A real-time quality sensing device and system for corn storage

Abstract

The invention discloses a real-time quality sensing device and system for corn storage, relates to the technical field of corn storage quality monitoring, and is used to solve the problem of poor real-time monitoring of corn storage quality; the device and system comprise a regional layout module, a dominant scale determination module, a scale correlation analysis module, a storage process monitoring module, and an alarm control module; the device and system deploy sensors in a corn accumulation layer to collect data such as gas concentration, temperature and humidity, and airflow velocity in real time, and transmit the data through a wireless network; after preprocessing, the gas concentration data is decomposed into multiple scales, the energy change at each scale is quantified, the dominant scale is identified, the fractal characteristics (self-similarity, multi-scale entropy) of the gas concentration data are extracted, the connection between each time scale is determined through multi-fractal spectrum analysis, the dynamic airflow vortex index and the trace volatile gas accumulation index are calculated, the environmental changes are monitored in real time, and when an abnormality is detected, the system automatically triggers a graded alarm and adjusts the control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of corn storage quality monitoring, and more specifically, to a real-time quality sensing device and system for corn storage. Background Art

[0002] As one of the most important food crops in the world, long-term storage of corn is crucial to the stability of the food supply chain. However, due to its high moisture and organic matter content, corn is easily affected by environmental factors during long-term storage, leading to problems such as mold, corruption, and insect pests. Parameters such as temperature, humidity, gas concentration (such as oxygen, carbon dioxide), and airflow conditions in the storage environment play a key role in the quality stability of corn.

[0003] The shortcomings of the existing technology are: at different depths and areas of the corn stacking layer, the distribution of gas in the stacking layer is uneven, especially in areas with high stacking density or limited ventilation, gas accumulation is more likely to cause mildew, corruption, etc.; secondly, there is a lack of analysis of gas concentration data between different layers of the corn stacking layer, and it is difficult to accurately quantify the changing characteristics on various time scales. Especially when the gas concentration fluctuates greatly, accurate data cannot be identified, resulting in the failure to effectively monitor key periods and areas of environmental changes. When airflow disturbances or volatile gas accumulation occur in the storage environment, it is difficult for the relevant systems to respond quickly, and ventilation and other strategies cannot be adjusted in time, thereby affecting the long-term storage quality of corn. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, there is a solution as follows to solve the problem of poor real-time monitoring of corn storage quality mentioned in the above-mentioned background technology.

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

[0006] A real-time quality sensing device and system for corn storage, comprising a regional layout module, a dominant scale determination module, a scale association analysis module, a storage process monitoring module and an alarm control module, wherein the modules are connected via signals;

[0007] The regional deployment module is used to deploy sensors at different depths and areas of the corn accumulation layer, and collect and transmit data in real time through a wireless network;

[0008] The dominant scale determination module is used to preprocess the gas concentration data collected by the sensor to generate continuous time series data, and to perform multi-scale decomposition on the preprocessed gas concentration data to quantify the energy of the gas concentration data at each scale in the time series and determine the dominant scale of the gas concentration change;

[0009] The scale correlation analysis module is used to extract the fractal characteristics of gas concentration data in the time series at the dominant scale, determine the self-similarity and multi-scale entropy of gas concentration data, and determine the relationship between scales based on the multi-fractal spectrum;

[0010] The storage process monitoring module calculates the dynamic airflow vortex index and trace volatile gas accumulation index based on the results of multi-scale decomposition, and combines fractal characteristics to monitor storage environment changes in real time and evaluate the dynamic distribution of gas concentration;

[0011] The alarm control module is used to trigger a graded alarm and adjust the control strategy of the storage environment when an abnormality occurs in the monitored storage environment.

[0012] In a preferred embodiment, the sensors are deployed in different depths and regions of the corn accumulation layer, and data is collected and transmitted in real time through a wireless network. The specific steps are as follows:

[0013] Sensors include gas concentration, temperature and humidity, pressure, and air flow velocity sensors;

[0014] Design the sensor layout strategy based on the structure of the corn storage warehouse, the height and density of the corn stacking layer, and deploy the sensors based on the spatial heterogeneity, airflow path, and temperature gradient inside the stacking layer;

[0015] During the sensor deployment process, uniform distribution and random distribution strategies are used to cover the entire accumulation area.

[0016] In a preferred embodiment, the method is used to preprocess the gas concentration data collected by the sensor to generate continuous time series data, and to perform multi-scale decomposition on the preprocessed gas concentration data, quantify the energy of the gas concentration data at each scale in the time series, and determine the dominant scale of the gas concentration change. The specific steps include:

[0017] The gas concentration data is interpolated using the local neighborhood nonlinear regression to determine the abnormal points and the cubic spline interpolation method to interpolate the gas concentration data;

[0018] The multi-scale box measurement method is used to divide the gas concentration data into scales, and the fractal dimension is calculated successively at different scales, and the final fractal dimension value is obtained by fitting.

