Pennisetum purpureum overwintering rate prediction method and device

Abstract

The invention provides a pennisetum purpureum wintering rate prediction method and device. The method comprises the following steps: acquiring soil cold accumulated temperature data and wintering rate data of pennisetum purpureum under different wintering management and protection measures; performing regression on the soil cold accumulated temperature data and the overwintering rate data under all the overwintering management and protection measures to obtain an overwintering rate prediction model; and acquiring actual cold accumulated temperature data of a to-be-predicted site, and inputting the actual cold accumulated temperature data into the overwintering rate prediction model to obtain an overwintering rate prediction result. Aiming at the defect that the overwintering rate prediction of the pennisetum purpureum is difficult to apply based on other crop models, the invention constructs an overwintering rate prediction model suitable for the pennisetum purpureum, and fills the blank of research on the aspect of pennisetum purpureum overwintering rate prediction. The overwintering rate prediction model obtained through regression analysis can be used for prediction based on the ubiquitous environmental factor of soil cold accumulated temperature, so that the prediction result has higher universality and transportability. The method can accurately predict the overwintering rate of the object grass at different places and under different overwintering management and protection measures.

Description

Technical Field

[0001] The invention relates to the field of agricultural science and technology, and in particular to a method and device for predicting the overwintering rate of elephant grass. Background Art

[0002] In the field of agricultural research and practice, the prediction of overwintering rate is crucial for the planting and management of economic crops. In previous studies, although there have been overwintering rate prediction models for cruciferous crops such as Chinese cabbage, these models are usually based on the morphological characteristics of Chinese cabbage itself as independent variables and the overwintering rate as the dependent variable, and accurate prediction is achieved by constructing regression simulation equations. However, the application scope of these models has significant limitations, mainly limited to the prediction of the overwintering rate of Chinese cabbage seedlings in experimental sites or specific areas.

[0003] Since Chinese cabbage and elephant grass belong to the Cruciferae and Poaceae respectively in plant classification, there are significant differences between the two in terms of biological characteristics, growth environment requirements and cold resistance. Therefore, the wintering prediction model of Chinese cabbage cannot be directly applied to the wintering rate prediction of elephant grass. Elephant grass is an important perennial forage grass of the genus Pennisetum in the Poaceae family. The prediction of its wintering rate has a far-reaching impact on the development of the forage industry. However, there are no relevant reports on the research on the prediction of the wintering rate of elephant grass, which constitutes a technical problem that needs to be solved in actual production.

[0004] Given the important position of elephant grass in the forage industry and the lack of research on its overwintering rate prediction, it is particularly urgent to conduct research on the overwintering rate prediction of elephant grass. The scientific method of building an elephant grass overwintering rate prediction model can not only provide a strong technical reference for grassroots technicians, but also help promote the promotion and application of high-quality elephant grass forage, thereby having a positive impact on the sustainable and healthy development of the forage industry. Summary of the invention

[0005] The present invention provides a method and device for predicting the overwintering rate of elephant grass, which is used to solve the defect that the overwintering prediction model of Chinese cabbage in the prior art cannot be directly applied to the overwintering rate prediction of elephant grass, and constructs an overwintering rate prediction model suitable for elephant grass, so as to accurately predict the overwintering rate of elephant grass under different locations and overwintering management measures. The technical solution proposed by the present invention is as follows: In a first aspect, the present invention provides a method for predicting the overwintering rate of elephant grass, comprising: Obtain soil cold accumulated temperature data and wintering rate data of elephant grass under different wintering management measures; The soil cold accumulated temperature data and wintering rate data under all wintering management measures were regressed to obtain the wintering rate prediction model; The actual cold accumulated temperature data of the location to be predicted is obtained, and the actual cold accumulated temperature data is input into the wintering rate prediction model to obtain the wintering rate prediction result.

[0006] Optionally, the soil cold accumulated temperature data under each wintering management measure is determined by a soil temperature data set obtained by a temperature monitoring device arranged in the soil.

