Method and system for judging pre-wetting time of straw

Abstract

The invention discloses a straw pre-wetting time judgment method and system, particularly relates to the technical field of agricultural waste treatment intelligent control, and is used for solving the problem of multi-target conflict between existing pre-wetting process parameters and tunnel fermentation energy consumption. The method comprises the following steps: acquiring initial moisture content and bulk density distribution data of straw in real time, identifying spatial distribution of a high-density blocking region and a low-density permeation channel in a stacking layer, and generating an effective length reflecting a moisture permeation path; generating a pre-wetting time reference value based on dynamic weight fusion of the current batch and historical pre-wetting data, and performing collaborative correction in combination with a coupling relationship between equipment idling time and ventilation energy consumption; the pre-wetting time threshold value is dynamically adjusted through a cooperative feedback mechanism of the main fiber direction of the straw stalks and the acoustic impedance characteristics of the stacking layers; finally, a termination instruction is triggered according to the continuous stable state of the surface layer moisture content and the fiber orientation, and precise balance between energy consumption and process efficiency is achieved while the pre-wetting uniformity is improved.

Description

Technical Field

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

[0002] Straw prewetting is a key pretreatment link in the resource utilization of organic waste and the bio-fermentation industry chain. The setting of its process parameters directly affects the stability of subsequent tunnel fermentation and the energy consumption of the system. Prewetting operations are mostly controlled based on fixed empirical thresholds or single indicators. Especially in large-scale production scenarios, due to factors such as complex raw material sources and uneven stacking density, the hygroscopic characteristics of straw show a nonlinear coupling relationship with the fermentation energy demand. The traditional prewetting process uses static time thresholds or manual experience judgment, which makes it difficult to coordinately optimize the prewetting quality and fermentation energy consumption.

[0003] In the existing technology, the multi-objective conflict problem between pre-wetting process parameters and tunnel fermentation energy consumption has not been effectively solved: if the pre-wetting time is too short, it will easily lead to a surge in ventilation energy consumption during the fermentation stage (increased material penetration resistance), and if the pre-wetting time is too long, it will cause equipment idling and waste of production capacity, resulting in a rigid contradiction between energy consumption control and process efficiency, which restricts 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, an embodiment of the present invention provides a straw pre-wetting time determination method and system thereof to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for determining straw pre-wetting time comprises the following steps: S1. Collecting real-time detection data of rice straw raw materials, including initial moisture content and bulk density distribution data at different positions of the stacking layer; S2. identifying high-density blocking areas and low-density penetration channels in the stacked layer according to the bulk density distribution data, and generating an effective moisture penetration length; S3. Allocate a 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 a pre-wetting time reference value according to the effective water 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 coordinated optimization of equipment idling time and ventilation energy consumption; S5. 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; S6. When the moisture content of the rice straw surface is continuously stable and the orientation of the rice straw stalk fibers is stable, a pre-wetting termination instruction is generated.

[0006] In a preferred embodiment, collecting real-time detection data of the straw raw material including initial moisture content and bulk density distribution data at different positions of the stacking layer includes: The initial moisture content of the straw raw material is detected in real time by using an infrared moisture detector with a preset near-infrared band; The stacking density distribution data of the straw stacking layer at different positions is measured by means of 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.

[0007] In a preferred embodiment, the high-density blocking area and the low-density permeation channel in the stacked layer are identified according to the bulk density distribution data, and the effective moisture permeation length is generated, including: Divide the stacking density distribution data at different positions of the stacking layer into a number of cubic grids according to the three-dimensional coordinate system, and the side length of each cubic grid is the set distance; The blocking state of each cube grid is determined according to the stacking density distribution data: if the stacking density distribution data of the cube grid is greater than the first set threshold, it is marked as a high-density blocking area; if the stacking density distribution data of the cube grid is less than the second set threshold and is connected with the adjacent grid, it is marked as a low-density permeation channel; The effective moisture penetration length is generated based on the spatial distribution relationship between high-density blocking areas and low-density infiltration channels.

[0008] In a preferred embodiment, generating the effective water penetration length based on the spatial distribution relationship between the high-density blocking area and the low-density permeation channel includes: identifying the boundary surface between the high-density blocking area and the low-density permeation channel based on the stacking density gradient distribution in the vertical direction and the horizontal direction of the stacked layer; The equivalent permeability resistance coefficient of the low-density permeability channel is calculated based on the angle between the main fiber arrangement direction of the rice straw stalk and the bulk density gradient direction. The equivalent permeability resistance coefficient is inversely proportional to the angle. 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.

[0009] 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: 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.

[0010] In a preferred embodiment, generating a pre-wetting time reference value according to the effective water penetration length and the initial water content includes: 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.

[0011] 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: 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.

