Method, system and equipment for predicting wadding floating period of willow, medium and product

Abstract

The invention discloses a method, a system, equipment, a medium and a product for predicting the wadding floating period of willow, and relates to the field of environmental pollution, and the method comprises the steps: constructing wadding floating period prediction models corresponding to different types of willow based on a unary linear regression modeling method; the different willow types comprise salix matsudana, populus tomentosa and black populus tomentosa; obtaining the activity accumulated temperature of different willow types in the current year; and inputting the current-year activity accumulated temperatures of different willow types into the corresponding willow wadding floating period prediction models to obtain predicted willow wadding floating period daily ordinal numbers corresponding to the different willow types, so that the accuracy of predicting the willow wadding floating period can be improved, accurate willow wadding floating period time is provided for municipal and garden departments, and the method is suitable for popularization and application. Therefore, pollution caused by poplar catkins is prevented in advance, and poplar catkins harm is reduced.

Description

Technical Field

[0001] The present application relates to the field of environmental pollution, and particularly to a method, system, device, medium and product for predicting the period of willow catkin floating. Background Art

[0002] Willows are widely distributed in North China, Northeast China, Northwest China, South China, East China, Central South and Southwest China due to their characteristics such as light-loving, well-developed root systems, cold tolerance, drought resistance and high adaptability. They are important shelter forests, timber forests and landscaping tree species in northern China. These tree species have become the first choice for early urban greening in the north because of their easy reproduction, easy survival, fast growth and quick results. In the northwestern part of Shandong Province, mainly planted are early-germinating tree species such as Populus tomentosa, Salix matsudana and Salix babylonica, as well as fast-growing poplars with later germination mainly of Populus nigra. In spring, when the fruits of willows mature, their seeds will float with the wind, forming a unique scene of willow catkin floating. The poems of Yong Yuzhi in the Tang Dynasty, "It doesn't reach the ground without wind, and fills the air with wind; because it's just like snow, don't let it near your hair." and Shi Mao in the Song Dynasty, "Floating with rain becomes falling snow, and flying away in spring becomes a flying ball." vividly describe this unique visual scene of willow catkin floating in spring. While evoking people's infinite beautiful imaginations, it also brings inconvenience to people's lives, travels and productions, and even causes fires. It is one of the main sources of ecological environmental pollution in spring, especially during the period from mid-April to early May when willow catkins are prevalent.

[0003] Therefore, accurately predicting the period of willow catkin floating (i.e., the seed shedding period, which is also the 6th phenological period after germination) and providing timely meteorological forecast services for preventing and controlling willow catkin pollution for municipal and gardening departments can reduce the harm of willow catkins and has important practical significance. Summary of the Invention

[0004] The purpose of the present application is to provide a method, system, device, medium and product for predicting the period of willow catkin floating, which can improve the accuracy of predicting the period of willow catkin floating, provide accurate time forecasts for the period of willow catkin floating of willows for municipal and gardening departments, thereby preventing and controlling the pollution caused by willow catkins in advance and reducing the harm of willow catkins.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In the first aspect, the present application provides a method for predicting the period of willow catkin floating, including:

[0007] Based on the unary linear regression modeling method, construct prediction models for the period of willow catkin floating corresponding to different willow types; different willow types include Salix matsudana, Populus tomentosa and Populus nigra;

[0008] Obtain the accumulated active temperature of different willow types this year; the accumulated active temperature includes: the accumulated active temperature before this day and the accumulated active temperature after this day; the accumulated active temperature before this day is the sum of the daily average temperatures greater than zero degrees Celsius from the initial day sequence number to the day sequence number of the day before this day; the accumulated active temperature after this day is the sum of the daily average temperatures greater than zero degrees Celsius from the day sequence number of this day to the day sequence number of the preset willow catkin period; the initial day sequence number is the day sequence number when the willow resumes growth;

[0009] Input the accumulated active temperature of different willow types this year into the corresponding willow catkin period prediction model to obtain the predicted willow catkin period day sequence numbers corresponding to different willow types.