[0007] Optionally, the soil temperature data set is a plurality of temperature data points within a preset time range, and the preset time range is divided into a plurality of preset time periods; The soil cold accumulated temperature data is determined by the following method: For each preset time period, calculating first temperature data according to each temperature data point within the preset time period; A preset critical temperature is obtained, and the soil cold accumulated temperature data is calculated according to the preset critical temperature and the first temperature data in each preset time period.

[0008] Optionally, calculating the first temperature data according to each temperature data point within the preset time period includes: The first temperature data is obtained by calculating the average value of all temperature data points within the preset time period.

[0009] Optionally, the soil cold accumulated temperature data is calculated according to the preset critical temperature and the first temperature data of each preset time period by the following formula: In the formula, is the soil cold accumulated temperature data, =1, 2, ..., , For the The first temperature data of a preset time period, is the preset critical temperature, is the number of preset time periods.

[0010] Optionally, the method further comprises: Obtain air temperature data and soil cold accumulated temperature data for each location; The air temperature data and soil cold accumulated temperature data of the same location are regressed to obtain the soil cold accumulated temperature prediction model of the corresponding location; The soil cold accumulated temperature prediction model of each location is combined with the overwintering rate prediction model to construct a comprehensive prediction model for the corresponding location. The comprehensive prediction model takes the temperature data as input and the overwintering rate of elephant grass as output.

[0011] In a second aspect, the present invention further provides a device for predicting the overwintering rate of elephant grass, comprising the following modules: The data acquisition module is used to obtain the soil cold accumulated temperature data and wintering rate data of elephant grass under different wintering management measures; The model building module is used to regress the soil cold accumulated temperature data and wintering rate data under all wintering management measures to obtain the wintering rate prediction model; The result prediction module is used to obtain the actual cold accumulated temperature data of the location to be predicted, and input the actual cold accumulated temperature data into the wintering rate prediction model to obtain the wintering rate prediction result.

[0012] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the method for predicting the overwintering rate of elephant grass as described in the first aspect above is implemented.

[0013] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting the overwintering rate of elephant grass as described in the first aspect above.

[0014] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for predicting the overwintering rate of elephant grass as described in the first aspect above.

[0015] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows: The method and device for predicting the overwintering rate of elephant grass provided by the present invention collect the soil cold accumulated temperature and overwintering rate data of elephant grass under different overwintering management measures, and construct a overwintering rate prediction model suitable for elephant grass. The overwintering rate prediction model obtained by regression analysis can be predicted based on the ubiquitous environmental factor of soil cold accumulated temperature, so that the prediction result has stronger versatility and portability. It is possible to accurately predict the overwintering rate of elephant grass under different locations and overwintering management measures. The present invention studies elephant grass and constructs a overwintering rate prediction model suitable for elephant grass, thereby solving the problem of model inapplicability caused by crop differences.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of the method for predicting the overwintering rate of elephant grass provided by the present invention.

[0020] Figure 2 This is a scatter distribution diagram of soil temperature under different wintering management measures from October 2023 to April 24, provided by the present invention.

[0021] Figure 3 It is a box-line scatter plot of soil temperature under different wintering management measures from October 23 to April 24, provided by the present invention.

[0022] Figure 4 It is a schematic diagram of a linear regression model for soil cold accumulation temperature and wintering rate provided by the present invention.

[0023] Figure 5 It is a structural schematic diagram of the elephant grass overwintering rate prediction device provided by the present invention.

[0024] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0026] The present invention proposes the concept of soil cold accumulated temperature. According to the fifth edition of the plant physiology textbook compiled by Lincoln et al., the low temperature stress suffered by plants is not only related to the instantaneous temperature suffered, but also to the duration of the low temperature stress suffered. For elephant grass, whether it can survive the winter is mainly related to the chilling injury or freezing injury suffered in the field, including instantaneous strong freezing injury or long-term weak chilling injury, which will cause the elephant grass to be unable to survive the winter. Therefore, the present invention uses the index of soil cold accumulated temperature to comprehensively reflect the degree of low temperature stress suffered by elephant grass, and then uses this index as the main independent variable to construct a regression equation, thereby realizing the rapid and accurate prediction of the overwintering rate of elephant grass in different locations.

