A method and system for determining the pre-wetting time of straw

Abstract

The present invention discloses a method and system for determining the pre-wetting time of straw, specifically relating to the field of intelligent control technology for agricultural waste treatment, and is used to solve the multi-objective conflict problem between the existing pre-wetting process parameters and the energy consumption of tunnel fermentation; by collecting the initial moisture content and bulk density distribution data of straw in real time, identifying the spatial distribution of high-density blocking areas and low-density permeation channels in the stacked layers, and generating the effective length reflecting the moisture penetration path; based on the dynamic weight fusion of the current batch and historical pre-wetting data, generating a pre-wetting time reference value, and making a collaborative correction in combination with the coupling relationship between the equipment idling time and the ventilation energy consumption; through the collaborative feedback mechanism of the main fiber direction of the straw stalks and the acoustic impedance characteristics of the stacked layers, dynamically adjusting the pre-wetting time threshold; finally, triggering a termination instruction according to the continuous and stable state of the surface moisture content and fiber orientation, achieving an accurate balance between energy consumption and process efficiency while improving the pre-wetting uniformity.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control for agricultural waste treatment, and more specifically, to a method and system for determining the pre-wetting time of rice straw. Background Art

[0002] As a key pretreatment link in the resource utilization of organic waste and the biological fermentation industrial chain, the setting of the process parameters of rice straw pre-wetting directly affects the stability of subsequent tunnel fermentation and the system energy consumption; the pre-wetting operation is mostly controlled based on fixed experience thresholds or single indicators. Especially in large-scale production scenarios, due to factors such as complex raw material sources and uneven stacking densities of rice straw, its moisture absorption characteristics and fermentation energy consumption requirements show a non-linear coupling relationship. The traditional pre-wetting process uses static time thresholds or manual experience judgment, resulting in difficulties in synergistically optimizing the pre-wetting quality and fermentation energy consumption.

[0003] In the prior art, the multi-objective conflict problem between the pre-wetting process parameters and the tunnel fermentation energy consumption has not been effectively solved: too short pre-wetting time is likely to cause a sharp increase in ventilation energy consumption during the fermentation stage (the material penetration resistance increases), and too long pre-wetting time will result in waste of equipment idling production capacity, forming a rigid contradiction between energy consumption control and process efficiency, restricting the overall energy efficiency improvement of the organic waste treatment system. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method and system for determining the pre-wetting time of rice straw to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for determining the pre-wetting time of rice straw, comprising the following steps:

[0007] S1. Collect real-time detection data of rice straw raw materials, including the initial moisture content and the stack density distribution data at different positions of the stacking layer;

[0008] S2. Identify the high-density blocking areas and low-density penetration channels in the stacking layer according to the stack density distribution data, and generate the effective moisture penetration length;

[0009] S3. Allocate the fusion weights of the real-time detection data and the historical pre-wetting data according to the similarity between the stack density distribution data of the current rice straw batch and the corresponding historical pre-wetting data; and generate the pre-wetting time reference value according to the effective moisture penetration length and the initial moisture content;

[0010] S4. Correct the pre-wetting time reference value based on the fusion weight to generate a pre-wetting time control strategy for synergistically optimizing the equipment idling time and ventilation energy consumption;

[0011] S5. Dynamically correct the output threshold of the pre-wetting time control strategy based on the synergistic feedback between the structural characteristics of the main fiber direction of the rice straw stalks and the physical response of the stacked layers;

[0012] S6. Generate a pre-wetting termination instruction when the moisture content on the surface of the rice straw is continuously stable and the fiber orientation of the rice straw stalks is stable.

[0013] In a preferred embodiment, collect real-time detection data of the rice straw raw materials including the initial moisture content and the bulk density distribution data at different positions of the stacked layers, including:

[0014] Use an infrared moisture detector with a preset near-infrared band to detect the initial moisture content of the rice straw raw materials in real time;

[0015] Measure the bulk density distribution data at different positions of the rice straw stacked layers through a pressure sensor array arranged according to a preset spatial distribution density, and the pressure sensor array covers the vertical stratification area and the horizontal distribution area of the stacked layers.

[0016] In a preferred embodiment, identify high-density blocking areas and low-density permeation channels in the stacked layers based on the bulk density distribution data, and generate an effective moisture penetration length, including:

[0017] Divide the bulk density distribution data at different positions of the stacked layers into several cubic grids in a three-dimensional coordinate system, and the side length of each cubic grid is a set distance;

[0018] Judge the blocking state of each cubic grid according to the bulk density distribution data: if the bulk density distribution data of the cubic grid is greater than the first set threshold, it is marked as a high-density blocking area; if the bulk density distribution data of the cubic grid is less than the second set threshold and is connected to the adjacent grid, it is marked as a low-density permeation channel;

[0019] Generate an effective moisture penetration length based on the spatial distribution relationship between the high-density blocking areas and the low-density permeation channels.

[0020] In a preferred embodiment, generate an effective moisture penetration length based on the spatial distribution relationship between the high-density blocking areas and the low-density permeation channels, including: identifying the boundary surface of the high-density blocking areas and the low-density permeation channels based on the bulk density gradient distribution in the vertical and horizontal directions of the stacked layers;

[0021] Combined with the angle between the main fiber arrangement direction of the rice straw stalks and the bulk density gradient direction, calculate the equivalent penetration resistance coefficient of the low-density permeation channels, and the equivalent penetration resistance coefficient is inversely proportional to the angle;

[0022] The connection weights between adjacent low-density infiltration channels are corrected according to the equivalent infiltration resistance coefficient to generate an optimal continuous infiltration path that avoids the high-density blocking area and is along the main fiber arrangement direction. The total length of the optimal continuous infiltration path is the effective moisture penetration length.

[0023] In a preferred embodiment, the fusion weight of the real-time detection data and the historical pre-wetting data is allocated according to the similarity between the bulk density distribution data of the current straw batch and the corresponding historical pre-wetting data, including:

[0024] A topological matching factor is generated based on the spatial distribution morphology of high-density blocking areas in the bulk density distribution data of the current rice straw batch and the morphological similarity with the historical pre-wetting data. The topological matching factor is corrected and the fusion weight is obtained by combining the synergy coefficient between the main fiber arrangement direction of the rice straw stalks and the bulk density gradient direction. The synergy coefficient is calculated by the cosine value of the angle between the main fiber arrangement direction and the bulk density gradient direction.

[0025] In a preferred embodiment, generating a pre-wetting time reference value according to the effective water penetration length and the initial water content includes:

[0026] Based on the fusion weight, the effective moisture penetration length in the historical prewetting data is weightedly screened, and the historical effective moisture penetration length consistent with the current bulk density gradient direction is selected as the benchmark parameter; the ratio of the current effective moisture penetration length to the benchmark parameter, and the ratio of the current initial moisture content to the historical benchmark moisture content are nonlinearly coupled to generate the prewetting time benchmark value.

