Asphalt mixture yard air duct airflow optimization control method based on temperature and humidity monitoring

Abstract

The application discloses an asphalt mixture stockyard air duct airflow optimization control method based on temperature and humidity monitoring, relates to the technical field of air duct control, and comprises the following steps: calculating the saturated water vapor pressure difference of the current atmosphere according to the environmental temperature and humidity, constructing a multidimensional decision matrix by using the evaporation dynamics principle, constructing a local airflow virtual resistance field model of the asphalt mixture stockyard based on three-dimensional geometric data, performing rapid airflow path deduction, obtaining a virtual resistance field deduction result, determining the required target ventilation mode and ventilation intensity by using the multidimensional decision matrix according to the saturated water vapor pressure difference, wind speed and direction data, average moisture content of the stockpile and the virtual resistance field deduction result, and applying the target ventilation mode and ventilation intensity to the asphalt mixture stockyard air duct after the target ventilation mode and ventilation intensity are converted into a control instruction set. The application achieves the intelligent, energy-saving and efficient stockyard environment regulation and control target through the air duct airflow control method.

Description

Technical Field

[0001] This invention relates to the field of ventilation control technology, and in particular to a method for optimizing airflow control in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring. Background Technology

[0002] In the field of road engineering material storage and management, significant progress has been made in environmental control methods for asphalt mixture stockpiles in recent years. With the integrated application of emerging methods such as intelligent sensing, the Internet of Things, and digital twins, stockpile management is gradually evolving from extensive to refined and intelligent. Currently, temperature and humidity monitoring has been widely deployed in large asphalt mixing plants and storage facilities. Real-time acquisition of the internal state of the stockpile is achieved by deploying distributed sensor networks. At the same time, linkage control strategies based on meteorological data and ventilation duct actuators are gradually introducing automation logic. Some even combine 3D scanning methods to obtain the geometric information of the stockpile, providing spatial basis for optimizing airflow organization. In addition, the development of computational fluid dynamics simulation and multiphysics coupling models has further promoted the understanding of the mechanism of heat and moisture transfer processes inside the stockpile, enabling ventilation intervention to shift from experience-driven to model-driven.

[0003] Nevertheless, existing methods for optimizing airflow control in ducts still have room for improvement. First, most ventilation control logics do not fully integrate the principles of evaporation kinetics and fail to incorporate saturated water vapor pressure difference as the core driving force into the decision-making framework, resulting in a lack of physical basis for ventilation timing and intensity. Second, existing duct control typically adopts the static resistance assumption and does not consider the local airflow virtual resistance field formed by the shape of the stack, pore structure, and surrounding structures, making it impossible to achieve rapid airflow path deduction and resistance correction for real stack topology. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an optimized control method for airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring, which solves the problems of lack of physical basis for ventilation duct control and inability to achieve rapid airflow path deduction and resistance correction for real stockpile topology.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring, comprising:

[0008] Collect data from humidity sensors inside the asphalt mixture stockpile, as well as ambient temperature and humidity, wind speed and direction data, and three-dimensional geometric data.

[0009] Based on the humidity sensor data inside the stockpile, a three-dimensional humidity field model of the asphalt mixture stockpile was constructed using a spatial interpolation algorithm, and the average moisture content of the stockpile was calculated.

[0010] The atmospheric saturated water vapor pressure difference is calculated based on the ambient temperature and humidity, and a multidimensional decision matrix is ​​constructed using the principle of evaporation kinetics.

[0011] Based on three-dimensional geometric data, a local airflow virtual resistance field model of asphalt mixture stockpile is constructed, and rapid airflow path deduction is performed to obtain the virtual resistance field deduction results.

[0012] Based on the saturated vapor pressure difference, wind speed and direction data, average moisture content of the stockpile, and virtual resistance field simulation results, a multi-dimensional decision matrix is ​​used to determine the current target ventilation mode and ventilation intensity.

[0013] After converting the target ventilation mode and ventilation intensity into a set of control commands, they are applied to the ventilation ducts of the asphalt mixture stockpile.

[0014] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the humidity sensor data inside the stockpile includes volumetric water content percentage, temperature reading, and the three-dimensional spatial coordinates of the corresponding humidity sensor.

[0015] The wind speed and direction data include wind speed scalar value, horizontal wind direction angle value, and vertical wind speed component value;

[0016] The ambient temperature and humidity include ambient temperature and ambient humidity;

[0017] The three-dimensional geometric data includes a set of three-dimensional point cloud coordinates and a triangular mesh.

[0018] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the following steps are taken: After constructing a three-dimensional humidity field model of the asphalt mixture stockpile using spatial interpolation algorithms based on humidity sensor data inside the stockpile, the average moisture content of the stockpile is calculated, specifically:

[0019] Using the three-dimensional spatial coordinates of the humidity sensor as interpolation nodes and the volumetric water content percentage as node value, spatial interpolation is performed in the entire three-dimensional spatial domain of the material pile to generate a three-dimensional humidity field model.

[0020] The average moisture content of the material pile is obtained by integrating the three-dimensional humidity field model over the entire volume of the pile.

[0021] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the step of calculating the current atmospheric saturated water vapor pressure difference based on ambient temperature and humidity specifically involves:

[0022] Based on ambient temperature, the saturated vapor pressure is calculated using the Magnus formula.

[0023] After converting the ambient humidity to a decimal form, the saturated vapor pressure difference is calculated in conjunction with the saturated vapor pressure.

[0024] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the method of constructing a multi-dimensional decision matrix using the principle of evaporation kinetics specifically includes:

[0025] The ventilation control logic is established by taking the saturated water vapor pressure difference as the main driving force, the wind speed scalar value as the convection exchange capacity, and the average moisture content of the material pile as the drying requirement.

