Straw drying state monitoring method based on multiple sensors

Abstract

The invention discloses a straw drying state monitoring method based on multiple sensors, particularly relates to the field of straw drying state monitoring and control, and aims to solve the problems of low drying efficiency, inconsistent quality and inaccurate control caused by uneven stacking density and poor airflow permeability. A dry bottleneck area is accurately identified; accurately predicting drying parameters of each region by using a regional drying demand model; an intelligent optimization algorithm and real-time feedback control are combined, and dynamic adjustment and accurate matching of the drying process are achieved; by means of the straw drying device, the straw drying efficiency and uniformity are remarkably improved, the problem of insufficient drying or excessive drying caused by density and permeability differences is solved, meanwhile, energy consumption and safety risks in the drying process are effectively reduced, and the consistency of drying quality and the reliability of equipment operation are ensured.

Description

Technical Field

[0001] The present invention relates to the field of straw drying state monitoring and control, and more specifically, to a straw drying state monitoring method based on multiple sensors. Background Art

[0002] Currently, straw drying mainly relies on traditional drying equipment and methods, such as natural drying, mechanical drying, and hot air drying. These methods usually rely on uniform drying parameters (such as temperature, humidity, and airflow speed) to process the entire straw pile. However, in order to ensure drying efficiency and product quality, some advanced technologies have introduced sensor monitoring systems, which mainly monitor overall temperature, humidity, and moisture content. These sensors are usually distributed in different locations of the drying equipment, and adjust the drying conditions through real-time data collection to ensure that the entire straw pile achieves the expected drying effect. In addition, some intelligent drying systems also combine automatic control algorithms, which can dynamically adjust drying parameters based on sensor data to improve the efficiency and consistency of the drying process.

[0003] However, existing technologies have significant shortcomings in dealing with the problem of uneven density and distribution of straw stacking. First, traditional sensor monitoring systems focus more on overall environmental parameters and lack accurate measurement of the internal density distribution of the straw, resulting in an inability to fully understand the drying status of each area inside the straw pile. Secondly, existing systems usually use unified drying parameters, ignoring the need for different drying strategies due to differences in filling density in different areas, resulting in some areas being over-dried or under-dried, and unable to meet the consistent requirements of demanders for drying uniformity and quality. In addition, existing technologies are relatively simple in data processing and analysis, and cannot fully utilize multi-sensor data to conduct in-depth analysis and modeling of the overall structural density of the straw. These shortcomings not only affect drying efficiency and product quality, but may also increase energy consumption and operating costs.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiment of the present invention provides a straw drying state monitoring method based on a multi-sensor. The straw drying state monitoring method based on a multi-sensor can accurately construct a three-dimensional density distribution model of straw accumulation, and combine the dynamic airflow permeability analysis to fully grasp the density and airflow characteristics in the drying environment. Further, the initial moisture content and environmental parameters are integrated to establish a regional drying demand model, generate the target drying curve of each region, and realize the accurate prediction and demand matching of the drying process. Advanced global optimization algorithms, such as genetic algorithms or particle swarm optimization, are used to dynamically adjust the temperature and airflow velocity of each region to ensure the optimal configuration of drying parameters. At the same time, the intelligent control module allocates and executes the optimal drying settings in real time to ensure the efficiency and uniformity of the drying process. This method effectively solves the problems of low drying efficiency and inconsistent quality caused by uneven straw accumulation density in traditional drying technology, and significantly improves the overall efficiency and product quality of the drying process to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The straw drying state monitoring method based on multiple sensors comprises the following steps: S1, collects data from density sensors and airflow sensors, generates a three-dimensional density distribution model of straw accumulation through multi-point interpolation and data fusion, and locates high-density areas and low-density areas.

[0007] S2, inject quantitative gas and monitor the airflow response data, monitor the airflow response data, and obtain the permeability distribution map through dynamic modeling.

[0008] S3, combining density distribution, permeability characteristics and initial moisture content data, establishes a regional drying demand model and generates target drying curves for each region.

[0009] S4, adjusting the corresponding drying parameters based on the target drying curve to achieve regional adaptive control.

[0010] In a preferred embodiment, step S1 includes the following contents: S1.1, evenly arrange matrix density sensors and airflow sensors on the inner wall of the drying equipment.

[0011] S1.2, using the multi-point measurement data of the density sensor, an initial density distribution model of the straw pile surface is established through the interpolation algorithm; using the inverse distance weighted interpolation method, the density value of each measurement point is weighted according to its distance to the target point to generate a density distribution on a two-dimensional plane.

