A multi-data real-time acquisition control system for wheat conditioning
By acquiring multi-parameter data and controlling water injection in stages, combined with saturation calculation and threshold determination, the problems of uneven moisture distribution during wheat rinsing and reliance on experience at the end of rinsing were solved, thus achieving precise control of the rinsing process and stability of milling quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TENGZHOU HOMETOWN WHEAT FLOUR CO LTD
- Filing Date
- 2025-11-28
- Publication Date
- 2026-07-28
AI Technical Summary
Existing wheat rinsing technology lacks quantitative data on the characteristics of different batches of raw grains. Uneven moisture distribution during the rinsing process leads to frequent problems of insufficient or excessive rinsing. Furthermore, there is a lack of online process data collection and analysis of moisture changes during the rinsing stage. The timing of the rinsing process relies on experience, resulting in unstable flour quality.
The system uses a multi-parameter acquisition unit to acquire raw wheat data, calculates the target moisture content and total water volume using a water ratio model, performs multiple small-dose interval water injections using a segmented water injection control unit, analyzes the differences and trends in moisture distribution using a saturation calculation unit, and automatically determines the timing for completing wheat saturation based on the saturation index using a threshold determination unit.
It enables precise control of water addition based on the characteristics of wheat batches, improves the uniformity and penetration depth of wheat wetting, reduces fluctuations in wheat wetting effect, and ensures stable milling quality.
Smart Images
Figure CN121501042B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of industrial control system technology, specifically to a real-time data acquisition and control system for wheat irrigation. Background Technology
[0002] In wheat milling, the pre-treatment step before milling is called "moistening wheat". On existing production lines, the target moisture content is usually set according to the single-point moisture test results and experience during the feeding process. Moistening is controlled by continuously adding water and fixing the moistening time. A few systems are equipped with online moisture meters and simple time controllers, but the overall adjustment is still mainly based on the average moisture content and human experience.
[0003] However, in existing technologies, the multi-source parameters of wheat before entering the wheat-moistening bin are not systematically collected and modeled, and the calculation of water-addition ratio lacks quantitative basis for the characteristics of different batches of raw grains. During the wheat-moistening process, a single-stage continuous watering method is generally adopted, without continuous watering in conjunction with stirring or turning. The distribution of moisture in the wheat-moistening bin is uneven in the vertical and radial directions, which easily leads to the outer layer of wheat grains having higher moisture content and insufficient internal penetration. This results in the phenomenon that although the wheat-moistening process has been carried out, the wheat is not thoroughly moistened. At the same time, existing technologies often lack online process data collection and analysis of moisture changes at different locations over time during the wheat-moistening stage, and have not established quantitative evaluation indicators that reflect the degree of moisture penetration inside and outside the wheat grains and the progress of wheat-moistening. When the wheat-moistening process ends, the machine usually judges based on the set time or the operator's experience, which leads to significant fluctuations in the wheat-moistening effect under different batches and different seasonal conditions. Problems of insufficient or excessive moistening occur frequently, making it difficult to maintain stable milling quality.
[0004] Therefore, a real-time data acquisition and control system for wheat irrigation is proposed to solve the above problems. Summary of the Invention
[0005] To achieve the above objectives, this application provides a real-time data acquisition and control system for wheat irrigation, comprising the following units;
[0006] The multi-parameter acquisition unit is used to acquire the first data of the target batch of wheat and generate a multi-parameter dataset based on the first data.
[0007] The water injection ratio calculation unit is used to calculate and determine the target moisture content and total water volume according to the preset water injection ratio model based on the multi-parameter dataset, and generate a segmented water injection ratio scheme.
[0008] The segmented water injection control unit is used to control the water injection actuator according to the segmented water injection ratio scheme, so as to inject water into the wheat in the stirring or turning state in multiple small doses at intervals.
[0009] The wettability calculation unit is used to introduce the wettability index. Secondary data is collected during the segmented watering and wheat resting stages. Based on the secondary data, the differences in moisture distribution and the trend of time change of wheat at different locations are analyzed, and the wettability index of the current batch of wheat is calculated according to the wettability evaluation model.
[0010] The threshold determination unit is used to compare the saturation index with the preset saturation target threshold. When the threshold is reached, a wheat saturation completion instruction is generated and the wheat is controlled to enter the subsequent process. When the threshold is not reached, an adjustment strategy is generated based on the saturation index deviation and changes in multiple parameters.
[0011] Preferably, the first data of the target batch of wheat is collected, and a multi-parameter dataset is generated based on the first data, specifically including:
[0012] The first data refers to the original multi-parameter data obtained during the feeding and temporary storage stages before the target batch of wheat enters the wheat conditioning process; the first data includes: the original moisture content, particle size distribution, bulk density, impurity content, broken grain ratio, variety type and corresponding hardness index of the target batch of wheat, as well as the temperature and relative humidity of the feeding environment.
[0013] A multi-parameter dataset is constructed based on the first data. The collected parameters are associated with the target batch number and the collection time identifier and stored. The multi-parameter dataset forms a set of input parameters for water addition ratio calculation, providing a data foundation for water addition ratio model calculation.
[0014] Preferably, the target moisture content and total water addition are calculated and determined according to a preset water addition ratio model based on a multi-parameter dataset, specifically including:
[0015] The water addition ratio model was constructed by statistically analyzing the relationship between the original moisture content, particle size distribution, bulk density, impurity content, broken grain ratio, variety type and hardness index of multiple batches of wheat, as well as the feeding environment temperature, relative humidity and the corresponding target moisture content after wheat soaking, and then using multiple regression fitting.
