Prediction method and system for optimal sowing rate of winter wheat

Abstract

The invention provides a method and a system for predicting the optimal sowing rate of winter wheat. The method comprises the following steps: determining a first key influence parameter and a second key influence parameter which influence the optimal sowing rate of winter wheat; calculating optimal basic seedling data based on the first key influence parameters; and calculating emergence rate data based on the second key influence parameter, and calculating optimal seeding rate data by using the optimal basic seedling data and the emergence rate data. According to the method, the influence of various factors on the seeding rate is considered, and the optimal seeding rate at the current moment can be dynamically given; the recommendation density of different regions can be given according to soil information, meteorological data and cultivated varieties of different regions, and the universality of model prediction is improved; the optimal winter wheat seeding rate prediction with higher mechanism, higher accuracy and better universality is realized.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural production, and in particular to a method and system for predicting an optimal sowing amount of winter wheat. Background Art

[0002] In agricultural production activities, the prediction of the optimal sowing amount for winter wheat is a key measure, which is of great significance for ensuring wheat yield, quality, and the economic benefits and sustainability of agricultural production.

[0003] Existing technologies generally use two methods to predict the optimal sowing amount of winter wheat. One is to predict based on the sowing time, and to increase the sowing amount of winter wheat by delaying the sowing period; the other is to predict based on field experiments, and to determine the optimal sowing amount by setting different sowing periods and conducting sowing amount experiments. However, traditional prediction methods have the following objective defects: they rely too much on manual experience, resulting in low prediction accuracy in different regions and years; and they do not consider the impact of factors such as climate and soil on the optimal sowing amount. Summary of the invention

[0004] To this end, the present invention provides a method and system for predicting the optimal sowing amount of winter wheat, aiming to solve the technical problem of low accuracy in predicting the optimal sowing amount of winter wheat in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: According to a first aspect of the present invention, the present invention provides a method for predicting an optimal sowing amount of winter wheat, the method comprising: Determine the first key influencing parameter and the second key influencing parameter affecting the optimal sowing rate of winter wheat; Calculating the most suitable basic seedling data based on the first key influencing parameter; and calculating the seedling emergence rate data based on the second key influencing parameter; Calculating optimal seeding rate data using the optimal basic seedling data and the emergence rate data; Among them, the first key influencing parameter is a key factor affecting the selection of the optimal basic seedlings; the second key influencing factor is a key factor affecting the seedling emergence rate data.

[0006] Furthermore, the first key influencing parameters include target yield, variety characteristics and accumulated temperature before winter; and / or, The second key influencing parameters include temperature, soil moisture, soil bulk density and pH value.

[0007] Further, the calculating of the most suitable basic seedling data based on the first key influencing parameter includes: The target yield and the variety characteristics are used to calculate the number of ears in the population. The calculation formula is as follows:

[0008] in, OSN It indicates the suitable number of ears in a population, with the unit of ears / mu; GYW Indicates target yield, in kg / mu; GN Indicates the number of grains per ear, with the unit of grains per ear; KGW Indicates thousand-grain weight, in g / 1000 grains; Furthermore, the number of ears that can be formed per plant is calculated using the variety characteristics and the accumulated temperature before winter, and the calculation formula is as follows:

[0009] in, PPSN Indicates the number of ears that can be formed on a single plant, with the unit of ear / plant; PPSMN The maximum number of ears that can form on a single plant, in ears / plant; k is the variety parameter; CUMGDD It indicates the accumulated temperature before winter; The optimum basic seedling data is calculated using the number of ears of the group and the number of ears that can be formed per plant, and the calculation formula is as follows:

[0010] in, OBS Indicates suitable basic seedling data, in units of plants / mu; OSN It indicates the number of ears in a group, with the unit of ears / mu; PPSN It indicates the number of ears that can be produced by a single plant, with the unit being ear / plant.