[0019] Multi-scale decomposition and energy distribution analysis of gas concentration data;

[0020] Select the Morlet wavelet function and combine it with the time-frequency resolution to determine the wavelet coefficients at different scales and time positions;

[0021] The scale of wavelet coefficients is decomposed to determine the energy concentration of gas concentration data at each scale, and the energy distribution curve is drawn to select the scale with the most concentrated energy as the dominant scale.

[0022] In a preferred embodiment, the fractal characteristics of gas concentration data in a time series at a dominant scale are extracted, the self-similarity and multi-scale entropy of the gas concentration data are determined, and the connection between the scales is determined according to the multi-fractal spectrum. The specific steps are as follows:

[0023] The wavelet coefficients at the dominant scale are inversely transformed, a model based on fractional Brownian motion is constructed, and the self-similarity of the dominant scale is obtained;

[0024] After completing the self-similarity analysis, a multi-scale entropy analysis is performed, and the information dimension of the gas concentration data in the time series is calculated as the entropy characteristic on the dominant scale.

[0025] In a preferred embodiment, determining the relationship between scales based on the multifractal spectrum includes the following steps:

[0026] Analyze the coupling characteristics between the long-range correlation and time scale of gas concentration data in time series;

[0027] The multifractal detrended fluctuation analysis method is used to analyze the long-range correlation of time series, and the fluctuation function at each scale is calculated to obtain the corresponding multifractal spectrum at each scale. The connection between scales is determined based on the multifractal spectrum.

[0028] In a preferred embodiment, based on the results of multi-scale decomposition, the dynamic airflow vortex index and the trace volatile gas accumulation index are calculated, and the storage environment changes are monitored in real time in combination with fractal characteristics to evaluate the dynamic distribution of gas concentration, including the following steps:

[0029] The fractal characteristics of gas concentration data in time series include Hurst exponent, multifractal spectrum, and information dimension;

[0030] The dynamic airflow vortex index indicates the instability of airflow in the storage environment and its impact on gas concentration distribution in the real-time perception of corn storage quality;

[0031] The trace volatile gas accumulation index indicates the distribution and instability of airflow during the real-time perception of corn storage quality;

[0032] The dynamic distribution of gas concentration is jointly evaluated based on fractal characteristics, dynamic airflow vortex index and trace volatile gas accumulation index.

[0033] In a preferred embodiment, when the monitored storage environment is abnormal, a hierarchical alarm is triggered and the control strategy of the storage environment is adjusted. The specific steps are as follows:

[0034] When the fractal characteristics change, a level 1 alarm is issued and storage quality assessment is performed;

[0035] The storage quality assessment process is as follows: if the dynamic airflow vortex index is greater than the dynamic airflow vortex threshold, and the trace volatile gas index is less than or equal to the trace volatile gas threshold, it is upgraded to a second-level alarm, and the ventilation system is immediately checked and adjusted;

[0036] If the dynamic airflow vortex index is greater than the dynamic airflow vortex threshold, and the trace volatile gas index is greater than the trace volatile gas threshold, a third-level alarm is triggered to increase monitoring of volatile gases.

[0037] As another aspect of an embodiment of the present disclosure, a device for real-time perception of corn storage quality is provided, comprising a computer-readable memory storing program code.

[0038] The technical effects and advantages of the real-time quality sensing device and system for corn storage of the present invention are as follows:

[0039] The present invention deploys sensors at different depths and key areas of the corn accumulation layer, so as to collect multiple parameters such as gas concentration, temperature and humidity, air flow velocity, etc. in the storage environment in real time and accurately, and transmits and processes the data through a wireless network. In the data preprocessing stage, continuous time series data is generated through denoising, outlier detection and interpolation processing to ensure the integrity and continuity of the data. Subsequently, the multi-scale decomposition technology is used to decompose and analyze the gas concentration data, quantify the energy changes at various scales, thereby identifying the dominant scale of the gas concentration change, and extract the fractal characteristics of the gas concentration data at the dominant scale, including self-similarity and multi-scale entropy. The connection and coupling relationship between each time scale is further determined through multi-fractal spectrum analysis, revealing the complex dynamic changes of gas concentration in the storage environment and capturing the details of long-term trends and local anomalies.

[0040] Then, through the calculation of the dynamic airflow vortex index and the trace volatile gas accumulation index, combined with fractal characteristics, the dynamic changes of the storage environment can be monitored in real time and accurately. Multi-scale decomposition and multi-fractal spectrum analysis further improve the ability to identify environmental anomalies. When an abnormality occurs, the system can automatically trigger a graded alarm and quickly adjust the control strategy to optimize key parameters such as ventilation, temperature and humidity to avoid further deterioration of the storage environment, thereby greatly improving the safety and stability of corn storage quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1The present invention is a structural schematic diagram of a real-time quality sensing device and system for corn storage. DETAILED DESCRIPTION

[0042] 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.

