Method, device and apparatus for simulating crop growth stages

Abstract

The application relates to the technical field of agricultural meteorology and crop model, and provides a simulation method, device and equipment for a crop growth period, which comprises the following steps: acquiring meteorological data and crop growth period observation data of a target area; determining a full-climate production potential time sequence and a climate fluctuation factor; determining biological parameters, human intervention parameters and climate correction parameters; determining a to-be-corrected growth period day sequence corresponding to a candidate sowing day by using the biological parameters and the human intervention parameters, and correcting the to-be-corrected growth period day sequence by using the climate fluctuation factor and the climate correction parameters to obtain a corrected growth period day sequence. The application comprehensively considers the cooperation of multiple climate elements, the human intervention effect and the climate fluctuation background, realizes high-precision dynamic simulation of the crop growth period, and can provide decision-making for agricultural production in response to climate change.

Description

Technical Field

[0001] This application relates to the field of agricultural meteorology and crop modeling technology, and in particular to methods, apparatus and equipment for simulating crop growth stages. Background Technology

[0002] Adjusting crop sowing dates is an important measure to proactively adapt to environmental changes such as climate warming, while accurate simulation of crop growth periods is the foundation for assessing the impact of climate change and optimizing planting systems.

[0003] However, existing crop growth stage simulation models are mostly driven by growing degree days (GDD) or single meteorological factors, making it difficult to comprehensively reflect the synergistic effects of multiple meteorological elements such as temperature, solar radiation, and water on crop development. Furthermore, existing models do not systematically quantify the impact of pre-sowing climate background (legacy effects) on crop growth. Ultimately, this leads to significant discrepancies between predicted and actual crop growth stages. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for simulating crop growth periods, which can integrate the synergistic effects of multiple climate factors and quantitatively analyze the impact of human interventions and climate fluctuations on the simulation of crop growth periods, thereby improving prediction accuracy.

[0005] This application provides a method for simulating crop growth period, comprising: acquiring meteorological data and crop growth period observation data of a target area; determining the climate production potential per unit leaf area based on the meteorological data to obtain a time series of total climate production potential potential; determining a standard curve of mean climate state based on the time series of total climate production potential potential, and calculating the ratio of the cumulative total climate production potential potential of the target year to the corresponding value of the standard curve to obtain a climate fluctuation factor; determining target model parameters based on crop growth period observation data and cumulative total climate production potential potential; wherein the target model parameters include biological parameters, anthropogenic influence parameters, and climate correction parameters; using biological parameters and anthropogenic influence parameters, determining the growth period sequence to be corrected corresponding to the candidate sowing date, and correcting the growth period sequence to be corrected using the climate fluctuation factor and climate correction parameters to obtain the corrected growth period sequence.

[0006] According to the crop growth period simulation method provided in this application, the growth period date sequence to be corrected is corrected by climate fluctuation factor and climate correction parameter. After obtaining the corrected growth period date sequence, the method further includes: determining the earliest sowing date and the latest sowing date based on the corrected growth period date sequence and crop maturity date, and the earliest sowing date and the latest sowing date constitute the crop sowing period window.

[0007] According to the crop growth period simulation method provided in this application, the target model parameters are determined based on crop growth period observation data and cumulative total climate production potential. This includes: fitting the target growth period date sequence and the corresponding cumulative total climate production potential based on crop growth period observation data to determine biological parameters; fitting the relative increment relationship between the target growth period date sequence and the cumulative total climate production potential relative to the sowing date sequence and the cumulative total climate production potential on the sowing date to determine anthropogenic influence parameters; and fitting the relationship between the actual and simulated difference of the target growth period and the climate fluctuation factor to determine climate correction parameters.

[0008] According to the crop growth period simulation method provided in this application, meteorological data and crop growth period observation data of the target area are obtained, and the climate production potential per unit leaf area is determined based on the meteorological data to obtain the time series of the total climate production potential. The method includes: calculating the total solar radiation per unit time of the target area by sequentially correcting it with the photosynthetic efficiency coefficient, temperature correction coefficient and water correction coefficient to obtain the climate production potential; dividing the climate production potential with the leaf area correction function to obtain the climate production potential per unit leaf area; and summing the climate production potential per unit leaf area according to the daily sequence to obtain the time series of the total climate production potential.

[0009] According to the crop growth period simulation method provided in this application, the target model parameters are determined based on crop growth period observation data and cumulative total climate production potential. The method includes: linearly fitting the crop growth period date sequence as the independent variable and the corresponding cumulative total climate production potential as the dependent variable; extracting the intercept and slope as biological parameters reflecting the inherent developmental characteristics of the crop variety; calculating the relative change rate of the cumulative climate production potential from the sowing date to the target growth period as the first increment; calculating the relative change rate of the time sequence from the sowing date to the target growth period as the second increment; linearly fitting the first increment and the second increment; extracting the fitting coefficient as a parameter quantifying the anthropogenic influence of sowing date variation on the development process regulation effect; subtracting the actual observed growth period date sequence from the simulated date sequence calculated based on the initial model to obtain the residual; and linearly regressing the residual with the climate fluctuation factor of the sowing period to obtain climate correction parameters.

[0010] According to the crop growth period simulation method provided in this application, biological parameters and anthropogenic influence parameters are used to determine the growth period sequence to be corrected corresponding to the candidate sowing date. The correction of the growth period sequence to be corrected is obtained by correcting the growth period sequence to be corrected using climate fluctuation factors and climate correction parameters. The method includes: combining the sequence of the target sowing date with the corresponding cumulative climate production potential and substituting it into a joint expression derived from biological parameters and anthropogenic influence parameters to solve for the growth period sequence to be corrected corresponding to the candidate sowing date; substituting the climate fluctuation factor into the linear regression equation constructed by the climate correction parameters to calculate the residual estimate; and adding the growth period sequence to be corrected with the residual estimate to finally output the corrected growth period sequence.