[0027] Combine the following Figure 1-Figure 5The present invention describes the method and device for predicting the overwintering rate of elephant grass.

[0028] Reference Figure 1 As shown, the method for predicting the overwintering rate of elephant grass includes the following: Step S110, obtaining soil cold accumulated temperature data and wintering rate data of elephant grass under different wintering management measures.

[0029] Select multiple representative test sites to ensure that the soil, climate and other conditions between sites are different for a more comprehensive analysis. At the same time, determine the number of samples for each site to ensure the reliability and statistical significance of the data. Install temperature monitoring equipment at the test site to ensure that the soil temperature can be accurately and continuously monitored. Implement different wintering management measures for elephant grass in different locations, such as covering with insulation materials, adjusting irrigation volume, and using cold-resistant fertilizers. Monitor the growth environment of the target grass under different wintering management measures, especially in the cold winter season, regularly record soil temperature data, and calculate soil cold accumulated temperature data. And record the wintering rate of elephant grass under the corresponding conditions at the same time. The wintering rate can be evaluated by the survival rate of elephant grass after spring greening. The wintering rate can be calculated based on the survival of elephant grass after spring greening.

[0030] The present invention arranges different overwintering management measures to achieve different soil temperature gradient conditions (Table 1), and a total of 12 overwintering management methods are arranged, thereby producing different elephant grass overwintering rate gradient effects.

[0031] Table 1

[0032] Step S120, regressing the soil cold accumulated temperature data and the wintering rate data under all wintering management measures to obtain a wintering rate prediction model.

[0033] The collected soil cold temperature accumulation data and wintering rate data were cleaned to remove outliers and missing values ​​to ensure the accuracy and completeness of the data. Appropriate regression analysis methods (such as linear regression, nonlinear regression, etc.) were selected to perform regression analysis on soil cold temperature accumulation and wintering rate to determine the mathematical relationship between the two. The prediction performance of the model was evaluated using methods such as cross-validation, and the model was optimized based on the evaluation results to improve the prediction accuracy. Through regression analysis, a mathematical model describing the relationship between soil cold temperature accumulation and wintering rate, namely the wintering rate prediction model, can be obtained.

[0034] Step S130, obtaining actual cold accumulated temperature data of the location to be predicted, and inputting the actual cold accumulated temperature data into the wintering rate prediction model to obtain a wintering rate prediction result.

[0035] After the model is built, the actual cold accumulated temperature data of the predicted location can be obtained to ensure the accuracy and timeliness of the data. These data can be obtained through meteorological monitoring or historical data records. Then, the actual cold accumulated temperature data is input into the wintering rate prediction model to obtain the wintering rate prediction results of elephant grass at the location. The prediction results are analyzed to evaluate the effects of different management and protection measures, providing a scientific basis for the wintering management of elephant grass. At the same time, the prediction results are applied to actual production to guide farmers to take reasonable management and protection measures to improve the wintering rate of elephant grass. After the prediction results are applied, the growth of elephant grass, including wintering rate, growth rate and health status, is continuously monitored to verify the accuracy and reliability of the prediction model. Keep communicating with farmers and growers, collect their feedback on the application effect of the prediction model, and understand the problems and room for improvement of the model in actual application. According to the monitoring results and feedback, the prediction model is iterated and optimized to continuously improve the prediction accuracy and practicality of the model.

[0036] The method for predicting the overwintering rate of elephant grass provided by the present invention collects soil cold accumulated temperature data and overwintering rate data of elephant grass under different overwintering management measures, and constructs a overwintering rate prediction model suitable for elephant grass. The overwintering rate prediction model obtained by regression analysis can be predicted based on the ubiquitous environmental factor of soil cold accumulated temperature, so that the prediction result has stronger versatility and portability. It is possible to accurately predict the overwintering rate of elephant grass under different locations and overwintering management measures. The present invention studies elephant grass and constructs a overwintering rate prediction model suitable for elephant grass, thereby solving the problem of model inapplicability caused by crop differences. The present invention aims at the problem that it is difficult to apply the overwintering rate prediction of elephant grass based on other crop models, and constructs an overwintering rate prediction model suitable for elephant grass for elephant grass, filling the gap in the research on the overwintering rate prediction of elephant grass, and providing a reference for front-line production technicians.