[0026] Based on the ventilation control logic, the numerical ranges of saturated water vapor pressure difference, wind speed scalar value, and average moisture content of the stockpile are divided into discrete interval levels.

[0027] Each zone level is mapped to specific control commands for vents, deflectors, and auxiliary fans, forming a multi-dimensional decision matrix.

[0028] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile based on temperature and humidity monitoring described in this invention, the construction of the local airflow virtual resistance field model of the asphalt mixture stockpile refers to using three-dimensional geometric data to discretize the stockpile shape and surrounding fixed structures into a grid, and assigning a corresponding local flow resistance coefficient to each discrete unit to form a local airflow virtual resistance field model.

[0029] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the step of performing rapid airflow path deduction refers to using wind speed and direction data as inlet boundary conditions, applying the solver of the Darcy-Fochheimer equation in the local airflow virtual resistance field model, performing rapid airflow path deduction, and obtaining the virtual resistance field deduction results.

[0030] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the step of determining the required target ventilation mode and ventilation intensity using a multi-dimensional decision matrix based on saturated water vapor pressure difference, wind speed and direction data, average moisture content of the stockpile, and virtual resistance field simulation results is as follows:

[0031] The saturated water vapor pressure difference, wind speed scalar value, and average moisture content of the stockpile are respectively mapped to the preset interval levels in the multidimensional decision matrix;

[0032] The overall drag coefficient is extracted from the virtual drag field simulation results. The overall drag coefficient is used as a correction factor to correct the saturated water vapor pressure difference range, wind speed range, and average moisture content range of the stockpile, and to obtain the comprehensive state index.

[0033] Use the comprehensive state index to query the multidimensional decision matrix and retrieve the corresponding target ventilation mode and ventilation intensity.

[0034] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the step of converting the target ventilation mode and ventilation intensity into a control command set specifically includes:

[0035] Construct an executor action mapping table;

[0036] The target ventilation mode is resolved by using the actuator action mapping table as the opening and closing state of the vent and the deflection angle of the guide vane, and the ventilation intensity is resolved by the opening value of the vent and the speed setting value of the auxiliary fan.

[0037] The control command set consists of the opening and closing status of the vents, the opening value of the vents, the deflection angle of the guide vanes, and the speed setting of the auxiliary fan.

[0038] As a preferred embodiment of the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring described in this invention, the step of converting the target ventilation mode and ventilation intensity into a control command set and applying it to the asphalt mixture stockpile ventilation ducts specifically involves:

[0039] The control command set is transmitted to the electric actuators, motors and frequency converters on the ventilation duct of the asphalt mixture stockpile via a wireless communication network;

[0040] The control instruction set drives the electric actuator to operate the louvers of the ventilation opening to achieve the opening and closing state and opening degree set by the instruction, drives the motor to adjust the guide plate to the deflection angle set by the instruction, and drives the frequency converter to control the auxiliary fan to achieve the speed set by the instruction.

[0041] The beneficial effects of this invention are as follows: By integrating multi-source sensing data to construct a three-dimensional humidity field and a virtual airflow resistance field, and combining a saturated water vapor pressure difference driving mechanism based on evaporation dynamics and a rapid flow field deduction method, it achieves optimized control of airflow in asphalt mixture stockpile ventilation ducts through physical mechanism-driven, spatial topology-adaptive, and precise matching of drying requirements. This not only overcomes the shortcomings of previous ventilation strategies that rely on empirical thresholds and ignore the geometry of the stockpile and the uneven distribution of internal moisture, but also dynamically adjusts the ventilation mode and intensity through a multi-dimensional decision matrix and resistance feedback correction mechanism, thereby improving moisture removal efficiency, energy utilization level, and response accuracy, and achieving the goal of intelligent, energy-saving, and efficient stockpile environmental control. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of an optimized airflow control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring.

[0044] Figure 2 A flowchart for obtaining the average moisture content of the stockpile.

[0045] Figure 3 A flowchart for obtaining a multidimensional decision matrix.

[0046] Figure 4 A flowchart for obtaining the simulation results of the virtual resistance field. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for optimizing and controlling airflow in the ventilation ducts of asphalt mixture stockpiles based on temperature and humidity monitoring, including the following steps:

[0051] S1. Collect humidity sensor data, ambient temperature and humidity, wind speed and direction data, and three-dimensional geometric data inside the asphalt mixture stockpile.

[0052] It should be noted that a network of humidity sensors embedded within the asphalt mixture stockpile reads and records the volumetric moisture content percentage and temperature readings of each sensor. Simultaneously, a total station records the three-dimensional spatial coordinates of each sensor, thus obtaining the humidity sensor data within the stockpile. A weather station is set up at the boundary of the asphalt mixture stockpile to continuously collect and record ambient temperature and humidity. A panoramic scan of the asphalt mixture stockpile is performed using a 3D laser scanner to obtain a 3D point cloud coordinate set composed of massive point clouds. This 3D point cloud coordinate set is then input into the Delaunay triangulation algorithm. The Delaunay triangulation algorithm calculates a triangular mesh satisfying the empty circle property based on the spatial coordinates of each point in the 3D point cloud coordinate set. Specifically, Delaunay... The Delaunay triangulation algorithm reads the 3D coordinates of each point in the 3D point cloud coordinate set and arbitrarily selects four non-coplanar points in the 3D point cloud coordinate set to form an initial tetrahedron. The Delaunay triangulation algorithm inserts the remaining points in the 3D point cloud coordinate set one by one into the initial tetrahedron. When a newly inserted point falls inside the circumsphere of the initial tetrahedron, the corresponding initial tetrahedron is identified as a conflicting tetrahedron and removed. The positions where conflicting tetrahedrons are removed form local cavity regions. Using the inserted points as common vertices, the boundary triangular facets of the cavity regions are connected to complete the reconstruction of the local triangular mesh. The Delaunay triangulation algorithm repeats the above insertion and reconstruction steps until all points in the 3D point cloud coordinate set have been processed, generating a triangular mesh composed of triangular facets.