[0027] In a preferred embodiment, the pre-wetting time reference value is corrected based on the fusion weight to generate a pre-wetting time control strategy for coordinated optimization of equipment idling time and ventilation energy consumption, including:

[0028] A correction coefficient is generated based on the correlation between the fusion weight and the idling time and ventilation energy consumption in the historical operation data of the equipment. The correlation is calibrated through the changing trend of the ratio of idling time to ventilation energy consumption in the historical data. The pre-wetting time baseline value is multiplied by the correction coefficient to obtain the preliminary optimization time. The ventilation energy consumption compensation amount is calculated based on the vertical temperature gradient distribution of the current stacking layer, and the ventilation energy consumption compensation amount increases linearly with the increase of the temperature gradient. The idle time increment corresponding to the preliminary optimization time and the ventilation energy consumption compensation amount is added to generate a pre-wetting time control strategy for the coordinated optimization of the equipment idling time and ventilation energy consumption.

[0029] In a preferred embodiment, based on the coordinated feedback of the structural characteristics of the main fiber direction of the straw stalk and the physical response of the stacked layer, the output threshold of the pre-wetting time control strategy is dynamically modified, including:

[0030] Detect the main fiber direction of the rice straw stalks, and determine the real-time included angle between the main fiber direction and the low-density penetration channel direction based on the spatial distribution of the low-density penetration channels;

[0031] Collect the acoustic impedance spectrum data of the stacked layers, extract the energy attenuation characteristics in the set frequency band, and generate a structural stability evaluation index by combining the historical fluctuation amplitude of the fusion weight;

[0032] When the real-time included angle is greater than the set angle threshold and the structural stability evaluation index is lower than the set stability threshold, trigger the enhanced correction mode and shorten the output threshold by the first set ratio;

[0033] When the real-time included angle is less than the set angle threshold and the structural stability evaluation index is higher than the set stability threshold, enable the conservative correction mode and extend the output threshold by the second set ratio.

[0034] In a preferred embodiment, when the moisture content on the surface of the rice straw is continuously stable and the fiber orientation of the rice straw stalks is stable, generate a pre-wetting termination instruction, including:

[0035] Real-time detect the change rate of the moisture content on the surface of the rice straw, and calculate the moisture content fluctuation amplitude within a continuously set time window;

[0036] Real-time detect the change rate of the fiber orientation of the rice straw stalks, and calculate the orientation angle fluctuation amplitude within a continuously set time window;

[0037] When the moisture content fluctuation amplitude is less than the set moisture content threshold and the orientation angle fluctuation amplitude is less than the set orientation threshold, determine that the stable condition is satisfied;

[0038] Compare the duration of satisfying the stable condition with the output threshold after dynamic correction. When the duration of satisfying the stable condition exceeds the output threshold after dynamic correction, generate a pre-wetting termination instruction.

[0039] On the other hand, the present invention provides a system for determining the pre-wetting time of rice straw, including:

[0040] Water-containing stacking and sampling module: Collect the real-time detection data of the rice straw raw materials, including the initial moisture content and the pile density distribution data at different positions of the stacked layers;

[0041] Blocking and penetration module: Identify the high-density blocking areas and low-density penetration channels in the stacked layers according to the pile density distribution data, and generate the effective moisture penetration length;

[0042] Weight reference module: Allocate the fusion weight of the real-time detection data and the historical pre-wetting data according to the similarity between the pile density distribution data of the current rice straw batch and the corresponding historical pre-wetting data; and generate a pre-wetting time reference value according to the effective moisture penetration length and the initial moisture content;

[0043] Idle energy consumption module: The pre-wetting time reference value is corrected based on the fusion weight, and a pre-wetting time control strategy is generated to coordinately optimize the equipment idling time and ventilation energy consumption;

[0044] Fiber IoT module: Based on the coordinated feedback of the structural characteristics of the main fiber direction of the straw stalk and the physical response of the stacked layer, the output threshold of the pre-wetting time control strategy is dynamically corrected;

[0045] Stable termination module: When the moisture content of the rice straw surface is continuously stable and the fiber orientation of the rice straw stem is stable, a pre-wetting termination instruction is generated.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. Through real-time collection of bulk density distribution and initial moisture content data, combined with dynamic identification of high-density blocking areas and low-density permeation channels, the permeation path analysis based on the structural characteristics of the material can accurately quantify the water penetration efficiency; at the same time, through the weight distribution mechanism of historical data and real-time detection, the pre-wetting time benchmark value is dynamically generated, which not only avoids the risk of misjudgment of a single data source, but also can adapt to the physical property fluctuations of different batches of raw materials; on this basis, by introducing a coordinated correction strategy for equipment idling and ventilation energy consumption, it ensures that the pre-wetting time achieves a dynamic balance between penetration adequacy and energy economy, alleviating the contradiction in the traditional process that too short a time leads to a surge in energy consumption and too long a time leads to idling waste.

[0048] 2. The fiber structure characteristics of rice straw stalks and the physical response of the stacking layer are incorporated into the dynamic correction system. By real-time monitoring of the spatial relationship between the main fiber direction and the stacking density gradient, combined with the physical feedback of the acoustic impedance spectrum, the implicit influence of the internal structure changes of the material on the water penetration can be dynamically perceived, thereby adaptively adjusting the prewetting threshold. This not only improves the environmental adaptability of the prewetting process, but also achieves accurate grasp of the termination timing through the dual stability judgment mechanism of moisture content and fiber orientation. Ultimately, while ensuring the uniformity of prewetting, the unit processing energy consumption is significantly reduced, and the reliability of efficient resource processing of organic waste is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a method for determining the pre-wetting time of rice straw according to the present invention;

[0050] Figure 2 The present invention is a schematic structural diagram of a straw pre-wetting time determination system. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1: Figure 1 A method for determining the pre-wetting time of rice straw according to the present invention is provided, which includes the following steps:

[0053] S1. Collect real-time detection data of rice straw raw materials, including the initial moisture content and the bulk density distribution data at different positions in the stacking layer;

[0054] S2. Identify the high-density blocking areas and low-density permeation channels in the stacking layer according to the bulk density distribution data, and generate the effective moisture penetration length;

[0055] S3. Assign the fusion weights of the real-time detection data and the historical pre-wetting data according to the similarity between the bulk density distribution data of the current rice straw batch and the corresponding historical pre-wetting data; and generate the pre-wetting time reference value according to the effective moisture penetration length and the initial moisture content;

[0056] S4. Correct the pre-wetting time reference value based on the fusion weights to generate a pre-wetting time control strategy that optimizes the equipment idling time and ventilation energy consumption;

[0057] S5. Dynamically correct the output threshold of the pre-wetting time control strategy based on the collaborative feedback of the structural characteristics of the main fiber direction of the rice straw stalk and the physical response of the stacking layer;

[0058] S6. Generate a pre-wetting termination instruction when the moisture content on the surface of the rice straw is continuously stable and the fiber orientation of the rice straw stalk is stable.