[0011] Further, the calculating of the seedling emergence rate data based on the second key influencing parameter includes: Calculating the key influencing factor parameters corresponding to the second key influencing factor; the key influencing factor parameters include temperature influencing factor parameters, moisture influencing factor parameters, pH influencing factor parameters and bulk density influencing factor parameters; The key influencing factor parameters are used to calculate the germination rate data, and the calculation formula is as follows:

[0012] in, SER It indicates the germination rate, the unit is %; TF Indicates the temperature influence factor parameter, with a value of 0.0-1.0; TFW Represents the weight of temperature influence factor; WF Indicates the moisture impact factor parameter, with a value of 0.0-1.0; WFW represents the weight of moisture influencing factor; pHF Indicates the pH impact factor parameter, with a value of 0.0-1.0; pHFW represents the weight of pH influencing factor; SBDFIndicates the parameter of the factor affecting bulk density; SBDFW Weights of factors affecting bulk density.

[0013] Furthermore, the calculation formula of the temperature influence factor parameter is as follows:

[0014] in, TF Indicates the temperature influence factor parameter, with a value of 0.0-1.0; TM It indicates the average temperature from sowing to emergence, in ℃; l Indicates the lower limit temperature for seed germination and emergence, in °C; o It indicates the optimum temperature for seed germination and emergence, in °C; u Indicates seed germination as the upper limit temperature of seed germination and emergence, in ℃.

[0015] Furthermore, the calculation formula of the moisture influencing factor parameter is as follows:

[0016] in, WF Indicates the moisture impact factor parameter, with a value of 0.0-1.0; SW It represents the average relative moisture content of the soil layer where the seeds are located from sowing to germination, in units of %; sl Indicates the lower limit of relative soil moisture content for seeds to absorb water and germinate, in %; so 1 represents the optimum lower limit of relative soil moisture content for seed germination, in units of %; so 2 represents the optimum upper limit of relative soil moisture content for seed absorption and germination, unit is %; su Indicates the lower limit of relative soil moisture content for seeds to absorb water and germinate, in %.

[0017] Furthermore, the calculation formula of the pH influencing factor parameter is as follows:

[0018] in, pHF Indicates the pH impact factor parameter, with a value of 0.0-1.0; pH Indicates the pH value of the soil layer where the seeds are located; pl Indicates the lower pH limit at which seeds can grow; po 1 represents the optimum lower limit of pH for seed growth; po 2 represents the upper limit of the optimum pH at which seeds can grow; pu Indicates the upper pH limit at which seeds can grow.

[0019] Furthermore, the calculation formula of the bulk density influencing factor parameter is as follows:

[0020] in, SBDF Represents the parameter of the factor affecting bulk density, with a value of 0.0-1.0; SBD Represents the bulk density of the soil layer where the seeds are located, in g / cm3; bdl Represents the lower limit of bulk density at which seeds can grow, in g / cm3; bdo 1 represents the optimum lower limit of bulk density for seed growth, in g / cm3; bdo 2 represents the upper limit of the bulk density at which the seeds can grow, in g / cm3; bdu Represents the upper limit of the bulk density at which the seeds can grow, expressed in g / cm3.

[0021] Further, the calculating of the optimal sowing amount data using the optimal basic seedling data and the emergence rate data comprises: The optimal seeding rate data is calculated using the following formula:

[0022] in, OSA Represents the optimal seeding rate data, in kg / mu; OBS Represents the most suitable basic seedling data, the unit is plant / mu; KGW Represents thousand-grain weight, in g / 1000 grains; SP Represents the purity of seeds, unit is %; SGR Represents the seed germination rate, unit is %; SER is the germination rate, in %.

[0023] According to a second aspect of the present invention, the present invention provides a prediction system for optimal sowing amount of winter wheat, the system comprising: An influencing parameter determination module, used to determine a first key influencing parameter and a second key influencing parameter that affect the optimal sowing amount of winter wheat; An intermediate data calculation module, used to calculate the most suitable basic seedling data based on the first key influencing parameter; and to calculate the seedling emergence rate data based on the second key influencing parameter; A sowing data prediction module, used for calculating the optimal sowing amount data using the optimal basic seedling data and the emergence rate data; Among them, the first key influencing parameter is a key factor affecting the selection of the optimal basic seedlings; the second key influencing factor is a key factor affecting the seedling emergence rate data.