[0053] An ultrasonic anemometer emits ultrasonic pulses along three mutually perpendicular measuring axes and measures the forward and reverse propagation times of the ultrasonic pulses between each pair of sensors. Based on the propagation times of the ultrasonic pulses in different directions, the velocity components of the airflow along the X, Y, and Z axes in a three-dimensional rectangular coordinate system are calculated using the following formulas:

[0054] ;

[0055] in, express The velocity components of the shaft, express Installation distance of axial ultrasonic transducer pairs. Indicates that ultrasound comes from The propagation time from the positive direction to the negative direction of the axis. Indicates that ultrasound comes from The propagation time from the negative direction to the positive direction.

[0056] The velocity components of the Y-axis and Z-axis and The formulas for calculating the velocity components of the shaft are consistent.

[0057] By combining the X-axis velocity components and the Y-axis velocity components, the scalar value of the horizontal wind speed can be calculated using the following formula:

[0058] ;

[0059] in, This represents the scalar value of horizontal wind speed. Represents the Y-axis velocity component. express The velocity component of the shaft.

[0060] The angle between the horizontal wind direction and the geographic north direction is calculated using the arctangent function, yielding the horizontal wind direction angle value. The formula is as follows:

[0061] ;

[0062] in, This indicates the horizontal wind direction angle value. Represents the arctangent function. Represents the Y-axis velocity component. express The velocity component of the shaft.

[0063] The Z-axis velocity component is directly taken as the vertical wind speed component value; the horizontal wind speed scalar value, the horizontal wind direction angle value, and the vertical wind speed component value are combined and recorded to form complete wind speed and wind direction data.

[0064] S2. Based on the humidity sensor data inside the stockpile, a three-dimensional humidity field model of the asphalt mixture stockpile is constructed using a spatial interpolation algorithm, and then the average moisture content of the stockpile is calculated.

[0065] S2.1 It should be noted that the three-dimensional spatial coordinates of the humidity sensor are used as interpolation nodes, and the volumetric moisture content percentage is used as the node value; a regular grid point array is defined in the three-dimensional spatial domain of the asphalt mixture stockpile, and the grid point array covers the entire three-dimensional geometry of the stockpile; the spatial locations where humidity sensors have been deployed and the volumetric moisture content percentage has been successfully collected are used as known grid points, and the grid nodes where humidity sensors have not been deployed are used as unknown grid points.

[0066] The semi-variogram value is calculated based on all known grid points, using the following formula:

[0067] ;

[0068] in, Represents the value of the semi-mutation function. Indicates that the spatial lag distance is equal to The number of known grid point pairs, This represents the spatial lag distance (the straight-line distance between two known grid points). This represents the percentage of water content per unit volume at a known grid point. Indicates the first The three-dimensional coordinates of each grid.

[0069] The semivariogram values ​​were plotted as a scatter plot to form the experimental semivariogram cloud. A spherical model was selected as the theoretical semivariogram model, which includes three parameters to be fitted: nugget value, sill value, and range. The three parameters of the spherical model—nugget value, sill value, and range—were adjusted using an iterative optimization algorithm to minimize the sum of squared residuals between the spherical model curve and the experimental semivariogram scatter plot. Specifically, the sum of squared residuals between the spherical model and the experimental semivariogram scatter plot was calculated. After initializing the nugget value, sill value, and range of the spherical model, the Levenberg-Marquardt optimization algorithm was used to update the nugget value, sill value, and range, as shown in the formula:

[0070] ;

[0071] in, This represents the updated parameter vector (gold nugget value, partial sill value, and range). Representing the Jacobian matrix The transpose of the matrix, Represents the Jacobian matrix. Indicates the damping factor. Represents the identity matrix. Represents the residual vector (a column vector of the differences between the semivariogram value and the nugget value, the partial sill value, and the range).

[0072] The Jacobian matrix acquisition process is as follows: Calculate the experimental semivariogram value and the residuals of the spherical model. Specifically, extract the experimental semivariogram value corresponding to each spatial lag distance from the experimental semivariogram cloud map; substitute the nugget value, partial sill value, and range into the spherical model formula to calculate the theoretical value of the spherical model under the same spatial lag distance; for each spatial lag distance, calculate the difference between the experimental semivariogram value and the theoretical value of the spherical model to obtain the residual. Calculate the partial derivatives of the residuals with respect to the three parameters of the spherical model (nugget value, partial sill value, and range). Specifically, when the spatial lag distance does not exceed the range, the partial derivative of the residuals with respect to the nugget value is equal to -1; the partial derivative of the residuals with respect to the partial sill value is equal to the negative coefficient of the spherical model sill term; the partial derivative of the residuals with respect to the range is equal to the derivative of the partial sill value multiplied by the coefficient of the spherical model sill term with respect to the range. When the spatial lag distance is greater than the range, the partial derivative of the residual with respect to the nugget value is negative one, the partial derivative of the residual with respect to the partial sill value is negative one, and the partial derivative of the residual with respect to the range is zero. These three partial derivatives are arranged in a row and sequentially filled to form a multi-row, three-column Jacobian matrix. The damping factor is dynamically generated and adjusted during the iteration of the Levenberg-Marquardt optimization algorithm, and the damping factor is the average of the sum of squares of the elements of the Jacobian matrix.