[0037] In an optional embodiment, the soil cold accumulated temperature data under each wintering management measure is determined by a soil temperature data set obtained by a temperature monitoring device installed in the soil.

[0038] Select a temperature monitoring device suitable for monitoring soil temperature, such as a soil temperature sensor. These sensors are usually characterized by high precision, high stability and long life, and can accurately reflect changes in soil temperature. According to the research needs and the actual situation of the test site, install the temperature monitoring device at a suitable position in the soil. The installation location should take into account factors such as soil depth, soil layer type, and root distribution to ensure that the monitored temperature data is representative. Set the sampling frequency of the temperature monitoring device to ensure that the soil temperature data can be recorded continuously and accurately. The sampling frequency should be determined according to the speed of soil temperature change and the accuracy requirements of the research. Regularly maintain and inspect the temperature monitoring equipment to ensure that it works properly. At the same time, collect and record all monitored soil temperature data to form a soil temperature data set. Using the collected soil temperature data set, calculate the soil cold accumulated temperature under each wintering management measure. Cold accumulated temperature refers to the accumulated degree of soil temperature below a certain preset critical temperature, which is used to evaluate the impact of cold seasons on plants. The above-mentioned preset critical temperature in the present invention is the critical temperature at which elephant grass is subjected to low temperature stress. The calculation method of cold accumulated temperature can be selected according to research needs, such as using the daily average temperature method, the accumulated temperature curve method, etc. During the calculation process, the accuracy and completeness of the data should be ensured to avoid misleading subsequent analysis.

[0039] The present invention can accurately reflect the change of soil temperature by using high-precision and high-stability temperature monitoring equipment, thereby improving the accuracy of soil cold accumulated temperature data. Continuous data recording and collection can ensure the integrity and continuity of data, and provide reliable data support for subsequent regression analysis. By using high-quality soil temperature data sets, more accurate soil cold accumulated temperature data can be obtained, thereby improving the prediction accuracy of wintering rate. Based on accurate soil cold accumulated temperature data, the effects of different wintering management and protection measures can be objectively evaluated. By comparing the soil cold accumulated temperature and wintering rate under different measures, the optimal management and protection measures can be screened out, providing a scientific basis for the wintering management of elephant grass. Accurate wintering rate prediction model can provide powerful technical support for the planting and management of elephant grass. By optimizing wintering management and protection measures, the survival rate and yield of elephant grass can be improved, thereby promoting the development of the elephant grass industry.

[0040] In an optional embodiment, the soil temperature data set is a plurality of temperature data points within a preset time range, and the preset time range is divided into a plurality of preset time periods. The soil cold accumulated temperature data is determined by: S210 . For each preset time period, calculate first temperature data according to each temperature data point within the preset time period.

[0041] First, the entire study period (i.e., the preset time range mentioned above) is divided into multiple shorter, continuous time periods, which can be fixed in length, such as daily, weekly, or monthly, depending on the needs of the study and the availability of data. In each preset time period, multiple temperature data points are collected through the soil temperature sensor. These data points should be distributed as evenly as possible within the time period to reflect the changes in soil temperature. For each preset time period, the first temperature data within the time period is calculated based on the collected temperature data points. This data can be the average temperature, median temperature, or other statistical indicators, depending on the purpose of the study and the characteristics of the data.

[0042] S220: Obtain a preset critical temperature, and calculate the soil cold accumulated temperature data according to the preset critical temperature and the first temperature data in each preset time period.

[0043] The above-mentioned preset critical temperature is the threshold value at which the soil temperature has an adverse effect on the plant (such as causing frost damage). This value can be determined according to the plant species, growth stage and climatic conditions. The above-mentioned preset critical temperature in the present invention is the critical temperature at which elephant grass is subjected to low temperature stress. For elephant grass, this critical temperature may be 0°C or lower. For each preset time period, if the first temperature data is lower than the preset critical temperature, the temperature difference in the time period (i.e., the critical temperature minus the first temperature data) is multiplied by the length of the time period to obtain the cold accumulated temperature of the time period. The cold accumulated temperatures of all time periods are added together to obtain the soil cold accumulated temperature data for the entire study period.