[0011] According to the crop growth period simulation method provided in this application, a standard curve of the average climate state is determined based on the time series of the total climate production potential potential, and the ratio of the cumulative total climate production potential potential of the target year to the corresponding value of the standard curve is calculated to obtain the climate fluctuation factor. The method includes: determining the multi-year daily cumulative climate production potential potential of the target area based on the time series of the total climate production potential potential; fitting the average Logistic function of the day sequence and the cumulative total climate production potential potential based on the multi-year daily cumulative climate production potential potential to obtain the standard curve of the average climate state; and determining the ratio of the cumulative total climate production potential potential of the target year to the corresponding value of the standard curve as the climate fluctuation factor.

[0012] This application also provides a crop growth period simulation device, comprising: a data input module for acquiring meteorological data and crop growth period observation data of a target area, determining the climate production potential per unit leaf area based on the meteorological data to obtain a time series of total climate production potential potential; a climate fluctuation module for determining a standard curve of average climate state based on the time series of total climate production potential potential, and calculating the ratio of the cumulative total climate production potential potential of the target year to the corresponding value of the standard curve to obtain a climate fluctuation factor; a model parameter determination module for determining target model parameters based on crop growth period observation data and cumulative total climate production potential potential; wherein the target model parameters include biological parameters, anthropogenic influence parameters, and climate correction parameters; and a growth period date sequence module for determining the growth period date sequence to be corrected corresponding to candidate sowing days using biological parameters and anthropogenic influence parameters, and correcting the growth period date sequence to be corrected using the climate fluctuation factor and climate correction parameters to obtain a corrected growth period date sequence.

[0013] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the crop growth period simulation method as described above.

[0014] This application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the crop growth period simulation method as described above.

[0015] This application also provides a computer program product, including a computer program that, when executed by a processor, implements a method for simulating the crop growth period as described above.

[0016] The method, apparatus, and equipment for simulating crop growth periods provided in this application extrapolate the overall climate production potential by acquiring meteorological and observational data and calculating the climate fluctuation factor. After determining the preliminary growth period sequence using biological and anthropogenic parameters, it is further corrected using climate correction parameters and the climate fluctuation factor. This method comprehensively considers multiple factors such as natural climate, crop physiology, and human intervention, quantifies the impact of interannual climate fluctuations, and thus improves the accuracy of crop growth period simulation, providing a more reliable reference for agricultural production time planning and field management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the crop growth period simulation method provided in this application embodiment.

[0019] Figure 2 This is a schematic diagram comparing the simulated and measured values ​​of the maize growth period provided in the embodiments of this application.

[0020] Figure 3 This is a schematic diagram illustrating the determination of the sowing period window provided in an embodiment of this application.

[0021] Figure 4 This is a schematic diagram of the structure of the crop growth period simulation device provided in the embodiments of this application.

[0022] Figure 5 This is a schematic diagram of the physical structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0025] This application provides a method for simulating crop growth period, which can accurately determine crop growth period and avoid prediction bias caused by ignoring the synergistic effects of multiple climate factors and pre-sowing climate background.

[0026] Specifically, this application establishes a dynamic simulation method for the growth period based on all-climate production potential factors, and independently determines biological parameters and human response parameters for each growth period, so as to achieve accurate simulation of any target growth period during crop growth, which can be used to determine the crop growth period and sowing window.

[0027] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for simulating crop growth stages according to an embodiment of this application. In this embodiment, the method for simulating crop growth stages may include steps S110 to S140, and the specific steps are as follows: S110: Obtain meteorological data and crop growth period observation data for the target area, determine the climate production potential per unit leaf area based on the meteorological data, and obtain the time series of the total climate production potential.

[0028] In this embodiment, the crop growth period refers to the collective term for a series of developmental stages of a crop from sowing, emergence, heading (or flowering) to maturity, with each stage characterized by a corresponding day sequence (i.e., the day of the year).

[0029] For example, the growth period of winter wheat can include several stages such as emergence, tillering, jointing, heading, flowering, grain filling, and maturity; while the growth period of rice can include several stages such as transplanting, tillering, booting, heading, milk stage, and maturity.

[0030] It should be understood that the crops in this application are not limited to the examples above. Crops can also be other crops such as corn, soybeans, cotton, and rapeseed. Accordingly, the growth period can also be divided into different stages depending on the type of crop.

[0031] Among them, crop growth period observation data refers to phenological observation data recorded for each key developmental stage of the target crop during its growth and development. Crop growth period observation data can come from long-term fixed-point observation records of agricultural meteorological experimental stations. However, it should be understood that this application is not limited to this. Crop growth period observation data can also come from field experiments, farmer surveys, or phenological period products based on remote sensing inversion.

[0032] In this embodiment, climate production potential refers to the theoretical upper limit of dry matter production that a crop population may achieve after climate factors such as water, temperature, and light are fully satisfied or corrected according to actual climate conditions.

[0033] Climate productivity per unit leaf area is an indicator that reflects the assimilation capacity per unit green leaf area, obtained by combining climate productivity with a leaf area correction function.

[0034] The total climate production potential potential refers to the cumulative curve of the climate production potential per unit leaf area, obtained by summing the daily changes over time. It can comprehensively characterize the cumulative effect of climate elements such as light, temperature, and water on the potential driving force of crop development.

[0035] The target area refers to the agricultural production area. This target area can be a large region or a specific site.

[0036] The acquired meteorological data may include, for example, the daily total solar radiation, maximum temperature, minimum temperature, average temperature, relative humidity, precipitation, wind speed, and other elements of the target area.

[0037] Optionally, meteorological data may be derived from ground meteorological station observations, satellite remote sensing inversion products, or reanalysis datasets, but this application is not limited to these, and meteorological data may also be derived from other available meteorological data sources.

[0038] Crop growth period observation data can be obtained from long-term phenological data of the growth period recorded by agricultural meteorological observation stations, including the actual occurrence dates of each developmental stage of the crop (such as sowing, emergence, heading, flowering, maturity, etc.) in multiple historical years.

[0039] S120: Determine the standard curve of the average climate state based on the time series of the total climate production potential potential, and calculate the ratio of the cumulative total climate production potential potential potential of the target year to the corresponding value of the standard curve to obtain the climate fluctuation factor.