[0016] The target moisture content and total water content are calculated based on the established water addition ratio model. The formula for calculating the target moisture content is as follows:
[0017]
[0018] In the formula, Indicates the target moisture content. This represents the original moisture content in the first data set. The hardness coefficient is obtained based on variety type and hardness index. The particle size coefficient is obtained based on particle size distribution and the proportion of broken particles. For the ambient temperature of the feeding environment, The relative humidity of the feeding environment, arrive These are the parameters of the water addition ratio model obtained through regression training.
[0019] The total amount of water added is calculated based on the target moisture content, the original moisture content in the first data, and the mass of the target batch of wheat.
[0020] Preferably, the segmented water injection ratio scheme specifically includes:
[0021] The segmented water injection ratio scheme is generated by allocating the total water volume according to a preset stage ratio. The segmented water injection ratio scheme includes the water volume for the pre-wetting stage, the main water injection stage, and the compensation stage. And the corresponding start and end times and intervals for water injection, and the corresponding water volume during the pre-wetting stage. The corresponding water volume during the main water filling stage The corresponding water addition during the compensation phase .
[0022] The segmented water injection ratio scheme is generated by distributing the total amount of water added according to a preset stage ratio. The calculation formula for the amount of water added in each stage is as follows:
[0023]
[0024] In the formula, These are the water addition amounts for the pre-wetting stage, the main water addition stage, and the compensation stage, respectively. The water addition ratio for each stage is adaptively set based on the hardness index and particle size distribution in the multi-parameter dataset.
[0025] Preferably, the water injection actuator is controlled according to the segmented water injection ratio scheme to inject water into the wheat during the mixing or turning of the feed in multiple small-dose intervals, specifically including:
[0026] The segmented water injection control unit adds water according to the pre-wetting stage, main water injection stage, and compensation stage in the segmented water injection ratio scheme. Set the corresponding number of water injections per cycle. ,in For integers greater than 1, calculate the single injection volume according to the following formula:
[0027]
[0028] In the formula, These represent the single water injection volume for the pre-wetting stage, the main water injection stage, and the compensation stage, respectively. The segmented water injection control unit divides the corresponding time intervals into segments based on the start and end times of each stage in the segmented water injection ratio scheme. Within a time window, the segmented water injection control unit reads the stirring and agitation status signals. When these signals indicate that the material is in a state of agitation, it outputs a water injection control command to the water injection actuator, triggering a single water injection at the corresponding stage. or Injection, the cumulative water injection volume in the corresponding stage reaches or Then the current stage of water injection will end.
[0029] Preferably, a moisture content index is introduced, and second data is collected during the segmented watering and wheat resting stages. The second data is used to analyze the differences in moisture distribution and the trend of its temporal variation at different locations in the wheat, specifically including:
[0030] The second data refers to the process monitoring data obtained from the current batch of wheat at preset time intervals during the segmented water injection stage and the wheat rinsing and settling stage. The second data includes: online moisture measurements at multiple monitoring locations set along the height and radial directions inside the wheat rinsing silo, temperature measurements at each monitoring location, surface moisture content and internal moisture content measurements of the discharged wheat obtained at preset time points, and the collection time identifier and monitoring location number corresponding to the above measurements. The second data is automatically collected and uploaded by a multi-parameter acquisition unit at preset time intervals at corresponding monitoring and discharge locations inside the wheat rinsing silo.
[0031] By statistically calculating the moisture measurements at different monitoring locations at the same collection time, a moisture distribution difference index is obtained for each collection time. The moisture distribution difference index includes the difference between the maximum and minimum moisture measurements and the variance of the moisture measurements. The moisture distribution difference index is used to represent the degree of unevenness of moisture distribution within the spatial range of Runmai.
[0032] By fitting the time series of moisture measurements taken at the same monitoring location at continuous collection times, the rate of moisture change and the moisture change curve are calculated. Furthermore, a joint analysis of the rate of moisture change at each monitoring location is conducted to obtain the time trend of moisture penetration into the wheat grains during the wheat hydration process.
[0033] Preferably, the wettability index of the current batch of wheat is calculated according to the wettability evaluation model, specifically including:
[0034] The wettability evaluation model is constructed based on the moisture measurements, corresponding temperature measurements, and collection times at different monitoring locations in the second data set, following the actual pattern of moisture gradually penetrating from the outer layer to the inner layer of wheat. It determines the moisture distribution difference value, which represents spatial uniformity, and the moisture change rate value, which represents the penetration speed. Then, a linear weighted combination method is used to combine the moisture distribution difference value, the moisture change rate value, and the wheat wetting time as variables affecting wettability, forming a wettability evaluation model to describe the current wheat wetting progress.
[0035] The wettability index of the current batch of wheat is calculated based on the established wettability evaluation model. The formula for calculating the wettability index is as follows:
[0036]
[0037] In the formula, This indicates the wettability index. The difference in the distribution of moisture measurements at each monitoring location in the second data is obtained by the difference between the maximum and minimum moisture values at the same collection time. It represents the average rate of change of moisture at each monitoring location over time, obtained by dividing the difference in moisture measurements at consecutive collection points by the time interval. This indicates the cumulative time for moistening the wheat from the start of the segmented watering process to the current moment; The coefficients of the wettability evaluation model are obtained by training based on historical batch wheat data; the wettability index The calculation results are used to represent the comprehensive state of the internal moisture penetration and overall uniformity of the current batch of wheat during the wheat-gathering stage, and serve as the input variable for subsequent threshold determination units.
