Large-scale straw storage method

Abstract

The invention relates to the technical field of agricultural waste treatment and resource utilization, and discloses a straw large-scale storage method which comprises the following steps: S1, a pretreatment step: performing rapid component analysis and characteristic judgment on harvested straws by combining a near infrared spectrum technology with an image recognition technology, determining the type, humidity, impurity content and lignin content of the straw; the straws are classified into different types according to the characteristics of the straws, namely feed use and power generation use; s2, a storage step: transporting the pretreated straws to a modularly designed storage facility, and storing the straws in corresponding areas according to the characteristics of the straws; s3, a collection and storage point operation management step: collecting straw price data, surrounding area straw yield data, market demand data for different purposes of straw and the like, and transmitting the data to a big data analysis platform in real time through a data transmission network; and adjusting the reserves of the storage points and adjusting the layout of the storage points through data analysis.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural waste treatment and resource utilization, and in particular to a large-scale straw storage method. Background Art

[0002] In agricultural production, straw is an agricultural waste with huge output. As a major agricultural country, my country produces a considerable amount of straw every year. If straw cannot be stored reasonably and on a large scale, it will not only cause a waste of resources, but also may cause environmental pollution and other problems. For example, a large amount of straw is randomly piled up, occupying land resources, and is easy to rot and deteriorate in the natural environment, breeding bacteria and pests, and having a negative impact on the surrounding ecological environment.

[0003] At present, large-scale straw storage faces many challenges. Traditional straw storage methods, such as open-air stacking or simple warehouse storage, are greatly affected by natural environmental factors. Open-air stacked straw is easily eroded by wind and rain and exposed to the sun, which leads to the decline of straw quality and the loss of nutrients in the straw, affecting its subsequent use as feed, fertilizer or industrial raw material. Moreover, open-air stacking also poses a fire hazard. Once a fire occurs, it will not only cause a large loss of straw resources, but also endanger the safety of surrounding facilities and personnel.

[0004] It can be seen from the data that although some existing straw storage technologies have solved some problems to a certain extent, they still have limitations. For example, Chinese patent CN201010159857 "A method for transporting, storing and processing straw for biogas production" transports and stores straw by crushing and briquetting it. Although it improves the transportation volume and storage safety, for large-scale storage scenarios, the cost of briquetting equipment, briquetting efficiency, and the quality maintenance of straw during long-term storage still need to be further optimized. Chinese patent CN201310293810 "Pretreatment method for quality storage of crop straw" focuses on the pretreatment of straw before storage to improve subsequent utilization, but the overall process optimization in the large-scale storage process, such as the efficient use of storage space and the impact of different pretreatment methods on large-scale storage management, still needs to be improved.

[0005] In addition, the layout and operation management of storage points are also involved in the large-scale storage process. According to Chinese patent CN202110957247 "Straw storage point location determination method, device, electronic equipment and storage medium" and Chinese patent CN202410027946 "A comprehensive management system, method and storage medium for straw storage and collection operations", the existing storage point location determination method and operation management system take into account factors such as cost and resource allocation, but in terms of deep integration with the large-scale straw storage process, such as how to optimize the operation process of storage points according to different storage methods to maximize overall benefits, there is still room for research and improvement.

[0006] In summary, developing an innovative large-scale straw storage method to solve the shortcomings of the existing technology in the large-scale straw storage process has important practical significance for improving the utilization rate of straw resources, reducing storage costs and reducing environmental pollution. Summary of the invention

[0007] To solve the above problems, the present invention adopts the following technical solutions.