[0073] The updated nugget value, partial sill value, and range are used to recalculate the residual sum of squares between the spherical model and the experimental semivariogram scatter plot. The new residual sum of squares is compared with that of the previous iteration. If the decrease in the residual sum of squares is less than a preset convergence threshold, the iteration stops, and the current nugget value, partial sill value, and range are output as the optimal fit. If the decrease in the residual sum of squares does not meet the convergence threshold, the partial derivative calculation, parameter update, and residual evaluation steps are repeated until the convergence threshold is met. The nugget value and the partial sill value in the optimal fit are added together to obtain the sill value.

[0074] The convergence threshold is set based on the accuracy requirements and computational efficiency of the optimization problem, and its value ranges from (10−12, 10−6], taking into account the balance between data measurement accuracy and engineering application requirements.

[0075] Based on the established spherical model, the spatial correlation strength between the location of unknown grid points and each known grid point is calculated using the following formula:

[0076] ;

[0077] in, This indicates the strength of the spatial correlation between unknown grid points and known grid points. Indicates the base value. This represents the distance between unknown grid points and known grid points.

[0078] The spatial correlation strength between unknown grid points and all known grid points is summed to obtain the total spatial correlation strength. The spatial correlation strength between the unknown grid point and each known grid point is then divided by the total spatial correlation strength to obtain the normalized spatial correlation strength (i.e., the weighting coefficient of the volumetric water content percentage of each known grid point to the unknown grid point). The weighting coefficient is multiplied by the corresponding volumetric water content percentage of the known grid point to obtain the contribution value of the known grid point to the unknown grid point. All contribution values ​​are summed to obtain the volumetric water content percentage of the unknown grid point. The volumetric water content percentage of all unknown grid points in the grid point array is calculated sequentially. The volumetric water content percentages of the unknown and known grid points constitute a complete set of grid point volumetric water content percentage data. This set of data is organized according to a three-dimensional spatial coordinate index to form a three-dimensional humidity field model.

[0079] S2.2. After integrating the three-dimensional humidity field model over the entire volume of the material pile, calculate the average moisture content of the material pile.

[0080] It should be noted that, based on the established three-dimensional humidity field model, the three-dimensional geometric space of the asphalt mixture stockpile is discretized into tiny voxel units; the volumetric moisture content percentage corresponding to the center point coordinates of each voxel unit is extracted (from the data set of volumetric moisture content percentages of grid points); the volume of each voxel unit is multiplied by the volumetric moisture content percentage at the corresponding center point coordinates to obtain the volumetric moisture equivalent of the voxel unit; the volumetric moisture equivalents of all voxel units are summed to obtain the total volumetric moisture equivalent of the stockpile; the volumes of all voxel units themselves are summed to obtain the total volume of the stockpile; the total volumetric moisture equivalent of the stockpile is divided by the total volume of the stockpile, and the result is the average moisture content of the stockpile.

[0081] S3. Calculate the current atmospheric saturated water vapor pressure difference based on the ambient temperature and humidity, and construct a multi-dimensional decision matrix based on the principle of evaporation kinetics.

[0082] S3.1 It should be noted that, based on the ambient temperature, the saturated vapor pressure is calculated using the Magnus formula, which is as follows:

[0083] ;

[0084] in, Indicates saturated water vapor pressure. Indicates ambient temperature.

[0085] The constants 6.112, 7.5 and 237.3 are all established constants in the Magnus formula; 6.112 represents the reference value of the saturated water vapor pressure at 0 degrees Celsius under standard atmospheric pressure, 7.5 is used to characterize the influence coefficient of temperature on the exponential growth rate of water vapor pressure, and 237.3 represents the characteristic temperature constant of water vapor pressure as a function of temperature.

[0086] After converting the ambient humidity to decimal form, the saturated vapor pressure difference is calculated using the saturated vapor pressure formula:

[0087] ;

[0088] in, Indicates the saturated water vapor pressure difference. Indicates saturated water vapor pressure. Indicates ambient humidity.

[0089] The constant 100 is the percentage conversion constant for ambient humidity, used to convert ambient humidity expressed as a percentage into a decimal form in the range of 0 to 1.

[0090] S3.2 It should be noted that, based on the principles of evaporation kinetics, a high saturated vapor pressure difference is defined as corresponding to high evaporation potential and triggering an active ventilation strategy; a wind speed scalar value above a certain level is defined as corresponding to effective convection and serving as a necessary condition for activating natural or auxiliary ventilation; and a high average moisture content of the material pile is defined as corresponding to urgent drying needs and determining the duration and intensity level of ventilation. The logical relationship between the three parameters—saturated vapor pressure difference, wind speed scalar value, and average moisture content of the material pile—is combined to form a series of deterministic rules that "if the [saturated vapor pressure difference condition], [wind speed scalar value condition], and [average moisture content of the material pile condition] are simultaneously satisfied, then the [specific ventilation action] is executed." The set of these deterministic rules constitutes the complete ventilation control logic.