[0012] 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: Detect the main fiber direction of the straw stalk, and determine the real-time angle between the main fiber direction and the low-density infiltration channel direction based on the spatial distribution of the low-density infiltration channel; Collect the acoustic impedance spectrum data of the stacked layers, extract the energy attenuation characteristics of the set frequency band, and generate the structural stability evaluation index by combining the historical fluctuation amplitude of the fusion weight; 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, the enhanced correction mode is triggered to shorten the output threshold by a first set ratio; 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, the conservative correction mode is enabled to extend the output threshold by a second set ratio.

[0013] In a preferred embodiment, when the moisture content of the surface layer of the rice straw is continuously stable and the orientation of the fiber of the rice straw stalk is stable, a pre-wetting termination instruction is generated, including: Real-time detection of the change rate of moisture content on the surface of the straw, and calculation of the fluctuation range of moisture content within a continuously set time window; Real-time detection of the change rate of the fiber orientation of the rice straw stalks and calculation of the fluctuation amplitude of the orientation angle within a continuously set time window; When the fluctuation range of the moisture content is less than the set moisture content threshold and the fluctuation range of the orientation angle is less than the set orientation threshold, it is determined that the stability condition is met; The duration of satisfying the stability condition is compared with the output threshold after dynamic correction, and when the duration of satisfying the stability condition exceeds the output threshold after dynamic correction, a pre-wetting termination instruction is generated.

[0014] In another aspect, the present invention provides a straw pre-wetting time determination system, comprising: Moisture content pile collection module: collects real-time detection data of rice straw raw materials, including initial moisture content and pile density distribution data at different positions of the stacking layer; Blockage 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 effective moisture penetration length; Weight reference module: assigns the fusion weight of real-time detection data and 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 generates the pre-wetting time reference value according to the effective water penetration length and the initial moisture content; 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; 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; 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.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 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.

[0016] 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

[0017] Figure 1 This is a flow chart of a method for determining the pre-wetting time of rice straw according to the present invention; Figure 2 The present invention is a schematic structural diagram of a straw pre-wetting time determination system. DETAILED DESCRIPTION

[0018] 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.

[0019] Embodiment 1: Figure 1 A method for determining the pre-wetting time of rice straw is provided, which comprises the following steps: S1. Collecting real-time detection data of rice straw raw materials, including initial moisture content and bulk density distribution data at different positions of the stacking layer; S2. identifying high-density blocking areas and low-density penetration channels in the stacked layer according to the bulk density distribution data, and generating an effective moisture penetration length; S3. Allocate a 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 a pre-wetting time reference value according to the effective water 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 coordinated optimization of equipment idling time and ventilation energy consumption; S5. 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; S6. When the moisture content of the rice straw surface is continuously stable and the orientation of the rice straw stalk fibers is stable, a pre-wetting termination instruction is generated.

[0020] S1. Collect real-time detection data of rice straw raw materials, including initial moisture content and bulk density distribution data at different positions of the stacking layer, including: The initial moisture content of the straw raw material is detected in real time by using an infrared moisture detector with a preset near-infrared band; The stacking density distribution data of the straw stacking layer at different positions is measured by means of 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.

[0021] The near infrared band ranges from 1400nm to 1500nm. For example, the characteristic absorption peak of moisture in rice straw is detected at a wavelength of 1450nm. The light source transmitting end and receiving end of the detector are located at the top and bottom of the stacked layer of rice straw raw materials, respectively, and the initial moisture content is calculated by reflectance spectrum analysis.

[0022] 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.

[0023] The arrangement of the pressure sensor array is as follows: the stacking layer is divided into multiple layered areas in the vertical direction, such as the upper, middle and lower layers, and each layer is distributed with a set number of detection points per square meter in the horizontal plane, such as at least two detection points per square meter. The range of the pressure sensor array covers the density range of the straw stacking layer, such as 0kg / m³ to 500kg / m³, and the measurement frequency is once per minute.

[0024] The measurement data of the pressure sensor array is aggregated through wired or wireless transmission, and the raw pressure value is converted into bulk density data. The conversion method is calculated based on the sensor force area and gravity acceleration. For example, the bulk density is equal to the sensor pressure value divided by the product of the sensor force area and gravity acceleration. 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 layer position.

[0025] The detection data of the infrared moisture detector and the pressure sensor array are aligned through time stamps to ensure that the collection time of the initial moisture content and the bulk density distribution data are synchronized.

[0026] The preset spatial distribution density is dynamically adjusted according to the size of the straw stacking layer. For example, when the stacking layer height exceeds the set threshold, the number of layers in the vertical direction increases; when the horizontal area exceeds the set threshold, the density of horizontal detection points increases. The layout of the pressure sensor array avoids the edge area of ​​the stacking layer. For example, the avoidance distance of the edge area is a set proportion of the total width of the stacking layer to avoid measurement errors caused by edge effects.