[0010] In a second aspect, the present application provides a willow catkin period prediction system, including:

[0011] A construction module for constructing a willow catkin period prediction model corresponding to different willow types based on the unary linear regression modeling method; different willow types include Salix matsudana, Populus tomentosa, and Populus nigra;

[0012] An acquisition module for acquiring the accumulated active temperature of different willow types this year; the accumulated active temperature includes: the accumulated active temperature before this day and the accumulated active temperature after this day; the accumulated active temperature before this day is the sum of the daily average temperatures greater than zero degrees Celsius from the initial day sequence number to the day sequence number of the day before this day; the accumulated active temperature after this day is the sum of the daily average temperatures greater than zero degrees Celsius from the day sequence number of this day to the day sequence number of the preset willow catkin period; the initial day sequence number is the day sequence number when the willow resumes growth;

[0013] A prediction module for inputting the accumulated active temperature of different willow types this year into the corresponding willow catkin period prediction model to obtain the predicted willow catkin period day sequence numbers corresponding to different willow types.

[0014] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the willow catkin period prediction method described in any one of the above.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the willow catkin period prediction method described in any one of the above.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the willow catkin period prediction method described in any one of the above.

[0017] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0018] The present application provides a method, system, device, medium and product for predicting the willow catkin flying period. By using the unary linear regression modeling method, prediction models corresponding to different willow types are constructed, so as to predict the willow catkin flying periods corresponding to different willow types more objectively, scientifically and reasonably. By obtaining the accumulated temperature of different willow types in the current year and using the prediction models corresponding to different willow types, the ordinal number of the day for predicting the willow catkin flying period can be obtained. Compared with the traditional methods of taking the average value and simple accumulated temperature, using the prediction model for prediction improves the accuracy of predicting the willow catkin flying period, thereby providing accurate time forecasts of the willow catkin flying periods for departments such as municipal and garden departments, enabling the municipal and garden departments to prevent the pollution caused by willow catkins in advance, and further reducing the harm of willow catkins. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic flowchart of a method for predicting the willow catkin flying period provided by an embodiment of the present application.

[0021] Figure 2 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0023] The growth rate of plants and the timing of the emergence of phenological phases are related to environmental factors, and among environmental factors, meteorological factors play a decisive role. The models for predicting plant phenological phases can be roughly divided into three types - the accumulated temperature model, the temperature base point model, and the statistical model. Since the influence degrees of meteorological factors such as air temperature, precipitation, relative humidity, sunshine hours, and wind speed on plant growth and development and phenological phases are closely related to the local soil texture, terrain, and climate background. The northwest of Shandong is located in the semi-humid monsoon climate zone of the north temperate zone. Although the precipitation is less in spring, poplars and willows are large trees with deep roots, reaching 2 - 3 meters. The influence of drought on the growth and development and phenological phases of poplars and willows is limited. In spring, the northwest of Shandong has sufficient sunlight, and sunshine hours are not a limiting factor. Through relevant analysis, wind speed and relative humidity have basically no influence on their spring phenological phases. That is to say, in spring in the northwest of Shandong, the phenological phases of woody plants are mainly affected by temperature, and it is suitable to use the theory of accumulated temperature to solve problems. Based on the theory of accumulated temperature, this application uses the method of mathematical statistics to establish a prediction model for the willow catkin flying period, breaking through the traditional modeling method. Moreover, the prediction parameters are few, the model is simple, and the prediction accuracy is high; breaking through the traditional method of establishing the accumulated temperature threshold by using the average value or several test data, this application uses the method of calculating the expected value based on a large number of observation data to establish the active accumulated temperature threshold. The accumulated temperature threshold established by this method is more stable and accurate.

[0024] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] In an exemplary embodiment, as Figure 1 shown, a method for predicting the willow catkin flying period is provided. This method is executed by a computer device, and specifically, it can be executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking the application of this method to a server as an example for illustration, it includes the following steps 1 to step 3. Among them:

[0026] Step 1: Based on the unary linear regression modeling method, construct a prediction model for the willow catkin flying period corresponding to different willow types; different willow types include Salix matsudana, Populus tomentosa, and Populus nigra. Specifically, step 1 includes:

[0027] Step 11: Obtain the data of different willow types in historical years; the data of historical years include: the active accumulated temperature in historical years and the day ordinal number of the willow catkin flying period in historical years; the active accumulated temperature in historical years is the sum of the daily average air temperatures greater than zero degrees Celsius from the initial day ordinal number in historical years to the day ordinal number of the willow catkin flying period in historical years.