[0012] S1.3, uses the pressure data of the airflow sensor to correct the initial density distribution model; the pressure difference reflects the resistance on the ventilation path, and the nonlinear relationship between the resistance and the local stacking density is quantified through modeling; the density distribution correction logic uses the pressure difference data to enhance the density weight of the difficult-to-ventilate area through exponential transformation, while weakening the interference of the low-resistance area.

[0013] S1.4, based on the density interpolation model and the airflow correction data, a three-dimensional density distribution model is generated using a constrained optimization algorithm; the objective function is optimized to balance the difference between the initial density model and the airflow correction data; through iterative solution, the density distribution value of each spatial point is finally obtained to construct a three-dimensional structural model of the straw pile.

[0014] S1.5, the process of marking high-density areas and low-density areas first analyzes the local density value of the three-dimensional density distribution model and the density gradient changes of its adjacent points to determine the density feature range in the area. The specific operation is: according to the optimized three-dimensional density model, the distribution range of the global density value is extracted, and the upper and lower quantiles of its statistical distribution are calculated as the initial reference thresholds for low density and high density; then at each density sampling point, the density change rate gradient of its adjacent points is used for local correction. The correction logic of the high-density threshold area is to detect whether the density change gradually tends to be stable within a certain range. If it meets the requirements, it is marked as a stable high-density area; for the low-density threshold area, it is judged whether the density value is less than the initial low-density threshold and the local gradient changes significantly. If the conditions are met, it is marked as an abnormally low-density area; finally, a regional high and low density distribution map is generated through high and low density marking points.

[0015] In a preferred embodiment, step S2 includes the following contents: S2.1, inject a certain amount of gas into the air inlet of the drying equipment, and ensure the uniformity of the airflow into the equipment by controlling the injection flow rate and initial pressure; the matrix airflow sensor arranged on the inner wall of the equipment records the airflow speed and pressure at the same time; the airflow sensor senses the airflow response at different points and outputs the corresponding airflow speed value and pressure value.

[0016] S2.2, based on the velocity and pressure difference recorded by the airflow sensor, preliminarily estimate the permeability distribution of the straw pile; the permeability of the airflow is closely related to the pressure drop and flow velocity along the path. By analyzing the flow velocity and pressure difference data at the sensor location point, the local permeability of each sensor point is calculated.

[0017] S2.3, combining the airflow response data collected at multiple times, constructing a dynamic model of airflow diffusion; the airflow diffusion process is significantly affected by the permeability and density distribution, and a dynamic diffusion model is used to describe the changes in airflow concentration in time and space; the airflow diffusion process is simulated through discrete solution using the finite difference method, and the model is fitted in combination with the actual sensor data, the initial permeability distribution is adjusted, and a corrected permeability distribution is generated.

[0018] S2.4, the process of marking high-permeability and low-permeability areas is based on the corrected permeability distribution data and is completed by analyzing the permeability gradient and regional characteristics. First, the permeability gradient of each spatial point is calculated, that is, the rate of change of the permeability values ​​of adjacent points, and the gradient mutation points are screened out as potential boundary areas. The permeability values ​​of areas with gradients below the standard are statistically analyzed, and the average value is taken as the global benchmark permeability threshold. When marking high-permeability areas, areas with continuous gradients and permeability values ​​higher than the corresponding threshold are screened to ensure that the ventilation path is unobstructed and evenly distributed. When marking low-permeability areas, areas with permeability values ​​lower than the corresponding threshold and drastic gradient changes are screened, and the airflow retention characteristics are analyzed in combination with the dynamic diffusion model to confirm whether there is potential airflow blockage. Finally, a regional distribution map is generated.

[0019] In a preferred embodiment, step S3 includes the following contents: S3.1, integrating the obtained three-dimensional density distribution model with the permeability distribution map, combined with the initial moisture content data of the straw pile, and in addition, collecting environmental parameter data.

[0020] S3.2, based on density distribution , Permeability distribution and initial moisture content , construct regional drying demand prediction function , predict the specific drying requirements of each spatial point during the drying process, and the drying requirement prediction function formula is: ;in: For space point Drying demand index; It is the drying demand sensitivity parameter, reflecting the nonlinear relationship between moisture, density and permeability; is the environmental parameter influence coefficient, which adjusts the influence of temperature and humidity on drying requirements; is the base of natural logarithms.

[0021] S3.3, using machine learning algorithms to optimize the drying demand prediction function.

[0022] S3.4, based on the optimized regional drying demand model, generate the target drying curve for each region ,in Indicates time.