[0027] The detection data of the infrared moisture detector and the pressure sensor array must pass the data validity verification. The verification rules include: if the detection point values ​​exceeding the set proportion in the bulk density data at the same time point exceed the measuring range, it is judged as a sensor abnormality and the equipment self-test program is triggered; if the fluctuation amplitude of the moisture content detection values ​​exceeds the set threshold for multiple consecutive times, it is judged as environmental interference and the redundant detection mechanism is activated to re-collect data.

[0028] S2. Identify high-density blocking areas and low-density penetration channels in the stacked layer based on the bulk density distribution data, and generate effective moisture penetration length, including: Divide the stacking density distribution data at different positions of the stacking layer into a number of cubic grids according to the three-dimensional coordinate system, and the side length of each cubic grid is the set distance; The blocking state of each cube grid is determined according to the stacking density distribution data: if the stacking density distribution data of the cube grid is greater than the first set threshold, it is marked as a high-density blocking area; if the stacking density distribution data of the cube grid is less than the second set threshold and is connected with the adjacent grid, it is marked as a low-density permeation channel; The effective water penetration length is generated based on the spatial distribution relationship between high-density blocking areas and low-density infiltration channels, including: Based on the stacking density gradient distribution in the vertical and horizontal directions of the stacked layers, the boundary surface between the high-density blocking area and the low-density permeation channel is identified; The equivalent permeability resistance coefficient of the low-density permeability channel is calculated based on the angle between the main fiber arrangement direction of the rice straw stalk and the bulk density gradient direction. The equivalent permeability resistance coefficient is inversely proportional to the angle. 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.

[0029] The stacking density distribution data at different positions of the stacking layer are divided into several cubic grids according to the three-dimensional coordinate system. The X-axis and Y-axis of the three-dimensional coordinate system represent the horizontal plane position, the Z-axis represents the vertical layer position, and the side length of each cubic grid is the set distance. The set distance is dynamically adjusted according to the overall scale of the stacking layer. For example, when the total height of the stacking layer is 3 meters, the side length of the cubic grid is set to 0.3 meters, and when the total height of the stacking layer is 5 meters, the side length is adjusted to 0.5 meters. The cubic grid is divided into layers along the Z-axis direction from the bottom of the stacking layer. The grids in each horizontal plane are arranged at equal intervals. After the grid division is completed, the coordinates of the center point of each cubic grid are associated with its corresponding stacking density distribution data and stored.

[0030] If the bulk density distribution data in a certain cubic grid is greater than the first set threshold, the cubic grid is marked as a high-density blocking area. The setting rule of the first set threshold is: determined by the critical value of the bulk density that causes a significant increase in the penetration resistance in the historical pre-wetting data, for example, the bulk density when the penetration resistance suddenly increases in the historical data is statistically analyzed, and the mean plus two times the standard deviation is taken as the first set threshold. When the statistical result of such critical values ​​in the historical data is 175kg / m³ to 185kg / m³, the first set threshold is set to 180kg / m³.

[0031] If the bulk density distribution data in a certain cubic grid is less than the second set threshold, and the bulk density distribution data of the cubic grid and at least one adjacent cubic grid in the horizontal or vertical direction are both less than the second set threshold, the cubic grid is marked as a low-density permeability channel. The setting rule of the second set threshold is: determined by the critical value of the bulk density when the permeability resistance is significantly reduced in the historical data, for example, the mean of the bulk density when the permeability resistance drops to the stable stage minus one standard deviation, when the statistical result is 115kg / m³ to 125kg / m³, the second set threshold is set to 120kg / m³. The judgment 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 by the east, south, west, and north faces of a cubic grid are adjacent; in the vertical direction, the grids corresponding to the upper and lower layers are adjacent.

[0032] Based on the stacking density gradient distribution in the vertical and horizontal directions of the stacked layer, the boundary surface of the high-density blocking area and the low-density permeability channel is identified. The calculation method of the stacking density gradient distribution is: for each cubic grid, calculate its stacking density change rate in the three directions of X, Y, and Z. For example, the stacking density gradient along the Z axis (vertical) is (upper grid stacking density-current grid stacking density) / grid side length. When the stacking density change rate in a certain direction exceeds the set gradient threshold, it is determined that there is a significant gradient change in that direction. The setting gradient threshold is determined based on the gradient statistics when the permeation path suddenly changes in the historical data. For example, when the vertical gradient exceeds 5kg / m³ / m or the horizontal gradient exceeds 3kg / m³ / m, it is determined to be a gradient significant change area. The generation method of the boundary surface is: connect the outer surfaces of the cubic grids in the area of ​​significant gradient change to form a continuous surface. For example, in the area of ​​significant vertical gradient, the interface between the adjacent high-density blocking area and the low-density permeability channel is extracted as a surface.