[0091] Historical saturated vapor pressure difference, historical wind speed scalar values, and historical average moisture content of material piles are collected, and typical value ranges are analyzed. For example, the common range for saturated vapor pressure difference is 0-3 kPa, the common range for wind speed scalar values ​​is 0-10 m / s, and the common range for average moisture content of material piles is 2%-10%. The interval levels are divided according to the needs of ventilation control logic. For example, saturated vapor pressure difference is divided into three interval levels: low (0-0.8 kPa), medium (0.8-1.5 kPa), and high (1.5-3 kPa); wind speed scalar values ​​are divided into three interval levels: low (0-1.5 m / s), medium (1.5-4 m / s), and high (4-10 m / s); and average moisture content of material piles is divided into three interval levels: low (2%-4%), medium (4%-7%), and high (7%-10%). Continuous parameter values ​​are mapped to the corresponding levels through interval judgment.

[0092] The range levels of saturated vapor pressure difference, wind speed scalar value, and average moisture content of the stockpile are set based on the analysis and verification of historical data. The values ​​are based on the influence of saturated vapor pressure difference on the evaporation driving force during the moisture evaporation process, the influence of wind speed on the airflow convection mass transfer efficiency and energy consumption characteristics, and the control requirements of changes in the average moisture content of the stockpile on the production quality and process stability of asphalt mixtures.

[0093] For all interval level combinations of the three parameters—saturated vapor pressure difference, wind speed scalar value, and average moisture content of the stockpile—a set of specific control instructions is assigned to each unique interval level combination according to the ventilation control logic in step S3.2. The control instructions clearly define the combination of opening and closing states of the ventilation openings and the target opening degree, the target deflection angle of the guide vanes, and the start / stop states and target speed of the auxiliary fans. The mapping relationship between all interval level combinations and the corresponding specific control instructions is organized and stored in the form of a three-dimensional lookup table, which is the final multi-dimensional decision matrix.

[0094] S4. Based on three-dimensional geometric data, construct a local airflow virtual resistance field model of the asphalt mixture stockpile and perform rapid airflow path deduction to obtain the virtual resistance field deduction results.

[0095] S4.1 It should be noted that the triangular mesh described by the three-dimensional geometric data in step S1 is used as the front edge; a triangular facet is selected from the front edge, and a new mesh node is generated in the direction of the normal vector of the triangular facet, so that the new node and the three vertices of the triangular facet form a tetrahedron; the newly generated tetrahedron is added to the volume mesh set, and the front edge is updated with the newly generated triangular facet; the process of selecting the front edge triangular facet, generating a new node, constructing a tetrahedron and updating the front edge is repeated until the front edge triangular facet is empty and a tetrahedral mesh is generated. The type of medium in which the grid is located is determined based on the center coordinates of each grid in the tetrahedral grid, and a corresponding flow resistance coefficient (including permeability and inertial drag coefficient) is assigned. Specifically, based on the center coordinates of each grid, if the grid center coordinates are located inside the stockpile, the grid is determined to be in the stockpile medium and is assigned a flow resistance coefficient calculated from the porosity of the asphalt mixture; if the grid center coordinates are located outside the stockpile but inside the shed structure, the grid is determined to be in the air medium and is assigned a flow resistance coefficient corresponding to standard air viscosity; if the center coordinates are located inside the fixed structure of the shed (such as beams or columns), the grid is determined to be in the solid barrier medium and is assigned a flow resistance coefficient approaching infinity.

[0096] By assembling all the grids and their corresponding drag coefficients in space, a local airflow virtual drag field model is constructed, which is based on the grids and uses the drag coefficients as field variables.

[0097] S4.2 It should be noted that the scalar value of wind speed and the horizontal wind direction angle value are read from the wind speed and wind direction data; the scalar value of wind speed is decomposed into eastward wind speed component and northward wind speed component using trigonometric functions, the formula is as follows:

[0098] ;

[0099] in, This indicates the eastward wind speed component. Indicates the northward wind speed component. Represents the scalar value of wind speed. This represents the wind direction angle in a mathematical coordinate system (the difference between 90 degrees and the horizontal wind direction angle).

[0100] The sum of the eastward and northward wind speed components constitutes the velocity vector.

[0101] In the global coordinate system of the local airflow virtual drag field model, the direction of the velocity vector is used as the line of sight. Each boundary grid in the model is processed sequentially, and its surface normal and velocity vectors are read. The dot product of the surface normal and velocity vectors is calculated. The dot product is checked for zero; if it is, the boundary grid is marked as the windward side grid. After traversing all boundary grids, all grids marked as windward side grids are identified. The Darcy-Fochheimer equation, including viscous drag and inertial drag terms, is established on each grid of the local airflow virtual drag field model. The formula is:

[0102] ;

[0103] in, Indicates the first Pressure gradient of each grid, Indicates the first The velocity vector of each grid, Indicates fluid dynamic viscosity, Indicates the first The penetration rate of each grid, Indicates the first The inertial drag coefficient of each grid, This indicates the fluid density.

[0104] By coupling the established Darcy-Fochheimer equation with the continuity equation of mass conservation, the pressure Poisson equation with pressure as the fundamental unknown is derived, as follows:

[0105] ;

[0106] in, Indicates the flow conductivity coefficient. The Laplace operator represents the pressure field. The gradient representing the flow conductivity coefficient, This represents the pressure gradient.

[0107] Using the velocity vector of the windward mesh as the velocity inlet boundary condition and the pressure value of the specified outlet surface as the pressure boundary condition, a large sparse linear system of equations is constructed for all mesh pressure unknowns. The formula is as follows:

[0108] ;

[0109] in, This represents the large sparse coefficient matrix obtained by discretizing the pressure Poisson equation. This represents the vector of all grid pressure values ​​to be determined. This represents the right-hand vector that includes the boundary conditions and the source term.