[0038] Preferably, the saturation index is compared with a preset saturation target threshold. When the threshold is reached, a wheat saturation completion instruction is generated and the wheat is controlled to proceed to the next process. Specifically, this includes:
[0039] Historical batch wheat-soaking data is obtained, and a target threshold for moisture content is set based on statistical analysis of the historical batch wheat-soaking data. The historical batch wheat-soaking data includes the moisture content index, original moisture content, particle size distribution, hardness index, total water added, wheat-soaking time, and corresponding subsequent milling quality score at the end of each batch wheat-soaking. Samples with milling quality scores not lower than the preset quality lower limit are considered valid samples. From the moisture content index distribution of the valid samples, a lower limit value of the moisture content index that can cover not less than 90% of the valid samples is selected as the target threshold for moisture content.
[0040] The threshold determination unit periodically reads the moisture index of the current batch of wheat and calculates its deviation from the target moisture threshold. The calculation formula is as follows:
[0041]
[0042] In the formula, Indicates the deviation value. This indicates the target threshold for infiltration. Indicates the wettability index; a judgment is made based on the calculated deviation value:
[0043] like The system determines that the current batch of wheat has met the rinsing standards, generates a rinsing completion instruction, and outputs a transfer signal to control the wheat to proceed to the next process.
[0044] like If the current wheat nutrient levels are not met, the adjustment strategy generation process will be initiated.
[0045] Preferably, when the threshold is not reached, an adjustment strategy is generated based on the wettability index deviation and changes in multiple parameters, specifically including:
[0046] Preset the first deviation threshold With the second deviation threshold The set first and second deviation thresholds must meet the following requirements. Simultaneously, a threshold for differences in moisture distribution is set. With the threshold of rate of change of moisture The adjustment strategy is based on the current wettability deviation value. Differences in real-time moisture distribution and rate of change in moisture The generation process follows these specific rules:
[0047] like and This leads to the generation of adjustment strategies to extend the wheat soaking time.
[0048] like and This leads to adjustment strategies that increase the ambient temperature of the wheat-moistening environment or increase the frequency of material turning.
[0049] like Furthermore, the rate of moisture change at some monitoring points Below This generates an adjustment strategy to continue implementing the water injection during the compensation phase.
[0050] The beneficial effects of this application are as follows:
[0051] 1. The multi-parameter acquisition unit collects data on the original moisture content, particle size distribution, bulk density, broken grain ratio, variety type, hardness index, feeding environment temperature, and feeding environment humidity of the target batch of wheat, and constructs a multi-parameter dataset. The water injection ratio calculation unit calculates the target moisture content and total water addition based on the water addition ratio model, and generates a segmented water injection ratio scheme that matches the parameters of the current batch. This solves the problem of setting the target moisture content based solely on single-point moisture detection and experience in the existing technology, so that the water addition decision has a clear data basis and reduces the fluctuation of wheat moisturizing effect caused by changes in the conditions of different batches of raw grain.
[0052] 2. By controlling the water injection actuator according to the segmented water injection ratio scheme through the segmented water injection control unit, the total water volume is divided into multiple small doses and injected intermittently while stirring or turning the material. This changes the traditional single-stage continuous water injection method, allowing water to enter the material layer in a smaller load and in a dispersed manner. This is conducive to the diffusion and balance of water in the height and radial directions of the wheat silo, alleviating the situation of high moisture content in the outer layer of wheat grains and insufficient internal penetration. At the same time, it reduces the risk of clumping and over-wetting caused by sudden increases in moisture in local areas, thereby improving the uniformity and penetration depth of wheat wetting.
[0053] 3. The wettability calculation unit collects second data during the segmented watering and wheat resting stages, analyzes the differences in moisture distribution and the trend of time change at different locations, calculates the wettability index of the current batch of wheat according to the wettability evaluation model, and the threshold judgment unit compares the wettability index with the preset wettability target threshold. When the threshold is reached, the wheat resting completion command is automatically output. When the threshold is not reached, the adjustment strategy is generated based on the wettability index deviation and changes in multiple parameters, including extending the wheat resting time, adjusting the segmented watering strategy, and adjusting the wheat resting process parameters. This solves the problem in the existing technology that only relies on fixed wheat resting time or manual experience to judge when to stop.
[0054] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0055] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0056] Figure 1 This is a schematic diagram of a real-time data acquisition and control system for wheat irrigation provided in an embodiment of this application.
[0057] Figure 2A dotted-line graph showing the infiltration index of the technical solutions provided in the embodiments of this application and existing related technologies. Detailed Implementation
[0058] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.
[0059] Please see Figure 1 , Figure 1 This is a schematic diagram of a real-time data acquisition and control system for wheat irrigation provided in an embodiment of this application.
[0060] In this embodiment, a real-time data acquisition and control system for wheat irrigation includes the following units:
[0061] A multi-parameter acquisition unit is used to acquire the first data of the target batch of wheat and generate a multi-parameter dataset based on the first data, specifically including:
[0062] The first data refers to the original multi-parameter data obtained during the feeding and temporary storage stages before the target batch of wheat enters the wheat conditioning process; the first data includes: the original moisture content, particle size distribution, bulk density, impurity content, broken grain ratio, variety type and corresponding hardness index of the target batch of wheat, as well as the temperature and relative humidity of the feeding environment.
[0063] It should be noted that, in this embodiment, the multi-parameter acquisition unit triggers an acquisition task when the target batch of wheat enters the feeding conveyor line. The original moisture content, particle size distribution, bulk density, impurity content, broken grain ratio, variety type, hardness index, feeding environment temperature and feeding environment relative humidity acquired within the same time window are uniformly marked as the first data of the same batch and assigned a unique target batch number and acquisition time identifier to form a structured record unit for constructing a multi-parameter dataset.