[0033] The equivalent permeability resistance coefficient of the low-density infiltration channel is calculated by combining the angle between the arrangement direction of the main fibers of the straw stalks and the direction of the bulk density gradient. The detection method of the arrangement direction of the main fibers of the straw stalks is as follows: a polarized light sensor array is arranged on the top of the stacking layer, polarized light is emitted to the surface of the straw stalks and the reflected light signal is received, and the average arrangement direction of the stalk fibers is determined 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 polarization angle offset of the reflected light is linearly related to the fiber direction. A mapping table of the offset and the actual fiber direction is established through calibration experiments to calculate the angle of the main fiber arrangement direction. The bulk density gradient direction is determined by pointing from the current low-density infiltration channel's cubic grid to the adjacent high-density blocking area. For example, when the bulk density of the current grid is 110kg / m³, and the adjacent grid on its east side is a high-density blocking area (bulk density 190kg / m³), the gradient direction is east.

[0034] The corresponding relationship between the equivalent permeability resistance coefficient and the angle is calibrated by pre-experiment: in a controllable experimental environment, the flow rate of water through the low-density permeation channel at different angles is measured, 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.

[0035] The connection weights between adjacent low-density permeation channels are corrected according to the equivalent permeability resistance coefficient. The connection weight is calculated as follows: the weight value is equal to the inverse 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 the optimal continuous permeation path that avoids the high-density blocking area and is along the main fiber arrangement direction.

[0036] The optimal continuous permeation path is generated by using a path search algorithm to traverse all possible low-density permeation channels from the bottom to the top of the stacked layer, and selecting the path with the largest total connection weight 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 weight, and retain the path with the largest cumulative weight. During the path search process, all cube grids marked as high-density blocking areas must be avoided.

[0037] The total length of the optimal continuous infiltration path is the effective moisture infiltration length. The effective moisture infiltration length is calculated by counting the number of all cube grids passed on the path and multiplying 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 continuity of the path is verified by checking whether the adjacent grids in the path meet the conditions of sharing a complete surface. If there is a jump or interruption, the path search is re-executed until a continuous path is generated.

[0038] S3. 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 straw batch and the corresponding historical pre-wetting data; and generate the pre-wetting time reference value according to the effective water penetration length and the initial moisture content, including: A topological matching factor is generated according to the spatial distribution morphology of the high-density blocking area in the bulk density distribution data of the current straw batch and the morphology similarity of the historical pre-wetting data; Combining the synergy coefficient between the arrangement direction of the main fibers of the rice straw and the bulk density gradient direction, the topological matching factor is modified to obtain the fusion weight. The synergy coefficient is calculated by the cosine value of the angle between the main fiber arrangement direction and the bulk density gradient direction; The effective water penetration length in the historical pre-wetting data is weighted and screened based on the fusion weight, and the historical effective water 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 a prewetting time benchmark value.

[0039] The topological matching factor is generated based on the spatial distribution morphology of the high-density blocking area in the current straw batch density distribution data and the morphological similarity of the historical pre-wetting data. The spatial distribution morphology of the high-density blocking area is analyzed by extracting the three-dimensional contours of the high-density blocking area in the current batch and historical data, smoothing the contours through morphological image processing methods, and calculating the overlap.

[0040] The morphological image processing method includes using a structure element of a preset size to perform an erosion operation on the contour of the high-density blocked area to remove isolated noise points, and then performing an expansion operation to restore the main contour. For example, the size of the structure element is set to one-third of the side length of the cube grid, and after the erosion operation, the high-density blocked area with a continuous area volume greater than a set threshold is retained.

[0041] The overlap degree is calculated by spatially superimposing the contours of the high-density blocked areas of the current batch with the three-dimensional contour model of the historical data, and counting the proportion of the volume of the overlapping part to the total volume of the historical contours. This proportion is the topological matching factor.

[0042] The topological matching factor is corrected by combining the synergy coefficient of the main fiber arrangement direction of the straw stalk and the bulk density gradient direction to obtain the fusion weight. The synergy coefficient is calculated by real-time detection of the main fiber arrangement direction of the straw stalk at the top of the stacked layer through the polarized light sensor array, and obtaining the bulk density gradient direction data generated in step S2, and calculating the cosine value of the angle between the two as the synergy coefficient. For example, when the angle between the main fiber direction and the bulk density gradient direction is 30 degrees, the synergy coefficient is cos(30°)=0.866.