[0008] A large-scale straw storage method comprises the following steps: S1 preprocessing steps: S11. Use near-infrared spectroscopy technology combined with image recognition technology to quickly analyze the composition and characteristics of the harvested straw to determine the type, moisture, impurity content and lignin content of the straw; S12. According to the characteristics of straw, it is divided into different categories, namely feed use, fuel use, base material use, fertilizer use and industrial raw material use; S13. For straw used for feed, low-temperature crushing is first performed, and then probiotics and enzyme preparations are added for fermentation pretreatment; for straw used for biomass power generation, high-temperature rapid crushing is used while adding a combustion aid, and straw used for base material, fertilizer and industrial raw material purposes is first crushed at room temperature; S2. Storage steps: S21. Transport the pre-treated straw to a modular storage facility, which is divided into multiple independent storage modules. Each module is equipped with an independent temperature and humidity control system, ventilation system and gas monitoring system. Each module is sealed to prevent air circulation; S22. Store the straw in the corresponding area according to its characteristics. For straw that is prone to mold, the ventilation system should be 8-10 times per hour, and the gas monitoring system should monitor the oxygen content in real time to ensure that the oxygen content is maintained at 18%-21%; S3. Operation and management steps of storage points: S31. Collect straw price data, straw production data in surrounding areas, market demand data for different uses of straw, etc., and transmit the data to the big data analysis platform in real time through the data transmission network; S32. The big data analysis platform uses machine learning algorithms to analyze the collected data and uses time series analysis based on the ARIMA (p, d, q) model to predict market demand trends, where p ranges from 1 to 3, d ranges from 1, and q ranges from 1 to 3. At the same time, a function is constructed ; Taking into account the impact of multiple factors on market demand, P is the market price value, S is an integer value assigned according to different seasons, and E is the economic growth rate value related to the straw industry in the region; S33. Use cluster analysis to optimize the layout of storage points, by defining the function ; Assess the rationality of the layout of storage points, (x i , yi) is the geographical coordinate value of the storage point, H i is the surrounding straw yield value, C i is the transportation cost value, (x 0 ,y 0 ) are the center coordinates of the entire storage area.

[0009] Preferably, before step S12, if the humidity of the straw is higher than 60%, it is first naturally dried to a humidity of 50%-60% and then crushed.

[0010] Preferably, in step S12, when the straw lignin content is lower than 15% and the fiber length is greater than 5 cm, it is judged to be suitable for feed use; if the straw lignin content is higher than 20% and the calorific value is higher than 3500 kcal / kg, it is judged to be suitable for fuel use, the fiber length is greater than 1.5 mm, and the lignin content is lower than 15%; it is judged to be suitable for industrial raw material use, bean straw, cereal straw, and potato vines are judged to be suitable for fertilizer use; cotton stalks, corn cobs, wheat straw, and rice straw are judged to be suitable for base material use.

[0011] Preferably, in step S13, the probiotics are lactic acid bacteria, and the added amount is 2-3 grams per kilogram of straw. The enzyme preparation is cellulase, and the added amount is 1-1.5 grams per kilogram of straw. The combustion aids are potassium carbonate and sodium carbonate, and the added amount of potassium carbonate is 0.5%-1% of the mass of the straw, and the added amount of sodium carbonate is 0.3%-0.7% of the mass of the straw.

[0012] Preferably, in step S13, the low-temperature crushing temperature is controlled at 30-40° C., and the particle size is controlled at 2-3 mm, and the high-temperature rapid crushing temperature is controlled at 80-100° C., and the particle size is controlled at 6-8 mm.

[0013] Preferably, in step S22, when the initial humidity of the straw is greater than 18% and the number of mold spores carried by the straw exceeds 500 CFU / g, the straw is determined to be moldy straw.

[0014] Preferably, when performing cluster analysis using a big data analysis platform, a K-means clustering algorithm is used, and the K value is determined based on the actual distribution of storage points, with a value range of 3-7.

[0015] Preferably, during the pretreatment process, if the impurity content of the straw exceeds 5%, it needs to be screened and removed.

[0016] Preferably, during storage, if the straw becomes slightly moldy, it is treated with ozone disinfection technology, with the ozone concentration controlled at 5-10 ppm and the treatment time being 30-60 minutes.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention combines near-infrared spectroscopy technology with image recognition technology to quickly and accurately analyze the composition and characteristics of straw. The pretreatment process is customized for straws with different characteristics, such as drying high-humidity straw before crushing, and using different crushing processes and additives for straws with different uses. These measures effectively reduce the probability of straw mold and rot during storage, ensure that the straw maintains good quality during storage, and provide high-quality raw materials for subsequent use.

