. Five types of multi-source parameters are collected, standardized, and organized to construct a data resource pool. Next, based on UAV aerial survey data and building information model, a digital twin material yard model is built, and parameters related to material extraction zones and filling work faces are extracted from the model and data resource pool. Subsequently, the actual moisture content, actual fine particle content, and actual dry density after transportation are calculated sequentially. Branching is used to determine whether the above parameters meet the quality target parameters: if they do not meet the requirements, the material extraction zone is excluded and the process is repeated; if they meet the requirements, the selection is completed, and a multi-objective optimization function with storage and demand constraints is constructed. An improved particle swarm optimization algorithm is used to solve the problem and generate an initial optimal allocation scheme. Afterward, construction process parameters are collected in real time, and branching is used to determine whether correction conditions are triggered, including parameter non-compliance, insufficient storage, or changes in demand. If triggered, the optimization function is resolved and a correction scheme is obtained; if not triggered, a unique RFID digital tag is generated, associated with all process parameters, and spatiotemporally bound to the digital twin material yard model to build a quality traceability chain and complete the process.

[0031] like Figure 3 The system structure diagram of an intelligent engineering soil material allocation system based on digital information is shown. This invention provides an intelligent engineering soil material allocation system based on digital information, comprising: Multi-source data acquisition and preprocessing module: includes a multi-source parameter acquisition unit and a data preprocessing unit; the multi-source parameter acquisition unit acquires intrinsic soil parameters, environmental parameters, transportation parameters, filling requirement parameters, and construction process parameters; the data preprocessing unit cleans and removes duplicates from the multi-source parameters, unifies timestamps, aligns spatial coordinates, and standardizes formats to build a data resource pool; The digital twin material yard construction module includes a 3D geometric modeling unit and a parameter mapping and correction unit. The 3D geometric modeling unit constructs a 3D geometric model of the material yard, transportation roads, and filling work face based on UAV aerial survey data and building information model. The parameter mapping and correction unit maps the intrinsic parameters of the soil to 3D mesh cells and dynamically corrects the parameters using the Bayesian update method to form a digital twin material yard model. The zoning and parameter extraction module includes a material extraction zone and soil parameter extraction unit and a filling and transportation parameter extraction unit. The material extraction zone and soil parameter extraction unit divides the material extraction zone in the digital twin material yard model and extracts the soil parameters and estimated exploitable reserves of each zone. The filling and transportation parameter extraction unit extracts the transportation distance, the amount of earthwork required for the filling face, and the quality target parameters from the data resource pool. The post-transport soil parameter calculation module includes three units: actual post-transport moisture content calculation unit, actual post-transport fine particle content calculation unit, and actual post-transport dry density calculation unit. The actual post-transport moisture content calculation unit calculates the moisture change based on environmental and transportation parameters to determine the actual post-transport moisture content of each extraction zone. The actual post-transport fine particle content calculation unit calculates the particle content change based on dust and crushing mechanisms to determine the actual post-transport fine particle content of each extraction zone. The actual post-transport dry density calculation unit calculates the actual post-transport dry density of each extraction zone by considering the effects of compaction, fine particles, and moisture content. The optimal allocation scheme generation module includes a qualified material extraction zone screening unit and a multi-objective optimization solution unit. The qualified material extraction zone screening unit compares the parameters of the soil after transportation with the quality target and selects the material extraction zones that meet the filling quality requirements. The multi-objective optimization solution unit constructs a constrained optimization model with the shortest transportation distance as the objective and obtains the initial optimal allocation scheme through an improved particle swarm optimization algorithm. Dynamic adjustment and quality traceability module: including construction data acquisition and scheme adjustment unit and quality traceability unit; among them, construction data acquisition and scheme adjustment unit collects data of each material picking zone in real time, and dynamically updates the initial optimal allocation scheme when parameters are not up to standard, storage is insufficient or demand changes; quality traceability unit generates RFID digital tags and binds them to the parameters of the whole process, forming a quality traceability chain with the digital twin material yard model.

[0032] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for intelligent allocation of engineering soil materials based on digital information, characterized in that, include: Collect parameters from multiple sources and standardize them to build a data resource pool; A digital twin material yard model was built based on geographic information systems and building information models. Extract parameters of each material extraction zone and filling face from the digital twin material yard model and data resource pool; For each material extraction zone, based on the parameters in the data resource pool, the change in soil moisture content during transportation is calculated to obtain the actual moisture content after transportation; Based on the actual moisture content after transportation, the change in fine particle content of soil during transportation is calculated to obtain the actual fine particle content after transportation. Based on the actual moisture content and actual fine particle content after transportation, the change in dry density of soil during transportation is calculated to obtain the actual dry density after transportation. Select material sampling zones that meet the quality target parameters in terms of actual moisture content, actual fine particle content, and actual dry density after transportation; analyze transportation distance; construct and solve a multi-objective optimization function to generate an initial optimal allocation scheme. Real-time collection of construction process parameters, updating of actual soil parameters after transportation, adjustment of the initial optimal allocation plan, generation of unique digital tags and association with parameters throughout the entire process, and construction of a quality traceability chain.

2. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, The process of collecting and standardizing multi-source parameters to construct a data resource pool includes: The multi-source parameters include intrinsic soil parameters, environmental parameters, transportation parameters, filling requirement parameters, and construction process parameters. Intrinsic soil parameters include initial moisture content, initial dry density, initial fine particle content, optimum moisture content, maximum dry density, and soil type coefficient. Environmental parameters include relative humidity, wind speed, and rainfall intensity. Transportation parameters include transportation time, cover condition coefficient, path slope, path bumpiness, stockpile height, rated stockpile height, and transportation distance, where the transportation distance is the distance from the extraction zone to the filling face. Filling requirement parameters include the required earthwork volume and quality target parameters for the filling face. Construction process parameters include the remaining reserves in the extraction zone, transportation status data, and compaction quality data of the filling face. The multi-source parameters are cleaned and deduplicated, unified with timestamps, spatial coordinates aligned and format standardized to form a data resource pool.

3. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, The construction of a digital twin material yard model based on Geographic Information System and Building Information Modeling includes: Based on UAV aerial survey data and building information model, a three-dimensional geometric model of the material yard, transportation road and filling working face is constructed; the intrinsic parameters of soil in the data resource pool are mapped to the three-dimensional mesh cells of the three-dimensional geometric model through co-kriging interpolation. Based on real-time collected soil testing data, the parameters within the three-dimensional grid cells are dynamically corrected using the Bayesian update method to form a digital twin material yard model.

4. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, The extraction of parameters for each material extraction zone and the filling working face from the digital twin material yard model and data resource pool includes: The material extraction zones are divided from the digital twin material yard model, and the initial moisture content, initial dry density, initial fine particle content and estimated exploitable reserves of each zone are extracted. The transportation distance, the required earthwork volume of the filling face and the quality target parameters are extracted from the data resource pool, including the target moisture content, target dry density and target fine particle content.

5. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, For each material extraction zone, based on parameters in the data resource pool, the change in soil moisture content during transportation is calculated to obtain the actual moisture content after transportation, including: The initial moisture content, optimum moisture content, relative humidity, wind speed, rainfall intensity, transportation time, cover state coefficient, and soil type coefficient of the collected material sampling area were used as calculation parameters. Multiply the initial moisture content, relative humidity, wind speed, and transportation time to obtain the water evaporation rate; multiply the ratio of the initial moisture content to the optimum moisture content, rainfall intensity, and transportation time to calculate the rainfall absorption rate. Subtract the water evaporation from the calculated rainfall absorption, multiply by the cover state coefficient and soil type coefficient to obtain the change in moisture content; add the initial moisture content to the change in moisture content to obtain the actual moisture content after transportation.

6. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, The calculation of the change in fine particle content of soil during transportation, based on the actual moisture content after transportation, yields the actual fine particle content after transportation, including: The initial moisture content, initial fine particle content, actual moisture content after transportation, wind speed, relative humidity, transportation time, cover state coefficient, path bumpiness, path slope, stacking height, rated stacking height, soil type coefficient, and optimum moisture content of the collected material collection area are used as calculation parameters. The initial fine particle content, wind speed, relative humidity, transportation time, and moisture content ratio are multiplied to calculate the dust loss; the initial fine particle content, path bumpiness, path slope, stacking height, rated stacking height, and transportation time are multiplied to calculate the particle breakage. Subtract the dust loss from the calculated particle breakage amount, multiply it by the cover state coefficient and soil type coefficient to obtain the change in fine particle content; add the initial fine particle content to the change in fine particle content to obtain the actual fine particle content after transportation.

7. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, The method of calculating the change in soil dry density during transportation based on the actual moisture content and actual fine particle content after transportation, and obtaining the actual dry density after transportation, includes: The initial dry density, maximum dry density, actual moisture content after transportation, actual fine particle content after transportation, path bumpiness, path slope, stacking height, rated stacking height, transportation time, soil type coefficient, and optimum moisture content of the collected material sampling area are used as calculation parameters. Calculate the mechanical compaction amount by multiplying the initial dry density, path bumpiness, path slope, stacking height, rated stacking height, and transportation time; calculate the fine particle correlation amount based on the ratio of initial dry density to fine particle content change; and calculate the moisture content adaptation amount based on the ratio of initial dry density to moisture content deviation. Add the calculated mechanical compaction amount, fine particle correlation amount, and moisture content adaptation amount together, and multiply by the soil type coefficient to obtain the change in dry density; add the initial dry density and the change in dry density together to obtain the actual dry density after transportation.

8. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, The process involves selecting sampling zones that meet the quality target parameters in terms of actual moisture content, actual fine particle content, and actual dry density after transportation; analyzing transportation distances; constructing and solving a multi-objective optimization function; and generating an initial optimal allocation scheme, including: By iterating through all material extraction zones, the calculated actual moisture content, actual fine particle content, and actual dry density after transportation are compared with the quality target parameters of the filling working face, and a set of material extraction zones that meet the quality requirements is selected. With the goal of minimizing transportation distance, a multi-objective optimization function is constructed, imposing reserves and demand constraints. The reserves constraint is that the amount of earth removed from the material extraction zone is less than or equal to the estimated recoverable reserves; the demand constraint is that the amount of earth moved into the filling face is greater than or equal to the required earthwork volume. An improved particle swarm optimization algorithm is used to solve the multi-objective optimization function and obtain the initial optimal allocation scheme.

9. The intelligent allocation method for engineering soil materials based on digital information according to claim 1, characterized in that, The process involves real-time collection of construction process parameters, updating actual soil parameters after transportation, adjusting the initial optimal allocation plan, generating unique digital tags and associating them with parameters throughout the entire process, and constructing a quality traceability chain, including: Real-time data collection of remaining reserves, transportation status, and compaction quality of filling surfaces in each material intake zone; when the post-transportation parameters of the material intake zone are not up to standard, the reserves are insufficient, or the demand changes, the optimization function is re-solved to correct the initial optimal allocation scheme; A unique digital tag is generated using RFID coding; the estimated exploitable reserves of the material extraction zone, initial soil parameters, transportation process parameters, actual soil parameters after transportation, transportation distance, earthwork volume required for the filling face, and compaction quality parameters are associated with the digital tag; the actual soil parameters after transportation include the actual moisture content after transportation, the actual fine particle content after transportation, and the actual dry density after transportation. By spatiotemporally binding tag data with digital twin material yard models, a queryable and traceable quality traceability chain is formed; the source of problems such as abnormal parameters, insufficient reserves, or mismatch between supply and demand is located through the quality traceability chain, and rectification is carried out and recorded.

10. A digital information-based intelligent soil material allocation system, using the digital information-based intelligent soil material allocation method according to any one of claims 1-9, characterized in that, include: Multi-source data acquisition and preprocessing module: includes a multi-source parameter acquisition unit and a data preprocessing unit; the multi-source parameter acquisition unit acquires intrinsic soil parameters, environmental parameters, transportation parameters, filling requirement parameters, and construction process parameters; the data preprocessing unit cleans and removes duplicates from the multi-source parameters, unifies timestamps, aligns spatial coordinates, and standardizes formats to build a data resource pool; The digital twin material yard construction module includes a 3D geometric modeling unit and a parameter mapping and correction unit. The 3D geometric modeling unit constructs a 3D geometric model of the material yard, transportation roads, and filling work face based on UAV aerial survey data and building information model. The parameter mapping and correction unit maps the intrinsic parameters of the soil to 3D mesh cells and dynamically corrects the parameters using the Bayesian update method to form a digital twin material yard model. The zoning and parameter extraction module includes a material extraction zone and soil parameter extraction unit and a filling and transportation parameter extraction unit. The material extraction zone and soil parameter extraction unit divides the material extraction zone in the digital twin material yard model and extracts the soil parameters and estimated exploitable reserves of each zone. The filling and transportation parameter extraction unit extracts the transportation distance, the amount of earthwork required for the filling face, and the quality target parameters from the data resource pool. The post-transport soil parameter calculation module includes three units: actual post-transport moisture content calculation unit, actual post-transport fine particle content calculation unit, and actual post-transport dry density calculation unit. The actual post-transport moisture content calculation unit calculates the moisture change based on environmental and transportation parameters to determine the actual post-transport moisture content of each extraction zone. The actual post-transport fine particle content calculation unit calculates the particle content change based on dust and crushing mechanisms to determine the actual post-transport fine particle content of each extraction zone. The actual post-transport dry density calculation unit calculates the actual post-transport dry density of each extraction zone by considering the effects of compaction, fine particles, and moisture content. The optimal allocation scheme generation module includes a qualified material extraction zone screening unit and a multi-objective optimization solution unit. The qualified material extraction zone screening unit compares the parameters of the soil after transportation with the quality target and selects the material extraction zones that meet the filling quality requirements. The multi-objective optimization solution unit constructs a constrained optimization model with the shortest transportation distance as the objective and obtains the initial optimal allocation scheme through an improved particle swarm optimization algorithm. Dynamic adjustment and quality traceability module: including construction data acquisition and scheme adjustment unit and quality traceability unit; among them, construction data acquisition and scheme adjustment unit collects data of each material picking zone in real time, and dynamically updates the initial optimal allocation scheme when parameters are not up to standard, storage is insufficient or demand changes; quality traceability unit generates RFID digital tags and binds them to the parameters of the whole process, forming a quality traceability chain with the digital twin material yard model.

Images