[0043] Embodiment 1, in order to achieve the above-mentioned object, Figure 1 A structural schematic diagram of a real-time quality perception system for corn storage of the present invention is given, which specifically includes a regional layout module, a dominant scale determination module, a scale association analysis module, a storage process monitoring module and an alarm control module, and each module is connected by a signal;

[0044] The regional deployment module is used to deploy sensors at different depths and areas of the corn accumulation layer, and collect and transmit data in real time through a wireless network;

[0045] The dominant scale determination module is used to preprocess the gas concentration data collected by the sensor to generate continuous time series data, and to perform multi-scale decomposition on the preprocessed gas concentration data to quantify the energy of the gas concentration data at each scale in the time series and determine the dominant scale of the gas concentration change;

[0046] The scale correlation analysis module is used to extract the fractal characteristics of gas concentration data in the time series at the dominant scale, determine the self-similarity and multi-scale entropy of gas concentration data, and determine the relationship between scales based on the multi-fractal spectrum;

[0047] The storage process monitoring module calculates the dynamic airflow vortex index and trace volatile gas accumulation index based on the results of multi-scale decomposition, and combines fractal characteristics to monitor storage environment changes in real time and evaluate the dynamic distribution of gas concentration;

[0048] The alarm control module is used to trigger a graded alarm and adjust the control strategy of the storage environment when an abnormality occurs in the monitored storage environment.

[0049] Integrating one or more sensors for real-time perception of corn storage quality can achieve comprehensive and real-time monitoring of corn storage environment data, thereby performing data analysis to ensure the stability of corn storage quality;

[0050] Corn storage quality analysis can integrate sensors such as oxygen, carbon dioxide, temperature and humidity, and pressure, and can monitor multiple key environmental parameters at the same time. This integration can provide a comprehensive perception of the corn storage environment;

[0051] According to the needs of corn storage quality perception, determine the type of sensors that need to be integrated. The sensors should include but are not limited to gas concentration sensors (used to monitor gas concentrations such as oxygen and carbon dioxide concentrations), temperature and humidity sensors, pressure sensors, and possible air flow velocity sensors;

[0052] Select highly sensitive electrochemical or infrared sensors to accurately measure the oxygen and carbon dioxide concentrations inside the accumulation layer; pressure sensors are used to monitor pressure changes inside the accumulation layer, especially in areas with high accumulation density, to prevent structural instability caused by excessive accumulation pressure; select air flow velocity sensors suitable for low air flow velocity monitoring to collect ventilation data inside the accumulation layer;

[0053] According to the structure of the corn storage warehouse, the height and density of the corn stacking layer, the sensor layout strategy is designed, and the sensor layout is based on factors such as spatial heterogeneity, airflow path, and temperature gradient inside the stacking layer;

[0054] Set up sensors at different depths of the accumulation layer to ensure that gas concentrations and environmental parameter changes at different levels can be monitored. For example, common layout levels include surface layer (0-0.5 meters), middle layer (1-1.5 meters) and deep layer (2-3 meters or deeper); increase the density of sensors in areas with high accumulation density or limited ventilation (such as near walls, corners or accumulation center) to ensure that environmental data in these areas can be fully collected;

[0055] During the sensor deployment process, the main deployment points are evenly distributed to cover the entire accumulation body; on this basis, a small number of randomly distributed sensors can be added to capture possible abnormal hot spots or microenvironment changes.

[0056] After the sensors are installed, an efficient data transmission and processing system is established. The system should be able to collect, transmit and process data from different sensors in real time to ensure the timeliness and accuracy of monitoring. A wireless data transmission module is set up in the warehouse to transmit the data collected by the sensors to the central processing system in real time. Low-power wide area network (LPWAN) technology is used to ensure the stability and coverage of data transmission.

[0057] In summary, the composition of the corn storage sensing device includes the following three main parts: sensor composition, sensor data collection processing and transmission part, and monitoring and early warning part.

[0058] Obtain gas concentration data collected by gas concentration sensors during corn storage. Gas concentration data usually comes from multiple sensors inside the accumulation layer. Gas concentration data may be affected by factors such as sensor drift, environmental noise, and data loss, resulting in insufficient data quality and continuity.

[0059] Before conducting an in-depth analysis of the gas concentration data during corn storage, the raw data must first be preprocessed. The preprocessing steps include denoising, outlier detection and processing, data interpolation and smoothing. The preprocessed time series data will be used for fractal dimension calculation to determine the complexity and multi-scale self-similarity characteristics of the data. The specific steps are as follows:

[0060] The steps of outlier detection and correction are as follows: during the data collection process, the gas concentration sensor may be disturbed by environmental noise, resulting in outlier components in the gas concentration data collected according to the time series. That is, the concentration value at each time point t is smoothed by using local neighborhood nonlinear regression to detect outliers that deviate from the expected trend. Suppose the original data sequence is x(t), and the smoothed sequence is , then the outlier The detection criteria are: ,in, represents the local gradient of the data smoothing sequence at the i-th time point, λ is an adaptive adjustment parameter that controls the sensitivity of outlier detection. When an outlier is detected, it is corrected using an adaptive nonlinear interpolation method that compensates based on the nonlinear fitting results of the surrounding data points;