[0059] S1. Collect real-time detection data of rice straw raw materials, including the initial moisture content and the bulk density distribution data at different positions in the stacking layer, including:

[0060] The initial moisture content of the rice straw raw materials is detected in real time by an infrared moisture detector with a preset near-infrared band;

[0061] The bulk density distribution data at different positions in the rice straw stacking layer is measured by a pressure sensor array arranged according to a preset spatial distribution density, and the pressure sensor array covers the vertical stratification area and the horizontal distribution area of the stacking layer.

[0062] The range of the near-infrared band includes the interval with wavelengths from 1400 nm to 1500 nm. For example, the characteristic absorption peak of moisture in straw is detected at a wavelength of 1450 nm. The light source emission end and the receiving end of the detector are respectively located at the top and bottom of the stacked layer of straw raw materials, and the initial moisture content is calculated through reflection spectrum analysis.

[0063] The detection accuracy of the infrared moisture detector is calibrated through standard moisture content samples. The calibration samples include straw samples with different moisture contents, such as samples with moisture contents of 10%, 30%, and 50%. The detection error is controlled within the set range.

[0064] The layout method of the pressure sensor array is as follows: it is divided into multiple stratified areas in the vertical direction of the stacked layer, such as the upper, middle, and lower three layers. In each horizontal plane, the detection points are distributed according to a set number per square meter, such as at least two detection points per square meter. The measurement range of the pressure sensor array covers the density range of the straw stacked layer, such as from 0 kg / m³ to 500 kg / m³, and the measurement frequency is once per minute.

[0065] The measurement data of the pressure sensor array is summarized through wired or wireless transmission methods. The original pressure value is converted into bulk density data, and the conversion method is calculated based on the force-bearing area of the sensor and the acceleration due to gravity. For example, the bulk density is equal to the sensor pressure value divided by the product of the force-bearing area of the sensor and the acceleration due to gravity. The bulk density distribution data is stored in a three-dimensional coordinate system, where the X-axis and Y-axis of the coordinate system represent the horizontal plane position, and the Z-axis represents the vertical stratification position.

[0066] The detection data of the infrared moisture detector and the pressure sensor array are aligned through timestamps to ensure the synchronization of the acquisition time of the initial moisture content and the bulk density distribution data.

[0067] The preset spatial distribution density is dynamically adjusted according to the scale of the straw stacked layer. For example, when the height of the stacked layer exceeds the set threshold, the number of vertical stratifications increases; when the horizontal area exceeds the set threshold, the density of detection points on the horizontal plane increases. The layout position of the pressure sensor array avoids the edge area of the stacked layer. For example, the avoidance distance of the edge area is a set proportion of the total width of the stacked layer to avoid measurement errors caused by edge effects.

[0068] The detection data of the infrared moisture detector and the pressure sensor array need to pass data validity verification. The verification rules include: if the values of more than a set proportion of the detection points in the bulk density data at the same time point exceed the measurement range, it is determined that the sensor is abnormal and the device self-check program is triggered; if the fluctuation amplitude of the moisture content detection values for consecutive multiple times exceeds the set threshold, it is determined that there is environmental interference and the redundant detection mechanism is started to re-collect data.

[0069] S2. Identify high-density blocking areas and low-density infiltration channels in the stacked layer based on the bulk density distribution data, and generate the effective moisture infiltration length, including:

[0070] Divide the bulk density distribution data at different positions in the stacked layer into several cubic grids according to a three-dimensional coordinate system, with the side length of each cubic grid being a set distance.

[0071] Judge the blocking state of each cubic grid according to the bulk density distribution data: if the bulk density distribution data of the cubic grid is greater than the first set threshold, it is marked as a high-density blocking area; if the bulk density distribution data of the cubic grid is less than the second set threshold and is connected to the adjacent grid, it is marked as a low-density infiltration channel.

[0072] Generate the effective moisture infiltration length based on the spatial distribution relationship between the high-density blocking areas and the low-density infiltration channels, including:

[0073] Identify the boundary surfaces of the high-density blocking areas and the low-density infiltration channels based on the bulk density gradient distribution in the vertical and horizontal directions of the stacked layer.

[0074] Combine the angle between the main fiber arrangement direction of the rice straw stalks and the bulk density gradient direction to calculate the equivalent infiltration resistance coefficient of the low-density infiltration channels. The equivalent infiltration resistance coefficient is inversely proportional to the angle.

[0075] Modify the connection weights between adjacent low-density infiltration channels according to the equivalent infiltration resistance coefficient, and generate an optimal continuous infiltration path that avoids the high-density blocking areas and follows the main fiber arrangement direction. The total length of the optimal continuous infiltration path is the effective moisture infiltration length.

[0076] Divide the bulk density distribution data at different positions in the stacked layer into several cubic grids according to a three-dimensional coordinate system. The X-axis and Y-axis of the three-dimensional coordinate system represent the horizontal plane positions, and the Z-axis represents the vertical stratification position. The side length of each cubic grid is a set distance. The set distance is dynamically adjusted according to the overall scale of the stacked layer. For example, when the total height of the stacked layer is 3 meters, the side length of the cubic grid is set to 0.3 meters; when the total height of the stacked layer is 5 meters, the side length is adjusted to 0.5 meters. The cubic grids are generated layer by layer along the Z-axis direction from the bottom of the stacked layer, and the grids in each horizontal plane are arranged at equal intervals. After the grid division is completed, the center point coordinates of each cubic grid are associated and stored with the corresponding bulk density distribution data.

[0077] If the bulk density distribution data within a certain cubic grid is greater than the first set threshold, then mark this cubic grid as a high-density blockage area. The setting rule for the first set threshold is: determined by the bulk density critical value that causes a significant increase in the penetration resistance in the historical pre-wetting data. For example, statistically analyze the bulk density when the penetration resistance suddenly increases in the historical data, and take the mean plus twice the standard deviation as the first set threshold. When the statistical result of such critical values in the historical data is between 175 kg / m³ and 185 kg / m³, the first set threshold is set to 180 kg / m³.

[0078] If the bulk density distribution data within a certain cubic grid is less than the second set threshold, and the bulk density distribution data of this cubic grid in the horizontal or vertical direction is less than the second set threshold with at least one adjacent cubic grid, then mark this cubic grid as a low-density penetration channel. The setting rule for the second set threshold is: determined by the bulk density critical value when the penetration resistance significantly decreases in the historical data. For example, take the mean of the bulk density when the penetration resistance drops to the stable stage minus one standard deviation. When the statistical result is between 115 kg / m³ and 125 kg / m³, the second set threshold is set to 120 kg / m³. The determination method for adjacent cubic grids is: two cubic grids sharing a complete face are considered adjacent. For example, in the horizontal plane, the grids connected to the east, south, west, and north faces of a cubic grid are adjacent; in the vertical direction, the corresponding grids of the upper layer and the lower layer are adjacent.