[0040] Specifically, the cumulative daily climate production potential of the target region is determined based on the time series of the total climate production potential potential; based on the cumulative daily climate production potential potential, the average Logistic function of the day series and the cumulative total climate production potential potential is fitted to obtain the standard curve of the average climate state.

[0041] The cumulative total climate productivity potential is a function of time. To eliminate the random effects of interannual climate fluctuations, a standard climate curve for the average climate state can be established based on the cumulative total climate productivity potential calculated from multi-year daily meteorological data, as shown below: ; In the formula, Let d represent the standard cumulative total climate productivity potential, and L represent the peak value of the cumulative total climate productivity potential. k For the growth rate parameter, The day sequence corresponding to the fastest accumulation rate.

[0042] The climate fluctuation factor is determined by the ratio of the cumulative total climate production potential potential for the target year to the corresponding value on the standard curve, as follows: ; In the formula, It is a climate fluctuation factor, which can represent the degree of deviation of the actual climate on day d of year i from the standard state. This represents the actual total climate production potential corresponding to day d of year i.

[0043] The climate fluctuation factor is a dimensionless quantity used to reflect the degree of deviation of the climate state of a target year from the multi-year average climate state. A value greater than 1 indicates that the climate conditions of the current year are higher than the multi-year average, and a value less than 1 indicates that the climate conditions of the current year are lower than the multi-year average. This indicates that the accumulation rate is too fast. This indicates that the accumulation rate is relatively slow.

[0044] S130: Determine the target model parameters based on crop growth period observation data and cumulative full-climate production potential; the target model parameters include biological parameters, anthropogenic influence parameters, and climate correction parameters.

[0045] Specifically, biological parameters can be determined by fitting the target growth period date sequence with the cumulative all-climate production potential of the corresponding date based on crop growth period observation data.

[0046] By fitting the relative increments of the target growth period date sequence and cumulative total climate production potential potential relative to the sowing date sequence and cumulative total climate production potential potential potential on the sowing date, the parameters of anthropogenic influence can be determined.

[0047] By fitting the relationship between the actual and simulated difference of the target fertility period and the climate fluctuation factor, climate correction parameters can be determined.

[0048] S140: Using biological parameters and anthropogenic influence parameters, determine the fertility period sequence to be corrected corresponding to the candidate sowing date, and correct the fertility period sequence to be corrected by climate fluctuation factor and climate correction parameter to obtain the corrected fertility period sequence.

[0049] This embodiment can obtain the corrected growth period sequence corresponding to the candidate sowing date. The corrected growth period sequence comprehensively considers the inherent developmental characteristics of the crop variety, the impact of sowing date adjustments, and the impact of climate fluctuations in the current year, thus more accurately reflecting the actual date on which the crop may reach the target growth period under specific sowing dates and specific climatic conditions.

[0050] Using the above method, the overall climate production potential is extrapolated by acquiring meteorological and observational data, and the climate fluctuation factor is calculated. After determining the preliminary growth period sequence using biological and anthropogenic parameters, it is further corrected using climate correction parameters and the climate fluctuation factor. This method comprehensively considers multiple factors such as natural climate, crop physiology, and human intervention, quantifies the impact of interannual climate fluctuations, and thus improves the accuracy of crop growth period simulation. It can provide a more reliable reference for agricultural production time planning and field management.

[0051] In some embodiments, after step S140, the method for simulating crop growth stages may further perform the following steps: S150: Based on the revised growth period date sequence and crop maturity period, the earliest sowing date and the latest sowing date are determined respectively, and the earliest sowing date and the latest sowing date constitute the sowing period window of the crop.

[0052] The crop sowing window refers to the range of sowing dates available while ensuring the crop can safely mature and has high yield potential. In this embodiment, the crop sowing window is determined based on both the earliest and latest sowing dates.

[0053] The earliest sowing date is the earliest candidate sowing date that ensures the revised critical growth period is no earlier than the safe development threshold; the latest sowing date is the latest candidate sowing date that ensures the revised maturity date is no later than the safe maturity threshold. The date range formed by the earliest and latest sowing dates constitutes the sowing window for the target crop in the target region and target year.

[0054] The sowing window is a feasible range that defines the flexibility of sowing activities in terms of time. The two endpoints of this range (the earliest sowing day and the latest sowing day) are boundaries determined based on factors such as crop growth cycle and climatic conditions.

[0055] Within this range, growers can flexibly choose the appropriate sowing date based on the actual situation. For example, if the sowing window is 150 to 180 days, then sowing can be done on any day within 160 to 170 days.

[0056] In some embodiments, agronomic constraints are set for key target growth stages of crops (such as flowering, grain-filling, or maturity), for example, requiring maturity no later than the date of frost or the date of clearing the stubble for the next crop.

[0057] The "stubble clearing period" refers to the time allotted after the harvest of the previous crop for preparation work such as tilling and land preparation for the next crop. Taking winter wheat-summer maize rotation areas as an example, 3 to 5 days are typically allocated in advance to complete land preparation, thus creating more favorable conditions for the growth of subsequent crops. To meet this requirement, a constraint period of 3 to 5 days should be allowed between the maturity of the first crop and the sowing of the second crop.

[0058] All candidate sowing dates that meet the constraints are combined into a set of feasible sowing dates. The earliest and latest sowing dates then constitute the sowing window for the crop. It should be noted that the constraints can be flexibly adjusted according to different regions, crops, and cultivation objectives.

[0059] According to the crop growth period simulation method in the above embodiments, this application calculates the time series of full-climate production potential based on meteorological data, which can comprehensively characterize the coupled driving effect of climate elements such as light, temperature, and water on crop development, and avoids the limitation of the traditional accumulated temperature method that only considers temperature factors.

[0060] This application, by introducing climate fluctuation factors and climate correction parameters, can effectively correct the nonlinear impact of interannual climate fluctuations on crop development processes and improve the robustness and reliability of growth period simulation. By simultaneously considering three types of parameters—biological parameters, anthropogenic influence parameters, and climate correction parameters—the model can reflect both the inherent developmental characteristics of crop varieties and the dual impacts of anthropogenic sowing date adjustments and climate fluctuations.