[0023] In a preferred embodiment, step S4 includes the following contents: S4.1, analyze the target drying curve of each area and extract the temperature required for each area at different time points and air velocity Through spatial mapping technology, each area in the three-dimensional space is matched with the specific control unit of the drying equipment to ensure that each control unit can independently receive and execute the corresponding drying parameters.

[0024] S4.2, based on the target drying curve, build an intelligent control algorithm and use a multivariable nonlinear optimization method to ensure that the dynamic adjustment of drying parameters can accurately match the needs of each area. The specific steps include: Establishment of multivariable optimization model: Establish a multivariable optimization model of temperature and airflow velocity. The model form is as follows: ;in: and The current area at time temperature and air velocity; and is the temperature and airflow velocity specified in the target drying curve; is the total drying time.

[0025] Constraint setting: Set upper and lower limits for temperature and airflow speed based on the physical limitations and safety parameters of the equipment: ; ;in, and Indicates a point in space The minimum and maximum drying temperatures permitted at the site; Indicates a point in space The minimum and maximum air velocity allowed at the location.

[0026] Application of optimization algorithm: Genetic algorithm or particle swarm optimization global optimization technology is used to solve the model and obtain the optimal temperature and airflow speed settings for each area at different time points. The obtained optimal temperature and airflow speed settings are distributed to the execution units of the corresponding areas in real time through the control module, and the operating parameters of the drying equipment are dynamically adjusted to match the target requirements of each area.

[0027] Technical effects and advantages of the straw drying state monitoring method based on multiple sensors of the present invention: The present invention achieves a systematic solution to the problems of uneven stacking density, airflow blockage, and difficulty in accurately matching drying conditions during the straw drying process through density distribution modeling, permeability analysis, drying demand prediction, and regionalized adaptive control. Through refined density and permeability modeling, the drying bottleneck area is accurately identified; the regionalized drying demand model is used to accurately predict the drying parameters of each area; combined with intelligent optimization algorithms and real-time feedback control, dynamic adjustment and precise matching of the drying process are achieved. This method significantly improves the efficiency and uniformity of straw drying, avoids the problem of insufficient or excessive drying caused by differences in density and permeability, and effectively reduces the energy consumption and safety risks of the drying process, ensuring the consistency of drying quality and the reliability of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figure is a flow chart of the straw drying state monitoring method based on multiple sensors of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Embodiment 1: Figure 1 The present invention provides a straw drying state monitoring method based on multiple sensors, comprising: S1, collects data from density sensors and airflow sensors, generates a three-dimensional density distribution model of straw accumulation through multi-point interpolation and data fusion, and locates high-density areas and low-density areas.

[0031] S2, inject a certain amount of gas and monitor the airflow response data, monitor the airflow response data, obtain the permeability distribution map through dynamic modeling, and identify the airflow obstruction or inefficient areas.

[0032] S3, combining density distribution, permeability characteristics and initial moisture content data, establishes a regional drying demand model and generates target drying curves for each region.

[0033] S4, adjusting the corresponding drying parameters based on the target drying curve to achieve regional adaptive control.

[0034] During the straw drying process, the distribution of straw stacking density directly determines the drying efficiency and uniformity. Since the straw pile is internally uneven during the stacking process, traditional methods cannot obtain internal density information. Existing solutions usually place sensors inside the straw pile, which not only affects the stacking structure, but also has problems such as limited detection range and difficult maintenance. This step uses matrix density sensors and airflow sensors arranged on the inner wall of the drying equipment. Through multi-point measurement, data fusion and reverse modeling, a three-dimensional density distribution model of the straw pile is constructed to locate high-density, gaps and abnormal areas in the pile.

[0035] Step S1 includes the following contents: S1.1, matrix density sensors and airflow sensors are evenly arranged on the inner wall of the drying equipment to avoid interference with the straw stacking structure through the layout of the inner wall of the equipment. The density sensor uses electromagnetic field induction to measure the local mass distribution of the straw area near the inner wall of the equipment and generate multi-point density data in a two-dimensional plane. The airflow sensor records the pressure difference data of each channel by sensing the pressure changes in the ventilation path to reflect the internal permeability characteristics of the stack. The collected data includes the spatial position, measurement values ​​and environmental conditions of each sensor, which serves as input for subsequent modeling.

[0036] S1.2, using the multi-point measurement data of the density sensor, the initial density distribution model of the straw pile surface is established through the interpolation algorithm. To improve the modeling accuracy, the inverse distance weighted interpolation method is used to assign weights to the density value of each measurement point according to its distance to the target point to generate a density distribution on a two-dimensional plane. The interpolation process constructs the quality characteristics of the local area through the spatial position relationship of the sensor, which better reflects the surface changes of the stacking structure.