[0064] A multi-parameter dataset is constructed based on the first data. The collected parameters are associated with the target batch number and the collection time identifier and stored. The multi-parameter dataset forms a set of input parameters for water addition ratio calculation, providing a data foundation for water addition ratio model calculation.
[0065] It should be noted that the first data is obtained through online moisture sensors, weight detectors, image acquisition units, and temperature and humidity sensors arranged in the feeding conveyor line and temporary storage bin. The original moisture content is measured by scanning the continuously passing material flow through the online moisture sensor. The particle size distribution, broken particle ratio, and impurity content are obtained by acquiring wheat surface images through the image acquisition unit and performing particle size statistics, broken particle identification, and foreign object identification by a preset image recognition algorithm. The bulk density is measured by the combination of the weight detector and the fixed-volume hopper to obtain the unit volume mass. The variety type and hardness index are obtained by matching the batch warehousing information with the preset variety parameter table to obtain the hardness coefficient. The above data are used for subsequent water addition ratio model calls.
[0066] It should be noted that the multi-parameter dataset uses the target batch number as an index and stores the first data fields of the above items in the same record in a fixed field order. The data storage format includes at least the batch number, collection time, original moisture content, particle size distribution parameters, bulk density, impurity content, broken particle ratio, variety type code, hardness index, feeding environment temperature and feeding environment relative humidity. When calling the water addition ratio calculation unit, the record in the multi-parameter dataset is directly used as the model input to avoid parameter omissions and numerical errors caused by manual entry.
[0067] The water injection ratio calculation unit is used to calculate and determine the target moisture content and total water volume according to a preset water injection ratio model based on a multi-parameter dataset, and to generate a segmented water injection ratio scheme, specifically including:
[0068] The water addition ratio model was constructed by statistically analyzing the relationship between the original moisture content, particle size distribution, bulk density, impurity content, broken grain ratio, variety type and hardness index of multiple batches of wheat, as well as the feeding environment temperature, relative humidity and the corresponding target moisture content after wheat soaking, and then using multiple regression fitting.
[0069] The target moisture content and total water content are calculated based on the established water addition ratio model. The formula for calculating the target moisture content is as follows:
[0070]
[0071] In the formula, Indicates the target moisture content. This represents the original moisture content in the first data set. The hardness coefficient is obtained based on variety type and hardness index. The particle size coefficient is obtained based on particle size distribution and the proportion of broken particles. For the ambient temperature of the feeding environment, The relative humidity of the feeding environment, arrive These are the parameters of the water addition ratio model obtained through regression training.
[0072] It should be noted that, in this embodiment, the parameters of the water addition ratio model... arrive Training was performed using data from no fewer than 100 historical production batches, with each batch containing its original moisture content. Select a target moisture content within the range of 10.0% to 13.5%. The range of 14.5% to 17.0% was selected. After the regression training was completed, the root mean square error of the residuals was calculated for all training samples. The root mean square error was controlled to be no more than 0.3 percentage points of quality. The model parameters were incrementally updated every 50 to 100 new batches to ensure that the water addition ratio model maintained stable accuracy under different seasons and different grain production areas.
[0073] The total amount of water added is calculated based on the target moisture content, the original moisture content in the first data, and the mass of the target batch of wheat.
[0074] The segmented water injection ratio scheme is generated by allocating the total water volume according to a preset stage ratio. The segmented water injection ratio scheme includes the water volume for the pre-wetting stage, the main water injection stage, and the compensation stage. And the corresponding start and end times and intervals for water injection, and the corresponding water volume during the pre-wetting stage. The corresponding water volume during the main water filling stage The corresponding water addition during the compensation phase ;
[0075] The segmented water injection ratio scheme is generated by distributing the total amount of water added according to a preset stage ratio. The calculation formula for the amount of water added in each stage is as follows:
[0076]
[0077] In the formula, These are the water addition amounts for the pre-wetting stage, the main water addition stage, and the compensation stage, respectively. The water addition ratio for each stage is adaptively set based on the hardness index and particle size distribution in the multi-parameter dataset.
[0078] It should be noted that the adaptive setting rule is as follows: The hardness index... Standardize to a range of 0 to 1, and adjust the coarse-grain ratio. Expressed as a percentage. Water addition ratio at each stage. Determined based on the following conditions:
[0079]
[0080] The rules in the formula allow for the adaptive setting of the water addition ratio at each stage under different hardness indices and particle size distribution conditions through explicit numerical configuration.
[0081] The segmented water injection control unit is used to control the water injection actuator according to the segmented water injection ratio scheme, injecting water into the wheat during the mixing or turning of the feed in multiple small-dose intervals. Specifically, it includes:
[0082] The segmented water injection control unit adds water according to the pre-wetting stage, main water injection stage, and compensation stage in the segmented water injection ratio scheme. Set the corresponding number of water injections per cycle. ,in For integers greater than 1, calculate the single injection volume according to the following formula:
[0083]
[0084] In the formula, These represent the single water injection volume for the pre-wetting stage, the main water injection stage, and the compensation stage, respectively. The segmented water injection control unit divides the corresponding time intervals into segments based on the start and end times of each stage in the segmented water injection ratio scheme. Within a time window, the segmented water injection control unit reads the stirring and agitation status signals. When these signals indicate that the material is in a state of agitation, it outputs a water injection control command to the water injection actuator, triggering a single water injection at the corresponding stage. or Injection, the cumulative water injection volume in the corresponding stage reaches or Then the current stage of water injection will end.