[0061] Data interpolation and smoothing are performed to ensure the continuity and smoothness of the time series. In particular, when there is data missing or uneven sampling, the gas concentration data needs to be interpolated and smoothed. The missing data are supplemented using cubic spline interpolation to retain the main trends and features of the data. For example, the time series value corresponding to the missing point is , the interpolated sequence is obtained by cubic spline interpolation ,Right now, ,in, , , , is the spline coefficient, i represents the time point;

[0062] Similarly, the fractal dimension of the gas concentration data is calculated based on the preprocessed gas concentration data. To determine the self-similarity and complexity of the data; use the multi-scale box measurement method to scale the gas concentration data (for example, according to the time range, such as 6 hours), calculate the fractal dimension at different scales, and get the final fractal dimension value by fitting. The fractal dimension calculation formula is: ,in, The scale is The minimum number of boxes covering the time series is , and the fractal dimension is obtained by multi-scale fitting;

[0063] The preprocessed gas concentration data and its corresponding fractal dimension will serve as the basic data for subsequent multi-scale decomposition and energy distribution analysis. The value of the fractal dimension will guide the scale selection of the subsequent wavelet decomposition and affect the calculation of the multi-scale energy distribution, ensuring that the subsequent analysis can accurately capture the complex dynamic characteristics of the gas concentration data in the time series.

[0064] After data preprocessing and fractal dimension calculation of the gas concentration data, multi-scale decomposition and energy distribution analysis are performed on the gas concentration data to determine the law of gas concentration changes at different time scales. The specific steps are as follows:

[0065] The Morlet wavelet function is selected to combine the time-frequency resolution and capture the multi-scale characteristics in the time series, so as to determine the short-term sudden changes and identify the long-term trend fluctuations when analyzing the changes in the gas concentration data in the time series;

[0066] After selecting the Morlet wavelet function, a continuous wavelet transform is performed. This process converts the gas concentration data to different scales under the change of the time series, so as to analyze the data characteristics in time and scale space. The continuous wavelet transform decomposes the time series into components of different scales, and each component represents the change characteristics of the gas concentration data at a specific scale of the time series. Through this transformation, the energy distribution of the time series at different time scales can be analyzed; specifically, the gas concentration data is convolved with the selected Morlet wavelet function to obtain the wavelet coefficients at different scales and time positions. The wavelet coefficients reflect the local change characteristics of the time series at different time scales. The scale is selected based on the fractal dimension calculated previously to ensure that the selected scale can determine the complexity of the time series.

[0067] Perform scale decomposition of wavelet coefficients and analyze the energy concentration of gas concentration data at each scale. The purpose of scale decomposition is to identify the scale of main energy concentration, that is, the dominant scale of gas concentration change, and decompose the complexity of gas concentration data in time series into multiple levels. Each level represents the change characteristics at different time scales. By calculating the energy of wavelet coefficients at each scale and drawing an energy distribution curve, the energy distribution curve shows the degree of energy concentration at each scale.

[0068] After completing the scale decomposition of the wavelet coefficients, energy distribution analysis is performed to quantify the energy of the gas concentration data in the time series at each scale and determine the characteristic scale of energy distribution. The analysis of the energy distribution curve will show the energy concentration at each scale, determine the scale with the most concentrated energy, and identify the dominant scale. Through energy distribution analysis, the scale with the most concentrated energy is determined as the dominant scale;

[0069] The determination of the dominant scale requires combining the extreme points in the energy distribution curve and the energy conversion trend between different scales. The selected dominant scale will be the focus in subsequent analysis to further extract its fractal characteristics to reveal the complex dynamic behavior of gas concentration data in time series.

[0070] After the dominant scale is determined, the complexity of the gas concentration change is quantified and fractal features are extracted. The specific steps are as follows:

[0071] The wavelet components at the dominant scale are reconstructed in time series. By selecting the dominant scale with the most concentrated energy and performing inverse wavelet transform on the wavelet coefficients of the scale, the corresponding time series is reconstructed. This time series retains the main features of gas concentration at the dominant scale.

[0072] On the reconstructed time series, self-similarity analysis is performed, and a model based on fractional Brownian motion is constructed to quantify the self-similarity and long-range dependence of the time series. The fractional Brownian motion model is defined as follows: ,in, Fractional Brownian motion process, H is the Hurst index, quantifying the degree of self-similarity; is the gamma function, is the increment of Brownian motion. The fractional Brownian motion model is obtained by fitting the reconstructed time series, thereby quantifying the self-similarity characteristics of the dominant scale under the time series.

[0073] Self-similarity reflects that time series have similar statistical characteristics at different time scales;

[0074] After completing the self-similarity analysis, the complexity characteristics of the time series are further extracted through multiscale entropy analysis (MSE). Multiscale entropy reflects the information richness of the time series at different scales and is used as an indicator to measure the complexity of the time series.