[0037] For example, using the three-dimensional inverse distance weighted interpolation method (IDW), the formula is as follows: ; in: For point The density value of .

[0038] For sensor The density measurement value.

[0039] For point With sensor The Euclidean distance of .

[0040] It is the distance decay exponent, which controls the decay speed of the interpolation weight, and its value usually ranges from 1.5 to 2.5.

[0041] The number of sampling points involved in the interpolation calculation.

[0042] The interpolation of density data is limited by the sensor distribution density and device structure. The interpolation model may have large errors for areas farther away from the sensor, and subsequent correction steps are required to improve the accuracy.

[0043] S1.3, using the pressure data of the airflow sensor, the initial density distribution model is corrected. The pressure difference reflects the resistance on the ventilation path, and the nonlinear relationship between the resistance and the local bulk density is quantified through modeling. The density distribution correction logic uses the pressure difference data to enhance the density weight of the difficult ventilation area through exponential transformation, while weakening the interference of the low resistance area. This process introduces the dynamic information of the airflow into the density distribution, making up for the limitations of the single density sensor measurement.

[0044] Assuming that the pressure difference of the ventilation path is nonlinearly related to the regional density, boundary restrictions will be imposed on abnormal points (such as local abnormally high pressure difference) to avoid excessive adjustment.

[0045] The density value is corrected using the following formula: ; in: is the corrected density.

[0046] For point of pressure difference.

[0047] is the maximum pressure difference in the area, used for normalization.

[0048] This process improves the accuracy of the model by increasing the weight of high-density areas and reducing the interference of low-density areas.

[0049] S1.4, based on the density interpolation model and airflow correction data, a three-dimensional density distribution model is generated using a constrained optimization algorithm. The optimization objective function balances the difference between the initial density model and the airflow correction data. The constraints include density range, density gradient continuity, and physical consistency requirements. Through iterative solution, the density distribution value of each spatial point is finally obtained, and a three-dimensional structural model of the straw pile is constructed. The optimized model can accurately reflect high-density stacking areas, potential ventilation bottlenecks, and low-density gaps.

[0050] Optimize the objective function: minimize the difference between the initial density model and the correction model. The formula is as follows: ; Constraints: Density range Reasonable.

[0051] Density gradient continuity .

[0052] S1.5, based on the optimized 3D density model, analyze the density gradient and local anomalies, and mark the high-density and low-density areas where there may be uneven drying or airflow obstruction. These areas will serve as the key basis for the subsequent optimization of drying strategy zoning. The model output includes a 3D density distribution map, anomaly area annotations and their physical characteristic data to ensure the accuracy of the subsequent drying control process.

[0053] The process of marking high-density and low-density areas is first analyzed based on the local density value of the three-dimensional density distribution model and the density gradient change of its adjacent points to determine the density feature range in the area. The specific operation is: according to the optimized three-dimensional density model, the distribution range of the global density value is extracted, and the upper and lower quantiles of its statistical distribution (such as the 25th percentile and the 75th percentile) are calculated as the initial reference thresholds for low density and high density; then at each density sampling point, the density change rate gradient of its adjacent points is used for local correction. The correction logic of the high-density threshold area is to detect whether the density change gradually tends to be stable within a certain range (that is, the gradient value is less than the set gradient upper limit). If it meets the requirements, it is marked as a stable high-density area; for the low-density threshold area, it is judged whether the density value is less than the initial low-density threshold and the local gradient changes significantly (that is, the density distribution shows a large-scale downward trend). If the conditions are met, it is marked as an abnormal low-density area. Finally, a regional high-low density distribution map is generated through these high- and low-density marking points, and the transition area boundary is adjusted in combination with the global characteristics of the model to form an accurate high- and low-density labeling result.

[0054] By arranging matrix density sensors and airflow sensors on the inner wall of the drying equipment, combined with multi-point interpolation and inverse modeling technology, step S1 accurately constructs a three-dimensional density distribution model of the straw pile. The model effectively characterizes the high-density areas, low-density gaps and density gradient changes inside the straw pile, laying a reliable data foundation for subsequent permeability analysis and zoning optimization of the drying process. The modeling method of density distribution integrates the dynamic correction of density and airflow data, improves the accuracy and applicability of the model, and ensures a more comprehensive and accurate description of the characteristics of the stacking structure.