[0110] The conjugate gradient method is applied iteratively to solve large sparse linear equation systems to obtain the updated pressure field. Specifically, the initial residual is calculated using the zero vector or the pressure field before the previous iteration as the initial guess solution; a tolerance for the conjugate gradient method is set (e.g., ...). The maximum number of iterations (e.g., 2000) is set. In each iteration, the search direction is updated, and the pressure field and initial residual are updated along the search direction. The norm of the updated initial residual is checked to see if it is less than the set tolerance. If it is less than the tolerance, the iteration is terminated, and the current pressure field is output as the updated pressure field. If the maximum number of iterations is reached and convergence is still not achieved, the pressure field of the last iteration is output as the updated pressure field.

[0111] The tolerance setting is based on ensuring that the numerical solution achieves the pressure field calculation accuracy required for engineering analysis, and that the residuals of the mass conservation equation are sufficiently small. The value is determined by the truncation error level of the discretization scheme for the pressure Poisson equation and the floating-point precision of the computer, and is typically taken as 10⁻⁻⁶ of the initial residual norm. 6 times.

[0112] The maximum number of iterations is set to prevent the solution process from running indefinitely when it fails to converge or converges very slowly. The value is determined by statistical analysis of historical convergence data of the conjugate gradient method under typical grid sizes, ensuring that there are sufficient iterations to achieve convergence in most operating conditions.

[0113] Based on the updated pressure field, the pressure gradient is calculated for each grid of the local airflow virtual drag field model using the least squares method. The calculated pressure gradient and the corresponding grid's drag coefficients (permeability and inertial drag coefficients) are then substituted into the Darcy-Fochheimer equations to obtain the updated velocity vector. This calculation is performed on all grids of the local airflow virtual drag field model to update the velocity vector field of the entire flow domain. Based on the updated velocity vector field, the divergence of the velocity vector is calculated at the center of each grid in the local airflow virtual drag field model, using the following formula:

[0114] ;

[0115] in, The divergence of the grid velocity vector is represented. Indicates the mesh on a certain surface velocity vector Represents surface area vector, Represents the volume of the mesh. Indicates the relationship with the first The set of all surfaces adjacent to a grid.

[0116] Combine the velocity vector divergences of all meshes into a single column vector, denoted as the velocity divergence vector; calculate the norm of the velocity divergence vector to obtain the mass conservation residual, as shown in the formula:

[0117] ;

[0118] in, This represents the residual due to mass conservation. Indicates the total number of grid cells. Indicates the first The velocity vector divergence of each grid.

[0119] The mass conservation residual is compared with a residual threshold. If the mass conservation residual is greater than the residual threshold, the flow conduction coefficient in the pressure Poisson equation is recalculated using the current velocity vector field. A large sparse linear system of equations with pressure as the unknown is then reconstructed and solved, initiating a new round of iterative updates to the pressure and velocity vector fields. This process of calculating the mass conservation residual, determining convergence, reconstructing and solving the pressure Poisson equation is repeated until the mass conservation residual is less than the residual threshold, at which point the iteration terminates. The stable pressure distribution at the termination of iteration is the final pressure field, and the corresponding stable velocity vector distribution is the final velocity vector field. Time integration of the final velocity vector field yields the trajectory of the virtual particles; this trajectory represents the main airflow path. The final pressure field, velocity vector field, and main airflow path together constitute the virtual drag field derivation result.

[0120] The residual threshold is set to ensure that the numerical solution achieves physical conservation accuracy that matches the grid discretization error, and its value ranges from 10⁻. 5 The value is up to 10⁻³, and the specific value is determined by the proportional relationship of the square of the grid size, the normalization result of the characteristic physical quantity, and the trade-off of iteration efficiency.

[0121] The specific process for determining the boundary grid is as follows: In all the grids of the local airflow virtual drag field model, identify at least one grid whose face is not shared by other grids; check all the faces of the grid, and if the adjacent grid index of a certain face is empty or is a boundary marker, then this face is determined to be a boundary surface, and the grid with the boundary surface is the boundary grid.

[0122] The process of obtaining the surface normal vector of the boundary mesh is as follows: For each boundary mesh in the local airflow virtual drag field model, extract the coordinates of all vertices that make up the surface of the boundary mesh; according to the surface geometry of the boundary mesh, if the surface is a triangular patch, calculate the surface normal vector by the cross product of the two edge vectors; if the surface is a quadrilateral patch, calculate the surface normal vector by the cross product of the diagonal vectors.

[0123] The area vector is obtained by calculating the directed area of ​​the geometric polygons on the mesh surface. Specifically, for triangular faces, half of the cross product of the edge vectors is used, and for quadrilateral faces, half of the cross product of the diagonal vectors is used.

[0124] S5. Based on the saturated water vapor pressure difference, wind speed and direction data, average moisture content of the material pile, and the results of the virtual resistance field simulation, use a multi-dimensional decision matrix to determine the current target ventilation mode and ventilation intensity.

[0125] It should be noted that the saturated vapor pressure difference, wind speed scalar value, and average moisture content of the stockpile are mapped to preset interval levels in the multidimensional decision matrix; the velocity field and pressure field data in the virtual resistance field simulation results are processed, and the dimensionless number characterizing the overall ventilation difficulty of the stockpile, i.e., the overall wind resistance coefficient, is obtained by calculating the ratio of the total pressure drop to the average volumetric flow rate; the wind resistance correction factor is obtained by calculating the ratio of the overall wind resistance coefficient to the preset benchmark wind resistance.

[0126] The baseline wind resistance setting is based on the theoretical ventilation resistance reflected in the material pile stacking shape and ideal flow field conditions, providing a comparative benchmark for evaluating the flow resistance change under actual working conditions. For example, the baseline wind resistance is set at 150 Pa·s / m³, and the value is based on the steady-state simulation results of a typical material pile geometric model under rated wind speed using computational fluid dynamics.