[0044] The actual cold accumulated temperature data of the location to be predicted in the above step S130 is also calculated using the above methods of S210 and S220.

[0045] The present invention can reflect the change of soil temperature more carefully by dividing the entire study period into multiple preset time periods and calculating the first temperature data of each time period, thereby improving the accuracy of data processing. Using the first temperature data based on the time period and the preset critical temperature to calculate the soil cold accumulated temperature can make the model more suitable for elephant grass under different climatic conditions, different growth stages and different management and protection measures. By comparing the soil cold accumulated temperature and wintering rate under different management and protection measures, the effects of various measures can be more accurately evaluated, thereby optimizing the wintering management and protection strategy and improving the survival rate and yield of elephant grass. This method provides a new idea and method for determining the soil cold accumulated temperature, which helps to promote research and technological innovation in agricultural science and related fields.

[0046] In an optional embodiment, the step S210 of calculating the first temperature data according to each temperature data point within the preset time period includes: S2101. Calculate the average value of all temperature data points within the preset time period to obtain the first temperature data.

[0047] First, all temperature data points within a preset time period are collected from the soil temperature sensor. These data points are stored in the form of a time series, which contains the soil temperature values ​​measured at regular intervals (such as every hour, every half day, or every day) within the time period. Before calculating the average, some preprocessing of the data is required, such as removing outliers (such as extremely high or low temperature readings due to equipment failure or environmental factors) and filling missing values ​​(such as missing data due to equipment failure or data transmission problems). Preprocessing can ensure that the calculated average value is more accurate and reliable. Next, calculate the average value of all temperature data points within the preset time period. This can be achieved by adding all temperature values ​​and then dividing by the total number of temperature values. The resulting average value is the first temperature data for the time period.

[0048] The present invention can quickly obtain the temperature profile within the preset time period by calculating the average value of all temperature data points within the preset time period without the need for complex analysis of each temperature data point. The average value can reflect the overall level of soil temperature within the preset time period. Even if there are individual abnormal values ​​or missing values, the average value can represent the temperature conditions of the time period to a certain extent. When constructing a wintering rate prediction model, using the first temperature data based on the average value can reduce the impact of data fluctuations on the model, thereby improving the stability of the model and the prediction accuracy.

[0049] In an optional embodiment, the soil cold accumulated temperature data is calculated by accumulating the difference between the first temperature data and the preset critical temperature in each preset time period. In the above step S220, the soil cold accumulated temperature data is calculated by the following formula based on the preset critical temperature and the first temperature data in each preset time period: , formula 1; In the formula, is the soil cold accumulated temperature data, For the The first temperature data of a preset time period, is the preset critical temperature, is the number of preset time periods.

[0050] For each preset time period ( =1, 2, ..., ),calculate and This difference indicates the degree to which the soil temperature is lower than the critical temperature during this period. The difference values ​​in all preset time periods are accumulated to obtain the soil cold accumulated temperature data. This value reflects the total cumulative amount of time that the soil temperature was below the critical temperature during the entire study period. Output calculated value as the final result of soil cold accumulated temperature data.

[0051] The present invention can accurately evaluate the soil cold accumulated temperature by accumulating the difference between the soil temperature and the critical temperature in each preset time period, providing a scientific basis for plant overwintering management. Based on accurate soil cold accumulated temperature data, a more accurate overwintering rate prediction model can be constructed. Thus, a more reasonable overwintering management strategy can be formulated to improve the overwintering survival rate of plants. By accurately evaluating the soil cold accumulated temperature and optimizing the overwintering management strategy, plant frost damage and death caused by low temperature can be reduced, thereby improving agricultural production efficiency and economic benefits.