[0079] Identify the boundary surface between the high-density blockage area and the low-density penetration channel based on the bulk density gradient distribution in the vertical and horizontal directions of the stacking layer. The calculation method for the bulk density gradient distribution is: for each cubic grid, calculate the change rate of the bulk density in the X, Y, and Z directions. For example, the bulk density gradient along the Z-axis direction (vertical) is (bulk density of the upper grid - bulk density of the current grid) / grid side length. When the change rate of the bulk density in a certain direction exceeds the set gradient threshold, it is determined that there is a significant gradient change in this direction. The set gradient threshold is determined according to the gradient statistical value when the penetration path suddenly changes in the historical data. For example, when the vertical gradient exceeds 5 kg / m³ / m or the horizontal gradient exceeds 3 kg / m³ / m, it is determined as a region with significant gradient change. The generation method for the boundary surface is: connect the outer surfaces of the cubic grids in the region with significant gradient change to form a continuous surface. For example, in the region with significant vertical gradient, extract the interface between the adjacent high-density blockage area and the low-density penetration channel as the surface.

[0080] Calculate the equivalent permeability resistance coefficient of the low-density permeation channels in combination with the angle between the main fiber arrangement direction of the rice straw stalks and the stacking density gradient direction. The detection method for the main fiber arrangement direction of the rice straw stalks is as follows: arrange a polarized light sensor array on the top of the stacking layer, emit polarized light to the surface of the rice straw stalks and receive the reflected light signal, and determine the average arrangement direction of the stalk fibers by analyzing the change in the polarization angle of the reflected light. For example, when the incident angle of the polarized light is 45 degrees, the offset of the polarization angle of the reflected light has a linear relationship with the fiber direction, and a mapping table between the offset and the actual fiber direction is established through a calibration experiment, so as to calculate the angle of the main fiber arrangement direction. The determination method for the stacking density gradient direction is: the direction from the cubic grid of the current low-density permeation channel to the adjacent high-density blocking area. For example, when the stacking density of the current grid is 110 kg / m³ and its adjacent grid on the east side is a high-density blocking area (stacking density 190 kg / m³), the gradient direction is eastward.

[0081] The corresponding relationship between the equivalent permeability resistance coefficient and the angle is calibrated through preliminary experiments: in a controllable experimental environment, measure the flow rate of water passing through the low-density permeation channels at different angles, and the reciprocal of the flow rate is normalized as the equivalent permeability resistance coefficient. For example, when the angle is 0 degrees (the fiber direction is consistent with the gradient direction), the equivalent permeability resistance coefficient is 1.0; when the angle is 30 degrees, the coefficient is 1.5; when the angle is 60 degrees, the coefficient is 2.0.

[0082] Correct the connection weights between adjacent low-density permeation channels according to the equivalent permeability resistance coefficient. The calculation method for the connection weight is: the weight value is equal to the reciprocal of the equivalent permeability resistance coefficient. For example, when the equivalent permeability resistance coefficients of two adjacent low-density permeation channels are 1.2 and 0.8 respectively, their connection weights are 0.83 and 1.25 respectively. The corrected connection weights are used to generate an optimal continuous permeation path that avoids high-density blocking areas and follows the main fiber arrangement direction.

[0083] The generation method for the optimal continuous permeation path is: from the bottom to the top of the stacking layer, use a path search algorithm to traverse all possible low-density permeation channels, and select the path with the largest sum of connection weights as the optimal path. For example, starting from a low-density permeation channel grid at the bottom, search for adjacent low-density channels in the east, south, west, north, and upper directions, recursively calculate the cumulative weights, and retain the path with the largest cumulative weight. All cubic grids marked as high-density blocking areas should be avoided during the path search process.

[0084] The total length of the optimal continuous infiltration path is the effective moisture infiltration length. The calculation method of the effective moisture infiltration length is as follows: count the number of all cube grids passed on the path and multiply it by the set side length of a single cube grid. For example, when the optimal path passes through 10 cube grids with a side length of 0.3 meters, the effective moisture infiltration length is 3 meters. The way to verify the continuity of the path is to check whether adjacent grids in the path meet the condition of sharing a complete surface. If there are jumps or interruptions, the path search is re-executed until a continuous path is generated.

[0085] S3. Assign the fusion weight of the real-time detection data and the historical pre-wetting data according to the similarity between the bulk density distribution data of the current straw batch and the corresponding historical pre-wetting data; and generate the pre-wetting time reference value according to the effective moisture infiltration length and the initial moisture content, including:

[0086] Generate a topological matching factor according to the morphological similarity between the spatial distribution pattern of the high-density blocking area in the bulk density distribution data of the current straw batch and the historical pre-wetting data;

[0087] Combine the coordination coefficient of the main fiber arrangement direction of the straw and the bulk density gradient direction to correct the topological matching factor to obtain the fusion weight;

[0088] The coordination coefficient is calculated by the cosine value of the angle between the main fiber arrangement direction and the bulk density gradient direction;

[0089] Based on the fusion weight, perform weighted screening on the effective moisture infiltration length in the historical pre-wetting data, and select the historical effective moisture infiltration length consistent with the current bulk density gradient direction as the reference parameter;

[0090] Non-linearly couple the ratio of the current effective moisture infiltration length to the reference parameter and the ratio of the current initial moisture content to the historical reference moisture content to generate the pre-wetting time reference value.

[0091] Generate a topological matching factor according to the morphological similarity between the spatial distribution pattern of the high-density blocking area in the bulk density distribution data of the current straw batch and the historical pre-wetting data. The analysis method of the spatial distribution pattern of the high-density blocking area is as follows: extract the three-dimensional contours of the high-density blocking areas in the current batch and historical data, and perform smoothing processing on the contours through morphological image processing methods and calculate the overlap degree.

[0092] The morphological image processing method includes using a structural element with a preset size to perform erosion operation on the contour of the high-density blocking area to remove isolated noise points, and then performing dilation operation to restore the main body contour. For example, the size of the structural element is set to one-third of the side length of the cube grid, and the high-density blocking areas with a continuous area volume greater than the set threshold are retained after the erosion operation.

[0093] The overlapping degree calculation method is as follows: spatially superimpose the contour of the high-density blocking area in the current batch with the three-dimensional contour model of historical data, and calculate the ratio of the volume of the overlapping part to the total volume of the historical contour. This ratio is the topological matching factor.

[0094] Modify the topological matching factor by combining the cooperation coefficient of the main fiber arrangement direction of the straw stalks and the stacking density gradient direction to obtain the fusion weight. The calculation method of the cooperation coefficient is: use a polarized light sensor array to detect the main fiber arrangement direction of the straw stalks at the top of the stacking layer in real time, and obtain the stacking density gradient direction data generated in step S2, and calculate the cosine value of the included angle between the two as the cooperation coefficient. For example, when the included angle between the main fiber direction and the stacking density gradient direction is 30 degrees, the cooperation coefficient is cos(30°) = 0.866.