[0075] Calculating information dimensions To quantify the entropy characteristics of gas concentration data on the dominant scale in time series, the information dimension is defined as: ,in, To reconstruct the time series at scale The probability distribution function under And calculate the information dimension, which can determine the multi-scale complexity and scale invariance of the time series;

[0076] At the dominant scale, in addition to the self-similarity and entropy characteristics, it is also necessary to analyze the long-range correlation of gas concentration data in time series and the coupling characteristics between time scales, that is, through time scale coupling analysis, determine the potential connection between gas concentration data at the dominant scale and other scales in time series, and use the multifractal detrended fluctuation analysis method to analyze the long-range correlation of time series, eliminate trend components, quantify the fluctuation behavior of time series at different scales, and define the multifractal spectrum: ,in, is the multifractal spectrum, represents the wave function on scale s, q is an adjustable parameter used to control the shape of the multifractal spectrum;

[0077] Assume that in the corn storage environment, a gas concentration sensor is used to monitor oxygen concentration data. The data is collected once an hour. After multi-scale decomposition and energy distribution analysis, it is determined that the time scale corresponding to the dominant scale is 24 hours (that is, the daily gas concentration fluctuation is the main feature affecting the storage environment). Now it is necessary to further determine the complex characteristics of the gas concentration data under the dominant scale in the time series and its coupling relationship with other scales through multi-fractal spectrum analysis.

[0078] Assume that the sensor collects oxygen concentration data for 30 consecutive days, recorded as x(t). After multi-scale decomposition and energy distribution analysis in the previous steps, it is determined that the main changes occur within a 24-hour period, indicating that the daily gas concentration fluctuation is the key influencing factor. Through detrending processing, the overall trend term T(t) (for example, the impact of temperature adjustment in the storage warehouse or airflow changes) is removed to obtain the fluctuation term, that is, the long-term trend other than daily fluctuations is eliminated, focusing on the short-term changes on the dominant scale and its surrounding scales;

[0079] On the basis of eliminating the trend, the fluctuation function at different time scales is calculated to measure the local fluctuation of gas concentration data in the time series. Specifically, each scale is divided into time periods, and the standardized fluctuation amplitude of the data in this period is calculated. For example, assuming the scale is 6 hours, the local fluctuation analysis is performed on the gas concentration data every 6 hours to obtain the fluctuation function at the corresponding scale, so as to identify the fluctuation characteristics of gas concentration data at different time scales such as 6 hours, 12 hours, and 24 hours, and gradually narrow the analysis scope to locate the dominant scale;

[0080] Calculate the wave function at each scale , introduce different q values ​​(multifractal adjustment parameters) to obtain the corresponding multifractal spectra. Under different values ​​of the multifractal adjustment parameters, the multifractal spectrum can reflect the fluctuation behaviors of different scales. For example, when q>2, it emphasizes the large fluctuations of gas concentration data in the time series, and when q<2, it emphasizes the relatively stable gas concentration fluctuations. Assuming that when q=2 on a 24-hour scale, the spectrum function is the largest, which indicates that on this time scale, the fluctuation of gas concentration data is most significant, which is also the dominant change scale;

[0081] Spectral analysis of multifractal spectra can also reveal whether there is a long-range correlation between scales. If the spectral function is found to show nonlinear changes at multiple scales, it means that there are complex correlations between gas concentration fluctuations at multiple time scales. For example, fluctuations at the 24-hour and 6-hour scales may have a common external environmental driving force (such as regular adjustment of the ventilation system);

[0082] The shape of the multifractal spectrum reflects the fluctuation characteristics and their connections between scales. The coupling and correlation between scales are mainly determined by observing the changes in the multifractal spectrum under different multifractal adjustment parameters. When the fluctuation characteristics at some scales significantly exceed those at other scales, or the fluctuations at multiple scales are synchronous, it can be considered that there is coupling and long-range correlation between these scales.

[0083] Long-range correlation is determined by identifying similar fluctuation characteristics at multiple scales. If the multifractal spectrum shows similar trend changes at multiple scales, it indicates that there is a strong long-range correlation between these scales. The change in gas concentration is not only affected by local environmental factors, but may also be driven by common factors at larger time ranges or longer time scales.

[0084] The multifractal spectrum can reveal the overall trend of the entire time series, and can also identify the local abnormal fluctuations and dominant fluctuation scales in the gas concentration data. For example, local anomalies caused by airflow disturbances or volatile gas accumulation can be analyzed through the multifractal spectrum to find prominent fluctuation characteristics on the scale, helping to locate specific abnormal time periods or areas.

[0085] Dynamic control and adaptive feedback adjustment: After completing the fractal feature extraction of gas concentration data in time series, the key parameters of the storage environment (such as ventilation, temperature, and humidity) are dynamically adjusted based on the real-time monitored gas concentration data and its fractal features. Through the adaptive feedback mechanism, the system responds to environmental changes in real time and gradually optimizes the control strategy. The specific steps are as follows:

[0086] By analyzing the multifractal spectrum, the analysis results of the multifractal spectrum can be used as the basis for calculating the dynamic airflow vortex index and the trace volatile gas accumulation index, which can more accurately capture the dominant scale and local fluctuations of gas concentration data and provide a basis for subsequent index calculations.