[0028] Determination of active accumulated temperature: When the daily average temperature rises to 0°C around the Beginning of Spring, the trees in spring start to sprout. Therefore, 0°C is taken as the biological lower temperature limit for poplars and willows. The specific types of poplars and willows are Populus tomentosa, Salix matsudana, and Populus nigra. The flower buds of Populus tomentosa and Salix matsudana begin to swell after the Beginning of Spring. Therefore, February 1st (ordinal number of the day is the 32nd day) is taken as the starting point (i.e., the initial ordinal number of the day) for the recovery growth of Populus tomentosa and Salix matsudana. The flower buds of Populus nigra begin to swell around the Waking of Insects. Therefore, March 1st (ordinal number of the day is the 60th or 61st day) is taken as the starting point for the recovery growth of Populus nigra. Then, the active accumulated temperature TT during the period from the starting point to the period of poplars and willows shedding catkins is:

[0029]

[0030] Among them, u is the ordinal number of the day corresponding to the starting date of this period, n is the ordinal number of the day corresponding to the ending date of this period, and T i is the daily average temperature on the i-th day, with the unit of °C. When T i is greater than zero degree Celsius, take T i ; when T i is less than or equal to zero degree Celsius, take zero degree Celsius.

[0031] Based on the phenological observation data of Salix matsudana and Populus tomentosa from 2006 to 2021 and the local meteorological data during the same period, calculate the ordinal number of the day corresponding to the period of poplars and willows shedding catkins each year for Salix matsudana and Populus tomentosa and the active accumulated temperature from February 1st to the period of poplars and willows shedding catkins. Based on the phenological observation data of Populus nigra from 2006 to 2021 and the local meteorological data during the same period, calculate the ordinal number of the day corresponding to the period of poplars and willows shedding catkins each year for Populus nigra and the active accumulated temperature from March 1st to the period of poplars and willows shedding catkins.

[0032] Step 12: Determine the expected value of the active accumulated temperature in the historical years as the active accumulated temperature threshold;

[0033] Specifically, the calculation formula for the accumulated temperature threshold is:

[0034]

[0035] Among them, GDD ford is the active accumulated temperature threshold; k is the number of elements of different active accumulated temperatures used to determine the active accumulated temperature threshold; TT j is the active accumulated temperature of the j-th element; P j is the probability of the occurrence of the active accumulated temperature of the j-th element.

[0036] Accumulated temperature theory: On the premise that other conditions are met, temperature plays a leading role in the development of plants. The relationship between the development speed and temperature is a linear relationship; plants require a certain lower temperature limit to start development. In the development period completed in the high-temperature season, there is also an upper temperature problem; plants need a certain accumulated temperature to complete a certain stage of development, that is, there is an accumulated temperature threshold. This application adopts the minimum active accumulated temperature threshold (GDDfore ) As the minimum accumulated temperature threshold. By analyzing the correlation between the day ordinal number during the willow catkin flying period and temperature, precipitation, sunshine, relative humidity, and wind speed, the results show that the day ordinal number during the willow catkin flying period is only related to temperature, meeting the prerequisite conditions of the accumulated temperature theory. According to the accumulated temperature theory, the condition for willow trees to enter the next phenological period is:

[0037] TT≥GDD fore .

[0038] Among them, TT is the active accumulated temperature from the starting point to the willow catkin flying period, with the unit of ℃·d; GDD fore is the active accumulated temperature threshold, with the unit of ℃·d.

[0039] Through the above formula, the calculation formula of the accumulated temperature threshold, and the data in step 11, the active accumulated temperature thresholds for Salix matsudana, Populus tomentosa, and Populus nigra can be determined to be 625.6℃·d, 526.5℃·d, and 780.8℃·d respectively.