[0024] The present invention adopts the above technical solution and has at least the following beneficial effects: Through the scheme of the present invention, the first key influencing parameter and the second key influencing parameter affecting the optimal sowing amount of winter wheat are determined; the optimal basic seedling data are calculated based on the first key influencing parameter; and the emergence rate data are calculated based on the second key influencing parameter; the optimal sowing amount data are calculated using the optimal basic seedling data and the emergence rate data. Thus, the influence of various factors on the sowing amount is taken into account, and the optimal sowing amount at the current moment can be dynamically given; and the recommended density for different regions can be given based on the soil information, meteorological data, and cultivated varieties of different regions, thereby increasing the universality of the model prediction. The present invention realizes a more mechanistic, more accurate, and more universal prediction of the optimal sowing amount of winter wheat.

[0025] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 A schematic diagram showing a flow chart of a method for predicting the optimal sowing amount of winter wheat provided by an embodiment of the present invention; Figure 2 A schematic diagram showing the structure of a prediction system for optimal sowing amount of winter wheat provided by an embodiment of the present invention is shown; Figure 3 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0028] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0029] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0030] The embodiment of the present invention provides a method for predicting the optimal sowing amount of winter wheat. Figure 1 As shown, at least the following steps S101 to S103 may be included: Step S101, determining a first key influencing parameter and a second key influencing parameter that affect the optimal seeding amount of winter wheat.

[0031] The embodiment of the present invention considers two types of data that affect the optimal sowing amount of winter wheat, namely, the most suitable basic seedling and the emergence rate. Among them, the first key influencing parameter is a key factor affecting the selection of the most suitable basic seedling; the second key influencing factor is a key factor affecting the emergence rate. By determining the first key influencing parameter affecting the selection of the most suitable basic seedling and the second key influencing parameter affecting the emergence rate, the optimal sowing amount is affected.

[0032] Specifically, the first key influencing parameter may include target yield, variety characteristics, and pre-winter accumulated temperature (accumulated temperature from winter wheat sowing to wintering), etc. The second key influencing parameter may include temperature, soil moisture, soil bulk density, and pH value, etc. Among them, variety characteristics can be parameterized into the number of grains per ear, thousand-grain weight, and the maximum number of ears per plant.

[0033] Step S102, calculating the most suitable basic seedling data based on the first key influencing parameter; and calculating the seedling emergence rate data based on the second key influencing parameter.

[0034] The calculation of the most suitable basic seedling data may specifically include the following steps: The target yield and variety characteristics are used to calculate the number of ears in a population. The calculation formula is as follows:

[0035] in, OSN It indicates the suitable number of ears in a population, with the unit of ears / mu; GYW Indicates target yield, in kg / mu; GN Indicates the number of grains per ear, with the unit of grains per ear;KGW Indicates thousand-grain weight, in g / 1000 grains; The number of ears that can be formed per plant is calculated using the characteristics of the variety and the accumulated temperature before winter. The calculation formula is as follows:

[0036] in, PPSN Indicates the number of ears that can be formed on a single plant, with the unit of ear / plant; PPSMN The maximum number of ears that can form on a single plant, in ears / plant; k is the variety parameter; CUMGDD It indicates the accumulated temperature before winter; The optimum basic seedling data is calculated using the number of ears in the population and the number of ears that can be formed per plant. The calculation formula is as follows:

[0037] in, OBS It indicates the most suitable basic seedling data, the unit is plant / mu; OSN It indicates the number of ears in a group, with the unit of ears / mu; PPSN It indicates the number of ears that can be produced by a single plant, with the unit being ear / plant.

[0038] Furthermore, since the wheat emergence rate is affected by sowing quality, seed quality, temperature, soil moisture, soil bulk density, and pH, in the embodiment of the present invention, for the calculation of the emergence rate data, the key influencing factor parameters corresponding to the second key influencing factor can be calculated first, and then the emergence rate data can be calculated using the key influencing factor parameters.

[0039] In the embodiment of the present invention, the key influencing factor parameters are parameters corresponding to the second key influencing factor, including temperature influencing factor parameters, moisture influencing factor parameters, pH influencing factor parameters, and bulk density influencing factor parameters.

[0040] Among them, the calculation formula of the temperature influence factor parameter is as follows:

[0041] in, TF Indicates the temperature influence factor parameter, with a value of 0.0-1.0; TM It indicates the average temperature from sowing to emergence, in ℃; l Indicates the lower limit temperature for seed germination and emergence, in °C; o It indicates the optimum temperature for seed germination and emergence, in °C; u Indicates seed germination as the upper limit temperature of seed germination and emergence, in ℃.