[0085] It should be noted that, in this embodiment, the number of water injections per cycle during the pre-wetting stage, the main water injection stage, and the compensation stage is specified. The value range is set to 3-10, when the quality of the target batch of wheat When the volume is between 5 tons and 20 tons, the total amount of water added is... The typical range is 150kg to 800kg, with single injection volumes at each stage. Through formula After calculation, the single water injection volume at each stage is controlled within the range of 5kg to 80kg, thereby ensuring that each water injection is a small-dose water injection, and that excessively high water content will not be formed in a local area instantaneously, while the entire water injection process can be completed gradually within multiple time windows.
[0086] It should be noted that, in this embodiment, the segmented water injection control unit determines the time window according to the start and end times of each stage of water injection, dividing the time intervals of the pre-wetting stage, the main water injection stage, and the compensation stage into separate time windows. The system operates within a continuous time window, with each window lasting from 2 to 10 minutes. The segmented water injection control unit reads the stirring and agitation status signals at 1 to 5-second intervals. When the stirring speed is within the set range of 40 to 80 r / min, and the agitation cycle count reaches 1 to 3 times within the current time window, the system determines that the material is in a state of agitation. Within 3 seconds of this determination, a single water injection control command is output to the water injection actuator, triggering the corresponding... or Inject, until the cumulative water injection volume reaches the current stage. or until.
[0087] The moisture content calculation unit is used to introduce the moisture content index. Secondary data is collected during the segmented watering and wheat resting stages. Based on this secondary data, the differences in moisture distribution and the trend of moisture change over time at different locations in the wheat are analyzed. The moisture content index of the current batch of wheat is calculated according to the moisture content evaluation model, specifically including:
[0088] The second data refers to the process monitoring data obtained from the current batch of wheat at preset time intervals during the segmented water injection stage and the wheat rinsing and settling stage. The second data includes: online moisture measurements at multiple monitoring locations set along the height and radial directions inside the wheat rinsing silo, temperature measurements at each monitoring location, surface moisture content and internal moisture content measurements of the discharged wheat obtained at preset time points, and the collection time identifier and monitoring location number corresponding to the above measurements. The second data is automatically collected and uploaded by a multi-parameter acquisition unit at preset time intervals at corresponding monitoring and discharge locations inside the wheat rinsing silo.
[0089] By statistically calculating the moisture measurements at different monitoring locations at the same collection time, a moisture distribution difference index is obtained for each collection time. The moisture distribution difference index includes the difference between the maximum and minimum moisture measurements and the variance of the moisture measurements. The moisture distribution difference index is used to represent the degree of unevenness of moisture distribution within the spatial range of Runmai.
[0090] It should be noted that, in the calculation of the moisture distribution difference index, the preferred number of monitoring locations inside the wheat storage facility is 4 to 12, distributed along the vertical and radial directions. At each sampling time... Record the moisture measurement values at the monitoring locations. ,in Total number of monitoring locations; moisture distribution difference index at the current moment. Calculated using the following formula:
[0091]
[0092] Simultaneously, the variance of the moisture measurements at the same time point was calculated. The variance is calculated using conventional statistical formulas, and the unit is the square of a percentage. In this embodiment, the early stage of wheat regrowth... In Within a certain percentage range of quality points, as the wheat replenishment process nears its end... Controlled Within a certain percentage of quality points Controlled Within a certain range, it is used to quantify the spatial uniformity of moisture at different sampling times.
[0093] By fitting the time series of moisture measurements taken at the same monitoring location at continuous collection times, the rate of moisture change and the moisture change curve are calculated. Furthermore, a joint analysis of the rate of moisture change at each monitoring location is conducted to obtain the time trend of moisture penetration into the wheat grains during the wheat hydration process.
[0094] It should be noted that, in calculating the trend of moisture change over time, the optimal sampling time interval is 5 to 15 minutes. For the same monitoring location... In continuous Moisture measurement values at each sampling time A time series was generated, and the rate of moisture change at the monitoring location was obtained by first-order linear fitting. The unit is mass percentage points per hour, obtained by averaging the rate of moisture change at all monitoring locations. The specific calculation formula is as follows:
[0095]
[0096] In the middle and late stages of wheat regrowth Numerical range controlled within Within the range, when When the moisture content is determined to be stable, it is used as a quantitative index of the infiltration rate in the wettability evaluation model to reflect the rate at which moisture permeates from the outer layer to the inner layer.
[0097] The wettability evaluation model is constructed based on the moisture measurements, corresponding temperature measurements, and collection times at different monitoring locations in the second data set, following the actual pattern of moisture gradually penetrating from the outer layer to the inner layer of wheat. It determines the moisture distribution difference value, which represents spatial uniformity, and the moisture change rate value, which represents the penetration speed. Then, a linear weighted combination method is used to combine the moisture distribution difference value, the moisture change rate value, and the wheat wetting time as variables affecting wettability, forming a wettability evaluation model to describe the current wheat wetting progress.
[0098] The wettability index of the current batch of wheat is calculated based on the established wettability evaluation model. The formula for calculating the wettability index is as follows:
[0099]
[0100] In the formula, This indicates the wettability index. The difference in the distribution of moisture measurements at each monitoring location in the second data is obtained by the difference between the maximum and minimum moisture values at the same collection time. It represents the average rate of change of moisture at each monitoring location over time, obtained by dividing the difference in moisture measurements at consecutive collection points by the time interval. This indicates the cumulative time for moistening the wheat from the start of the segmented watering process to the current moment; The coefficients of the wettability evaluation model are obtained by training based on historical batch wheat data; the wettability index The calculation results are used to represent the comprehensive state of the internal moisture penetration and overall uniformity of the current batch of wheat during the wheat-gathering stage, and serve as the input variable for subsequent threshold determination units.