[0043] The direction of the bulk density gradient is the vector direction from the low-density permeability channel to the adjacent high-density blocking area. It is calculated by taking the vector difference of the coordinates of the center point of the adjacent high-density blocking area minus the coordinates of the center point of the current low-density permeability channel. The fusion weight is generated by multiplying the topological matching factor by the synergy coefficient and mapping it to the interval of 0 to 1 through linear normalization. For example, if the topological matching factor is 0.8 and the synergy coefficient is 0.9, the product is 0.72, and the fusion weight after normalization is 0.8.

[0044] The effective water penetration length in the historical pre-wetting data is weighted and screened based on the fusion weight, and the historical effective water penetration length that is consistent with the current bulk density gradient direction is selected as the benchmark parameter. The weighted screening rule is: calculate the cosine value of the angle between the bulk density gradient direction and the current direction in each historical data, and screen the historical data entries whose cosine value is greater than the set consistency threshold.

[0045] The consistency threshold is set to be determined by the statistical results of the directional matching degree when the pre-wetting effect is optimal in the historical data. For example, when the cosine values ​​of the entries with the optimal directional matching degree in the historical data are all greater than 0.8, the consistency threshold is set to 0.8.

[0046] The filtered historical data are sorted from high to low according to the fusion weight, and the historical effective water penetration length with the highest weight value is selected as the benchmark parameter. If there is no historical data that meets the consistency threshold, the current effective water penetration length is directly used as the benchmark parameter.

[0047] 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 a prewetting time benchmark value.

[0048] The rule of nonlinear coupling is: take the square root of the product of the two ratios and multiply it by the basic prewetting time. For example, if the current effective water penetration length is 3 meters, the benchmark parameter is 2 meters, the current initial moisture content is 40%, and the historical benchmark moisture content is 35%, then the ratio is (3 / 2) × (35 / 40) = 1.3125, and the square root is about 1.145, multiplied by the basic prewetting time of 20 minutes, the prewetting time benchmark value is about 22.9 minutes.

[0049] The basic pre-wetting time is pre-set according to the characteristics of the rice straw variety. For example, the basic pre-wetting time for japonica rice is 20 minutes, and that for indica rice is 18 minutes. The setting basis is the average value of the time required to reach the target moisture content in laboratory pre-wetting experiments.

[0050] S4. Based on the fusion weight, the pre-wetting time reference value is corrected to generate a pre-wetting time control strategy for coordinated optimization of equipment idling time and ventilation energy consumption, including: Generate a correction coefficient 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 by the changing trend of the ratio of idling time to ventilation energy consumption in the historical data. Multiply the pre-wetting time benchmark value by the correction factor to obtain the initial optimization time; The ventilation energy consumption compensation is calculated according to the temperature gradient distribution in the vertical direction of the current stacking layer. The ventilation energy consumption compensation increases linearly with the increase of the temperature gradient. The initial optimization time is added to the idling time increment corresponding to the ventilation energy consumption compensation amount to generate a pre-wetting time control strategy for the coordinated optimization of equipment idling time and ventilation energy consumption.

[0051] The correction coefficient is generated 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 fusion weight comes from the weight value generated in step S3 based on the spatial distribution morphology of the high-density blocking area and the main fiber arrangement direction.

[0052] The correlation relationship is calibrated by analyzing the changing trend of the ratio of idling time to ventilation energy consumption in historical data. The specific method is as follows: the operating condition data with the best pre-wetting effect is selected from the historical database, and the average value of the ratio of idling time to ventilation energy consumption is calculated as the benchmark ratio, and the deviation of the actual ratio from the benchmark ratio under different fusion weights is counted. For example, when the fusion weight is high, if the actual ratio in the historical data deviates less from the benchmark ratio, the correction coefficient approaches 1; if the deviation is large, the correction coefficient is adjusted proportionally.

[0053] The historical database contains multiple sets of historical pre-wetting data, each set of data records the bulk density distribution, effective moisture penetration length, initial moisture content and corresponding equipment operating parameters.

[0054] The pre-wetting time reference value is multiplied by the correction coefficient to obtain the initial optimization time. The pre-wetting time reference value is a value generated in step S3 by nonlinear coupling of the ratio of the current effective water penetration length to the historical reference parameter and the ratio of the current initial water content to the historical reference water content. For example, if the pre-wetting time reference value is high and the correction coefficient is small, the initial optimization time is shortened accordingly.

[0055] The ventilation energy consumption compensation is calculated based on the temperature gradient distribution in the vertical direction of the current stacking layer. The temperature gradient distribution data is collected in real time through the arranged temperature sensor array, and the sensor array is arranged in layers in the vertical direction of the stacking layer. The temperature gradient is calculated by determining the temperature change rate per unit height based on the ratio of the temperature difference between adjacent layers in the vertical direction to the layer spacing. The calculation rule for the ventilation energy consumption compensation is that for every increase in the temperature gradient by a certain amplitude, the compensation amount increases according to the preset basic compensation value ratio. For example, when the temperature gradient is high, the ventilation energy consumption increases significantly, and the idling time needs to be increased to balance the energy consumption.