[0040] Step 13: Using the active accumulated temperature of historical years as the independent variable, the day ordinal number of the willow catkin flying period in historical years as the dependent variable, and the active accumulated temperature threshold as the constraint condition, a unary linear regression modeling method is used to construct an initial willow catkin flying period prediction model.

[0041] Specifically, the unary linear regression modeling method of Origin data analysis software is used to analyze the correlation between the willow catkin flying period corresponding to Salix matsudana, Populus tomentosa, and Populus nigra and the active accumulated temperature, and construct the initial willow catkin flying period prediction models corresponding to Salix matsudana, Populus tomentosa, and Populus nigra.

[0042] Step 14: Solve the coefficients of the initial willow catkin flying period prediction model to determine the willow catkin flying period prediction model.

[0043] Specifically, the active accumulated temperature of each year of Salix matsudana, Populus tomentosa, and Populus nigra, as well as the day ordinal number of the willow catkin flying period corresponding to Salix matsudana, Populus tomentosa, and Populus nigra, are respectively input into the initial willow catkin flying period prediction models corresponding to Salix matsudana, Populus tomentosa, and Populus nigra, so as to obtain the coefficients of the initial willow catkin flying period prediction models corresponding to Salix matsudana, Populus tomentosa, and Populus nigra, and determine the willow catkin flying period prediction model.

[0044] Specifically, the expressions of the willow catkin flying period prediction models corresponding to different willow types are:

[0045] Y=a + b×(TT f-past + TT f ).

[0046]

[0047] TT f-past + TT f ≥GDD ford .

[0048] Among them, Y is the ordinal number of the predicted poplar and willow catkin period corresponding to different poplar and willow types; both a and b are the coefficients of the poplar and willow catkin period prediction model. When the poplar and willow type is Salix matsudana, a = 42.51 and b = 0.104. When the poplar and willow type is Populus tomentosa, a = 53.81 and b = 0.087. When the poplar and willow type is Populus nigra, a = 90.94 and b = 0.038; TT f-past is the accumulated active temperature before this day; TT f is the accumulated active temperature after this day; m is the preset ordinal number of the poplar and willow catkin period day; T i is the daily average temperature on the i-th day; 1d is one day; GDD ford is the accumulated active temperature threshold.

[0049] In a specific embodiment, the poplar and willow catkin period prediction model corresponding to Salix matsudana:

[0050] Y l = 42.51 + 0.104×(TT fl-past + TT fl ).

[0051]

[0052] TT fl-past + TT fl ≥ 625.6℃·d.

[0053] Among them, Y l is the predicted ordinal number of the Salix matsudana catkin period day; TT fl-past is the sum of the daily average temperatures greater than zero degrees Celsius from February 1st (ordinal number is the 32nd day) of Salix matsudana to the day before this day; TT fl is the sum of the daily average temperatures greater than zero degrees Celsius from the ordinal number of this day of Salix matsudana to the ordinal number m of the preset poplar and willow catkin period (the poplar and willow catkin period corresponding to Salix matsudana).

[0054] The poplar and willow catkin period prediction model corresponding to Populus tomentosa:

[0055] Y b = 53.81 + 0.087×(TT fb-past + TT fb ).

[0056]

[0057] TT fb-past + TT fb ≥ 526.5℃·d.

[0058] Among them, Y b is the predicted ordinal number of the Populus tomentosa catkin period day; TT fb-pastis the sum of the daily average temperatures greater than zero degrees Celsius for each day from February 1 (ordinal day number 32) of Populus tomentosa to the ordinal day number of the previous day before this day; TT fb is the sum of the daily average temperatures greater than zero degrees Celsius for each day from the ordinal day number of this day of Populus tomentosa to the ordinal day number m of the preset willow catkin floating period (the willow catkin floating period corresponding to Populus tomentosa).

[0059] Prediction model for the willow catkin floating period corresponding to Populus nigra:

[0060] Y h = 90.94 + 0.038×(TT fh-past + TT fh ).

[0061]

[0062] TT fh-past + TT fh ≥ 780.8℃·d.