[0042] The calculation formula of the moisture influencing factor parameter is as follows:

[0043] in, WF Indicates the moisture impact factor parameter, with a value of 0.0-1.0; SW It represents the average relative moisture content of the soil layer where the seeds are located from sowing to germination, in units of %; sl Indicates the lower limit of relative soil moisture content for seeds to absorb water and germinate, in %; so 1 represents the optimum lower limit of relative soil moisture content for seed germination, in units of %; so 2 represents the optimum upper limit of relative soil moisture content for seed absorption and germination, unit is %; su Indicates the lower limit of relative soil moisture content for seeds to absorb water and germinate, in %.

[0044] The calculation formula of pH influencing factor parameters is as follows:

[0045] in, pHF Indicates the pH impact factor parameter, with a value of 0.0-1.0; pH Indicates the pH value of the soil layer where the seeds are located; pl Indicates the lower pH limit at which seeds can grow; po 1 represents the optimum lower limit of pH for seed growth; po 2 represents the upper limit of the optimum pH at which seeds can grow; pu Indicates the upper pH limit at which seeds can grow.

[0046] The calculation formula of the bulk density influencing factor parameter is as follows:

[0047] in, SBDF Represents the parameter of the factor affecting bulk density, with a value of 0.0-1.0; SBD Represents the bulk density of the soil layer where the seeds are located, in g / cm3; bdl Represents the lower limit of bulk density at which seeds can grow, in g / cm3; bdo 1 represents the optimum lower limit of bulk density for seed growth, in g / cm3; bdo 2 represents the upper limit of the bulk density at which the seeds can grow, in g / cm3; bdu Represents the upper limit of the bulk density at which the seeds can grow, expressed in g / cm3.

[0048] Furthermore, after determining the temperature influencing factor parameters, water influencing factor parameters, pH influencing factor parameters and bulk density influencing factor parameters, the following formula can be used to calculate the germination rate data:

[0049] in, SER It indicates the germination rate, the unit is %;TF Indicates the temperature influence factor parameter, with a value of 0.0-1.0; TFW Represents the weight of temperature influence factor; WF Indicates the moisture impact factor parameter, with a value of 0.0-1.0; WFW represents the weight of moisture influencing factor; pHF Indicates the pH impact factor parameter, with a value of 0.0-1.0; pHFW represents the weight of pH influencing factor; SBDF Indicates the parameter of the factor affecting bulk density; SBDFW Weights of factors affecting bulk density.

[0050] It should be noted that, in practical applications, the parameter weights corresponding to the parameters of each key influencing factor can be set according to actual needs, and the present invention does not limit this.

[0051] Step S103, calculating the optimal seeding amount data using the optimal basic seedling data and the seedling emergence rate data.

[0052] Specifically, the optimal seeding rate data is calculated using the following formula:

[0053] in, OSA Represents the optimal seeding rate data, in kg / mu; OBS Represents the most suitable basic seedling data, the unit is plant / mu; KGW Represents thousand-grain weight, in g / 1000 grains; SP Represents the purity of seeds, in %, and the national standard is 98%; SGR Represents the seed germination rate, in %, and the national standard can be referred to as 95%; SER is the germination rate, in %.

[0054] The embodiment of the present invention provides a method for predicting the optimal sowing amount of winter wheat, which predicts the optimal sowing amount of winter wheat at the current moment through soil, meteorological and variety characteristics. Specifically, the first key influencing parameter and the second key influencing parameter that affect the optimal sowing amount of winter wheat are determined; the most suitable basic seedling data are calculated based on the first key influencing parameter; and the emergence rate data are calculated based on the second key influencing parameter; the optimal sowing amount data is calculated using the most suitable basic seedling data and the emergence rate data. Through the present invention, the influence of various factors on the sowing amount is taken into account, and the optimal sowing amount at the current moment can be given dynamically; and the recommended density of different regions can be given according to the soil information, meteorological data and cultivated varieties of different regions, which increases the universality of the model prediction; and realizes the prediction of the optimal sowing amount of winter wheat with stronger mechanism, higher accuracy and better universality.