[0056] The basic compensation value is calibrated by simulating the changes in ventilation energy consumption under different temperature gradients in the laboratory to ensure that the compensation amount matches the trend of energy consumption changes.

[0057] The idle time increment corresponding to the initial 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 idle time and ventilation energy consumption. For example, when the initial optimization time is short but the temperature gradient is high, the compensation amount is large, and the final strategy time is extended accordingly to reduce ventilation energy consumption. The corresponding relationship between the idle time increment and the compensation amount is calibrated through the equipment operation parameters to ensure that the idle time adjustment amount is proportional to the energy consumption optimization target.

[0058] After the pre-wetting time control strategy is generated, the strategy parameters are transmitted to the pre-wetting equipment controller in real time. The controller adjusts the operation time of the water injection system and the start-stop cycle of the ventilation equipment according to the strategy value. The specific implementation method is: in the early stage of the pre-wetting stage, the water injection operation is performed according to the strategy value, and in the later stage, the water injection amount is dynamically fine-tuned according to the real-time detection of the change rate of the moisture content of the straw surface to ensure that the moisture content uniformity meets the standard. For example, if the strategy time is long and the moisture content fluctuates slightly, the water injection rate can be appropriately reduced to reduce the waste of idling time.

[0059] S5. 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, including: Detect the main fiber direction of the straw stalk, and determine the real-time angle between the main fiber direction and the low-density infiltration channel direction based on the spatial distribution of the low-density infiltration channel; Collect the acoustic impedance spectrum data of the stacked layers, extract the energy attenuation characteristics of the set frequency band, and generate the structural stability evaluation index by combining the historical fluctuation amplitude of the fusion weight; 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, the enhanced correction mode is triggered to shorten the output threshold by a first set ratio; 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, the conservative correction mode is enabled to extend the output threshold by a second set ratio.

[0060] The main fiber direction of the straw stalk is detected in real time by a polarized light sensor. The polarized light sensor array is arranged on the top of the stacked layer, and polarized light is emitted to the surface of the straw stalk at a preset incident angle, and the reflected light signal is received. 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 according to the vertical stratified areas of the stacked layer, and an average direction angle is output for each group of areas. The detection frequency of the polarized light sensor is synchronized with the acquisition frequency of the stacking density distribution data in step S1 to ensure data timestamp alignment.

[0061] Based on the spatial distribution data of the low-density permeation channel generated in step S2, determine the real-time angle between the main fiber direction and the low-density permeation channel direction. The low-density permeation channel direction is the channel extension direction generated by the bulk density gradient distribution analysis in step S2. For example, the horizontal extension direction of a low-density permeation channel is 30 degrees east of south. The real-time angle is calculated by taking the angle between the main fiber direction and the low-density permeation channel direction projected on the horizontal plane, and the angle range is 0 degrees to 90 degrees. For example, if the main fiber direction is 15 degrees east of north and the low-density permeation channel direction is 30 degrees east of south, the real-time angle is 45 degrees. The horizontal plane projection angle is calculated by vector direction decomposition without relying on a three-dimensional space geometric model.

[0062] The acoustic impedance spectrum data of the stacked layer is collected by an 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, 100Hz to 500Hz). The receiving end records the energy attenuation data of the signal after passing through the stacked layer. The energy attenuation feature extraction method of the set frequency band is: statistically calculate the energy attenuation rate of each sub-interval in the set frequency band, for example, divide the frequency band into 10 equal-width sub-intervals, and calculate the average attenuation rate of each sub-interval. The energy attenuation rate is negatively correlated with the internal pore connectivity of the stacked layer. The higher the attenuation rate, the looser the structure. The energy attenuation feature data and the fusion weight historical data generated in step S3 are aligned by timestamp to ensure data synchronization.

[0063] Combined with the historical fluctuation range of the fusion weight in step S3, the structural stability evaluation index is generated. The historical fluctuation range is calculated by counting the standard deviation of the fusion weight in the past set time period, for example, the standard deviation of the fusion weight in the past 30 minutes. The larger the standard deviation, the higher the historical fluctuation range.

[0064] The structural stability evaluation index is generated by adding the energy decay rate and the historical fluctuation range according to the preset weights, for example, the energy decay rate accounts for 60% of the weight and the historical fluctuation range accounts for 40% of the weight. The weighted result is normalized to the range of 0 to 1. The lower the value, the more unstable the structure. The weight ratio is calibrated by inverse calculation of the parameter combination when the pre-wetting effect is optimal in the historical data, without relying on the optimization algorithm.