[0063] Among them, Y h is the predicted ordinal day number of the willow catkin floating period of Populus nigra; TT fh-past is the sum of the daily average temperatures greater than zero degrees Celsius for each day from March 1 (ordinal day number 60 or 61) of Populus nigra to the ordinal day number of the previous day before this day; TT fh is the sum of the daily average temperatures greater than zero degrees Celsius for each day from the ordinal day number of this day of Populus nigra to the ordinal day number m of the preset willow catkin floating period (the willow catkin floating period corresponding to Populus nigra).

[0064] The above three constructed models are tested and prediction verified. According to the phenological observation frequency requirements of the "Agricultural Meteorological Observation Specification", the observation is carried out every other day, and the phenological period prediction error standard is ≤ 2d.

[0065] Regression model performance evaluation:

[0066] Root mean square error (RMSE) and coefficient of determination (R 2 ) are used as indicators to evaluate the performance of the willow catkin floating period prediction models corresponding to different willow types. The root mean square error represents the degree of deviation between the simulated value and the actual value. The smaller the value, the better the simulated result. R 2 represents the fitting degree between the simulated value and the actual value, ranging from 0 to 1. The larger the value, the better the simulated result.

[0067]

[0068] Among them, Y' i is the ordinal day number of the actually observed willow catkin floating period in the i-th year; Y i is the ordinal day number of the simulated willow catkin floating period in the i-th year; n is the number of samples.

[0069] R 2= SSR / SST.

[0070]

[0071] Wherein, SSR is the regression sum of squares; SST is the total sum of squares; is the average value of the ordinal numbers of the actual observed days of willow catkin floating.

[0072] R 2 grading of, R 2 < 0.2, the predicted value is not related to the prediction factor, and the goodness of fit is extremely poor; 0.2 < R 2 < 0.4, the predicted value is related to the prediction factor, and the goodness of fit is poor; 0.4 ≤ R 2 ≤ 0.7, the predicted value is highly related to the prediction factor, and the goodness of fit is good; 0.7 < R 2 ≤ 1, the predicted value is highly correlated with the prediction factor, and the goodness of fit is good. In meteorology, the accuracy rate required for short-term climate prediction (prediction over 10 days) is 70 - 80%, so the requirement for the goodness of fit of linear simulation is relatively low. Generally, R 2 greater than 0.7 is regarded as high goodness of fit, with good linear fit, and 0.4 - 0.7 is regarded as relatively high goodness of fit, with good linear fit. This application basically adopts this standard). The "Specifications for Agricultural Meteorological Observation" (1993 edition) stipulates that natural phenological observations are made every other day. Therefore, the prediction error ≤ 2d is used as the standard for correct prediction, 3d is used as the standard for relatively accurate prediction, and more than 3d is used as the standard for incorrect prediction. The prediction accuracy rate is the percentage of the number of correct predictions in the total number of predictions; the relatively accurate prediction rate is the percentage of the sum of the number of correct and relatively accurate predictions in the total number of predictions; the prediction error rate is the percentage of the number of incorrect predictions in the total number of predictions.

[0073] The RMSE of Salix matsudana, Populus tomentosa, and Populus nigra in the simulation results are 1.2, 0.7, and 0.6 respectively, all meeting the accurate standard of ≤ 2d; the R 2 of Salix matsudana, Populus tomentosa, and Populus nigra are 0.7726, 0.7756, and 0.5594 respectively, all passing the test with a confidence level of 0.01. Among them, the simulation effects of Salix matsudana and Populus tomentosa are good, and the simulation effect of Populus nigra is relatively good.

[0074] Prediction verification: Using the observation data from 2022 to 2024, the prediction models for the catkin periods of Salix matsudana, Populus tomentosa, and Populus nigra were adopted, and the ordinal numbers of the corresponding days of their catkin periods are shown in Table 1. The prediction results show that according to the standard that the prediction is ≤ 2 days according to the phenological period, 3 days is relatively accurate, and more than 3 days is inaccurate. For the prediction model of Salix matsudana, among the 3 predictions, 2 results are correct and 1 is relatively accurate, with a prediction accuracy of 67% and a relatively accurate rate of 100%, and the prediction effect is good. For the prediction models of Populus tomentosa and Populus nigra, all 3 prediction results are accurate, with a prediction accuracy of 100% and a very good prediction effect. For the willow and poplar prediction model, the average prediction accuracy is close to 90%, the relatively accurate rate is 100%, and the prediction effect is good.