[0095] The stacking density gradient direction is the vector direction from the low-density infiltration channel to the adjacent high-density blocking area, and its calculation method is to take the vector difference of the central point coordinates of the adjacent high-density blocking area minus the central point coordinates of the current low-density infiltration channel. The generation method of the fusion weight is: after multiplying the topological matching factor by the cooperation coefficient, linearly normalize it to the interval of 0 to 1. For example, if the topological matching factor is 0.8 and the cooperation coefficient is 0.9, the product is 0.72, and the normalized fusion weight is 0.8.

[0096] Based on the fusion weight, perform weighted screening on the effective moisture penetration length in the historical pre-wetting data, and select the historical effective moisture penetration length consistent with the current stacking density gradient direction as the reference parameter. The rule of weighted screening is: calculate the cosine value of the included angle between the stacking density gradient direction and the current direction in each historical data, and screen the historical data entries with cosine values greater than the set consistency threshold.

[0097] The set consistency threshold is determined by the statistical result of the direction matching degree when the pre-wetting effect is optimal in the historical data. For example, when the cosine values of the entries with the best direction matching degree in the historical data are all greater than 0.8, the set consistency threshold is 0.8.

[0098] The screened historical data are sorted from high to low according to the fusion weight, and the historical effective moisture penetration length with the highest weight value is selected as the reference parameter. If there is no historical data that meets the consistency threshold, the current effective moisture penetration length is directly used as the reference parameter.

[0099] Non-linearly couple the ratio of the current effective moisture penetration length to the reference parameter and the ratio of the current initial moisture content to the historical reference moisture content to generate the pre-wetting time reference value.

[0100] The rule of non - linear coupling is: take the square root of the product of two ratios, and then multiply by the basic pre - wetting time. For example, if the current effective moisture penetration length is 3 meters, the reference parameter is 2 meters, the current initial moisture content is 40%, and the historical reference moisture content is 35%, then the ratio is (3 / 2)×(35 / 40)=1.3125, the square root is approximately 1.145, multiply by the basic pre - wetting time of 20 minutes, and the reference value of the pre - wetting time is approximately 22.9 minutes.

[0101] The basic pre - wetting time is preset according to the characteristics of the rice straw variety. For example, the basic pre - wetting time for japonica rice is 20 minutes, and for indica rice is 18 minutes. The setting basis is the average value of the time required to reach the target moisture content in the laboratory pre - wetting experiment.

[0102] S4. Modify the reference value of the pre - wetting time based on the fusion weight to generate a pre - wetting time control strategy that optimizes the equipment idle time and ventilation energy consumption synergistically, including:

[0103] Generate a correction coefficient according to the correlation between the fusion weight and the idle time and ventilation energy consumption in the historical operation data of the equipment. The correlation is calibrated by the change trend of the ratio of the idle time to the ventilation energy consumption in the historical data;

[0104] Multiply the reference value of the pre - wetting time by the correction coefficient to obtain the preliminary optimized time;

[0105] Calculate the ventilation energy consumption compensation amount according to the vertical temperature gradient distribution of the current stacking layer. The ventilation energy consumption compensation amount increases linearly with the increase of the temperature gradient;

[0106] Add the preliminary optimized time and the idle time increment corresponding to the ventilation energy consumption compensation amount to generate a pre - wetting time control strategy that optimizes the equipment idle time and ventilation energy consumption synergistically.

[0107] Generate a correction coefficient according to the correlation between the fusion weight and the idle time and ventilation energy consumption in the historical operation data of the equipment. The fusion weight comes from the weight value generated synergistically based on the spatial distribution pattern of the high - density blocking area and the main fiber arrangement direction in step S3.

[0108] The correlation is calibrated by analyzing the change trend of the ratio of the idle time to the ventilation energy consumption in the historical data. The specific method is: screen the working condition data with the best pre - wetting effect in the historical database, calculate the average value of the ratio of its idle time to the ventilation energy consumption as the reference ratio, and count the deviation amplitude of the actual ratio relative to the reference ratio under different fusion weights. For example, when the fusion weight is high, if the deviation amplitude of the actual ratio in the historical data from the reference ratio is small, the correction coefficient approaches 1; if the deviation amplitude is large, the correction coefficient is adjusted proportionally.

[0109] The historical database contains multiple sets of historical pre-wetting data, and each set of data records the bulk density distribution, effective moisture penetration length, initial moisture content, and corresponding equipment operation parameters.

[0110] Multiply the pre-wetting time reference value by a correction factor to obtain the preliminary optimized time. The pre-wetting time reference value is a value generated by non-linearly coupling the ratio of the current effective moisture penetration length to the historical reference parameter and the ratio of the current initial moisture content to the historical reference moisture content in step S3. For example, if the pre-wetting time reference value is high and the correction factor is small, the preliminary optimized time is correspondingly shortened.

[0111] Calculate the ventilation energy consumption compensation amount according to the temperature gradient distribution in the vertical direction of the current stacking layer. The temperature gradient distribution data is collected in real time by the arranged temperature sensor array, and the sensor array is arranged in layers in the vertical direction of the stacking layer. The temperature gradient calculation method is: determine the temperature change rate per unit height according to the ratio of the temperature difference between adjacent layers in the vertical direction to the layer spacing. The calculation rule of the ventilation energy consumption compensation amount is: when the temperature gradient rises by a certain amplitude, the compensation amount increases proportionally according to the preset basic compensation value. For example, when the temperature gradient is high, the ventilation energy consumption rises significantly, and the idling time needs to be increased to balance the energy consumption.

[0112] The basic compensation value is calibrated by simulating the ventilation energy consumption changes under different temperature gradients in the laboratory to ensure that the compensation amount matches the energy consumption change trend.

[0113] Add the preliminary optimized time to the idling time increment corresponding to the ventilation energy consumption compensation amount to generate a pre-wetting time control strategy for the collaborative optimization of the equipment idling time and ventilation energy consumption. For example, when the preliminary optimized time is short but the temperature gradient is high, the compensation amount is large, and the final strategy time is correspondingly extended to reduce the ventilation energy consumption. The corresponding relationship between the idling time increment and the compensation amount is calibrated by the equipment operation parameters to ensure that the adjustment amount of the idling time is proportional to the energy consumption optimization target.

[0114] After generating the pre-wetting time control strategy, transmit the strategy parameters to the pre-wetting equipment controller in real time. The controller adjusts the operation duration of the water injection system and the start-stop cycle of the ventilation equipment according to the strategy value. The specific execution method is: perform the water injection operation according to the strategy value in the early stage of the pre-wetting stage, and dynamically fine-tune the water injection amount according to the change rate of the moisture content on the surface of the straw detected in real time in the later stage to ensure that the moisture content uniformity meets the standard. For example, if the strategy time is long and the moisture content fluctuation is small, the water injection rate can be appropriately reduced to reduce the waste of idling time.