[0052] In an optional embodiment, the temperature data and soil temperature data of different locations can be monitored simultaneously by a temperature monitoring device, and a regression model of the soil cold accumulated temperature data and the temperature data can be constructed to achieve the effect of accurately predicting the soil cold accumulated temperature through the temperature data, and then construct a regression model of the temperature data and the overwintering rate of elephant grass, that is, the overwintering rate of elephant grass can be accurately predicted by knowing the temperature data of a certain place. However, due to factors such as different soil types and different water contents, the regression models of the temperature and soil cold accumulated temperature data of different locations are not single, but diverse. The present invention constructs regression models for the temperature data and soil cold accumulated temperature data for different locations and different soil types. The method also includes: S310, obtaining air temperature data and soil cold accumulated temperature data at each location.

[0053] In this step, in order to build an accurate and reliable prediction model, it is necessary to widely collect temperature data and soil cold accumulated temperature data from different geographical locations and climatic conditions. Temperature data can usually be obtained through channels such as meteorological stations, meteorological satellites, and meteorological forecasting systems. These data record historical temperature changes and can contain real-time or predicted data. The soil cold accumulated temperature data is calculated by the method of S210 to S220 described previously, which involves collecting soil temperature data within a preset time period and calculating the soil cold accumulated temperature data based on these data.

[0054] S320, regressing the air temperature data and soil cold accumulated temperature data of the same location to obtain a soil cold accumulated temperature prediction model for the corresponding location.

[0055] In this step, regression analysis is performed using the temperature data of the same location as the independent variable (input) and the soil cold accumulated temperature data as the dependent variable (output). The purpose of regression analysis is to find the mathematical relationship between the temperature data and the soil cold accumulated temperature data, so as to establish a soil cold accumulated temperature prediction model. This model can predict the soil cold accumulated temperature based on the temperature data, providing a basis for subsequent wintering rate prediction. There are many methods of regression analysis, such as linear regression, polynomial regression, nonlinear regression, etc. Those skilled in the art can weigh the characteristics of the data and the complexity of the model and select a specific regression method.

[0056] S330, combining the soil cold accumulated temperature prediction model of each location with the overwintering rate prediction model, respectively, to construct a comprehensive prediction model for the corresponding location, wherein the comprehensive prediction model takes the temperature data as input and the overwintering rate of elephant grass as output.

[0057] The previously established soil cold temperature prediction model is combined with the wintering rate prediction model. By using the output of the soil cold temperature prediction model (predicted soil cold temperature) as one of the inputs of the wintering rate prediction model, a comprehensive prediction model with temperature data as input and elephant grass wintering rate as output can be constructed. This model can comprehensively consider the impact of temperature and soil cold temperature on the wintering rate, and improve the accuracy and reliability of the prediction.

[0058] The present invention, by combining air temperature data and soil cold accumulated temperature data, can more comprehensively consider the factors affecting the overwintering rate of elephant grass, thereby improving the accuracy and reliability of prediction. The comprehensive prediction model can provide agricultural producers with more accurate overwintering rate prediction, helping them to formulate more reasonable planting and management strategies, such as selecting cold-resistant varieties, adjusting planting time, strengthening insulation measures, etc. The establishment and research of the comprehensive prediction model help to reveal the influence mechanism of air temperature and soil cold accumulated temperature on the overwintering rate of plants, and promote the research and development of agricultural science and related fields. Through more accurate prediction, agricultural producers can use resources more effectively, such as reducing unnecessary use of insulation materials, optimizing irrigation plans, etc., thereby improving resource utilization efficiency and economic benefits.

[0059] The present invention conducts field trials in site A, and the selected elephant grass material is giant fungus grass. The climate conditions in site A are just around the critical threshold for elephant grass to survive the winter. Under natural bare conditions, different varieties of elephant grass can show good differences in wintering rates, which is an important reason for choosing site A to carry out the main experiment.

[0060] In this area, different soil temperature gradient conditions were achieved by setting up different wintering management measures such as thermal insulation cotton and plastic sheets (Table 1 above). A total of 12 wintering management methods were set up, which produced different gradient effects of elephant grass wintering rates. The soil temperature data under each wintering management measure was continuously monitored. The instrument was set to record temperature data every half an hour, starting from early October 2023 and continuing until the end of April 2024, generating a total of 41,334 data.