[0127] The corrected saturated vapor pressure difference interval boundary is obtained by multiplying the wind resistance correction factor with the interval level boundary value of the saturated vapor pressure difference; the corrected wind speed interval boundary is obtained by multiplying the wind resistance correction factor with the interval level boundary value of the wind speed scalar value; and the corrected average moisture content interval boundary is obtained by multiplying the wind resistance correction factor with the interval level boundary value of the average moisture content of the stockpile.

[0128] The saturated vapor pressure difference is compared one by one with the boundary values ​​of the corrected saturated vapor pressure difference range to determine which boundary range the saturated vapor pressure difference value falls within. The level corresponding to the boundary range is the level of the corrected range to which the saturated vapor pressure difference belongs. Similarly, the wind speed scalar value is compared one by one with the boundary values ​​of the corrected wind speed range to determine which boundary range the wind speed scalar value falls within. The level corresponding to the boundary range is the level of the corrected range to which the wind speed scalar value belongs. Finally, the average moisture content of the stockpile is compared one by one with the boundary values ​​of the corrected average moisture content range to determine which boundary range the average moisture content of the stockpile falls within. The level corresponding to the boundary range is the level of the corrected range to which the average moisture content of the stockpile belongs.

[0129] The corrected saturated vapor pressure difference range level, corrected wind speed range level, and corrected average pile moisture content range level form a comprehensive state index. This comprehensive state index is used as the joint retrieval key. A location query is performed within the multidimensional decision matrix using this joint retrieval key. The multidimensional decision matrix is ​​a lookup table indexed by the saturated vapor pressure difference range level, wind speed range level, and average pile moisture content range level. The query operation locates the unique corresponding storage unit within the multidimensional decision matrix. The pre-stored data entry in this storage unit represents the target ventilation mode and ventilation intensity that matches the current comprehensive state index.

[0130] S6. After converting the target ventilation mode and ventilation intensity into a control instruction set, apply it to the ventilation duct of the asphalt mixture stockpile.

[0131] S6.1 It should be noted that, based on the physical layout and control logic of all ventilation openings, deflectors, and auxiliary fans in the asphalt mixture stockpile ventilation duct, a corresponding set of specific actuator target states is defined for each possible target ventilation mode. For example, when the target ventilation mode is "powerful ventilation mode", the corresponding set of actuator target states is defined as follows: ventilation openings 1 and 3 are opened to 100% opening, ventilation openings 2 and 4 are closed, all deflectors are deflected to a 45-degree angle, and auxiliary fans 1 and 2 are started and run at 90% of their rated speed. When the target ventilation mode is "maintaining ventilation mode", the corresponding set of actuator target states is defined as follows: only ventilation opening 1 is opened to 30% opening, the remaining ventilation openings are closed, the deflectors are kept at a neutral 0-degree angle, and all auxiliary fans are stopped. The target state set clearly defines the opening and closing status and target opening value of each vent, the target deflection angle of each deflector, and the start / stop status and target speed value of each auxiliary fan. All the defined target ventilation modes and the corresponding actuator target state sets are organized into a lookup table with the target ventilation mode as the index key and the detailed list of actuator target state parameters as the corresponding values. This lookup table is the actuator action mapping table.

[0132] S6.2 It should be noted that the target ventilation mode is used as the query key to search the actuator action mapping table and match the corresponding data entries. From the matched data entries, the opening and closing status commands of all vents and the target deflection angle commands of all guide vanes are directly read. At the same time, the specific parameters corresponding to the ventilation intensity are read from the same data entry, namely the target opening value of all vents that need to be opened and the target speed setting value of all auxiliary fans that need to be operated. The opening and closing status commands of vents, the opening value commands of vents, the deflection angle commands of guide vanes, and the speed setting value commands of auxiliary fans are collected, and each command is appended with the corresponding actuator device identifier and execution timestamp. All commands collected with device identifiers and timestamps are encoded and packaged according to the communication protocol format to form a control command set.

[0133] The control command set is transmitted to the electric actuators, motors and frequency converters on the ventilation duct of the asphalt mixture stockpile via a wireless communication network;

[0134] The control instruction set drives the electric actuator to operate the louvers of the ventilation opening to achieve the opening and closing state and opening degree set by the instruction, drives the motor to adjust the guide plate to the deflection angle set by the instruction, and drives the frequency converter to control the auxiliary fan to achieve the speed set by the instruction.

[0135] This embodiment also provides a computer device applicable to the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as proposed in the above embodiment.