[0055] Further, as Figure 1In a specific implementation, an embodiment of the present invention provides a prediction system for the optimal sowing amount of winter wheat, such as Figure 2 As shown, the system may include: an influencing parameter determination module 210 , an intermediate data calculation module 220 and a seeding data prediction module 230 .

[0056] The influencing parameter determination module 210 may be used to determine a first key influencing parameter and a second key influencing parameter that affect the optimal sowing amount of winter wheat; The intermediate data calculation module 220 can be used to calculate the most suitable basic seedling data based on the first key influencing parameter; and calculate the seedling emergence rate data based on the second key influencing parameter; The sowing data prediction module 230 can be used to calculate the optimal sowing amount data using the optimal basic seedling data and the emergence rate data; Among them, the first key influencing parameter is the key factor affecting the selection of the most suitable basic seedlings; the second key influencing factor is the key factor affecting the seedling emergence rate data.

[0057] It should be noted that for other corresponding descriptions of the functional modules involved in the prediction system for optimal sowing amount of winter wheat provided by the embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0058] Based on the above Figure 1 The method shown, accordingly, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for predicting the optimal seeding amount of winter wheat described in any of the above embodiments are implemented.

[0059] Based on the above Figure 1 The method shown and Figure 2 The embodiment of the system shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 3 As shown, the computer device may include a communication bus, a processor, a memory and a communication interface, and may also include an input / output interface and a display device, wherein each functional unit may communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the steps of the method for predicting the optimal sowing amount of winter wheat described in the above embodiment.

[0060] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they are not further described here.

[0061] In addition, the functional units in various embodiments of the present invention may be physically independent of each other, or two or more functional units may be integrated together, or all functional units may be integrated into one processing unit. The above integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.

[0062] A person skilled in the art can understand that if the integrated functional unit is implemented in the form of software and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, which includes several instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention when running the instructions. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0063] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware associated with program instructions (such as a computing device such as a personal computer, a server, or a network device), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of the computing device, the computing device executes all or part of the steps of the method described in the various embodiments of the present invention.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate from the protection scope of the present invention.

Claims

1. A method for predicting the optimal sowing rate of winter wheat, characterized in that: The method comprises: Determine the first key influencing parameter and the second key influencing parameter affecting the optimal sowing rate of winter wheat; Calculating the most suitable basic seedling data based on the first key influencing parameter; and calculating the seedling emergence rate data based on the second key influencing parameter; Calculating optimal seeding rate data using the optimal basic seedling data and the emergence rate data; Among them, the first key influencing parameter is a key factor affecting the selection of the optimal basic seedlings; the second key influencing factor is a key factor affecting the seedling emergence rate data.

2. The method according to claim 1, characterized in that: The first key influencing parameters include target yield, variety characteristics and accumulated temperature before winter; and / or, The second key influencing parameters include temperature, soil moisture, soil bulk density and pH value.

3. The method according to claim 2, characterized in that The calculating of the most suitable basic seedling data based on the first key influencing parameter comprises: The target yield and the variety characteristics are used to calculate the number of ears in the population. The calculation formula is as follows: in, OSN It indicates the suitable number of ears in a population, with the unit of ears / mu; G JZ Indicates target yield, in kg / mu; GN Indicates the number of grains per ear, with the unit of grains per ear; KGW Indicates thousand-grain weight, in g / 1000 grains; Furthermore, the number of ears that can be formed per plant is calculated using the variety characteristics and the accumulated temperature before winter, and the calculation formula is as follows: in, PPSN Indicates the number of ears that can be formed on a single plant, with the unit of ear / plant; PPSMN The maximum number of ears that can form on a single plant, in ears / plant; k is the variety parameter; CUMGDD It indicates the accumulated temperature before winter; The optimum basic seedling data is calculated using the number of ears of the group and the number of ears that can be formed per plant, and the calculation formula is as follows: in, OBS Indicates suitable basic seedling data, in units of plants / mu; OSN It indicates the number of ears in a group, with the unit of ears / mu; PPSN It indicates the number of ears that can be produced by a single plant, with the unit being ear / plant.