[0061] The soil cold accumulated temperature data corresponding to each wintering management measure are calculated using the cold accumulated temperature calculation formula (Formula 1 above). Figure 2 and Figure 3 As shown in the figure, the soil cold accumulated temperature treated with different wintering management measures is significantly different, showing a good differentiation gradient. Through the linear regression equation, the soil cold accumulated temperature is used as the independent variable for fitting, so as to construct a model that can accurately predict the wintering rate based on the soil cold accumulated temperature data. Figure 2 and Figure 3 Different colors in represent different wintering management measures, Temperature represents soil temperature data, Time represents time, and Group represents the category of wintering management measures. WHAHA, WHAHB, WHAHC, WHAHD, WHAHE, WHAHF, WHAHG, WHAHH, WHAHI, WHAHJ, WHAHK, and WHAHL correspond to the wintering management measures in Table 1 above. The box plot represents the distribution of soil temperature data under the corresponding wintering management measures. g, ef, a, f, f, f, de, d, ab, c, a, and b above the box plot are used to indicate the significance of the difference in soil temperature data distribution under different wintering management measures. If the lowercase English letters above the box plot are completely different, it means that the difference is significant; if the lowercase English letters above the box plot are the same or contain the same English letters, it means that the difference is not significant.

[0062] By conducting field planting experiments in site A, combined with artificial control experiments, and using temperature monitoring equipment set up in the soil to obtain soil temperature data, the corresponding soil cold accumulated temperature data was calculated. The soil temperature data obtained by the temperature monitoring equipment is accurate, simple to operate, highly feasible, easy to implement long-term monitoring, and can accurately obtain the corresponding soil cold accumulated temperature data. Different gradients of wintering management treatments were set up using insulation cotton, plastic sheeting, and cold-proof cloth. The wintering rate data of different treatment groups in the second year were observed, and a wintering rate prediction model was established based on the soil cold accumulated temperature data and wintering rate data under different wintering management measures. The results showed that the wintering rate data (Survival rate) was significantly correlated with the soil cold accumulated temperature data (Cold accumulated temperature). By fitting a linear regression model, the wintering rate prediction model of elephant grass was obtained: wintering rate data = 0.001291 × soil cold accumulated temperature data + 1.172255 (such as Figure 4 as shown).

[0063] Based on this model, field predictions were carried out in sites B and C to test the accuracy of the overwintering rate prediction model.

[0064] In site B, elephant grass could not survive the winter normally in the absence of any artificial management in the field. Therefore, winter management was carried out at the end of the elephant grass growing season in 2023. A soil layer about 20 cm thick was laid on the mowed elephant grass stubble, and then a layer of rice husk about 5 cm thick was further laid on the soil layer. Finally, a layer of transparent plastic sheet was laid on the top, and the surrounding area was compacted with soil. The soil cold accumulated temperature data monitored by the soil temperature continuous monitoring equipment was -786.79±107.18℃, the wintering rate test result predicted by the model was 15.65%, and the actual wintering rate of elephant grass in the field obtained through actual investigation was 16.67% (SD value=6.89), with an error value of 6.12%. In site C, elephant grass can survive the winter under natural conditions. In 2023, a natural wintering test of elephant grass in the field was also arranged at another test site in the same place. The soil cold accumulated temperature value detected and calculated by the instrument is -692.02±84.13℃. The wintering rate test result predicted by the model is 27.89%, and the wintering rate test result obtained by the actual survey is 30.64%, with an error value of 8.98%. It can be seen that the wintering rate prediction model can be applied to the wintering rate prediction in many places. The accuracy of the model will continue to improve after the relevant research continues, and the main parameters of the model will be updated in time.

[0065] By continuously monitoring soil temperature data and calculating soil cold accumulated temperature data, the wintering rate of elephant grass in different locations can be effectively and accurately predicted. Technicians in different locations can directly predict the wintering effect of elephant grass in the local area by using soil temperature data, thereby providing guidance for the planting and cold-resistant wintering management of elephant grass, thereby promoting the promotion and application of elephant grass, optimizing planting costs, and reducing losses. At the same time, it can shorten the test cycle for subsequent scientific research work and effectively reduce unnecessary resource investment.