[0136] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0137] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the airflow optimization control method for asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring, as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0138] In summary, this invention achieves optimized control of airflow in asphalt mixture stockpile ventilation ducts by: constructing a three-dimensional humidity field and a virtual airflow resistance field by integrating multi-source sensing data; combining a saturated water vapor pressure difference driving mechanism based on evaporation dynamics and a rapid flow field deduction method; driving the physical mechanism of airflow, adapting spatial topology, and precisely matching drying requirements. This not only overcomes the shortcomings of previous ventilation strategies that rely on empirical thresholds and ignore the geometry of the stockpile and the uneven distribution of internal moisture, but also dynamically adjusts the ventilation mode and intensity through a multi-dimensional decision matrix and resistance feedback correction mechanism, thereby improving moisture removal efficiency, energy utilization level, and response accuracy, and achieving the goal of intelligent, energy-saving, and efficient stockpile environmental control.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring, characterized in that: include, Collect humidity sensor data, ambient temperature and humidity, wind speed and direction data, and three-dimensional geometric data inside the asphalt mixture stockpile; Based on the humidity sensor data inside the stockpile, a three-dimensional humidity field model of the asphalt mixture stockpile was constructed using a spatial interpolation algorithm, and the average moisture content of the stockpile was calculated. The current atmospheric saturated water vapor pressure difference is calculated based on the ambient temperature and humidity, and a multidimensional decision matrix is ​​constructed using the principle of evaporation kinetics. Based on three-dimensional geometric data, a local airflow virtual resistance field model of asphalt mixture stockpile is constructed, and rapid airflow path deduction is performed to obtain the virtual resistance field deduction results. Based on the saturated vapor pressure difference, wind speed and direction data, average moisture content of the stockpile, and virtual resistance field simulation results, a multi-dimensional decision matrix is ​​used to determine the current target ventilation mode and ventilation intensity. After converting the target ventilation mode and ventilation intensity into a set of control instructions, they are applied to the ventilation ducts of the asphalt mixture stockpile.

2. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 1, characterized in that: The humidity sensor data inside the material pile includes the volumetric water content percentage, temperature reading, and the three-dimensional spatial coordinates of the corresponding humidity sensor. The wind speed and direction data include wind speed scalar value, horizontal wind direction angle value, and vertical wind speed component value; The ambient temperature and humidity include ambient temperature and ambient humidity; The three-dimensional geometric data includes a set of three-dimensional point cloud coordinates and a triangular mesh.

3. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 2, characterized in that: After constructing a three-dimensional humidity field model of the asphalt mixture stockpile using spatial interpolation algorithms based on humidity sensor data inside the stockpile, the average moisture content of the stockpile is calculated, specifically: Using the three-dimensional spatial coordinates of the humidity sensor as interpolation nodes and the volumetric water content percentage as node value, spatial interpolation is performed in the entire three-dimensional spatial domain of the material pile to generate a three-dimensional humidity field model. The average moisture content of the material pile is obtained by integrating the three-dimensional humidity field model over the entire volume of the pile.

4. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 3, characterized in that: The calculation of the current atmospheric saturated water vapor pressure difference based on ambient temperature and humidity is specifically as follows: Based on ambient temperature, the saturated vapor pressure is calculated using the Magnus formula. After converting the ambient humidity to a decimal form, the saturated vapor pressure difference is calculated in conjunction with the saturated vapor pressure.

5. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 4, characterized in that: The construction of a multidimensional decision matrix using the principles of evaporation kinetics is specifically as follows: The ventilation control logic is established by taking the saturated water vapor pressure difference as the main driving force, the wind speed scalar value as the convection exchange capacity, and the average moisture content of the material pile as the drying requirement. Based on the ventilation control logic, the numerical ranges of saturated water vapor pressure difference, wind speed scalar value, and average moisture content of the stockpile are divided into discrete interval levels. Each zone level is mapped to specific control commands for vents, deflectors, and auxiliary fans, forming a multi-dimensional decision matrix.

6. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 5, characterized in that: The construction of the local airflow virtual resistance field model for asphalt mixture stockpile refers to using three-dimensional geometric data to discretize the stockpile shape and surrounding fixed structures into a grid, and assigning a corresponding local flow resistance coefficient to each discrete unit to form a local airflow virtual resistance field model.

7. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 6, characterized in that: The aforementioned rapid airflow path simulation refers to using wind speed and direction data as inlet boundary conditions, applying the solver of the Darcy-Fochheimer equation in a local airflow virtual resistance field model, and performing rapid airflow path simulation to obtain the virtual resistance field simulation results.

8. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 7, characterized in that: Based on the saturated vapor pressure difference, wind speed and direction data, average moisture content of the stockpile, and the results of virtual resistance field simulation, a multi-dimensional decision matrix is ​​used to determine the current target ventilation mode and ventilation intensity. Specifically: The saturated water vapor pressure difference, wind speed scalar value, and average moisture content of the stockpile are respectively mapped to the preset interval levels in the multidimensional decision matrix; The overall drag coefficient is extracted from the virtual drag field simulation results. The overall drag coefficient is used as a correction factor to correct the saturated water vapor pressure difference range, wind speed range, and average moisture content range of the stockpile, and to obtain the comprehensive state index. Use the comprehensive state index to query the multidimensional decision matrix and retrieve the corresponding target ventilation mode and ventilation intensity.

9. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 8, characterized in that: The specific steps for converting the target ventilation mode and ventilation intensity into a control command set are as follows: Construct an executor action mapping table; The target ventilation mode is resolved by using the actuator action mapping table as the opening and closing state of the vent and the deflection angle of the guide vane, and the ventilation intensity is resolved by the opening value of the vent and the speed setting value of the auxiliary fan. The control command set consists of the opening and closing status of the vents, the opening value of the vents, the deflection angle of the guide vanes, and the speed setting of the auxiliary fan.

10. The method for optimizing and controlling airflow in asphalt mixture stockpile ventilation ducts based on temperature and humidity monitoring as described in claim 9, characterized in that: The process of converting the target ventilation mode and ventilation intensity into a set of control commands and applying them to the ventilation ducts of the asphalt mixture stockpile specifically involves: The control command set is transmitted to the electric actuators, motors and frequency converters on the ventilation duct of the asphalt mixture stockpile via a wireless communication network; The control instruction set drives the electric actuator to operate the louvers of the ventilation opening to achieve the opening and closing state and opening degree set by the instruction, drives the motor to adjust the guide plate to the deflection angle set by the instruction, and drives the frequency converter to control the auxiliary fan to achieve the speed set by the instruction.

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