[0087] The system needs to continuously collect real-time data from the gas concentration sensor, and combine it with the dynamic airflow vortex index (DAVI) and the trace volatile gas accumulation index (TVGAI) to monitor the current storage environment through the previously extracted fractal features. The key fractal features of the gas concentration data in the time series, such as Hurst index, multifractal spectrum, information dimension, etc., will be used together with the changes in the dynamic airflow vortex index and the trace volatile gas accumulation index to identify whether the current storage environment is abnormal;

[0088] The dynamic airflow vortex index is an indicator that quantifies the instability of airflow in a storage environment and its impact on the gas concentration distribution. It reflects the rotation intensity of the airflow in the storage environment, the formation of vortices and their spatial distribution. By calculating the vorticity in the airflow velocity vector field, identifying the vortex structure generated in the airflow, and combining the intensity and range of the vortex, a value representing the degree of airflow disturbance is finally obtained.

[0089] The acquisition logic of the dynamic airflow vortex index is as follows: through the airflow sensor network installed in the storage environment, the velocity vector data v(x, y, z, t) of the airflow is collected in real time, where x, y, z are spatial coordinates and t is time. Each sensor records the local airflow velocity. , including the three directional components of the airflow, obtain the curl operator, calculate the vorticity, and the calculation expression is: , where represents the curl operator; the average rotation intensity of the vortex is calculated using the following expression: , where is the vortex radius;

[0090] The number, average rotation intensity and vortex radius of all vortices in the corn storage space are obtained, and the dynamic airflow vortex index DAVI is calculated. The calculation formula is: , where is the number of vortices detected in the corn storage space, and are the rotation intensity and radius of the i-th vortex, respectively, and L is the characteristic length of the system (such as the average size of the storage space), which is used for normalization.

[0091] It should be noted that the curl operator is a vector differential operator used to calculate the curl of a vector field. Curl is a measure of the degree of rotation of a vector field at a certain point. The curl operator is not a fixed value, but a calculation process used to operate on a given vector field. Specifically, the curl is obtained by performing partial derivative operations on the components of the vector field.

[0092] The trace volatile gas accumulation index is an indicator that measures the accumulation rate and concentration distribution of specific trace volatile gases (such as ethylene, formaldehyde, etc.) in the storage environment in a time series, and quantifies the concentration changes, diffusion, and accumulation trends of these gases in the storage environment in a local area.

[0093] The dynamic airflow vortex index is mainly used to monitor the distribution and instability of airflow, help optimize the ventilation system, and prevent abnormal local gas concentration caused by airflow disturbance. The trace volatile gas accumulation index is used to analyze the accumulation of trace gases in the storage environment. By detecting the concentration and diffusion of these gases, it helps prevent the quality deterioration and corruption of stored objects. The combination of the two can comprehensively monitor and control the storage environment to ensure the stable quality of stored objects.

[0094] Monitoring the concentration of trace volatile gases in corn storage environments , T represents the monitoring time, and the concentration gradient of trace volatile gases in the storage space is calculated. The calculation expression is: , where X, Y, and Z represent the horizontal, vertical, and height coordinates in three-dimensional space;

[0095] Obtain the volume of a local area based on the concentration gradient and the gas velocity vector , calculate the local accumulation rate of trace volatile gases in a specific area, the calculation expression is: , the accumulation rates of each local area are combined to calculate the overall trace volatile gas accumulation index, and the calculation expression is: , where , , They represent the accumulation rate, volume and gas concentration of the jth region respectively, M is the number of monitoring regions in the storage space, Indicates the base concentration of the gas.

[0096] It should be noted that a network of highly sensitive gas sensors is used to monitor the concentration of trace volatile gases in the storage environment, such as ethylene and formaldehyde, in real time, and each sensor records the concentration of a specific gas over time.

[0097] For example, if the Hurst index is found to decrease suddenly, this may mean that the long-range dependence of the system is destroyed, indicating that the short-term fluctuations in gas concentration may be abnormally drastic. At the same time, if the dynamic airflow vortex index increases significantly, it indicates that the instability of the airflow vortex has intensified, which may lead to local accumulation and uneven distribution of gas concentration; if the trace volatile gas accumulation index rises significantly, this indicates that the accumulation rate of certain trace volatile gases (such as ethylene and formaldehyde) is accelerating, which may pose a potential threat to the quality of stored materials. At this time, the system will automatically trigger an early warning, notify the manager to conduct environmental intervention or system maintenance, and dynamically adjust the ventilation and environmental control strategies according to the specific changes in the dynamic airflow vortex index and the trace volatile gas accumulation index to mitigate potential risks;

[0098] At the same time, the system can indicate the severity of the current abnormality through an alarm mechanism, ensuring that more active intervention measures are taken to prevent the corn storage environment from further deteriorating when the fractal characteristics of gas concentration data, dynamic airflow vortex index, and trace volatile gas accumulation index are all abnormal.