[0101] It should be noted that the parameters in the wettability evaluation model... In the process of determining the sample, 100 historical batches were selected as training samples. Each sample recorded the data of the corresponding batch at several collection times before the end of the wheat soaking process. And the target wetness index calibrated after manual confirmation of the wheat's wetness status. ; Training sample selection is in Within the range, Training sample selection is in Within the range, Training samples were selected at 6 hours. Within a 32-hour timeframe, a least squares method was used for multiple regression fitting to obtain... The value of the training sample The root mean square error of the fit was controlled within 0.05, and this was further increased during subsequent runs. Each new batch performs an incremental update to the model parameters, thereby ensuring that the saturation index calculation results can stably reflect the internal moisture penetration and overall uniformity of the wheat in the current batch.
[0102] The threshold determination unit compares the moisture content index with a preset moisture content target threshold. When the threshold is reached, it generates a wheat moistening completion instruction and controls the wheat to proceed to subsequent processes. When the threshold is not reached, it generates an adjustment strategy based on the moisture content index deviation and changes in multiple parameters, specifically including:
[0103] Historical batch wheat-soaking data is obtained, and a target threshold for moisture content is set based on statistical analysis of the historical batch wheat-soaking data. The historical batch wheat-soaking data includes the moisture content index, original moisture content, particle size distribution, hardness index, total water added, wheat-soaking time, and corresponding subsequent milling quality score at the end of each batch wheat-soaking. Samples with milling quality scores not lower than the preset quality lower limit are considered valid samples. From the moisture content index distribution of the valid samples, a lower limit value of the moisture content index that can cover not less than 90% of the valid samples is selected as the target threshold for moisture content.
[0104] It should be noted that the preset quality lower limit is set by statistically analyzing the flour quality scores of historical batches. Under a scoring system with a full score of 100, batches that have passed the company's internal control inspection and have been supplied stably are selected. The distribution range of the flour quality scores of the statistical samples is 80 to 95 points. 80 points is set as the preset quality lower limit for the flour quality score, which is used to screen effective samples and determine the target threshold for wettability.
[0105] The threshold determination unit periodically reads the moisture index of the current batch of wheat and calculates its deviation from the target moisture threshold. The calculation formula is as follows:
[0106]
[0107] In the formula, Indicates the deviation value. This indicates the target threshold for infiltration. Indicates the wettability index; a judgment is made based on the calculated deviation value:
[0108] like The system determines that the current batch of wheat has met the rinsing standards, generates a rinsing completion instruction, and outputs a transfer signal to control the wheat to proceed to the next process.
[0109] like If the current wheat nutrient levels are not met, the adjustment strategy generation process will be initiated.
[0110] Preset the first deviation threshold With the second deviation threshold The set first and second deviation thresholds must meet the following requirements. Simultaneously, a threshold for differences in moisture distribution is set. With the threshold of rate of change of moisture The adjustment strategy is based on the current wettability deviation value. Differences in real-time moisture distribution and rate of change in moisture The generation process follows these specific rules:
[0111] like and This leads to the generation of adjustment strategies to extend the wheat soaking time.
[0112] like and This leads to adjustment strategies that increase the ambient temperature of the wheat-moistening environment or increase the frequency of material turning.
[0113] like Furthermore, the rate of moisture change at some monitoring points Below This generates an adjustment strategy to continue implementing the water injection during the compensation phase.
[0114] It should be noted that, in this embodiment, the wettability deviation threshold... and The specific range of values is set as follows:
[0115]
[0116] In the formula, , .
[0117] Moisture distribution difference threshold With the threshold of rate of change of moisture Set as:
[0118]
[0119] The threshold determination unit reads the current immersion index every 5 minutes. And update the wettability deviation. Differences in moisture distribution and rate of change in moisture When the monitoring data meets the above-mentioned preset numerical conditions, the system will automatically trigger and execute the corresponding adjustment strategy.
[0120] In addition, to demonstrate the reproducibility of this embodiment, the technical solution was substituted with the above parameters to conduct three batches of wheat soaking tests, selecting batches A, B, and C respectively, with the following initial parameters:
[0121] Batch A: Initial moisture content Batch quality Hardness coefficient coarse-grain ratio ;
[0122] Batch B: Initial Moisture Batch quality Hardness coefficient coarse-grain ratio ;
[0123] Batch C: Initial Moisture Batch quality Hardness coefficient coarse-grain ratio Ambient temperature for material feeding relative humidity .
[0124] The system calculates the following based on the water mixing ratio model:
[0125]
[0126]
[0127]
[0128] Set the water addition ratio for each stage according to the aforementioned adaptive rules:
[0129]
[0130]
[0131]
[0132] The amount of water added at each stage is calculated based on the ratio, with the unit of water added being kg:
[0133]
[0134]
[0135]
[0136] The number of water injections in each stage are set as follows: Corresponding single injection volume Controlled Within the range.
[0137] The monitoring data during the wheat soaking process are as follows:
[0138] Batch A: Differences in moisture distribution after segmented water injection. The moisture content gradually decreased from an initial 3.4 to 0.8, indicating a change in moisture rate. ,Depend on Reduced to 0.04, wheat soaking time At that time, the wettability index .
[0139] Batch B: In hour, .
[0140] Batch C: In hour, Wetting deviation .