[0061] Furthermore, this application, by determining the sowing window based on the modified fertility period date sequence, can provide agricultural producers with more accurate and actionable sowing period recommendations. It achieves high-precision dynamic simulation of the fertility period and scientific definition of the suitable sowing window, which can provide decision-making support for agricultural production in response to climate change.

[0062] In some embodiments, the steps of acquiring meteorological data and crop growth period observation data for a target area, and determining the climate production potential per unit leaf area based on the meteorological data to obtain a time series of the total climate production potential may specifically include: The total solar radiation per unit time in the target area is calculated by correcting it sequentially using the photosynthetic efficiency coefficient, temperature correction coefficient, and moisture correction coefficient to obtain the climate production potential. The climate production potential is then divided by the leaf area correction function to obtain the climate production potential per unit leaf area. Finally, the climate production potential per unit leaf area is summed up in diurnal order to obtain the time series of the total climate production potential.

[0063] Among them, the temperature correction coefficient is a piecewise function constructed based on the average temperature, the optimum temperature, and the upper and lower limit temperature thresholds, while the water correction coefficient is a piecewise function constructed based on the actual water vapor pressure deficit and the upper and lower limit thresholds of water vapor pressure deficit that limit plant photosynthesis.

[0064] In this embodiment, daily meteorological data of the target area over many years can be obtained, such as daily average temperature, precipitation, solar radiation, relative humidity, etc., as well as at least 3 sets of reproductive period records.

[0065] Based on meteorological data, the climate production potential per unit leaf area is calculated daily using a crop climate production potential model. ), and calculate the daily cumulative total starting from January 1st of each year. Values ​​were obtained to obtain the time series of the total climate production potential. .

[0066] Optionally, the crop climate production potential model is configured with a stepwise correction method for daily calculations.

[0067] The total climatic productivity potential refers to the climatic productivity potential per unit leaf area, which is a comprehensive reflection of the influence of daily meteorological conditions and the theoretical leaf area index. The specific calculation is as follows: ; ; In the formula, Climate production potential per unit leaf area on a given date (kg ha) -1 ), The time series of total climate production potential for a certain period (kg ha) -1 ), Indicates the cumulative number of days (d). It is a counting variable. Climate production potential (kg ha) -1 ), This is the leaf area correction function.

[0068] This can be calculated according to the methods recommended by the Food and Agriculture Organization of the United Nations (FAO). Based on this, this embodiment can also be corrected for temperature and moisture, as detailed in the following calculations: ;

[0069] ; ; In the formula, Photosynthetic efficiency (g kJ) -1 Q represents the total solar radiation per unit time (MJ / m²). -2 ), k This refers to a unit conversion function, which can take the value 10000. ε This refers to the ratio of photosynthetic radiation to total radiation, and can be taken as 0.49. The quantum efficiency of photosynthesis can take a value of 0.224. Ω This refers to the ratio of a crop's photosynthetic CO2 fixation capacity, and can be set to a value of 1.0. α This refers to the reflectance of the plant community, and can take a value of 0.08. β The transmittance of a dense plant community can be 0.06. ρ This refers to the proportion of radiation intercepted by non-photosynthetic organs, and can be taken as 0.1. This refers to the proportion of light exceeding the light saturation point, and can take a value of 0.01. ω This refers to the ratio of respiratory consumption to photosynthetic products, and can be 0.3. This refers to the moisture content of mature grains, and can be taken as 0.15. This refers to the proportion of inorganic ash content in plants, and can be taken as 0.08. q Heat per unit dry matter (MJ / kg) -1 ); s The economic coefficient of a crop.

[0070] Alternatively, when the crop is corn, q It can take the value 17.8. s It can take the value 0.4.

[0071] Alternatively, when the crop is rice, q It can take the value 18.0. s It can take the value 0.45.

[0072] Alternatively, when the crop is wheat, q It can take the value 17.0. s It can take the value 0.4.

[0073] It should be noted that the parameter values ​​can be adjusted within the above range based on the specific crop (such as soybeans, peanuts, etc.) and the corresponding growth stage, or further determined by referring to field trial data. The values ​​provided above are general recommended ranges based on practical experience.

[0074] This is the temperature correction factor. This indicates the average temperature (°C). This indicates the lower limit temperature (°C) for crop growth. This indicates the upper limit of temperature for crop growth (°C). This indicates the optimal temperature (°C) for crop growth.

[0075] Alternatively, when the crop is corn, , and The values ​​are 8℃, 38℃ and 25℃ respectively.

[0076] Alternatively, when the crop is wheat, , and The values ​​are 3℃, 30℃ and 20℃ respectively.

[0077] Alternatively, when the crop is rice, , and The values ​​are 10℃, 35℃ and 26℃ respectively.

[0078] This is the moisture correction factor. This is due to a water vapor pressure deficit (kPa). and These are the maximum and minimum thresholds that limit plant photosynthesis, respectively.

[0079] Optionally, and The values ​​are 4.3 and 0.65 respectively.

[0080] In some embodiments, the step of determining the target model parameters based on crop growth period observation data and cumulative all-climate production potential may specifically include: A linear fit was performed using the crop growth period date as the independent variable and the corresponding cumulative climate production potential potential as the dependent variable. The intercept and slope were extracted as biological parameters reflecting the inherent developmental characteristics of crop varieties. The relative change rate of the cumulative climate production potential potential from the sowing date to the target growth period was calculated as the first increment, and the relative change rate of the time date from the sowing date to the target growth period was calculated as the second increment. The first increment and the second increment were linearly fitted, and the fitting coefficient was extracted as a parameter to quantify the anthropogenic influence of the sowing date variation on the development process. The residual was obtained by subtracting the actual observed growth period date from the simulated date date calculated based on the initial model. The residual was linearly regressed with the climate fluctuation factor of the sowing period to obtain the climate correction parameter.