[0099] The system is equipped with a multi-level alarm mechanism, which can evaluate the storage quality based on the comprehensive situation of the fractal characteristics of gas concentration data, dynamic airflow vortex index and trace volatile gas accumulation index, automatically evaluate the severity of the current abnormality, and issue a detailed alarm report to the management personnel. For example, when the Hurst index of gas concentration changes significantly (which can be analyzed by the preset significant change threshold), the system will issue a first-level alarm to indicate the risk of intensified short-term fluctuations. If the dynamic airflow vortex index rises at the same time, it indicates that the intensified airflow vortex may cause the gas concentration in the local area to be significantly higher (compared with the preset dynamic airflow vortex threshold), and the system will upgrade to a second-level alarm, suggesting that the ventilation system should be checked and adjusted immediately to eliminate the local concentration abnormality caused by airflow disturbance.

[0100] Furthermore, if the trace volatile gas accumulation index also shows an abnormal increase under the above circumstances (compared with the preset trace volatile gas threshold), it indicates that trace volatile gases (such as ethylene or formaldehyde) are accumulating at an accelerated rate. In this case, the system will trigger a level 3 alarm, which requires not only immediate adjustment of the ventilation and humidity control systems, but also enhanced monitoring of these volatile gases, and may require an increase in the supply of fresh air or the use of specific filtering equipment to prevent these gases from reaching the hazard threshold;

[0101] For example, during a specific period of time, a decrease in the Hurst index indicates an increase in the volatility of gas concentrations in the short term; at the same time, the dynamic airflow vortex index shows that airflow disturbances have formed high-intensity vortices in certain areas, causing the gas concentrations in these areas to rise rapidly; coupled with a significant increase in the trace volatile gas accumulation index, it indicates that ethylene gas has accumulated in these areas. At this time, the system will trigger a level 3 alarm and automatically execute a series of intervention measures, such as increasing local ventilation, lowering the temperature or adjusting the direction of airflow to ensure that the concentration of volatile gases returns to a safe range, and notify managers to conduct on-site inspections to prevent further deterioration of the storage environment and ensure that the storage quality of corn is not affected.

[0102] It should be noted that the threshold information in this embodiment is pre-set by professionals and will not be explained in detail here. Some parameter English letters in the embodiments have the same situation, but different meanings are explained when used, which will not be explained one by one here.

[0103] The present invention deploys sensors at different depths and key areas of the corn accumulation layer, so as to collect multiple parameters such as gas concentration, temperature and humidity, air flow velocity, etc. in the storage environment in real time and accurately, and transmits and processes the data through a wireless network. In the data preprocessing stage, continuous time series data is generated through denoising, outlier detection and interpolation processing to ensure the integrity and continuity of the data. Subsequently, the multi-scale decomposition technology is used to decompose and analyze the gas concentration data, quantify the energy changes at various scales, thereby identifying the dominant scale of the gas concentration change, and extract the fractal characteristics of the gas concentration data at the dominant scale, including self-similarity and multi-scale entropy. The connection and coupling relationship between each time scale is further determined through multi-fractal spectrum analysis, revealing the complex dynamic changes of gas concentration in the storage environment and capturing the details of long-term trends and local anomalies.

[0104] Then, through the calculation of the dynamic airflow vortex index and the trace volatile gas accumulation index, combined with fractal characteristics, the dynamic changes of the storage environment can be monitored in real time and accurately. Multi-scale decomposition and multi-fractal spectrum analysis further improve the ability to identify environmental anomalies. When an abnormality occurs, the system can automatically trigger a graded alarm and quickly adjust the control strategy to optimize key parameters such as ventilation, temperature and humidity to avoid further deterioration of the storage environment, thereby greatly improving the safety and stability of corn storage quality.

[0105] Example 2, a real-time quality perception device for corn storage, includes a computer-readable memory, wherein the computer-readable memory stores program code, and when the computer program code runs on a computer, the functions of each module of the real-time quality perception system for corn storage in Example 1 are implemented.

[0106] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0107] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0108] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0109] 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.

[0110] 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.

[0111] 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 real-time quality perception system for corn storage, characterized in that: It includes a regional layout module, a dominant scale determination module, a scale association analysis module, a storage process monitoring module and an alarm control module, and each module is connected through signals; The regional deployment module is used to deploy sensors at different depths and areas of the corn accumulation layer, and collect and transmit data in real time through a wireless network; The dominant scale determination module is used to preprocess the gas concentration data collected by the sensor to generate continuous time series data, and to perform multi-scale decomposition on the preprocessed gas concentration data to quantify the energy of the gas concentration data at each scale in the time series and determine the dominant scale of the gas concentration change; The scale correlation analysis module is used to extract the fractal characteristics of gas concentration data in the time series at the dominant scale, determine the self-similarity and multi-scale entropy of gas concentration data, and determine the relationship between scales based on the multi-fractal spectrum; The storage process monitoring module calculates the dynamic airflow vortex index and trace volatile gas accumulation index based on the results of multi-scale decomposition, and combines fractal characteristics to monitor storage environment changes in real time and evaluate the dynamic distribution of gas concentration; The alarm control module is used to trigger a graded alarm and adjust the control strategy of the storage environment when an abnormality occurs in the monitored storage environment.

