Method for improving dry matter prediction of rice under ozone influence and improved device

Abstract

The application relates to an improved method and device for predicting rice dry matter under the influence of ozone, wherein the method comprises the following steps: constructing a quantitative response relationship of the maximum dry matter in the rice growth period to the influence of ozone and relative effective accumulated temperature; determining a key threshold when the dry matter reduction target percentage is reached based on the quantitative response relationship, and combining an influence equation of ozone on dry matter distribution to determine a coupling influence factor of ozone stress and effective accumulated temperature; establishing a Logistic growth model of standardized dry matter and the coupling influence factor, and optimizing an empirical coefficient suitable for evaluating the influence of ozone concentration; and dynamically predicting the dry matter based on the growth period air temperature and ozone data through the quantitative response equation of the maximum dry matter to ozone and relative effective accumulated temperature and the Logistic growth model. Thus, the problem that related technologies do not consider the influence of environmental stress factors such as ozone on crop growth, resulting in obvious deviation of the model prediction result in seriously polluted or complex environmental areas, is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information processing, in particular to a rice dry matter prediction improvement method and device under the influence of ozone. BACKGROUND

[0002] In related technologies, crop dry matter accumulation models are mainly constructed based on effective accumulated temperature theory, and usually assume a linear or nonlinear relationship between crop growth rate and accumulated temperature, such as the Logistic model or Gompertz equation, which has the characteristics of simple modeling and wide application.

[0003] However, in related technologies, the model does not fully consider environmental stress factors during the calculation process, especially the inhibitory effect of ozone pollution on crop growth. Ozone can enter plant leaves through stomata, induce photosynthetic structure damage, accelerate leaf senescence, and change the distribution pattern of dry matter, thereby significantly reducing crop productivity. This leads to large prediction errors of the model under high pollution or complex environmental conditions, and seriously affects its applicability under complex environmental conditions, and other problems, which need to be solved urgently. SUMMARY

[0004] The present application provides a rice dry matter prediction improvement method and device under the influence of ozone, an electronic device and a storage medium, to solve the technical problem that the model does not consider the adverse effects of environmental stress factors such as ozone on crop photosynthesis, leaf senescence and dry matter distribution in related technologies, resulting in significant prediction errors of the model in severely polluted or complex environmental areas.

[0005] The first aspect of the present application provides a rice dry matter prediction improvement method under the influence of ozone, comprising the following steps: constructing a quantitative response relationship between the maximum dry matter during the growth period of rice and the influence of ozone and relative effective accumulated temperature; determining the key threshold when the dry matter reduction target percentage is based on the quantitative response relationship, and combining the influence equation of ozone on dry matter distribution to determine the coupling influence factor of ozone stress and effective accumulated temperature; establishing a Logistic growth model of standardized dry matter and the coupling influence factor, and optimizing the empirical coefficient applicable to evaluate the influence of ozone concentration; through the quantitative response equation of the maximum dry matter to ozone and relative effective accumulated temperature and the Logistic growth model, the dry matter is dynamically predicted based on the air temperature and ozone data during the growth period.

[0006] Through the above technical means, based on the quantitative response relationship of the maximum dry matter in the growth period of rice to ozone and relative effective accumulated temperature, combined with the dynamic prediction of rice dry matter by the Logistic growth model, fully considering the influence of ozone stress on crop growth, the prediction accuracy of the dynamic change of rice dry matter can be improved, the scientificity and timeliness of growth simulation can be improved, and more practical guiding significance of the crop monitoring results can be brought, so as to provide reliable data support and decision basis for agricultural production management.

[0007] Optionally, in an embodiment of the present application, the quantitative response relationship can be expressed as:

[0008] Y e,max =a1*[AOT 40 ] m *(1-GDD / GDD opt )+a2,

[0009] Wherein: Y e,max is the simulation value of the maximum dry matter under different ozone concentrations; e is the simulation value; max is the maximum value; [AOT 40 ] m is the cumulative value of ozone concentration greater than 40ppb when the dry matter is maximum; m is the day sequence when the dry matter is maximum; GDD is the actual effective accumulated temperature; GDD opt is the optimum effective accumulated temperature; opt is the optimum value; a1 and a2 are regression coefficients.

[0010] Through the above technical means, the quantitative response relationship between ozone concentration and effective accumulated temperature is established, which can more directly reveal the influence law of ozone concentration on the utilization efficiency of heat resources of crops, and is helpful to quantify the interference degree of environmental factors on the growth process of crops, and provides key parameter basis for the dynamic simulation model of crop growth.