[0141] The system according to Furthermore, the rate of change in moisture at some monitoring points The judgment result automatically triggered the water injection stage and extended the wheat moistening time by 2 hours; ultimately, batch C was... hour, Increased to 0.81.
[0142] Please also refer to Figure 2 To provide a direct comparison with existing technologies, a comparative experiment was conducted using conventional wheat-moistening control methods, while maintaining the same initial moisture content, particle size distribution, hardness, and batch quality for batches A, B, and C. In this comparative experiment, water was added once according to an empirical formula during the feeding stage, and a fixed moistening time of 18 hours was set, without performing multiple small-dose interval water injections or closed-loop adjustment of the wettability. The experimental results showed that the wettability indices of batches A, B, and C under this technical solution were 0.87, 0.83, and 0.81, respectively, while the corresponding wettability indices of existing technologies were 0.71, 0.67, and 0.61.
[0143] Thus, a real-time data acquisition and control system for wheat irrigation has been completed.
[0144] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0145] Those skilled in the art will recognize that the algorithms or steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.
[0146] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A real-time data acquisition and control system for wheat irrigation, characterized in that, Includes the following units; The multi-parameter acquisition unit is used to acquire the first data of the target batch of wheat and generate a multi-parameter dataset based on the first data. The water injection ratio calculation unit is used to calculate and determine the target moisture content and total water addition based on a multi-parameter dataset and a preset water injection ratio model, and to generate a segmented water injection ratio scheme. The segmented water injection control unit is used to control the water injection actuator according to the segmented water injection ratio scheme, so as to inject water into the wheat in the stirring or turning state in multiple small doses at intervals. The wettability calculation unit is used to introduce the wettability index. Second data is collected during the segmented watering and wheat resting stages. Based on the second data, the differences in moisture distribution and the trend of time change of wheat at different locations are analyzed, and the wettability index of the current batch of wheat is calculated according to the wettability evaluation model. The wettability evaluation model is constructed based on the moisture measurements, corresponding temperature measurements, and collection times at different monitoring locations in the second data set, following the actual pattern of moisture gradually penetrating from the outer layer to the inner layer of wheat. It determines the moisture distribution difference value, which represents spatial uniformity, and the moisture change rate value, which represents the penetration rate. Then, a linear weighted combination method is used to combine the moisture distribution difference value, the moisture change rate value, and the wheat wetting time as variables affecting wettability, forming a wettability evaluation model to describe the current wheat wetting progress. The wettability index of the current batch of wheat is calculated based on the established wettability evaluation model. The formula for calculating the wettability index is as follows: ； In the formula, This indicates the wettability index. The difference in the distribution of moisture measurements at each monitoring location in the second data is obtained by the difference between the maximum and minimum moisture values at the same collection time. It represents the average rate of change of moisture at each monitoring location over time, obtained by dividing the difference in moisture measurements at consecutive collection points by the time interval. This indicates the cumulative time for moistening the wheat from the start of the segmented watering process to the current moment; The coefficients of the wettability evaluation model are obtained by training based on historical batch wheat data; the wettability index The calculation results are used to represent the comprehensive state of the internal moisture penetration and overall uniformity of the current batch of wheat during the wheat rinsing stage, and serve as the input variable for the subsequent threshold determination unit; The threshold determination unit is used to compare the saturation index with the preset saturation target threshold. When the threshold is reached, a wheat saturation completion instruction is generated and the wheat is controlled to enter the subsequent process. When the threshold is not reached, an adjustment strategy is generated based on the saturation index deviation and changes in multiple parameters.
2. The multi-data real-time acquisition and control system for wheat irrigation as described in claim 1, characterized in that, Collect the first data of the target batch of wheat, and generate a multi-parameter dataset based on the first data, specifically including: The first data refers to the original multi-parameter data obtained during the feeding and temporary storage stages before the target batch of wheat enters the wheat conditioning process; the first data includes: the original moisture content, particle size distribution, bulk density, impurity content, broken grain ratio, variety type and corresponding hardness index of the target batch of wheat, as well as the temperature and relative humidity of the feeding environment; A multi-parameter dataset is constructed based on the first data. The collected parameters are associated with the target batch number and the collection time identifier and stored. The multi-parameter dataset forms a set of input parameters for water addition ratio calculation, providing a data foundation for water addition ratio model calculation.
3. The multi-data real-time acquisition and control system for wheat irrigation as described in claim 2, characterized in that, Based on a multi-parameter dataset and a preset water addition ratio model, the target moisture content and total water addition are calculated and determined, specifically including: The water addition ratio model was constructed by statistically analyzing the relationship between the original moisture content, particle size distribution, bulk density, impurity content, broken grain ratio, variety type and hardness index of multiple batches of wheat, as well as the feeding environment temperature, relative humidity and the corresponding target moisture content after wheat soaking, and then using multiple regression fitting. The target moisture content and total water content are calculated based on the established water addition ratio model. The formula for calculating the target moisture content is as follows: ； In the formula, Indicates the target moisture content. This represents the original moisture content in the first data set. The hardness coefficient is obtained based on variety type and hardness index. The particle size coefficient is obtained based on particle size distribution and the proportion of broken particles. For the ambient temperature of the feeding environment, The relative humidity of the feeding environment, arrive These are the parameters of the water addition ratio model obtained through regression training; The total amount of water added is calculated based on the target moisture content, the original moisture content in the first data, and the mass of the target batch of wheat.