[0081] Furthermore, the steps to revise the reproductive date sequence may specifically include: By combining the date sequence of the target sowing date with the corresponding cumulative climate production potential, and substituting it into the joint expression derived from biological parameters and anthropogenic influence parameters, the date sequence of the fertility period corresponding to the candidate sowing date is solved; the climate fluctuation factor is substituted into the linear regression equation constructed by the climate correction parameters to calculate the residual estimate; the date sequence of the fertility period to be corrected is added to the residual estimate to finally output the corrected fertility period date.

[0082] In this embodiment, biological parameters and human influence parameters can be obtained by fitting the following two empirical equations based on at least three sets of reproductive period records: ① A model relating cumulative total climate production potential to target fertility period: ; In the formula, For a certain growth period of a crop, for The corresponding time series of all-climate production potential (kg ha) -1 ), a and b are biological parameters that reflect the inherent developmental characteristics of crop varieties.

[0083] ②Assume the actual crop sowing date sequence The corresponding time series of all-climate production potential is (kgha) -1 The relative increment relationship from sowing to the target growth period is as follows: ; In the formula, c and d are human-induced parameters, which quantify the regulatory effect of changes in sowing date on subsequent development.

[0084] Combining the two formulas above in this embodiment, the expression for calculating any growth period of a crop can be obtained: (1) In the formula, For a certain growth period of a crop, This refers to the actual crop sowing date. for The corresponding time series of all-climate production potential (kg ha) -1 After determining the parameters a, b, c and d based on the target growth period, the growth period sequence corresponding to the candidate sowing date can be solved based on equation (1).

[0085] Furthermore, since any growth period is influenced by the pre-sowing climatic background, this influence is related to the local climate state. Therefore, it can be based on climate fluctuation factors. The residual estimates are determined using the climate correction parameters (e, f). ,Right now Then the above As the reproductive date sequence to be corrected and the residual estimate Adding them together, we finally obtain the corrected reproductive period date sequence. as follows: (2) Among them, climate fluctuation factors The climate correction parameters (e, f) can be determined by calculating the ratio of the cumulative total climate production potential in the target year to the corresponding value on the standard curve. First, using the actual observed fertility date sequence The result obtained from equation (1) The difference between ,Right now: Then, based on the actual climate fluctuation factors of the corresponding period ,Establish and The linear regression relationship is as follows: Through this regression analysis, the climate correction parameters e and f can be determined.

[0086] The above embodiments of this application provide a method for simulating crop growth period. First, the required meteorological data and crop growth period record data are input. Based on the input data, the cumulative climate production potential is calculated daily. Then, based on the existing data, multiple key parameters required by the model are automatically fitted. Using the fitted parameters, the specific dates of each growth period of the crop are simulated and predicted. The simulation results are then corrected for residuals to improve accuracy. Finally, under the premise of meeting the conditions for safe crop maturity, the earliest and latest sowing dates that are theoretically feasible are solved through daily iterative calculations, thereby determining the sowing window.

[0087] Furthermore, this embodiment can output the simulation and analysis results in the form of charts, which may include time series curves, spatial distribution maps, etc., and provide relevant accuracy evaluation indicators.

[0088] In one specific embodiment, the modified reproductive period model is validated to assess its accuracy and reliability. The specific validation metrics and calculation formulas used are as follows: ① ; RMSE, or root mean square error, is used to reflect the overall deviation between simulated and measured day sequence values.

[0089] ② ; in, The coefficient of determination is used to reflect the model's ability to explain the variables. The closer the value is to 1, the better the model fits.

[0090] ③ ; Wherein, MBE is the mean bias error, which is used to reflect the systematic error of the model. MBE>1 indicates that the model overestimates overall, and MBE<1 indicates that the model underestimates overall. The closer it is to 0, the higher the accuracy of the model.

[0091] ④ ; MAE stands for Mean Absolute Error, which reflects the average error magnitude of the predicted value.

[0092] above, Let i be the measured date sequence of the i-th sample. For the simulated day sequence of the corresponding samples, is the average of the measured day sequence of all samples, and n is the total number of samples participating in the verification.

[0093] For example, this embodiment provides a method for simulating the maize growth period based on the full climate production potential, which can determine the date sequence of any target growth period, and may specifically include the following steps: 1) Calculate the time series of the full climate production potential.

[0094] Based on daily meteorological data of the target area, the climate production potential per unit leaf area is calculated daily using a climate production potential model. Starting from January 1st of each year, the number is accumulated daily. Time series of full-climate production potential were obtained. .

[0095] 2) Construct the average climatological curve.

[0096] Based on the cumulative daily climate productivity potential of the target region over many years, the average Logistic function of the daily sequence and cumulative total climate productivity potential is fitted to obtain the standard curve of the average climate state. The ratio of the actual cumulative total climate productivity potential to the corresponding value of the standard climate curve in the target year is determined as the climate fluctuation factor.

[0097] 3) Determine the model parameters for the target reproductive period.

[0098] Based on at least three sets of crop growth period observation data in the target area, the cumulative all-climate production potential of the target growth period date sequence and the corresponding date is fitted to determine biological parameters a and b.

[0099] By fitting the relative increment relationship between the target growth period date sequence and the cumulative total climate production potential potential relative to the sowing date sequence and the cumulative total climate production potential potential potential on the sowing date, the anthropogenic influence parameters c and d are determined.

[0100] By fitting the relationship between the actual and simulated difference of the target fertility period and the climate fluctuation factor, the climate correction parameters e and f are determined.

[0101] 4) Simulate the target reproductive period.

[0102] Using the target growth period determination parameters (a, b, c, and d), the growth period sequence to be corrected corresponding to any sowing date is determined. Then, the growth period sequence to be corrected in the simulation results is corrected for climate fluctuations using climate fluctuation factors and climate correction coefficients (e and f) to obtain the final corrected growth period.

[0103] As mentioned above, the growth period can include multiple stages. If the target growth period is determined to be the maturity period, then parameters related to the maturity period can be further combined to clarify the suitable sowing period range for the crop. Specific details are as follows: 5) Determine the earliest sowing date.