[0065] 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, the enhanced correction mode is triggered. The set angle threshold is determined by the statistical value of the angle between the main fiber direction and the low-density permeation channel direction when the pre-wetting effect is optimal in the historical data. For example, the 50 groups of data with the lowest fermentation energy consumption in the historical data are screened, and the average value of the angle plus two times the standard deviation is calculated as the threshold. The set stability threshold is determined by the top 20% quantile of the structural stability evaluation index in the historical data. For example, the minimum value of the top 20% of the stability index in the historical data is taken as the threshold.

[0066] The operation method of the enhanced correction mode is: shorten the output threshold by a first set ratio, and the first set ratio is dynamically adjusted according to the extent by which the real-time angle exceeds the set angle threshold. For example, for every 1 degree the angle exceeds the threshold, the shortening ratio increases by 0.5%.

[0067] 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, the conservative correction mode is enabled. The conservative correction mode operates as follows: the output threshold is extended by a second set ratio, and the second set ratio is dynamically adjusted according to the extent to which the structural stability evaluation index exceeds the set stability threshold. For example, for every 0.1 that the index exceeds the threshold, the extension ratio increases by 0.3%. The range of the first set ratio and the second set ratio is calibrated by the statistical value of the ratio when the correction effect is optimal in the historical data. For example, in the historical data, when the angle exceeds the standard by 5 degrees, the shortening ratio of 8% is the best effect.

[0068] The dynamically corrected output threshold is transmitted to the pre-wetting device controller in real time. The controller adjusts the water injection rate and ventilation frequency in 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 reduced to avoid local over-wetting. The parameter execution logic of the controller is: in the early stage of the pre-wetting stage, a fixed water injection rate is executed according to the corrected threshold.

[0069] S6. When the moisture content of the rice straw surface is continuously stable and the orientation of the rice straw stalk fibers is stable, a pre-wetting termination instruction is generated, including: Real-time detection of the change rate of moisture content on the surface of the straw, and calculation of the fluctuation range of moisture content within a continuously set time window; Real-time detection of the change rate of the fiber orientation of the rice straw stalks and calculation of the fluctuation amplitude of the orientation angle within a continuously set time window; When the fluctuation range of the moisture content is less than the set moisture content threshold and the fluctuation range of the orientation angle is less than the set orientation threshold, it is determined that the stability condition is met; Based on the dynamically corrected output threshold, the duration for satisfying the stability condition is compared with the dynamically corrected output threshold, and when the duration for satisfying the stability condition exceeds the dynamically corrected output threshold, a pre-wetting termination instruction is generated.

[0070] The rate of change of 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 consistent with the set frequency of the initial moisture content detection in step S1, for example, once per minute. The moisture content change rate is calculated by counting the moisture content detection values ​​within a continuous set time window (for example, 5 minutes), and calculating its standard deviation as the fluctuation range. 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 range 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.

[0071] The change rate of the fiber orientation of the straw stalk is detected in real time by a polarized light sensor array. The layout position of the polarized light sensor array is consistent with the sensor array for main fiber direction detection in step S5, and the detection frequency is synchronized with the moisture content detection. The fiber orientation change rate is calculated by counting the fiber direction angle detection values ​​within a continuous set time window (for example, 5 minutes), and calculating the range 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 and minimum values, which ensures that the calculation logic is simple and feasible.

[0072] 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 stability condition is met. The moisture content threshold is determined by the moisture content fluctuation statistical value 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 orientation threshold is calibrated by the working condition data of 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 mean value of the moisture content fluctuation amplitude in the historical optimal data is 0.2% and the standard deviation is 0.05%, the moisture content threshold is set to 0.25%; if the mean value of the fiber orientation fluctuation range is 2 degrees and the standard deviation is 1 degree, the orientation threshold is set to 4 degrees.

[0073] Based on the dynamically corrected output threshold in step S5, the duration for satisfying the stability 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 for satisfying the stability condition is the length of time continuously counted since the first time the moisture content and orientation fluctuation thresholds are simultaneously met. The comparison method is: 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 duration for satisfying the stability condition is accumulated to 23 minutes, the termination instruction is triggered.

[0074] Generate a pre-wetting termination command and shut down the pre-wetting equipment. The termination command is transmitted to the water injection system and ventilation equipment of the pre-wetting equipment through a control signal. The water injection system immediately stops injecting water, and the ventilation equipment switches to a low-speed operation mode to maintain basic ventilation needs. For example, when the termination command is triggered, the water injection valve is completely closed, and the speed of the ventilation motor is reduced from 1200 revolutions per minute to 300 revolutions per minute. The equipment shutdown logic must ensure that there is no residual moisture accumulation. For example, after the water injection stops, the drain valve is opened for 10 seconds to drain the residual moisture in the pipe.