4. The method according to claim 2, characterized in that: The step of calculating the emergence rate data based on the second key influencing parameter comprises: Calculating the key influencing factor parameters corresponding to the second key influencing factor; the key influencing factor parameters include temperature influencing factor parameters, moisture influencing factor parameters, pH influencing factor parameters and bulk density influencing factor parameters; The key influencing factor parameters are used to calculate the germination rate data, and the calculation formula is as follows: in, SER It indicates the germination rate, the unit is %; TF Indicates the temperature influence factor parameter, with a value of 0.0-1.0; TFW Represents the weight of temperature influence factor; WF Indicates the moisture impact factor parameter, with a value of 0.0-1.0; WFW represents the weight of moisture influencing factor; pHF Indicates the pH impact factor parameter, with a value of 0.0-1.0; FWf represents the weight of pH influencing factor; SBDF Indicates the parameter of the factor affecting bulk density; SBDFW Weights of factors affecting bulk density.

5. The method according to claim 4, characterized in that The calculation formula of the temperature influence factor parameter is as follows: in, TF Indicates the temperature influence factor parameter, with a value of 0.0-1.0; TM It indicates the average temperature from sowing to emergence, in ℃; l Indicates the lower limit temperature for seed germination and emergence, in °C; o It indicates the optimum temperature for seed germination and emergence, in °C; u Indicates seed germination as the upper limit temperature of seed germination and emergence, in ℃.

6. The method according to claim 4, characterized in that The calculation formula of the moisture influencing factor parameter is as follows: in, WF Indicates the moisture impact factor parameter, with a value of 0.0-1.0; SW It represents the average relative moisture content of the soil layer where the seeds are located from sowing to germination, in units of %; sl Indicates the lower limit of relative soil moisture content for seeds to absorb water and germinate, in %; so 1 represents the optimum lower limit of relative soil moisture content for seed germination, in units of %; so 2 represents the optimum upper limit of relative soil moisture content for seed germination, unit is %; su Indicates the lower limit of relative soil moisture content for seeds to absorb water and germinate, in %.

7. The method according to claim 4, characterized in that The calculation formula of the pH influencing factor parameter is as follows: in, pHF Indicates the pH impact factor parameter, with a value of 0.0-1.0; pH Indicates the pH value of the soil layer where the seeds are located; pl Indicates the lower pH limit at which seeds can grow; po 1 represents the optimum lower limit of pH for seed growth; po 2 represents the upper limit of the optimum pH at which seeds can grow; pu Indicates the upper pH limit at which seeds can grow.

8. The method according to claim 4, characterized in that The calculation formula of the bulk density influencing factor parameter is as follows: in, SBDF Represents the parameter of the factor affecting bulk density, with a value of 0.0-1.0; SBD Represents the bulk density of the soil layer where the seeds are located, in g / cm3; bdl Represents the lower limit of bulk density at which seeds can grow, in g / cm3; bdo 1 represents the optimum lower limit of bulk density for seed growth, in g / cm3; bdo 2 represents the upper limit of the bulk density at which the seeds can grow, in g / cm3; bdu Represents the upper limit of the bulk density at which the seeds can grow, expressed in g / cm3.

9. The method according to claim 1, characterized in that: The method of calculating the optimal sowing amount data using the optimal basic seedling data and the emergence rate data comprises: The optimal seeding rate data is calculated using the following formula: in, OSA Represents the optimal seeding rate data, in kg / mu; OBS Represents the most suitable basic seedling data, the unit is plant / mu; KGW Represents thousand-grain weight, in g / 1000 grains; SP Represents the purity of seeds, unit is %; SGR Represents the seed germination rate, unit is %; SER is the germination rate, in %.

10. A prediction system for optimal sowing amount of winter wheat, characterized in that: The system comprises: An influencing parameter determination module, used to determine a first key influencing parameter and a second key influencing parameter that affect the optimal sowing amount of winter wheat; An intermediate data calculation module, used to calculate the most suitable basic seedling data based on the first key influencing parameter; and to calculate the seedling emergence rate data based on the second key influencing parameter; A sowing data prediction module, used for calculating the optimal sowing amount data using the optimal basic seedling data and the emergence rate data; Among them, the first key influencing parameter is a key factor affecting the selection of the optimal basic seedlings; the second key influencing factor is a key factor affecting the seedling emergence rate data.

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