[0066] The following is a description of the elephant grass overwintering rate prediction device provided by the present invention. The elephant grass overwintering rate prediction device described below and the elephant grass overwintering rate prediction method described above can be referenced to each other.

[0067] The elephant grass wintering rate prediction device provided by the present invention refers to Figure 5 As shown, including: The data acquisition module 410 is used to obtain soil cold accumulated temperature data and wintering rate data of elephant grass under different wintering management measures; The model building module 420 is used to regress the soil cold accumulated temperature data and the wintering rate data under all wintering management measures to obtain a wintering rate prediction model; The result prediction module 430 is used to obtain actual cold accumulated temperature data of the location to be predicted, and input the actual cold accumulated temperature data into the wintering rate prediction model to obtain the wintering rate prediction result.

[0068] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the method for predicting the overwintering rate of elephant grass.

[0069] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0070] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the elephant grass overwintering rate prediction method provided by the above methods.

[0071] In yet another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the elephant grass overwintering rate prediction method provided by the above methods.

[0072] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0073] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the overwintering rate of elephant grass, characterized in that: include: Obtain soil cold accumulated temperature data and wintering rate data of elephant grass under different wintering management measures; The soil cold accumulated temperature data and wintering rate data under all wintering management measures were regressed to obtain the wintering rate prediction model; The actual cold accumulated temperature data of the location to be predicted is obtained, and the actual cold accumulated temperature data is input into the wintering rate prediction model to obtain the wintering rate prediction result.

2. The method for predicting the overwintering rate of elephant grass according to claim 1, characterized in that: The soil cold accumulated temperature data under each wintering management measure is determined by a soil temperature data set obtained by a temperature monitoring device installed in the soil.

3. The method for predicting the overwintering rate of elephant grass according to claim 2, characterized in that: The soil temperature data set is a plurality of temperature data points within a preset time range, and the preset time range is divided into a plurality of preset time periods; The soil cold accumulated temperature data is determined by the following method: For each preset time period, calculating first temperature data according to each temperature data point within the preset time period; A preset critical temperature is obtained, and the soil cold accumulated temperature data is calculated according to the preset critical temperature and the first temperature data in each preset time period.

4. The method for predicting the overwintering rate of elephant grass according to claim 3, characterized in that: Calculating first temperature data according to each temperature data point within the preset time period includes: The first temperature data is obtained by calculating the average value of all temperature data points within the preset time period.

5. The method for predicting the overwintering rate of elephant grass according to claim 3, characterized in that: According to the preset critical temperature and the first temperature data of each preset time period, the soil cold accumulated temperature data is calculated by the following formula: In the formula, is the soil cold accumulated temperature data, =1, 2, ..., , For the The first temperature data of a preset time period, is the preset critical temperature, is the number of preset time periods.

6. The method for predicting the overwintering rate of elephant grass according to claim 1, characterized in that: The method further comprises: Obtain air temperature data and soil cold accumulated temperature data for each location; The air temperature data and soil cold accumulated temperature data of the same location are regressed to obtain the soil cold accumulated temperature prediction model of the corresponding location; The soil cold accumulated temperature prediction model of each location is combined with the overwintering rate prediction model to construct a comprehensive prediction model for the corresponding location. The comprehensive prediction model takes the temperature data as input and the overwintering rate of elephant grass as output.

7. A device for predicting the overwintering rate of elephant grass, characterized in that: include: The data acquisition module is used to obtain the soil cold accumulated temperature data and wintering rate data of elephant grass under different wintering management measures; The model building module is used to regress the soil cold accumulated temperature data and wintering rate data under all wintering management measures to obtain the wintering rate prediction model; The result prediction module is used to obtain the actual cold accumulated temperature data of the location to be predicted, and input the actual cold accumulated temperature data into the wintering rate prediction model to obtain the wintering rate prediction result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for predicting the overwintering rate of elephant grass as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the overwintering rate of elephant grass according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the overwintering rate of elephant grass according to any one of claims 1 to 6 is implemented.

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