[0055] During the straw drying process, the permeability of the airflow directly affects the drying efficiency and uniformity. Due to the uneven density distribution inside the straw pile, the ventilation path will show a significant nonlinear distribution, resulting in airflow blockage or inefficient ventilation in some areas. However, static monitoring of airflow velocity and pressure difference data alone cannot fully characterize the complex permeability distribution. To solve this problem, this step injects a certain amount of gas into the drying equipment, dynamically monitors the response data of the airflow sensor, combines dynamic modeling with path reconstruction, and generates a high-resolution distribution map reflecting the internal permeability to accurately locate the ventilation bottleneck area.

[0056] Step S2 includes the following contents: S2.1, inject a certain amount of gas into the air inlet of the drying equipment, and ensure uniform airflow into the equipment by precisely controlling the injection flow and initial pressure. The matrix airflow sensor arranged on the inner wall of the equipment records the airflow speed and pressure at the same time. The airflow sensor senses the airflow response at different points and outputs the corresponding airflow speed and pressure values. Each sensor records the time, position coordinates and response data, which form the basis for analyzing the transparency. Data collection is completed in multiple rounds, each lasting a period of time to ensure that the dynamic diffusion process of the airflow inside the straw pile is fully captured.

[0057] S2.2, based on the velocity and pressure difference recorded by the airflow sensor, preliminarily estimate the permeability distribution of the straw pile. The permeability of the airflow is closely related to the pressure drop and flow rate on the path. By analyzing the flow rate and pressure difference data at the sensor location point, the local permeability of each sensor point is calculated. The path length is used as a geometric constraint on the area through which the airflow passes, combined with the relationship between flow and pressure loss, to obtain a preliminary permeability distribution.

[0058] The velocity and pressure recorded by the airflow sensor are used to model the diffusion path of the airflow in the straw pile. Assuming that the permeability of the airflow is nonlinearly related to the pressure drop along the path, the initial permeability distribution is estimated by the following formula: ; in: For path Permeability (unit: ).

[0059] Injection volume for air flow.

[0060] is the path length, calculated by the geometric distance of the sensor locations in three-dimensional space.

[0061] For path of pressure difference.

[0062] S2.3, combined with the collected airflow response data at multiple times, a dynamic model of airflow diffusion is constructed. The airflow diffusion process is significantly affected by the permeability and density distribution. The dynamic diffusion model is used to describe the changes in airflow concentration in time and space. The initial permeability distribution is corrected based on the concentration change trend of the airflow sensor to better reflect the actual situation. The dynamic model iteratively simulates the airflow diffusion path and gradually adjusts the permeability distribution so that the final result can fit the dynamic concentration distribution characteristics.

[0063] Based on the airflow response data at each moment in the sampling period, a dynamic diffusion model is constructed to analyze the regional characteristics of the airflow. It is assumed that the airflow diffusion satisfies the following nonlinear diffusion equation: ; in: For location In time air flow concentration.

[0064] is the diffusion coefficient, which is related to density and permeability.

[0065] is the dissipation coefficient, which reflects the weakening effect of air flow resistance on diffusion.

[0066] The equation is discretely solved by the finite difference method to simulate the airflow diffusion process, and the model is fitted with the actual sensor data to adjust the initial permeability distribution and generate the corrected permeability distribution. The dissipation coefficient is further corrected by the correlation function with the pressure gradient to optimize the airflow area distribution.

[0067] S2.4, the process of marking high-permeability and low-permeability areas is based on the corrected permeability distribution data and is completed by analyzing the permeability gradient and regional characteristics. First, the permeability gradient of each spatial point is calculated, that is, the rate of change of the permeability values ​​of adjacent points, and the gradient mutation points are screened out as potential boundary areas; the permeability values ​​of areas with gradients below the standard are statistically analyzed, and the average value is taken as the global benchmark permeability threshold; when marking high-permeability areas, the areas with continuous gradients and permeability values ​​above the corresponding threshold are screened to ensure that the ventilation path is unobstructed and evenly distributed; when marking low-permeability areas, the areas with permeability values ​​below the corresponding threshold and drastic gradient changes are screened, and the airflow retention characteristics are further analyzed in combination with the dynamic diffusion model to confirm whether there is potential airflow obstruction. Finally, a regional distribution map is generated, in which the boundaries of high-permeability areas and low-permeability areas are composed of gradient change points, and the stability and accuracy of the results are verified through multiple rounds of data.