[0011] Optionally, in an embodiment of the present application, before determining the coupling influence factor of ozone stress and effective accumulated temperature, it further includes: according to the average effective accumulated temperature of the region in many years and the response relationship, establishing the influence equation of ozone stress in the growth period, wherein the calculation formula of the influence equation can be expressed as:

[0012]

[0013] Wherein: is the correction coefficient of ozone to effective accumulated temperature; O3 is ozone; is the cumulative value of ozone concentration greater than 40ppb at the end of tillering of rice; t1 is the last day of the end of tillering; t is the day sequence; a3 is a parameter; [AOT 40 ] 50 is the critical [AOT40 ]。

[0014] Through the above technical means, the correction coefficient of ozone to effective accumulated temperature can dynamically reflect the cumulative influence of ozone stress on the development process of rice, adjust the deviation of the accumulated temperature index in representing the growth rate, and thus improve the accuracy and adaptability of crop growth simulation and dry matter prediction in high ozone environment.

[0015] Optionally, in an embodiment of the present application, the calculation formula of the dry matter can be represented as:

[0016]

[0017] wherein Y is the dry matter prediction value; Y e,max is the dry matter simulation value based on the maximum dry matter response relationship; e is the simulation value; max is the maximum value; x t is the effective accumulated temperature standardized data after ozone stress correction at day sequence t; and t is the day sequence.

[0018] Through the above technical means, the influence equation of ozone stress on effective accumulated temperature during the growth period is constructed, which can improve the influence of ozone on the estimation accuracy of dry matter, improve the simulation accuracy of the dry matter accumulation process, and bring more scientific and fine rice growth monitoring results, thereby enhancing the application ability of the model under ozone conditions.

[0019] The second aspect embodiment of the present application provides an improved device for predicting rice dry matter under the influence of ozone, comprising: a construction module for constructing a quantitative response relationship of the maximum dry matter of rice during the growth period to the influence of ozone and relative effective accumulated temperature; a determination module for determining a key threshold when the target percentage of dry matter reduction is based on the quantitative response relationship, and determining a coupling influence factor of ozone stress and effective accumulated temperature by combining the influence equation of ozone on dry matter distribution; an optimization module for establishing a Logistic growth model of standardized dry matter and the coupling influence factor, and optimizing the empirical coefficient suitable for evaluating the influence of ozone concentration; and a prediction module for dynamically predicting dry matter based on the air temperature and ozone data during the growth period through the quantitative response equation of the maximum dry matter of rice to ozone and relative effective accumulated temperature and the Logistic growth model.

[0020] Through the above technical means, based on the quantitative response relationship of the maximum dry matter of rice during the growth period to the influence of ozone and relative effective accumulated temperature, the Logistic growth model is used to dynamically predict the dry matter of rice, fully considering the influence of ozone stress on crop growth, which can improve the prediction accuracy of the dynamic change of rice dry matter, improve the scientificity and timeliness of growth simulation, and bring more practical and guiding significance of crop monitoring results, thereby providing reliable data support and decision basis for agricultural production management.

[0021] Optionally, in an embodiment of the present application, the quantitative response relationship can be expressed as:

[0022] Y e,max = a1 * [AOT 40 ] m *(1-GDD / GDD opt )+a2,

[0023] Wherein: Y e,max is the maximum dry matter simulation value under different ozone concentrations; e is the simulation value; max is the maximum value; [AOT 40 ] m is the cumulative value of ozone concentration greater than 40 ppb when the dry matter is maximum; m is the day sequence when the dry matter is maximum; GDD is the actual effective accumulated temperature; GDD opt is the optimum effective accumulated temperature; opt is the optimum value; a1 and a2 are regression coefficients.

[0024] Through the above technical means, the quantitative response relationship between ozone concentration and effective accumulated temperature is established, which can more directly reveal the influence law of ozone concentration on the heat resource utilization efficiency of crops, helps to quantify the interference degree of environmental factors on the growth process of crops, and provides key parameter basis for crop growth dynamic simulation model.

[0025] Optionally, in an embodiment of the present application, the determining module comprises: an establishing unit, configured to establish the influence equation of ozone stress in the growth period according to the average effective accumulated temperature of the region in multiple years and the response relationship, wherein the calculation formula of the influence equation can be expressed as:

[0026]

[0027] Wherein: is the correction coefficient of ozone on effective accumulated temperature; O3 is ozone; is the cumulative value of ozone concentration greater than 40 ppb at the end of rice tillering; t1 is the last day of the end of tillering; t is the day sequence; a3 is a parameter; [AOT 40 ] 50 is the critical [AOT 40 ] when the dry matter is reduced by 50% under the temperature conditions in the region.