[0018] Second, the present invention uses cluster analysis and the defined function F to optimize the layout of storage points, taking into full account the geographical location of the storage points, the surrounding straw production and transportation costs, and other factors to reduce construction and operation costs. At the same time, a more reasonable layout reduces the transportation distance of straw, reduces transportation costs, and improves the economic benefits of the entire storage link.

[0019] 3. The present invention uses a big data analysis platform to apply multiple machine learning algorithms and ARIMA (p, d, q) models to predict market demand, and combines function D to comprehensively consider the impact of multiple factors on market demand. Through accurate market forecasting, it is possible to reasonably arrange the storage volume according to market demand, avoid straw backlog or insufficient supply caused by inaccurate market demand forecasting, and reduce resource waste and economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a stereogram of the present invention; DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0022] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0023] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "mounted / connected", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. Example

[0024] like Figure 1 As shown, the present invention provides a straw large-scale storage method, comprising the following steps: S1 preprocessing steps: First, a near-infrared spectrometer is used to scan within the wavelength range of 780-2526nm, and the scanning time is controlled within 3 minutes to obtain the cellulose, moisture, lignin and other component information of the straw. At the same time, an image recognition system based on a deep learning algorithm is used to identify the appearance, color and other characteristics of the straw. In this embodiment, the stored straw is a batch of corn straw with a darker color and more water stains on the surface. It is preliminarily judged that the humidity is high. The near-infrared spectrum analysis shows that the humidity of this batch of straw is 72%, the impurity content is 6%, and the lignin content is 18%; Then, according to the test results, the batch of corn stalks with a humidity higher than 60% and an impurity content of more than 5% are first screened and treated to remove impurities such as soil, sand and gravel. Then they are placed in an open and ventilated place for natural drying, and the stalks are turned regularly every day to speed up the drying speed. After 3 days of drying, the humidity of the stalks dropped to 55%, and then they were crushed. Since the lignin content of this batch of stalks was 18%, the calorific value was tested to be 3600 kcal / kg, which was determined to be suitable for biomass power generation.

[0025] For this batch of straw suitable for biomass power generation, high-temperature rapid crushing equipment is used for crushing. During the crushing process, the crushing temperature is strictly controlled at 80-100℃ to make the straw particle size reach 6-8 mm. At the same time, potassium carbonate is added at 0.8% of the straw mass and sodium carbonate is added at 0.5% as a combustion aid. For example, for 2,000 kg of this batch of straw, 16 kg of potassium carbonate and 10 kg of sodium carbonate are added and mixed thoroughly; During the pretreatment process, the moisture content of the straw during drying is monitored in real time using a humidity sensor. When the moisture content reaches 12% - 13%, heating and drying is stopped immediately.

[0026] S2. Storage steps: Build modular storage facilities, with each module made of high-strength, corrosion-resistant composite materials to ensure a service life of no less than 10 years. The facility is divided into multiple independent storage areas, each equipped with an independent temperature and humidity control system, ventilation system, and gas monitoring system. Transport qualified pre-treated straw to the storage facility and store it according to its characteristics. For example, store the straw suitable for biomass power generation in a specific area.

[0027] For straw prone to mold with an initial humidity greater than 18% and a mold spore count of more than 500 CFU / g, such as a batch of rice straw with an initial humidity of 22% and a mold spore count of 650 CFU / g, it is stored in a special straw prone to mold storage area. The humidity in this area is controlled at 52% - 55% and the temperature is controlled at 12 - 15°C through the temperature and humidity control system. The ventilation system changes air 8 - 10 times per hour, and the gas monitoring system monitors the oxygen content in real time to ensure that the oxygen content remains at 18% -21%.

[0028] S22. Store the straw in the corresponding area according to its characteristics. For straw that is prone to mold, the ventilation system should be 8-10 times per hour, and the gas monitoring system should monitor the oxygen content in real time to ensure that the oxygen content is maintained at 18%-21%; S3. Operation and management steps of storage points: First, the market straw price data, seasonal data, straw production data in surrounding areas, regional economic growth rates related to the straw industry, and straw market demand data for the corresponding period in the past five years were collected, and the data were transmitted to the big data analysis platform in real time through the data transmission network; The big data analysis platform collects market prices, seasonal data (coded in quarters), regional economic development indicators (such as regional GDP growth rate) and straw market demand data for the past five years. Then, the multivariate linear regression analysis algorithm is used to analyze the collected data, and the time series analysis based on the ARIMA (p, d, q) model is used to predict the market demand trend, where the p value range is 1-3, the d value is 1, and the q value range is 1-3. At the same time, a function is constructed ;