[0075] Embodiment 2: Figure 2 A structural schematic diagram of a straw pre-wetting time determination system of the present invention is provided. The straw pre-wetting time determination system comprises: Moisture content pile collection module: collects real-time detection data of rice straw raw materials, including initial moisture content and pile density distribution data at different positions of the stacking layer; Blockage 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 effective moisture penetration length; Weight reference module: assigns the fusion weight of real-time detection data and 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 generates the pre-wetting time reference value according to the effective water penetration length and the initial moisture content; 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; 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; 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.

[0076] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0077] It should be noted that the present invention can be deployed on the device itself to realize embedded applications, and can also be run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0078] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0079] 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 aforementioned method embodiments and will not be repeated here.

[0080] 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 only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0081] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0083] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0084] 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.

[0085] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining the pre-wetting time of rice straw, characterized in that: The steps include: S1. Collecting real-time detection data of rice straw raw materials, including initial moisture content and bulk density distribution data at different positions of the stacking layer; S2. identifying high-density blocking areas and low-density penetration channels in the stacked layer according to the bulk density distribution data, and generating an effective moisture penetration length; S3. Allocate a 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 a pre-wetting time reference value according to the effective water 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 coordinated optimization of equipment idling time and ventilation energy consumption; S5. 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; S6. When the moisture content of the rice straw surface is continuously stable and the orientation of the rice straw stalk fibers is stable, a pre-wetting termination instruction is generated.

2. A method for determining the prewetting time of rice straw according to claim 1, characterized in that: Collect real-time detection data of rice straw raw materials including initial moisture content and bulk density distribution data at different positions of the stacking layer, including: The initial moisture content of the straw raw material is detected in real time by using an infrared moisture detector with a preset near-infrared band; The stacking density distribution data of the straw stacking layer at different positions is measured by means of 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.

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

4. A method for determining the straw prewetting time according to claim 3, characterized in that: The effective water penetration length is generated based on the spatial distribution relationship between the high-density blocking area and the low-density penetration channel, including: based on the stacking density gradient distribution in the vertical and horizontal directions of the stacked layer, the boundary surface between the high-density blocking area and the low-density penetration channel is identified; The equivalent permeability resistance coefficient of the low-density permeability channel is calculated based on the angle between the main fiber arrangement direction of the rice straw stalk and the bulk density gradient direction. The equivalent permeability resistance coefficient is inversely proportional to the angle. 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.

5. A method for determining the straw prewetting time according to claim 1, characterized in that: 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: 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.

6. A method for determining the straw prewetting time according to claim 1, characterized in that: Generate prewetting time benchmark value based on effective water penetration length and initial moisture content, including: 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.

7. A method for determining the pre-wetting time of rice straw according to claim 1, characterized in that: The pre-wetting time reference value is corrected based on the fusion weight, and a pre-wetting time control strategy for coordinated optimization of equipment idling time and ventilation energy consumption is generated, including: 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.

8. A method for determining the straw prewetting time according to claim 1, characterized in that: 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, including: Detect the main fiber direction of the straw stalk, and determine the real-time angle between the main fiber direction and the low-density infiltration channel direction based on the spatial distribution of the low-density infiltration channel; Collect the acoustic impedance spectrum data of the stacked layers, extract the energy attenuation characteristics of the set frequency band, and generate the structural stability evaluation index by combining the historical fluctuation amplitude of the fusion weight; 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, the enhanced correction mode is triggered to shorten the output threshold by a first set ratio; 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, the conservative correction mode is enabled to extend the output threshold by a second set ratio.

9. A method for determining the pre-wetting time of rice straw according to claim 1, characterized in that: When the moisture content of the rice straw surface is continuously stable and the orientation of the rice straw stalk fibers is stable, a pre-wetting termination instruction is generated, including: Real-time detection of the change rate of moisture content on the surface of the straw, and calculation of the fluctuation range of moisture content within a continuously set time window; Real-time detection of the change rate of the fiber orientation of the rice straw stalks and calculation of the fluctuation amplitude of the orientation angle within a continuously set time window; When the fluctuation range of the moisture content is less than the set moisture content threshold and the fluctuation range of the orientation angle is less than the set orientation threshold, it is determined that the stability condition is met; The duration of satisfying the stability condition is compared with the output threshold after dynamic correction, and when the duration of satisfying the stability condition exceeds the output threshold after dynamic correction, a pre-wetting termination instruction is generated.

10. A straw pre-wetting time determination system, used to implement a straw pre-wetting time determination method according to any one of claims 1 to 9, characterized in that: include: Moisture content pile collection module: collects real-time detection data of rice straw raw materials, including initial moisture content and pile density distribution data at different positions of the stacking layer; Blockage 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 effective moisture penetration length; Weight reference module: assigns the fusion weight of real-time detection data and 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 generates the pre-wetting time reference value according to the effective water penetration length and the initial moisture content; 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; 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; 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.

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