[0075] Table 1 Prediction effects of the willow and poplar catkin period models corresponding to Salix matsudana, Populus tomentosa, and Populus nigra in the past 3 years

[0076]

[0077] In Table 1, A represents the prediction absolute error, that is, the absolute value of the difference between the predicted value and the observed value.

[0078] Step 2: Obtain the accumulated temperature of different willow and poplar types in the current year; the accumulated temperature includes: the accumulated temperature before this day and the accumulated temperature after this day; the accumulated temperature before this day is the sum of the daily average temperatures greater than zero degrees Celsius from the initial ordinal number of days to the day before this day; the accumulated temperature after this day is the sum of the daily average temperatures greater than zero degrees Celsius from the ordinal number of this day to the ordinal number of the preset willow and poplar catkin period; the initial ordinal number of days is the ordinal number of days when the willow and poplar resume growth.

[0079] Specifically, the calculation formula for the daily average temperature is:

[0080] T i = λ1 + λ2T iG + λ3T iD .

[0081] Among them, T i is the daily average temperature on the i-th day; λ1, λ2, and λ3 are all coefficients of the daily average temperature; T iG is the highest temperature on the i-th day; T iD is the lowest temperature on the i-th day.

[0082] In a specific embodiment, the meteorological prediction of future weather temperature is limited to the daily highest temperature and the daily lowest temperature. It is necessary to convert the predicted daily highest temperature and daily lowest temperature into the prediction of the daily average temperature. The relationship between the daily average temperature and the daily highest temperature and daily lowest temperature in the northwestern Shandong region:

[0083]

[0084] Among them, is the daily average temperature on the i-th day in the northwestern Shandong region.

[0085] Step 3: Input the accumulated active temperature of different types of poplars and willows in the current year into the corresponding prediction models for the poplar and willow catkin flying periods to obtain the predicted day numbers of the poplar and willow catkin flying periods corresponding to different types of poplars and willows.

[0086] Before step 3, it also includes: judging whether the accumulated active temperature of poplars and willows in the current year is greater than or equal to the accumulated active temperature threshold; if so, input the accumulated active temperature of poplars and willows in the current year into the prediction model for the poplar and willow catkin flying period; if not, increase the preset day number of the poplar and willow catkin flying period by one day (i.e., 1d) until the accumulated active temperature of poplars and willows in the current year is greater than or equal to the accumulated active temperature threshold.

[0087] The beneficial effects of a method for predicting the poplar and willow catkin flying period proposed in this application are mainly manifested in:

[0088] (1) A prediction model for the poplar and willow catkin flying period is constructed by using the unary linear regression modeling method, so as to predict the poplar and willow catkin flying period more objectively, scientifically and reasonably. By obtaining the historical accumulated active temperature of poplars and willows and using the prediction model for the poplar and willow catkin flying period, the predicted day number of the poplar and willow catkin flying period can be obtained. Compared with the traditional method of taking the average value, using the prediction model for prediction improves the accuracy of predicting the poplar and willow catkin flying period, so as to provide accurate poplar and willow catkin flying period time for departments such as municipal and garden departments, enabling municipal and garden departments to prevent and control the pollution caused by poplar and willow catkins in advance, and thus reducing the harm of poplar and willow catkins.

[0089] (2) In the prediction models for the poplar and willow catkin flying period, the determination coefficients (R 2 ) of the prediction models for the catkin flying periods of Salix matsudana, Populus tomentosa and Populus nigra are 0.7726, 0.7756 and 0.5594 respectively, and all pass the test with a confidence level of 0.01, indicating that the day number of the poplar and willow catkin flying period is linearly correlated with the accumulated active temperature of ≥0°C from the flower bud swelling period to the poplar and willow catkin flying period. The root mean square error of the simulation results is between 0.6 and 1.2d, meeting the accuracy standard of ≤2d, and the simulation effect is good, which can meet the requirements of meteorological prediction services. The prediction accuracy of the trial forecast in the recent 3 years is close to 90%, and compared with the accuracy of 100%, the prediction accuracy is relatively high, further proving that the model simulation effect is good.