[0115] S5. Based on the collaborative feedback of the structural characteristics of the main fiber direction of the straw stem and the physical response of the stacking layer, dynamically correct the output threshold of the pre-wetting time control strategy, including:

[0116] Detect the main fiber direction of the rice straw stem, and based on the spatial distribution of the low-density penetration channels, determine the real-time angle between the main fiber direction and the low-density penetration channel direction;

[0117] Collect the acoustic impedance spectrum data of the stacked layer, extract the energy attenuation characteristics of the set frequency band, and generate a structural stability evaluation index in combination with the historical fluctuation range of the fusion weight;

[0118] When the real-time angle is greater than the set angle threshold and the structural stability evaluation index is lower than the set stability threshold, trigger the enhanced correction mode and shorten the output threshold by the first set ratio;

[0119] When the real-time angle is less than the set angle threshold and the structural stability evaluation index is higher than the set stability threshold, enable the conservative correction mode and extend the output threshold by the second set ratio.

[0120] The main fiber direction of the rice straw stem is detected in real time by a polarized light sensor. The polarized light sensor array is arranged on the top of the stacked layer, emits polarized light to the surface of the rice straw stem at a preset incident angle, and receives the reflected light signal. The main fiber arrangement direction is determined by analyzing the polarization angle offset of the reflected light. For example, when the main fiber direction is consistent with the polarization direction of the incident light, the polarization angle offset of the reflected light is 0 degrees; when there is an angle, the offset increases linearly with the increase of the angle. The main fiber direction detection results are grouped by the vertical stratification area of the stacked layer, and an average direction angle is output for each group area. The detection frequency of the polarized light sensor is synchronized with the acquisition frequency of the stack density distribution data in step S1 to ensure that the data timestamps are aligned.

[0121] Based on the spatial distribution data of the low-density penetration channels generated in step S2, determine the real-time angle between the main fiber direction and the low-density penetration channel direction. The low-density penetration channel direction is the channel extension direction generated by the stack density gradient distribution analysis in step S2. For example, the horizontal extension direction of a certain low-density penetration channel is 30 degrees south by east. The calculation method of the real-time angle is: take the angle between the projections of the main fiber direction and the low-density penetration channel direction on the horizontal plane, and the angle range is 0 degrees to 90 degrees. For example, if the main fiber direction is 15 degrees north by east and the low-density penetration channel direction is 30 degrees south by east, the real-time angle is 45 degrees. The horizontal plane projection angle is calculated by vector direction decomposition and does not require a three-dimensional space geometric model.

[0122] Collect the acoustic impedance spectrum data of the stacked layer through the acoustic wave transmitting-receiving device. The acoustic wave transmitting-receiving device is arranged on both sides of the stacked layer, and the transmitting frequency covers the set frequency band defined in step S1 (for example, 100 Hz to 500 Hz). The receiving end records the energy attenuation data of the signal after passing through the stacked layer. The method for extracting the energy attenuation characteristics of the set frequency band is: statistically calculate the energy attenuation rate of each sub-interval within the set frequency band. For example, divide the frequency band into 10 equal-width sub-intervals and calculate the average value of the attenuation rate of each sub-interval. The energy attenuation rate is negatively correlated with the pore connectivity inside the stacked layer, and the higher the attenuation rate, the looser the structure. The energy attenuation characteristic data is aligned with the historical data of the fusion weights generated in step S3 through timestamps to ensure data synchronization.

[0123] Generate a structural stability evaluation index by combining the historical fluctuation amplitude of the fusion weights in step S3. The historical fluctuation amplitude is calculated by statistically calculating the standard deviation of the fusion weights within the past set time period. For example, statistically calculate the standard deviation of the fusion weights within the past 30 minutes. The larger the standard deviation, the higher the historical fluctuation amplitude.

[0124] The method for generating the structural stability evaluation index is: add the energy attenuation rate and the historical fluctuation amplitude according to the preset weights. For example, the energy attenuation rate accounts for 60% of the weight, and the historical fluctuation amplitude accounts for 40% of the weight. The weighted result is normalized to the interval of 0 to 1. The lower the value, the more unstable the structure. The weight ratio is calibrated by reverse deduction based on the parameter combination with the best pre-wetting effect in the historical data, without relying on an optimization algorithm.

[0125] When the real-time angle is greater than the set angle threshold and the structural stability evaluation index is lower than the set stability threshold, trigger the reinforcement correction mode. The set angle threshold is determined by the statistical value of the angle between the main fiber direction and the low-density penetration channel direction with the best pre-wetting effect in the historical data. For example, screen 50 groups of data with the lowest fermentation energy consumption in the historical data and calculate the average value of their angles plus twice the standard deviation as the threshold. The set stability threshold is determined by the 20% quantile value of the structural stability evaluation index in the historical data. For example, take the minimum value of the top 20% of the stability index rankings in the historical data as the threshold.

[0126] The operation method of the reinforcement correction mode is: shorten the output threshold by the first set ratio, and the first set ratio is dynamically adjusted according to the amplitude of the real-time angle exceeding the set angle threshold. For example, for every 1 degree that the angle exceeds the threshold, the shortening ratio increases by 0.5%.

[0127] When the real-time included angle is less than the set angle threshold and the structural stability evaluation index is higher than the set stability threshold, the conservative correction mode is enabled. The operation method of the conservative correction mode is as follows: extend the output threshold by the second set ratio, and the second set ratio is dynamically adjusted according to the amplitude by which the structural stability evaluation index exceeds the set stability threshold. For example, for every 0.1 by which the index exceeds the threshold, the extension ratio increases by 0.3%. The ranges of the first set ratio and the second set ratio are calibrated by the ratio statistical values when the correction effect is optimal in historical data. For example, in historical data, when the included angle exceeds the standard by 5 degrees, the shortening ratio of 8% gives the best effect.

[0128] The dynamically corrected output threshold is transmitted to the pre-wetting equipment controller in real time, and the controller adjusts the water injection rate and ventilation frequency during the pre-wetting stage according to the corrected output threshold. For example, when the output threshold is shortened, the controller increases the water injection rate to accelerate penetration; when the output threshold is extended, the water injection rate is decreased to avoid local over-wetting. The parameter execution logic of the controller is: execute a fixed water injection rate according to the corrected threshold in the early stage of the pre-wetting stage.

[0129] S6. When the moisture content on the surface of the straw is continuously stable and the fiber orientation of the straw stalks is stable, a pre-wetting termination instruction is generated, including:

[0130] Real-time detect the change rate of the moisture content on the surface of the straw, and calculate the fluctuation amplitude of the moisture content within a continuously set time window;

[0131] Real-time detect the change rate of the fiber orientation of the straw stalks, and calculate the fluctuation amplitude of the orientation angle within a continuously set time window;

[0132] When the fluctuation amplitude of the moisture content is less than the set moisture content threshold and the fluctuation amplitude of the orientation angle is less than the set orientation threshold, it is determined that the stable condition is met;

[0133] Based on the dynamically corrected output threshold, compare the duration when the stable condition is met with the dynamically corrected output threshold. When the duration when the stable condition is met exceeds the dynamically corrected output threshold, a pre-wetting termination instruction is generated.