[0068] By injecting a certain amount of gas into the drying equipment and monitoring the response data of the airflow sensor, step S2 uses dynamic diffusion modeling technology to generate a permeability distribution map inside the straw pile. This distribution map clearly identifies the airflow blockage area and high permeability area of ​​the straw pile. Combined with the correction method of the dynamic characteristics of the airflow, the resolution and accuracy of the analysis results are improved, providing a key support basis for the regional optimization and risk control of the subsequent drying strategy. The permeability distribution analysis is further combined with the dynamic diffusion law to ensure the accurate positioning of bottleneck areas and potential inefficient areas.

[0069] In the first two steps, the internal structural characteristics of the straw pile have been fully understood through the density distribution model (step S1) and the permeability distribution map (step S2). In order to achieve an efficient and uniform drying process, these structural characteristics must be combined with the initial moisture content data to accurately predict the drying needs of each area. This step aims to generate a target drying curve for each area through multi-dimensional data fusion and complex model establishment, ensuring that the drying process can be adaptively adjusted to meet the specific needs of different areas.

[0070] Step S3 includes the following contents: S3.1, integrating the three-dimensional density distribution model and permeability distribution map obtained in steps S1 and S2, combined with the initial moisture content data of the straw pile. The initial moisture content is measured in each area by a portable moisture sensor, and the initial moisture content of each area is recorded. In addition, environmental parameter data such as outside temperature is collected and humidity , so that dynamic adjustments can be made in the model.

[0071] S3.2, based on density distribution , Permeability distribution and initial moisture content , construct regional drying demand prediction function , whose purpose is to predict the specific drying requirements of each spatial point during the drying process. This function takes into account the moisture migration dynamics of the straw, the effect of permeability on airflow, and the dynamic changes of environmental parameters.

[0072] Drying demand prediction function formula: ; in: For space point Drying demand index.

[0073] It is the drying demand sensitivity parameter, reflecting the nonlinear relationship between moisture, density and permeability.

[0074] It is the environmental parameter influence coefficient, which adjusts the influence of temperature and humidity on drying requirements.

[0075] is the base of natural logarithms.

[0076] The function reflects the complex effects of density and permeability on water migration through nonlinear combinations, while dynamically responding to changes in environmental parameters to ensure that the drying demand model can adapt to real-time conditions.

[0077] S3.3, optimize the drying demand prediction function using machine learning algorithms (such as support vector machine regression or neural network). The model is trained to improve the prediction accuracy through historical drying data and real-time monitoring data. The optimization process includes parameter adjustment, model verification and cross-validation to ensure that the model has good generalization ability under different straw pile structures and environmental conditions.

[0078] During the model optimization phase, a data-driven approach is introduced to improve the accuracy and robustness of drying demand forecasting, ensuring that the generated target drying curve can truly reflect the specific needs of each region.

[0079] S3.4, based on the optimized regional drying demand model, generate the target drying curve for each region ,in Indicates time. The target drying curve includes the dynamic change trend of drying temperature, air flow velocity and drying time, ensuring that the needs of each area during the drying process are accurately met. Through spatial mapping technology, the target drying curve is assigned to a specific regional control unit to achieve regional drying parameter settings.

[0080] By integrating density distribution, permeability characteristics and initial moisture content data, step S3 constructs a refined regional drying demand model and generates target drying curves for each region. The model uses a nonlinear drying demand prediction function combined with machine learning optimization technology to ensure high-precision prediction and dynamic adaptation of drying demand. The generated target drying curve provides specific drying parameter guidance for each region, achieves accurate matching and optimization of the drying process, significantly improves drying efficiency and uniformity, and ensures that the final product meets high quality requirements.

[0081] In the above steps, the internal structural characteristics of the straw pile have been fully understood through the density distribution model (step S1) and the permeability distribution map (step S2), and the target drying curves of each region have been generated through the regional drying demand model (step S3). These target curves describe in detail the temperature, airflow velocity and time requirements of each region during the drying process. In order to achieve efficient and uniform drying, these target drying curves must be converted into specific drying parameter adjustment operations to ensure that the drying conditions of each region can dynamically respond to its specific needs. This step aims to achieve regionalized adaptive drying control by accurately adjusting the operating parameters of the drying equipment based on the target drying curve through an intelligent control system.

[0082] Step S4 includes the following contents: S4.1, the target drying curves of each region generated in step S3 Analyze and extract the temperature required for each area at different time points and air velocity Through spatial mapping technology, each area in the three-dimensional space corresponds to the specific control unit of the drying equipment, ensuring that each control unit can independently receive and execute the corresponding drying parameters.