[0090] (3) The established accumulated active temperature thresholds from the catkin germination period to the poplar and willow catkin flying period of poplar and willow catkins using phenological observation data are 625.6°C·d, 526.5°C·d and 780.8°C·d respectively, filling the gaps in northwestern Shandong, and even in North China and the Huang-Huai-Hai region. The method of establishing the accumulated active temperature threshold using the expected value theory is more scientific and reasonable than the traditional method of taking the average value, and the threshold stability is higher.

[0091] Based on the same inventive concept, an embodiment of the present application further provides a prediction system for the willow catkin period. The implementation solutions provided by this system to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the willow catkin period prediction system provided below can refer to the limitations on the willow catkin period prediction method in the above text, and will not be repeated here.

[0092] In an exemplary embodiment, a willow catkin period prediction system is provided, including:

[0093] A construction module, configured to construct a willow catkin period prediction model corresponding to different willow types based on the unary linear regression modeling method; different willow types include Salix matsudana, Populus tomentosa, and Populus nigra.

[0094] An acquisition module, configured to acquire the accumulated temperature of different willow types this year; the accumulated temperature includes: the accumulated temperature before this day and the accumulated temperature after this day; the accumulated temperature before this day is the sum of the daily average temperatures greater than zero degrees Celsius from the initial day ordinal number to the day ordinal number of the day before this day; the accumulated temperature after this day is the sum of the daily average temperatures greater than zero degrees Celsius from the day ordinal number of this day to the preset willow catkin period day ordinal number; the initial day ordinal number is the day ordinal number when the willow resumes growth.

[0095] A prediction module, configured to input the accumulated temperature of different willow types this year into the corresponding willow catkin period prediction model to obtain the predicted willow catkin period day ordinal numbers corresponding to different willow types.

[0096] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 2 shown. This computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of this computer device is used to store the predicted willow catkin period day ordinal numbers. The input / output interface of this computer device is used to exchange information between the processor and external devices. The communication interface of this computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a willow catkin period prediction method.

[0097] Those skilled in the art can understand, Figure 2The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0098] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0099] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0100] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0101] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0102] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0103] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0104] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for predicting the period of willow catkin flying, characterized in that, The poplar and willow catkin period prediction method includes: Based on the unary linear regression modeling method, construct poplar and willow catkin period prediction models corresponding to different poplar and willow types; different poplar and willow types include Salix matsudana, Populus tomentosa, and Populus nigra. Obtain the accumulated temperature of different poplar and willow types this year; the accumulated temperature includes: the accumulated temperature before this day and the accumulated temperature after this day; the accumulated temperature before this day is the sum of the daily average temperatures greater than zero degrees Celsius from the initial day ordinal number to the day ordinal number of the day before this day; the accumulated temperature after this day is the sum of the daily average temperatures greater than zero degrees Celsius from the day ordinal number of this day to the day ordinal number of the preset poplar and willow catkin period; the initial day ordinal number is the day ordinal number when the poplar and willow resume growth. Input the accumulated temperature of different poplar and willow types this year into the corresponding poplar and willow catkin period prediction model to obtain the predicted poplar and willow catkin period day ordinal numbers corresponding to different poplar and willow types.

2. The poplar and willow catkin period prediction method according to claim 1, wherein, Before inputting the accumulated temperature of different poplar and willow types this year into the corresponding poplar and willow catkin period prediction model to obtain the predicted poplar and willow catkin period day ordinal numbers corresponding to different poplar and willow types, it further includes: Judge whether the accumulated temperature of different poplar and willow types this year is greater than or equal to the accumulated temperature threshold. If so, input the accumulated temperature of different poplar and willow types this year into the corresponding poplar and willow catkin period prediction model. If not, increase the preset poplar and willow catkin period day ordinal number by one day until the accumulated temperature of different poplar and willow types this year is greater than or equal to the corresponding accumulated temperature threshold.