[0134] The change rate of the moisture content on the surface of the straw is detected in real time by an infrared moisture detector. The detection frequency of the infrared moisture detector is the same as the set frequency of the initial moisture content detection in step S1, for example, once per minute. The calculation method of the change rate of the moisture content is as follows: count the moisture content detection values within a continuously set time window (for example, 5 minutes), and calculate its standard deviation as the fluctuation amplitude. For example, when the moisture content values detected within 5 minutes are 40.5%, 40.3%, 40.7%, 40.4%, and 40.6% respectively, the standard deviation is 0.15%, that is, the fluctuation amplitude is 0.15%. The length of the set time window is dynamically adjusted according to the scale of the straw stacking layer. For example, when the stack height exceeds 3 meters, the window is extended to 8 minutes.

[0135] The change rate of the fiber orientation of the rice straw stalk is detected in real time by a polarized light sensor array. The layout position of the polarized light sensor array is the same as that of the sensor array for detecting the main fiber direction in step S5, and the detection frequency is synchronized with the moisture content detection. The calculation method of the fiber orientation change rate is as follows: the detection values of the fiber direction angles within a continuously set time window (such as 5 minutes) are statistically analyzed, and the range is calculated as the fluctuation amplitude. For example, when the fiber direction angles detected within 5 minutes are 15 degrees, 16 degrees, 14 degrees, 15 degrees, and 17 degrees, the range is 3 degrees, that is, the fluctuation amplitude is 3 degrees. The range calculation method is the difference between the maximum value and the minimum value, ensuring that the calculation logic is simple and feasible.

[0136] When the fluctuation amplitude of the moisture content is less than the set moisture content threshold and the fluctuation amplitude of the orientation angle is less than the set orientation threshold, it is determined that the stable condition is satisfied. The set moisture content threshold is determined by the statistical value of the moisture content fluctuation when the pre-wetting effect is optimal in the historical data. For example, the average value of the fluctuation amplitude in the historical optimal data plus one standard deviation is taken as the threshold. The set orientation threshold is calibrated by the working condition data with stable fiber orientation in the historical data. For example, the minimum value of the fiber orientation fluctuation range in the historical data plus two standard deviations is taken as the threshold. For example, if the average value of the moisture content fluctuation amplitude in the historical optimal data is 0.2% and the standard deviation is 0.05%, the set moisture content threshold is 0.25%; if the average value of the fiber orientation fluctuation range is 2 degrees and the standard deviation is 1 degree, the set orientation threshold is 4 degrees.

[0137] Based on the dynamically corrected output threshold in step S5, the duration that satisfies the stable condition is compared with the dynamically corrected output threshold. The dynamically corrected output threshold is the adjusted pre-wetting time threshold generated in step S5. For example, the corrected threshold is 22.1 minutes. The duration that satisfies the stable condition is the time length continuously counted since the moisture content and the orientation fluctuation thresholds are simultaneously satisfied for the first time. The comparison method is as follows: when the duration exceeds the dynamically corrected output threshold, it is determined that the pre-wetting is completed. For example, if the dynamically corrected output threshold is 22.1 minutes and the cumulative duration that satisfies the stable condition reaches 23 minutes, a termination instruction is triggered.

[0138] A pre-wetting termination instruction is generated and the pre-wetting equipment is shut down. The termination instruction is transmitted to the water injection system and the ventilation equipment of the pre-wetting equipment through a control signal. The water injection system immediately stops water injection, and the ventilation equipment switches to a low-speed operation mode to maintain the basic ventilation requirement. For example, when the termination instruction is triggered, the water injection valve is completely closed, and the rotation speed of the ventilation motor drops from 1200 revolutions per minute to 300 revolutions per minute. The equipment shutdown logic needs to ensure that there is no accumulation of residual moisture. For example, after the water injection stops, the drain valve is opened for 10 seconds to drain the residual moisture in the pipeline.

[0139] Embodiment 2: Figure 2The structural schematic diagram of a rice straw pre-wetting time determination system according to the present invention is given. A rice straw pre-wetting time determination system includes:

[0140] Water-containing stacking and mining module: Collect real-time detection data of rice straw raw materials, including the initial moisture content and the pile density distribution data at different positions of the stacking layer;

[0141] Blocking and penetration module: Identify high-density blocking areas and low-density penetration channels in the stacking layer according to the pile density distribution data, and generate the effective moisture penetration length;

[0142] Weight reference module: Assign the fusion weight of the real-time detection data and the historical pre-wetting data according to the similarity between the pile density distribution data of the current rice straw batch and the corresponding historical pre-wetting data; and generate the pre-wetting time reference value according to the effective moisture penetration length and the initial moisture content;

[0143] Idle energy consumption module: Correct the pre-wetting time reference value based on the fusion weight, and generate a pre-wetting time control strategy for the coordinated optimization of the equipment idle time and ventilation energy consumption;

[0144] Fiber Internet of Things module: Dynamically correct the output threshold of the pre-wetting time control strategy based on the coordinated feedback of the structural characteristics of the main fiber direction of the rice straw stalk and the physical response of the stacking layer;

[0145] Stable termination module: Generate a pre-wetting termination instruction when the moisture content on the surface of the rice straw is continuously stable and the fiber orientation of the rice straw stalk is stable.

[0146] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by software simulation of collecting a large amount of data to obtain a formula closest to the real situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0147] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can be run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0148] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0149] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0150] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0151] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, in each embodiment of the present application, each functional module may be integrated into one processing module, may exist separately physically for each module, or two or more modules may be integrated into one module.

[0153] If the above-mentioned function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0154] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0155] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the pre-wetting time of straw, characterized in that, It includes the following steps: S1. Collect real-time detection data of straw raw materials, including the initial moisture content and the bulk density distribution data at different positions of the stacked layer; S2. Identify high-density blocking areas and low-density permeation channels in the stacked layer according to the bulk density distribution data, and generate the effective moisture penetration length; S3. Assign the fusion weight of the real-time detection data and the historical pre-wetting data according to the similarity between the bulk density distribution data of the current straw batch and the corresponding historical pre-wetting data; and generate the pre-wetting time reference value according to the effective moisture penetration length and the initial moisture content; S4. Correct the pre-wetting time reference value based on the fusion weight to generate a pre-wetting time control strategy for collaborative optimization of the equipment idle time and ventilation energy consumption; S5. Dynamically correct the output threshold of the pre-wetting time control strategy based on the collaborative feedback of the structural characteristics of the straw stalk main fiber direction and the physical response of the stacked layer, including: Detect the main fiber direction of the straw stalk, and determine the real-time included angle between the main fiber direction and the low-density permeation channel direction based on the spatial distribution of the low-density permeation channels; Collect the acoustic impedance spectrum data of the stacked layer, extract the energy attenuation characteristics of the set frequency band, and generate a structural stability evaluation index in combination with the historical fluctuation range of the fusion weight; When the real-time included angle is greater than the set angle threshold and the structural stability evaluation index is lower than the set stability threshold, trigger the enhanced correction mode and shorten the output threshold by the first set ratio; When the real-time included angle is less than the set angle threshold and the structural stability evaluation index is higher than the set stability threshold, enable the conservative correction mode and extend the output threshold by the second set ratio; S6. Generate a pre-wetting termination instruction when the surface moisture content of the straw is continuously stable and the fiber orientation of the straw stalk is stable.