[0104] Based on biological parameters related to maturity ( , ) and human influence parameters ( , The actual crop sowing date is determined daily, starting from January 1st each year. Calculate Corresponding time series of all-climate production potential And calculate the theoretical reproductive period corresponding to the maturity date based on the determined parameters and equation (1). Combined with climate correction parameters related to maturity ( , The simulated theoretical maturity date is corrected using equation (2) to obtain the corrected final simulated maturity date. ,if It is a finite positive value and is greater than If the above conditions are met, then sowing can be determined on that day. The date on which the above conditions are first met is the earliest sowing date.

[0105] 6) Determine the latest sowing date.

[0106] The planting period for subsequent crops is determined according to local cropping systems. Crop planting systems can be divided into two types: One type is single-season crop areas, where only one crop is grown per year. The planting period for subsequent crops is determined based on the safe harvest period of the local crop, with the date set as the longest day of the year. For example, December 31st in a common year is the 365th day, and in a leap year it is the 366th day. The other type is multi-season crop areas, where two or three crops are grown per year. The planting period for subsequent crops is determined by the sowing deadline for the subsequent crop.

[0107] After determining the planting period for the subsequent crop, the search proceeds day by day backward from the day before that date until the first crop that meets the planting conditions is found, and the final simulated maturity date of the crop after planting on that day is also determined. The date that does not exceed the crop planting period shall be regarded as the latest sowing date.

[0108] 7) Determine the appropriate sowing period window.

[0109] The time period consisting of the earliest and latest sowing days is called the sowing window. The length of the window is the difference between the two days plus one day.

[0110] The following verification is based on summer maize in a maize-growing area of ​​a certain region. A staggered sowing experiment was conducted at agricultural meteorological research stations in areas A and B of the region, setting up four sowing date experiments (SD, based on the usual field sowing date): 10 days (d) earlier, 0 days (d), 10 days (d) later, and 20 days (d) later. A total of 48 sets of field experiment data were collected from 2019 to 2024. Model parameters were determined using experimental data from 2021 to 2023, and independent verification was performed using reserved experimental station data from 2019-2020 and 2024, as well as data from all stations in the region.

[0111] Table 1. Varieties and sowing dates at different sites

[0112] Please see Figure 2 , Figure 2 This is a schematic diagram comparing the simulated and measured values ​​of the maize growth period provided in the embodiments of this application.

[0113] exist Figure 2 The graph uses a scatter plot to show the comparison between simulated values ​​(Y, in days) and measured values ​​(x, in days) for a specific reproductive period. Two auxiliary lines are included: a dashed line (1:1 reference line) and a solid line (fitted line based on sample data).

[0114] Each dot in the diagram represents a validation sample. The shade of the dot's color reflects the local sample density around that dot; darker colors (e.g., orange, red, and dark red) indicate a denser sample count around the dot, while lighter colors (e.g., white and yellow) indicate a sparser sample count around the dot.

[0115] The fitting equations differ across regions. The fitting equation for a certain region is Y = 0.76x + 65.57, with a coefficient of determination of R0. 2 =0.59; the fitted equation for location A is Y=0.80x+47.85, and the coefficient of determination is R. 2 =0.96; the fitted equation for location B is Y=0.75x+68.23, with a coefficient of determination of R0.96. 2 =0.80.

[0116] The verification results show that the simulated values ​​of the stations (location A and location B) and a certain area maintain a high correlation with the measured values, and are distributed around the 1:1 line. The errors are all within an acceptable range, indicating that the model has strong applicability at the regional scale.

[0117] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating the determination of the sowing period window provided in an embodiment of this application.

[0118] exist Figure 3 In the diagram, the horizontal axis represents the sowing time in days (d), and the vertical axis represents the maturity time in days (d). The sowing (period) window is the earliest sowing date indicated by the arrow. ) to the latest sowing date ( (Time span) Indicates the planting period (day sequence) for the subsequent crop. Subsequent crop planting period It refers to the allowable time frame between the harvest of the previous season's crop and the completion of sowing or transplanting of a specific crop in the next season.

[0119] For maturity, any candidate sowing date ( , ) and determine the corresponding biological parameters at maturity ( , ) and human influence parameters ( , Substitute into equation (1) to calculate the theoretical crop maturity date sequence. And combined with the corresponding climate correction parameters for the maturity period ( , ) and equation (2) Climate correction is performed, and the determined revised fertility date sequence is the final maturity date. .

[0120] like It is a finite positive value and is greater than If the conditions are met, then sowing is deemed permissible on that day; otherwise, sowing is not permitted. Starting from January 1st, each day will be assessed, and the date on which the above conditions are first met will be the earliest sowing day. .

[0121] Then, based on the planting period of the subsequent crop. (Date sequence), from Search forward day by day to find the corresponding final maturity date that meets the sowing conditions and is calculated by equations (1) and (2). and through The maximum sowing date is determined by the constraints and recorded as the latest sowing date. .

[0122] Finally, the sowing window was determined to be... The window length represents the number of days available for sowing, specifically... sky.

[0123] It should be noted that although the above example uses corn as an example, this application is not limited to this. The method of the embodiments of this application is also applicable to the determination of the sowing period window of other crops such as winter wheat, rice, cotton, and rapeseed.

[0124] This application also provides a crop growth period simulation device. The crop growth period simulation device provided in this application will be described below. The crop growth period simulation device described below can be referred to in correspondence with the crop growth period simulation method described above.

[0125] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a crop growth period simulation device provided in an embodiment of this application. In this embodiment, the crop growth period simulation device may include a data input module 410, a climate fluctuation module 420, a model parameter determination module 430, and a growth period date sequence module 440.

[0126] The data input module 410 is used to acquire meteorological data and crop growth period observation data of the target area, and to determine the climate production potential per unit leaf area based on the meteorological data in order to obtain the time series of the total climate production potential.