3. The poplar catkin period prediction method according to claim 1, wherein Based on the unary linear regression modeling method, construct poplar and willow catkin period prediction models corresponding to different poplar and willow types, specifically including: Obtain the data of different poplar and willow types in historical years; the data in historical years includes: the accumulated temperature in historical years and the poplar and willow catkin period day ordinal numbers in historical years; the accumulated temperature in historical years is the sum of the daily average temperatures greater than zero degrees Celsius from the initial day ordinal number in historical years to the poplar and willow catkin period day ordinal number in historical years. Determine the expected value of the accumulated temperature in historical years as the accumulated temperature threshold. Taking the accumulated temperature in historical years as the independent variable, the poplar and willow catkin period day ordinal numbers in historical years as the dependent variable, and the accumulated temperature threshold as the constraint condition, use the unary linear regression modeling method to construct the initial poplar and willow catkin period prediction models corresponding to different poplar and willow types. Solve the coefficients of the initial poplar and willow catkin period prediction model to determine the poplar and willow catkin period prediction models corresponding to different poplar and willow types.

4. The method for predicting the willow catkin period according to claim 1, characterized in that, The calculation formula for the daily average temperature is: T i = λ1 + λ2T iG + λ3T iD ; Among them, T i is the daily average temperature on the i-th day; λ1, λ2, and λ3 are all coefficients of the daily average temperature; T iG is the highest temperature on the i-th day; T iD is the lowest temperature on the i-th day.

5. The willow catkin period prediction method according to claim 3, characterized in that, The calculation formula for the accumulated temperature threshold is: Among them, GDD ford is the active accumulated temperature threshold; k is the number of elements of different active accumulated temperatures used to determine the active accumulated temperature threshold; TT j is the active accumulated temperature of the j-th element; P j is the probability of the occurrence of the active accumulated temperature of the j-th element.

6. The method for predicting the willow catkin flying period according to claim 3, wherein The expression of the poplar and willow catkin period prediction model corresponding to different poplar and willow types is: Y = a + b×(TT f-past + TT f ); TT f-past +TT f ≥GDD ford ; Among them, Y is the ordinal number of the predicted catkin flying period corresponding to different willow and poplar types; both a and b are coefficients of the catkin flying period prediction model of willow and poplar. When the willow and poplar type is Salix matsudana, a = 42.51 and b = 0.

104. When the willow and poplar type is Populus tomentosa, a = 53.81 and b = 0.

087. When the willow and poplar type is Populus nigra, a = 90.94 and b = 0.038; TT f-past is the accumulated active temperature before this day; TT f is the accumulated active temperature after this day; m is the preset ordinal number of the catkin flying period of willow and poplar; T i is the daily average temperature on the i-th day; 1d is one day; GDD ford is the threshold of accumulated active temperature.

7. A prediction system for the period of willow catkin flying, characterized in that, The poplar and willow catkin period prediction system includes: A construction module for constructing poplar and willow catkin period prediction models corresponding to different poplar and willow types based on the unary linear regression modeling method; different poplar and willow types include Salix matsudana, Populus tomentosa, and Populus nigra. An acquisition module for acquiring the accumulated active temperature of different willow types in the current year; the accumulated active temperature includes: the accumulated active temperature before the current day and the accumulated active temperature after the current day; the accumulated active temperature before the current day is the sum of the daily average temperatures greater than zero degrees Celsius from the initial day sequence number to the day before the current day; the accumulated active temperature after the current day is the sum of the daily average temperatures greater than zero degrees Celsius from the current day sequence number to the preset willow catkin flying period day sequence number; the initial day sequence number is the day sequence number when the willow resumes growth. A prediction module for inputting the accumulated active temperature of different willow types in the current year into the corresponding willow catkin flying period prediction model to obtain the predicted willow catkin flying period day sequence numbers corresponding to different willow types.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the willow catkin flying period prediction method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the willow catkin flying period prediction method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the willow catkin flying period prediction method according to any one of claims 1-6.

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