2. A real-time quality perception system for corn storage according to claim 1, characterized in that: The sensors are deployed in different depths and areas of the corn accumulation layer, and real-time data collection and transmission is carried out through a wireless network. The specific steps are as follows: Sensors include gas concentration, temperature and humidity, pressure, and air flow velocity sensors; Design the sensor layout strategy based on the structure of the corn storage warehouse, the height and density of the corn stacking layer, and deploy the sensors based on the spatial heterogeneity, airflow path, and temperature gradient inside the stacking layer; During the sensor deployment process, uniform distribution and random distribution strategies are used to cover the entire accumulation area.

3. A real-time quality perception system for corn storage according to claim 2, characterized in that: It is used to preprocess the gas concentration data collected by the sensor, generate continuous time series data, and perform multi-scale decomposition on the preprocessed gas concentration data, quantify the energy of the gas concentration data at each scale in the time series, and determine the dominant scale of gas concentration changes. The specific steps include: The gas concentration data is interpolated using the local neighborhood nonlinear regression to determine the abnormal points and the cubic spline interpolation method to interpolate the gas concentration data; The multi-scale box measurement method is used to divide the gas concentration data into scales, and the fractal dimension is calculated successively at different scales, and the final fractal dimension value is obtained by fitting. Multi-scale decomposition and energy distribution analysis of gas concentration data; Select the Morlet wavelet function and combine it with the time-frequency resolution to determine the wavelet coefficients at different scales and time positions; The scale of wavelet coefficients is decomposed to determine the energy concentration of gas concentration data at each scale, and the energy distribution curve is drawn to select the scale with the most concentrated energy as the dominant scale.

4. A real-time quality perception system for corn storage according to claim 3, characterized in that: It is used to extract the fractal characteristics of gas concentration data in the time series under the dominant scale, determine the self-similarity and multi-scale entropy of gas concentration data, and determine the connection between scales according to the multi-fractal spectrum. The specific steps are as follows: The wavelet coefficients at the dominant scale are inversely transformed, a model based on fractional Brownian motion is constructed, and the self-similarity of the dominant scale is obtained; After completing the self-similarity analysis, a multi-scale entropy analysis is performed, and the information dimension of the gas concentration data in the time series is calculated as the entropy characteristic on the dominant scale.

5. A real-time quality perception system for corn storage according to claim 4, characterized in that: Determining the relationship between scales based on the multifractal spectrum includes the following steps: Analyze the coupling characteristics between the long-range correlation and time scale of gas concentration data in time series; The multifractal detrended fluctuation analysis method is used to analyze the long-range correlation of time series, and the fluctuation function at each scale is calculated to obtain the corresponding multifractal spectrum at each scale. The connection between scales is determined based on the multifractal spectrum.

6. A real-time quality perception system for corn storage according to claim 5, characterized in that: Based on the results of multi-scale decomposition, the dynamic airflow vortex index and trace volatile gas accumulation index are calculated, and the storage environment changes are monitored in real time in combination with fractal characteristics to evaluate the dynamic distribution of gas concentration, including the following steps: The fractal characteristics of gas concentration data in time series include Hurst exponent, multifractal spectrum, and information dimension; The dynamic airflow vortex index indicates the instability of airflow in the storage environment and its impact on gas concentration distribution in the real-time perception of corn storage quality; The trace volatile gas accumulation index indicates the distribution and instability of airflow during the real-time perception of corn storage quality; The dynamic distribution of gas concentration is jointly evaluated based on fractal characteristics, dynamic airflow vortex index and trace volatile gas accumulation index.

7. A real-time quality perception system for corn storage according to claim 6, characterized in that: It is used to trigger graded alarms when the monitored storage environment is abnormal, and adjust the control strategy of the storage environment. The specific steps are as follows: When the fractal characteristics change, a level 1 alarm is issued and storage quality assessment is performed; The storage quality assessment process is as follows: if the dynamic airflow vortex index is greater than the dynamic airflow vortex threshold, and the trace volatile gas index is less than or equal to the trace volatile gas threshold, it is upgraded to a second-level alarm, and the ventilation system is immediately checked and adjusted; If the dynamic airflow vortex index is greater than the dynamic airflow vortex threshold, and the trace volatile gas index is greater than the trace volatile gas threshold, a third-level alarm is triggered to increase monitoring of volatile gases.

8. A device for real-time perception of corn storage quality, comprising a computer-readable memory, wherein the computer-readable memory stores program code, and when the program code stored in the computer-readable memory runs on a computer, the computer-readable memory executes the system as described in any one of claims 1 to 7.

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