[0127] The climate fluctuation module 420 is used to determine the standard curve of the average climate state based on the time series of the total climate production potential potential, and to calculate the ratio of the cumulative total climate production potential potential potential of the target year to the corresponding value of the standard curve, thereby obtaining the climate fluctuation factor.

[0128] The model parameter determination module 430 is used to determine the target model parameters based on crop growth period observation data and cumulative full-climate production potential; the target model parameters include biological parameters, anthropogenic influence parameters and climate correction parameters.

[0129] The reproductive period sequence module 440 is used to determine the reproductive period sequence to be corrected corresponding to the candidate sowing date using biological parameters and human influence parameters, and to correct the reproductive period sequence to be corrected by climate fluctuation factors and climate correction parameters to obtain the corrected reproductive period sequence.

[0130] In some embodiments, the crop growth period simulation device may further include a sowing period window module, which may be specifically used for: Based on the revised growth period date sequence and crop maturity period, the earliest and latest sowing dates are determined, and the earliest and latest sowing dates constitute the sowing period window for crops.

[0131] In some embodiments, the model parameter determination module 430 is specifically used for: Based on crop growth period observation data, the biological parameters are determined by fitting the target growth period date sequence and the cumulative total climate production potential potential of the corresponding date; the anthropogenic influence parameters are determined by fitting the relative increment relationship between the target growth period date sequence and the cumulative total climate production potential potential potential of the planting date and the cumulative total climate production potential potential of the planting date; and the climate correction parameters are determined by fitting the relationship between the actual and simulated difference of the target growth period and the climate fluctuation factor.

[0132] In some embodiments, the data input module 410 is specifically used for: The total solar radiation per unit time in the target area is calculated by correcting it sequentially using the photosynthetic efficiency coefficient, temperature correction coefficient, and moisture correction coefficient to obtain the climate production potential. The climate production potential is then divided by the leaf area correction function to obtain the climate production potential per unit leaf area. Finally, the climate production potential per unit leaf area is summed up in diurnal order to obtain the time series of the total climate production potential.

[0133] Optionally, the temperature correction factor is a piecewise function constructed based on the average temperature, the optimum temperature, and the upper and lower limit temperature thresholds, and the water correction factor is a piecewise function constructed based on the actual water vapor pressure deficit and the upper and lower limit thresholds of water vapor pressure deficit that limit plant photosynthesis.

[0134] In some embodiments, the model parameter determination module 430 is specifically used for: A linear fit was performed using the crop growth period date as the independent variable and the corresponding cumulative climate production potential potential as the dependent variable. The intercept and slope were extracted as biological parameters reflecting the inherent developmental characteristics of crop varieties. The relative change rate of the cumulative climate production potential potential from the sowing date to the target growth period was calculated as the first increment, and the relative change rate of the time date from the sowing date to the target growth period was calculated as the second increment. The first increment and the second increment were linearly fitted, and the fitting coefficient was extracted as a parameter to quantify the anthropogenic influence of the sowing date variation on the development process. The residual was obtained by subtracting the actual observed growth period date from the simulated date date calculated based on the initial model. The residual was linearly regressed with the climate fluctuation factor of the sowing period to obtain the climate correction parameter.

[0135] In some embodiments, the reproductive date sequence module 440 is specifically used for: By combining the date sequence of the target sowing date with the corresponding cumulative climate production potential, and substituting it into the joint expression derived from biological parameters and anthropogenic influence parameters, the date sequence of the fertility period corresponding to the candidate sowing date is solved; the climate fluctuation factor is substituted into the linear regression equation constructed by the climate correction parameters to calculate the residual estimate; the date sequence of the fertility period to be corrected is added to the residual estimate to finally output the corrected fertility period date.

[0136] In some embodiments, the climate fluctuation module 420 is specifically used for: The cumulative daily climate production potential of the target region is determined based on the time series of the total climate production potential potential. Based on the cumulative daily climate production potential potential, the average Logistic function of the day series and the cumulative total climate production potential potential is fitted to obtain the standard curve of the average climate state. The ratio of the cumulative total climate production potential potential potential of the target year to the corresponding value of the standard curve is determined as the climate fluctuation factor.

[0137] On the other hand, this application also provides an electronic device, please refer to... Figure 5 , Figure 5 This is a schematic diagram of the physical structure of the electronic device provided in the embodiments of this application, such as... Figure 5 As shown, the electronic device may include a memory 520, a processor 510, and a computer program stored in the memory 520 and executable on the processor 510. When the processor 510 executes the program, it can implement a method for simulating crop growth stages, which may include: Meteorological data and crop growth period observation data for the target area are acquired. The climate production potential per unit leaf area is determined based on the meteorological data to obtain a time series of the total climate production potential potential. A standard curve of the mean climate state is determined based on the time series of the total climate production potential potential, and the ratio of the cumulative total climate production potential potential for the target year to the corresponding value on the standard curve is calculated to obtain the climate fluctuation factor. Target model parameters are determined based on crop growth period observation data and the cumulative total climate production potential potential. These target model parameters include biological parameters, anthropogenic influence parameters, and climate correction parameters. Using the biological parameters and anthropogenic influence parameters, the growth period sequence to be corrected corresponding to the candidate sowing date is determined, and the growth period sequence to be corrected is corrected using the climate fluctuation factor and climate correction parameters to obtain the corrected growth period sequence.

[0138] Optionally, the electronic device may further include a communication bus 530 and a communication interface 540, wherein the processor 510, the communication interface 540, and the memory 520 communicate with each other via the communication bus 530. The processor 510 can call the computer program in the memory 520 to execute the crop growth period simulation method provided by the above methods.

[0139] Furthermore, the logical instructions in the aforementioned memory 520 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] On the other hand, this application also provides a computer program product, which includes a computer program that 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 crop growth period simulation method provided by the above methods. The steps and principles of the simulation method have been described in detail in the above methods and will not be repeated here.

[0141] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the crop growth period simulation method provided by the above methods. The steps and principles of the simulation method have been described in detail in the above methods and will not be repeated here.