[0029] Comprehensively consider the impact of multiple factors on market demand. P is the market price value, which is the key economic factor affecting the market demand for straw. Price fluctuations will directly affect consumers' willingness and purchase volume of straw. S is an integer value assigned according to different seasons. There are differences in the demand for straw in different seasons. For example, the demand for straw as fuel may increase in winter, while the demand for straw used as feed may be relatively stable in summer. Seasonal factors affect the overall market demand by affecting the demand for different uses of straw. E is the economic growth rate value related to the straw industry in the region. r 1 、r 2 、r 3 The coefficient is determined by multivariate linear regression analysis of the market price P, seasonal data S (coded in quarters) for at least the past five years, the economic growth rate value E related to the straw industry in the region, and the straw market demand D data for the corresponding period. The error term ε represents the impact of unexplained random factors in the model on market demand, which can be estimated by residual analysis of historical data. The S value encodes the seasons according to the conventional quarterly division, dividing the year into four quarters and assigning different integer values ​​to each quarter. For example: Spring (first quarter): S = 1, Summer (second quarter): S = 2, Autumn (third quarter): S = 3, Winter (fourth quarter): S = 4. This encoding method is simple and intuitive, easy to understand and operate, and can initially reflect the cyclical changes of the seasons.

[0030] By comparing the prediction results of the two models, we can find that the ARIMA model focuses more on capturing the historical trends and cyclical changes in market demand, while the function D(P, S, E) pays more attention to the impact of external factors such as market prices, seasonal factors and regional economic development levels on market demand. Combining the two can more comprehensively and accurately predict the trend of straw market demand. Then cluster analysis is used to optimize the layout of storage points, and the function is defined ; Assess the rationality of the layout of storage points, (x i , yi) is the geographical coordinate value of the storage point, which is used to determine the location of the storage point in the geographical space. The geographical location directly affects the radiation range and service convenience of the storage point. iis the surrounding straw yield value, reflecting the amount of straw resources that can be collected around the collection and storage point. The higher the yield, the richer the straw raw materials available at the collection and storage point. i is the transportation cost value, which covers the transportation cost from the straw collection site to the storage point and from the storage point to the use site. The transportation cost affects the operating cost and economic benefits of the storage point. (x 0 ,y 0 ) is the center coordinate of the entire storage area, Where α and β are weight coefficients (0 <α, β< 1) and α+β = 1), which are determined according to actual operation objectives and cost-benefit analysis. The smaller the calculated F value, the more reasonable the layout of the storage point is. However, there is no fixed absolute value to determine how small F is. Instead, a relative judgment is made by comparing the F values ​​of different storage points. For example, assuming there are 3 storage points, the geographical coordinates of storage point 1 are (10, 20), P=500, C=20; the geographical coordinates of storage point 2 are (30, 40), P=800, C=30; the geographical coordinates of storage point 1 are (50, 60), P=600, C=25, and the central coordinates of the entire storage area are (30, 30), α=0.6, β=0.4, and by calculating F 1 =23.94, F 2 =20, F 3 =28.82, Compare F 1 、F 2 、F 3 From the size of , we can see that the F value of storage point 2 is the smallest and the layout is relatively reasonable. For storage point 3 with a larger F value, we can consider adjusting its location or optimizing the transportation route to reduce the F value, thereby optimizing the layout of the entire storage point. When using the big data analysis platform for cluster analysis, the K-means clustering algorithm is used. The K value is determined according to the actual distribution of the storage points, and the value range is 3-7.