2. The method for determining the pre-wetting time of straw according to claim 1, wherein Collect real-time detection data of straw raw materials including the initial moisture content and the bulk density distribution data at different positions of the stacked layer, including: Real-time detect the initial moisture content of the straw raw materials through an infrared moisture detector with a preset near-infrared band; Measure the bulk density distribution data at different positions of the straw stacked layer through a pressure sensor array arranged according to the preset spatial distribution density, and the pressure sensor array covers the vertical stratification area and the horizontal distribution area of the stacked layer.

3. The method for determining the pre-wetting time of straw according to claim 1, wherein Identify high-density blocking areas and low-density permeation channels in the stacked layer according to the bulk density distribution data, and generate the effective moisture penetration length, including: Divide the bulk density distribution data at different positions of the stacked layer into several cubic grids in a three-dimensional coordinate system, and the side length of each cubic grid is the set distance; Judge the blocking state of each cubic grid according to the bulk density distribution data: if the bulk density distribution data of the cubic grid is greater than the first set threshold, it is marked as a high-density blocking area, and if the bulk density distribution data of the cubic grid is less than the second set threshold and is connected to the adjacent grid, it is marked as a low-density permeation channel; Generate the effective moisture penetration length based on the spatial distribution relationship between the high-density blocking area and the low-density permeation channel.

4. The method for determining the pre-wetting time of straw according to claim 3, characterized in that, Generate the effective moisture penetration length based on the spatial distribution relationship between the high-density blocking area and the low-density permeation channel, including: identifying the boundary surface of the high-density blocking area and the low-density permeation channel based on the bulk density gradient distribution in the vertical and horizontal directions of the stacked layer; Combined with the angle between the main fiber arrangement direction of the rice straw stalk and the direction of the bulk density gradient, calculate the equivalent permeability resistance coefficient of the low-density permeation channels. The equivalent permeability resistance coefficient is inversely proportional to the angle. Modify the connection weight between adjacent low-density permeation channels according to the equivalent permeability resistance coefficient, and generate an optimal continuous permeation path that avoids high-density blocking areas and is along the main fiber arrangement direction. The total length of the optimal continuous permeation path is the effective moisture permeation length.

5. A method for determining the pre-wetting time of straw according to claim 1, characterized in that, Allocate the fusion weight of the real-time detection data and the historical pre-wetting data according to the similarity between the bulk density distribution data of the current rice straw batch and the corresponding historical pre-wetting data, including: Generate a topological matching factor according to the morphological similarity between the spatial distribution pattern of the high-density blocking areas in the bulk density distribution data of the current rice straw batch and the pattern of the historical pre-wetting data. Combine the cooperation coefficient between the main fiber arrangement direction of the rice straw stalk and the direction of the bulk density gradient to modify the topological matching factor to obtain the fusion weight. The cooperation coefficient is calculated by the cosine value of the angle between the main fiber arrangement direction and the direction of the bulk density gradient.

6. A method for determining the pre-wetting time of straw according to claim 1, characterized in that, Generate a pre-wetting time reference value according to the effective moisture permeation length and the initial moisture content, including: Based on the fusion weight, perform weighted screening on the effective moisture permeation lengths in the historical pre-wetting data, and select the historical effective moisture permeation length that is consistent with the current bulk density gradient direction as the reference parameter. Non-linearly couple the ratio of the current effective moisture permeation length to the reference parameter and the ratio of the current initial moisture content to the historical reference moisture content to generate the pre-wetting time reference value.

7. A method for determining the pre-wetting time of straw according to claim 1, characterized in that, Modify the pre-wetting time reference value based on the fusion weight to generate a pre-wetting time control strategy that co-optimizes the idling time and ventilation energy consumption of the equipment, including: Generate a correction coefficient according to the correlation between the fusion weight and the idling time and ventilation energy consumption in the historical operation data of the equipment. The correlation is calibrated by the change trend of the ratio of the idling time to the ventilation energy consumption in the historical data. Multiply the pre-wetting time reference value by the correction coefficient to obtain the preliminary optimized time. Calculate the ventilation energy consumption compensation amount according to the temperature gradient distribution in the vertical direction of the current stacked layer. The ventilation energy consumption compensation amount increases linearly with the increase of the temperature gradient. Add the preliminary optimized time and the idling time increment corresponding to the ventilation energy consumption compensation amount to generate a pre-wetting time control strategy that co-optimizes the idling time and ventilation energy consumption of the equipment.

8. A method for determining the pre-wetting time of straw according to claim 1, characterized in that When the moisture content on the surface of the rice straw is continuously stable and the fiber orientation of the rice straw stalk is stable, generate a pre-wetting termination instruction, including: Real-time detect the change rate of the moisture content on the surface of the rice straw, and calculate the moisture content fluctuation amplitude within a continuously set time window; Real-time detect the change rate of the fiber orientation of the rice straw stalk, and calculate the orientation angle fluctuation amplitude within a continuously set time window; When the moisture content fluctuation amplitude is less than the set moisture content threshold and the orientation angle fluctuation amplitude is less than the set orientation threshold, it is determined that the stable condition is met; Compare the duration of meeting the stable condition with the dynamically corrected output threshold. When the duration of meeting the stable condition exceeds the dynamically corrected output threshold, generate a pre-wetting termination instruction.

9. A rice straw pre-wetting time determination system for implementing the rice straw pre-wetting time determination method according to any one of claims 1-8, characterized in that, Including: Water-containing stacking and sampling module: Collect real-time detection data of the rice straw raw material, including the initial moisture content and the bulk density distribution data at different positions of the stacked layer; Blocking and Penetration Module: Identify high-density blocking areas and low-density penetration channels in the stacked layer based on the bulk density distribution data, and generate the effective moisture penetration length; Weight Benchmark Module: Assign the fusion weight of the real-time detection data and the historical pre-wetting data according to the similarity between the bulk density distribution data of the current straw batch and the corresponding historical pre-wetting data; and generate the pre-wetting time benchmark value according to the effective moisture penetration length and the initial moisture content; Idle Energy Consumption Module: Correct the pre-wetting time benchmark value based on the fusion weight, and generate the pre-wetting time control strategy for the collaborative optimization of the equipment idle time and ventilation energy consumption; Fiber Internet of Things Module: Dynamically correct the output threshold of the pre-wetting time control strategy based on the collaborative feedback of the structural characteristics of the main fiber direction of the straw stalk and the physical response of the stacked layer; Stable Termination Module: Generate the pre-wetting termination instruction when the moisture content on the straw surface is continuously stable and the fiber orientation of the straw stalk is stable.

Images