[0083] S4.2, based on the target drying curve, build an intelligent control algorithm and use a multivariable nonlinear optimization method to ensure that the dynamic adjustment of drying parameters can accurately match the needs of each area. The specific steps include: Establishment of multivariable optimization model: Establish a multivariable optimization model of temperature and airflow velocity, taking into account the temporal and spatial variation characteristics of the target curves in each area. The model form is as follows: ; in: and The current area at time temperature and air flow rate.

[0084] and is the temperature and airflow velocity specified in the target drying curve.

[0085] is the total drying time.

[0086] Constraint setting: Set upper and lower limits for temperature and airflow speed based on the physical limitations and safety parameters of the equipment: ; ; and Indicates a point in space The minimum and maximum drying temperatures allowed.

[0087] Indicates a point in space The minimum and maximum air velocity allowed at the location.

[0088] Application of optimization algorithm: Global optimization techniques such as genetic algorithm or particle swarm optimization are used to solve the model and obtain the optimal temperature and airflow speed settings for each area at different time points. The obtained optimal temperature and airflow speed settings are distributed to the execution units of the corresponding areas in real time through the control module, and the operating parameters of the drying equipment are dynamically adjusted to match the target requirements of each area.

[0089] Step S4: By intelligently analyzing the target drying curve, the temperature and airflow velocity requirements of each area are accurately mapped to the control unit of the drying equipment, and the drying parameters are dynamically adjusted using a multivariable nonlinear optimization algorithm to ensure that the drying conditions of each area are highly matched to their specific requirements. At the same time, the system monitors the actual temperature and airflow velocity in real time through high-precision sensors, compares and analyzes them with the target curve, and uses an adaptive control algorithm to instantly correct deviations to ensure continuous optimization and stable operation of the drying process.

[0090] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0091] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0092] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0093] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A straw drying state monitoring method based on multiple sensors, characterized in that: Includes steps: S1, collects data from density sensors and airflow sensors, generates a three-dimensional density distribution model of straw accumulation through multi-point interpolation and data fusion, and locates high-density areas and low-density areas; S2, injecting a certain amount of gas and monitoring the airflow response data, monitoring the airflow response data, and obtaining a permeability distribution map through dynamic modeling; S3, combining density distribution, permeability characteristics and initial moisture content data, establishes a regional drying demand model and generates target drying curves for each region; S4, adjusting the corresponding drying parameters based on the target drying curve to achieve regional adaptive control.

2. The method for monitoring the drying state of straw based on multiple sensors according to claim 1, characterized in that: Step S1 includes the following contents: S1.1, evenly arrange matrix density sensors and airflow sensors on the inner wall of the drying equipment; S1.2, using the multi-point measurement data of the density sensor, an initial density distribution model of the straw pile surface is established through an interpolation algorithm; using the inverse distance weighted interpolation method, the density value of each measurement point is weighted according to its distance to the target point to generate a density distribution in a two-dimensional plane; S1.3, using the pressure data of the airflow sensor, the initial density distribution model is corrected; the pressure difference reflects the resistance on the ventilation path, and the nonlinear relationship between the resistance and the local bulk density is quantified through modeling; the density distribution correction logic uses the pressure difference data to enhance the density weight of the ventilation difficult area through exponential transformation, while weakening the interference of the low resistance area; S1.4, based on the density interpolation model and the airflow correction data, a three-dimensional density distribution model is generated using a constrained optimization algorithm; the objective function is optimized to balance the difference between the initial density model and the airflow correction data; through iterative solution, the density distribution value of each spatial point is finally obtained to construct a three-dimensional structural model of the straw pile.

3. The method for monitoring the drying state of straw based on multiple sensors according to claim 2, characterized in that: Step S1 also includes the following contents: S1.5, the process of marking high-density areas and low-density areas first analyzes the local density value of the three-dimensional density distribution model and the density gradient changes of its adjacent points to determine the density feature range in the area. The specific operation is: according to the optimized three-dimensional density model, the distribution range of the global density value is extracted, and the upper and lower quantiles of its statistical distribution are calculated as the initial reference thresholds for low density and high density; then at each density sampling point, the density change rate gradient of its adjacent points is used for local correction. The correction logic of the high-density threshold area is to detect whether the density change gradually tends to be stable within a certain range. If it meets the requirements, it is marked as a stable high-density area; for the low-density threshold area, it is judged whether the density value is less than the initial low-density threshold and the local gradient changes significantly. If the conditions are met, it is marked as an abnormally low-density area; finally, a regional high and low density distribution map is generated through high and low density marking points.