[0142] Non-transitory computer-readable storage media can be any available medium or data storage device that can be accessed by a processor, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0143] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for simulating crop growth stages, characterized in that, include: Acquire meteorological data and crop growth period observation data for the target area, and determine the climate production potential per unit leaf area based on the meteorological data to obtain a time series of the total climate production potential. The standard curve of the average climate state is determined based on the time series of the total climate production potential, and the ratio of the cumulative total climate production potential of the target year to the corresponding value of the standard curve is calculated to obtain the climate fluctuation factor. The target model parameters are determined based on the crop growth period observation data and the cumulative full-climate production potential; wherein the target model parameters include biological parameters, anthropogenic influence parameters, and climate correction parameters. Using the biological parameters and the anthropogenic influence parameters, the fertility period sequence to be corrected corresponding to the candidate sowing date is determined, and the fertility period sequence to be corrected is corrected by the climate fluctuation factor and the climate correction parameter to obtain the corrected fertility period sequence.

2. The method for simulating crop growth period according to claim 1, characterized in that, After correcting the fertility date sequence to be corrected using the climate fluctuation factor and the climate correction parameter to obtain the corrected fertility date sequence, the process further includes: Based on the modified growth period date sequence and crop maturity date, the earliest sowing date and the latest sowing date are determined respectively, and the earliest sowing date and the latest sowing date constitute the sowing period window of the crop.

3. The method for simulating crop growth period according to claim 1, characterized in that, The determination of target model parameters based on the crop growth period observation data and the cumulative all-climate production potential includes: Based on the crop growth period observation data, the cumulative all-climate production potential of the target growth period date sequence and the corresponding date is fitted to determine the biological parameters; By fitting the relative increment relationship between the target growth period date sequence and the cumulative total climate production potential potential relative to the sowing date sequence and the cumulative total climate production potential potential potential on the sowing date, the anthropogenic influence parameters are determined. By fitting the relationship between the actual and simulated difference of the target fertility period and the climate fluctuation factor, the climate correction parameters are determined.

4. The method for simulating crop growth period according to claim 1, characterized in that, The acquisition of meteorological data and crop growth period observation data of the target area, and the determination of climate production potential per unit leaf area based on the meteorological data to obtain a time series of total climate production potential, includes: The total solar radiation per unit time in the target area is calculated by correcting it sequentially using the photosynthetic efficiency coefficient, temperature correction coefficient, and moisture correction coefficient to obtain the climate production potential. The climate productivity potential is divided by the leaf area correction function to obtain the climate productivity potential per unit leaf area. The climate production potential per unit leaf area is summed up daily to obtain the time series of the total climate production potential.

5. The method for simulating crop growth period according to claim 1, characterized in that, The determination of target model parameters based on the crop growth period observation data and the cumulative all-climate production potential includes: Linear fitting was performed using the crop growth period date sequence as the independent variable and the corresponding cumulative all-climate production potential potential as the dependent variable. The intercept and slope were extracted as the biological parameters reflecting the inherent developmental characteristics of crop varieties. The relative rate of change of cumulative climate production potential from the sowing date to the target growth period is calculated as the first increment, and the relative rate of change of time sequence from the sowing date to the target growth period is calculated as the second increment. The first increment and the second increment are linearly fitted, and the fitting coefficient is extracted as the anthropogenic influence parameter for quantifying the regulatory effect of sowing date changes on the development process. The residual is obtained by subtracting the actual observed fertile date sequence from the simulated date sequence calculated based on the initial model. The residual is then fitted with the climate fluctuation factor of the sowing period through linear regression to obtain the climate correction parameter.

6. The method for simulating crop growth period according to claim 1, characterized in that, The process of determining the fertility period sequence to be corrected corresponding to the candidate sowing date using the biological parameters and the anthropogenic influence parameters, and correcting the fertility period sequence to be corrected using the climate fluctuation factor and the climate correction parameter to obtain the corrected fertility period sequence includes: By combining the date sequence of the target sowing date with the corresponding cumulative climate production potential, and substituting it into the joint expression derived from the biological parameters and the anthropogenic influence parameters, the date sequence of the fertility period to be corrected corresponding to the candidate sowing date is solved. Substituting the climate fluctuation factor into the linear regression equation constructed from the climate correction parameters, the residual estimate is calculated. The corrected fertility date sequence is added to the residual estimate to finally output the corrected fertility date sequence.

7. The method for simulating crop growth period according to claim 1, characterized in that, The process of determining the standard curve of the average climate state based on the time series of the total climate production potential, and calculating the ratio of the cumulative total climate production potential of the target year to the corresponding value of the standard curve to obtain the climate fluctuation factor includes: The multi-year daily cumulative climate production potential of the target area is determined based on the time series of the full climate production potential potential. Based on the cumulative daily climate production potential potential over many years, the average Logistic function of the daily sequence and cumulative total climate production potential potential potential is fitted to obtain the standard curve of the average climate state. The climate fluctuation factor is defined as the ratio of the cumulative total climate production potential for the target year to the corresponding value on the standard curve.

8. A device for simulating the growth period of crops, characterized in that, include: The data input module is used to acquire meteorological data and crop growth period observation data of the target area, and determine the climate production potential per unit leaf area based on the meteorological data to obtain the time series of the total climate production potential. The climate fluctuation module is used to determine the standard curve of the average climate state based on the time series of the total climate production potential, and to calculate the ratio of the cumulative total climate production potential of the target year to the corresponding value of the standard curve, so as to obtain the climate fluctuation factor. The model parameter determination module is used to determine target model parameters based on the crop growth period observation data and the cumulative all-climate production potential; wherein the target model parameters include biological parameters, anthropogenic influence parameters, and climate correction parameters. The reproductive period sequence module is used to determine the reproductive period sequence to be corrected corresponding to the candidate sowing date using the biological parameters and the human influence parameters, and to correct the reproductive period sequence to be corrected using the climate fluctuation factor and the climate correction parameter to obtain the corrected reproductive period sequence.

9. 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, it implements the crop growth period simulation method as described in any one of claims 1 to 7.

10. 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, it implements the crop growth period simulation method as described in any one of claims 1 to 7.

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