[0031] The above are only preferred specific implementations of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and improved concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A straw large-scale storage method, characterized in that: The following steps are involved: S1 preprocessing steps: S11. Use near-infrared spectroscopy technology combined with image recognition technology to quickly analyze the composition and characteristics of the harvested straw to determine the type, moisture, impurity content and lignin content of the straw; S12. According to the characteristics of straw, it is divided into different categories, namely feed use, fuel use, base material use, fertilizer use and industrial raw material use; S13. For straw used for feed, low-temperature crushing is first performed, and then probiotics and enzyme preparations are added for fermentation pretreatment; for straw used for biomass power generation, high-temperature rapid crushing is used while adding a combustion aid, and straw used for base material, fertilizer and industrial raw material purposes is first crushed at room temperature; S2. Storage steps: S21. Transport the pre-treated straw to a modular storage facility, which is divided into multiple independent storage modules. Each module is equipped with an independent temperature and humidity control system, ventilation system and gas monitoring system. Each module is sealed to prevent air circulation; S22. Store the straw in the corresponding area according to its characteristics. For straw that is prone to mold, the ventilation system should be 8-10 times per hour, and the gas monitoring system should monitor the oxygen content in real time to ensure that the oxygen content is maintained at 18%-21%; S3. Operation and management steps of storage points: S31. Collect straw price data, straw production data in surrounding areas, market demand data for different uses of straw, etc., and transmit the data to the big data analysis platform in real time through the data transmission network; S32. The big data analysis platform uses machine learning algorithms to analyze the collected data and uses time series analysis based on the ARIMA (p, d, q) model to predict market demand trends, where p ranges from 1 to 3, d ranges from 1, and q ranges from 1 to 3. At the same time, a function is constructed ； Taking into account the impact of multiple factors on market demand, P is the market price value, S is the integer value assigned according to different seasons, and E is the economic growth rate value related to the straw industry in the region; the market demand is predicted through two models, so as to adjust the reserves; S33. Use cluster analysis to optimize the layout of storage points, by defining the function ； Assess the rationality of the layout of storage points, (x i ,y i ) is the geographical coordinate value of the storage point, H i is the surrounding straw yield value, C i is the transportation cost value, (x0, y0) is the center coordinate of the entire storage area.

2. A straw large-scale storage method according to claim 1, characterized in that: Before step S12, if the humidity of the straw is higher than 60%, it is first naturally dried to a humidity of 50%-60% and then crushed.

3. A straw large-scale storage method according to claim 1, characterized in that: In step S12, when the straw lignin content is lower than 15% and the fiber length is greater than 5 cm, it is judged to be suitable for feed use; if the straw lignin content is higher than 20% and the calorific value is higher than 3500 kcal / kg, it is judged to be suitable for fuel use; if the fiber length is greater than 1.5 mm and the lignin content is lower than 15%, it is judged to be suitable for industrial raw material use.

4. A straw large-scale storage method according to claim 1, characterized in that: In step S13, the probiotics are lactic acid bacteria, and the addition amount is 2-3 grams per kilogram of straw. The enzyme preparation is cellulase, and the addition amount is 1-1.5 grams per kilogram of straw. The combustion aids are potassium carbonate and sodium carbonate. The addition amount of potassium carbonate is 0.5%-1% of the mass of the straw, and the addition amount of sodium carbonate is 0.3%-0.7% of the mass of the straw.

5. The large-scale straw storage method according to claim 1, characterized in that: In step S13, the low-temperature crushing temperature is controlled at 30-40° C., and the particle size is controlled at 2-3 mm. The high-temperature rapid crushing temperature is controlled at 80-100° C., and the particle size is controlled at 6-8 mm.

6. A straw large-scale storage method according to claim 1, characterized in that: In step S22, when the initial humidity of the straw is greater than 18% and the number of mold spores carried by the straw exceeds 500 CFU / g, it is determined to be moldy straw.

7. The large-scale straw storage method according to claim 1, characterized in that: When using the big data analysis platform for cluster analysis, the K-means clustering algorithm is adopted. The K value is determined according to the actual distribution of storage points, and the value range is 3-7.

8. The large-scale straw storage method according to claim 1, characterized in that: During the pretreatment process, if the impurity content of the straw exceeds 5%, it needs to be screened and removed.

9. The large-scale straw storage method according to claim 1, characterized in that: During storage, if the straw becomes slightly moldy, it will be treated with ozone disinfection technology. The ozone concentration is controlled at 5-10ppm and the treatment time is 30-60 minutes.

Images