4. The method for monitoring the drying state of straw based on multiple sensors according to claim 3, characterized in that: Step S2 includes the following contents: S2.1, inject a fixed amount of gas into the air inlet of the drying equipment, and ensure the uniformity of the airflow into the equipment by controlling the injection flow rate and initial pressure; the matrix airflow sensor arranged on the inner wall of the equipment records the airflow speed and pressure at the same time; the airflow sensor senses the airflow response at different points and outputs the corresponding airflow speed value and pressure value; S2.2, based on the velocity and pressure difference recorded by the airflow sensor, preliminarily estimate the permeability distribution of the straw pile; the permeability of the airflow is closely related to the pressure drop and flow velocity along the path. By analyzing the flow velocity and pressure difference data at the sensor location point, the local permeability of each sensor point is calculated; S2.3, combining the airflow response data collected at multiple times, constructing a dynamic model of airflow diffusion; the airflow diffusion process is significantly affected by the permeability and density distribution, and a dynamic diffusion model is used to describe the changes in airflow concentration in time and space; the airflow diffusion process is simulated through discrete solution using the finite difference method, and the model is fitted in combination with the actual sensor data, the initial permeability distribution is adjusted, and a corrected permeability distribution is generated.

5. The method for monitoring the drying state of straw based on multiple sensors according to claim 4, characterized in that: Step S2 includes the following contents: S2.4, the process of marking high-permeability and low-permeability areas is based on the corrected permeability distribution data and is completed by analyzing the permeability gradient and regional characteristics; First, the permeability gradient of each spatial point is calculated, that is, the rate of change of the permeability values ​​of adjacent points, and the gradient mutation points are screened out as potential boundary areas; the permeability values ​​of areas with gradients below the standard are statistically analyzed, and the average value is taken as the global benchmark permeability threshold; when marking high-permeability areas, areas with continuous gradients and permeability values ​​above the corresponding threshold are screened to ensure that the ventilation path is unobstructed and evenly distributed; when marking low-permeability areas, areas with permeability values ​​below the corresponding threshold and drastic gradient changes are screened, and the airflow retention characteristics are analyzed in combination with the dynamic diffusion model to confirm whether there is potential airflow blockage; finally, a regional distribution map is generated.

6. The method for monitoring the drying state of straw based on multiple sensors according to claim 5, characterized in that: Step S3 includes the following contents: S3.1, integrating the obtained three-dimensional density distribution model and permeability distribution map, combined with the initial moisture content data of the straw pile, and in addition, collecting environmental parameter data; S3.2, based on density distribution , Permeability distribution and initial moisture content , construct regional drying demand prediction function , predict the specific drying requirements of each spatial point during the drying process, and the drying requirement prediction function formula is: ;in: For space point Drying demand index; It is the drying demand sensitivity parameter, reflecting the nonlinear relationship between moisture, density and permeability; is the environmental parameter influence coefficient, which adjusts the influence of temperature and humidity on drying requirements; is the base of natural logarithms; S3.3, optimizing the drying demand prediction function using a machine learning algorithm; S3.4, based on the optimized regional drying demand model, generate the target drying curve for each region ,in Indicates time.

7. The method for monitoring the drying state of straw based on multiple sensors according to claim 6, characterized in that: Step S4 includes the following contents: S4.1, analyze the target drying curve of each area and extract the temperature required for each area at different time points and air velocity Through spatial mapping technology, each area in the three-dimensional space corresponds to the specific control unit of the drying equipment, ensuring that each control unit can independently receive and execute the corresponding drying parameters; S4.2, based on the target drying curve, build an intelligent control algorithm and use a multivariable nonlinear optimization method to ensure that the dynamic adjustment of drying parameters can accurately match the needs of each area. The specific steps include: Establishment of multivariable optimization model: Establish a multivariable optimization model of temperature and airflow velocity. The model form is as follows: ;in: and The current area at time temperature and air velocity; and is the temperature and airflow velocity specified in the target drying curve; is the total drying time; Constraint setting: Set upper and lower limits for temperature and airflow speed based on the physical limitations and safety parameters of the equipment: ; ;in, and Indicates a point in space The minimum and maximum drying temperatures permitted at the site; Indicates a point in space The minimum and maximum air velocity allowed at the location; Application of optimization algorithm: Genetic algorithm or particle swarm optimization global optimization technology is used to solve the model and obtain the optimal temperature and airflow speed settings for each area at different time points. The obtained optimal temperature and airflow speed settings are distributed to the execution units of the corresponding areas in real time through the control module, and the operating parameters of the drying equipment are dynamically adjusted to match the target requirements of each area.

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