4. The multi-data real-time acquisition and control system for wheat irrigation as described in claim 3, characterized in that, The segmented water injection ratio scheme specifically includes: The segmented water injection ratio scheme is generated by allocating the total water volume according to a preset stage ratio. The segmented water injection ratio scheme includes the water volume for the pre-wetting stage, the main water injection stage, and the compensation stage. And the corresponding start and end times and intervals for water injection, and the corresponding water volume during the pre-wetting stage. The corresponding water volume during the main water filling stage The corresponding water addition during the compensation phase ; The segmented water injection ratio scheme is generated by distributing the total amount of water added according to a preset stage ratio. The calculation formula for the amount of water added in each stage is as follows: ； In the formula, These are the water addition amounts for the pre-wetting stage, the main water addition stage, and the compensation stage, respectively. The water addition ratio for each stage is adaptively set based on the hardness index and particle size distribution in the multi-parameter dataset.
5. The multi-data real-time acquisition and control system for wheat irrigation as described in claim 4, characterized in that, The water injection actuator is controlled according to the segmented water injection ratio scheme to inject water into the wheat during the mixing or turning of the feed in multiple small-dose intervals. Specifically, this includes: The segmented water injection control unit adds water according to the pre-wetting stage, main water injection stage, and compensation stage in the segmented water injection ratio scheme. Set the corresponding number of water injections per cycle. ,in For integers greater than 1, calculate the single injection volume according to the following formula: ； In the formula, These represent the single water injection volume for the pre-wetting stage, the main water injection stage, and the compensation stage, respectively. The segmented water injection control unit divides the corresponding time intervals into segments based on the start and end times of each stage in the segmented water injection ratio scheme. Within a time window, the segmented water injection control unit reads the stirring and agitation status signals. When these signals indicate that the material is in a state of agitation, it outputs a water injection control command to the water injection actuator, triggering a single water injection at the corresponding stage. or Injection, the cumulative water injection volume in the corresponding stage reaches or Then the current stage of water injection will end.
6. The multi-data real-time acquisition and control system for wheat irrigation as described in claim 1, characterized in that, A moisture content index was introduced, and secondary data were collected during the segmented watering and wheat resting stages. Based on this secondary data, the differences in moisture distribution and its temporal trends at different locations in the wheat were analyzed, specifically including: The second data refers to the process monitoring data obtained from the current batch of wheat at preset time intervals during the segmented watering stage and the wheat-moistening and settling stage. The second data includes: online moisture measurements at multiple monitoring locations set along the height and radial directions inside the wheat-moistening silo, temperature measurements at each monitoring location, surface moisture content and internal moisture content measurements of the discharged wheat obtained at preset time points, and the collection time identifier and monitoring location number corresponding to the above measurements. The second data is automatically collected and uploaded by a multi-parameter acquisition unit at preset time intervals at corresponding monitoring locations and discharge locations inside the wheat-moistening silo. By statistically calculating the moisture measurements at different monitoring locations at the same collection time, a moisture distribution difference index is obtained for each collection time. The moisture distribution difference index includes the difference between the maximum and minimum moisture measurements and the variance of the moisture measurements. The moisture distribution difference index is used to represent the degree of unevenness of moisture distribution within the spatial range of Runmai. By fitting the time series of moisture measurements taken at the same monitoring location at continuous collection times, the rate of moisture change and the moisture change curve are calculated. Furthermore, a joint analysis of the rate of moisture change at each monitoring location is conducted to obtain the time trend of moisture penetration into the wheat grains during the wheat hydration process.
7. The multi-data real-time acquisition and control system for wheat irrigation as described in claim 1, characterized in that, The moisture content index is compared with a preset moisture content target threshold. When the threshold is reached, a wheat moistening completion instruction is generated, and the wheat is controlled to proceed to the next process. Specifically, this includes: Historical batch wheat-soaking data is obtained, and a target threshold for moisture content is set based on statistical analysis of the historical batch wheat-soaking data. The historical batch wheat-soaking data includes the moisture content index, original moisture content, particle size distribution, hardness index, total water added, wheat-soaking time, and corresponding subsequent milling quality score at the end of each batch wheat-soaking. Samples with milling quality scores not lower than the preset quality lower limit are considered valid samples. From the moisture content index distribution of the valid samples, a lower limit value of the moisture content index that can cover not less than 90% of the valid samples is selected as the target threshold for moisture content. The threshold determination unit periodically reads the moisture index of the current batch of wheat and calculates its deviation from the target moisture threshold. The calculation formula is as follows: ； In the formula, Indicates the deviation value. This indicates the target threshold for infiltration. Indicates the wettability index; a judgment is made based on the calculated deviation value: like The system determines that the current batch of wheat has met the rinsing standards, generates a rinsing completion instruction, and outputs a transfer signal to control the wheat to proceed to the next process. like If the current wheat nutrient levels are not met, the adjustment strategy generation process will be initiated.
8. A real-time data acquisition and control system for wheat irrigation as described in claim 7, characterized in that, When the threshold is not reached, an adjustment strategy is generated based on the infiltration index deviation and changes in multiple parameters, specifically including: Preset the first deviation threshold With the second deviation threshold The set first and second deviation thresholds must meet the following requirements. Simultaneously, a threshold for differences in moisture distribution is set. With the threshold of rate of change of moisture The adjustment strategy is based on the current wettability deviation value. Differences in real-time moisture distribution and rate of change in moisture The generation process follows these specific rules: like and This generates an adjustment strategy to extend the wheat soaking time; like and This leads to adjustment strategies that increase the ambient temperature of the wheat-moistening environment or increase the frequency of material turning. like Furthermore, the rate of moisture change at some monitoring points Below This generates an adjustment strategy to continue implementing the water injection during the compensation phase.