[0028] Through the above technical means, the correction coefficient of ozone on effective accumulated temperature calculated can dynamically reflect the cumulative influence of ozone stress on the development process of rice, adjust the representation deviation of accumulated temperature index on growth rate, and thus improve the accuracy and adaptability of crop growth simulation and dry matter prediction in high ozone environment.

[0029] Optionally, in one embodiment of the present application, the calculation formula of the dry matter can be expressed as:

[0030]

[0031] Wherein, Y is the dry matter prediction value; Y e,max is the dry matter simulation value based on the maximum dry matter response relationship; e is the simulation value; max is the maximum value; x t is the effective accumulated temperature standardized data after ozone stress correction of day sequence t; t is the day sequence.

[0032] Through the above technical means, the equation of the influence of ozone stress on effective accumulated temperature during the growth period is constructed, which can improve the influence of ozone on the estimation accuracy of dry matter in the dry matter prediction, improve the simulation accuracy of the dry matter accumulation process, bring more scientific and fine rice growth monitoring results, and thus enhance the application ability of the model under the condition of ozone.

[0033] The third aspect embodiment of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to realize the rice dry matter prediction improvement method under the influence of ozone as described in the above embodiments.

[0034] The fourth aspect embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the rice dry matter prediction improvement method under the influence of ozone as above.

[0035] The fifth aspect embodiment of the present application provides a computer program product, comprising a computer program, which is executed to realize the rice dry matter prediction improvement method under the influence of ozone as above.

[0036] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter in the description of the application. BRIEF DESCRIPTION OF DRAWINGS

[0037] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description of embodiments, taken in conjunction with the accompanying drawings, in which:

[0038] Figure 1 A flowchart of a rice dry matter prediction improvement method under the influence of ozone according to an embodiment of the present application is provided;

[0039] Figure 2 A schematic diagram of the result of predicting the dry matter of 2023 using the dry matter prediction improvement method of one embodiment of the present application is provided;

[0040] Figure 3 A block schematic diagram of an improved device for predicting dry matter of rice under ozone influence according to an embodiment of the present application is provided.

[0041] Figure 4 A structural schematic diagram of an electronic device according to an embodiment of the present application is provided.

[0042] Reference signs:

[0043] 10 - improved device for predicting dry matter of rice under ozone influence; 100 - construction module, 200 - determination module, 300 - optimization module and 400 - prediction module; 401 - memory, 402 - processor and 403 - communication interface. DETAILED DESCRIPTION

[0044] Embodiments of the present application are described below in detail, examples of which are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0045] The ozone-influenced rice dry matter prediction improvement method, device, electronic equipment and storage medium of the embodiments of the present application are described below with reference to the drawings. In view of the technical problem that the crop dry matter accumulation model does not consider the adverse effects of environmental stress factors such as ozone on crop photosynthesis, leaf senescence and dry matter distribution, resulting in significant deviation of the model prediction results in severely polluted or complex environmental areas, the present application provides an ozone-influenced rice dry matter prediction improvement method. In the method, by integrating the quantitative response equation of the maximum dry matter to ozone and the relative effective accumulated temperature with the Logistic growth model, the dry matter is dynamically predicted based on the temperature and ozone data during the growth period, which can accurately depict the change rule of the dry matter of rice under ozone stress, effectively improve the adaptability and accuracy of the dry matter prediction under high ozone scenarios, and through the quantitative evaluation of the current environmental ozone concentration, combined with the prediction model, the influence of the future ozone concentration increase on the yield formation process of rice can be simulated, providing scientific and reliable technical support for the risk assessment and adaptive management of agricultural production under the background of climate change and atmospheric pollution combined stress. Thus, the problem that the related art does not consider the adverse effects of environmental stress factors such as ozone on crop photosynthesis, leaf senescence and dry matter distribution, resulting in significant deviation of the model prediction results in severely polluted or complex environmental areas, is solved.

[0046] Specifically, Figure 1 A flowchart of an ozone-influenced rice dry matter prediction improvement method according to an embodiment of the present application is provided.

[0047] As Figure 1 shown, the rice dry matter prediction improvement method under the influence of ozone includes the following steps:

[0048] In step S101, a quantitative response relationship of the maximum dry matter in the rice growth period to ozone and relative effective accumulated temperature is constructed.

[0049] The maximum dry matter is a balance state reached in the growth process of crops, that is, the accumulation of dry matter in the plant body no longer increases or increases very slowly, which usually occurs in the mature period or near the mature period of crops. The maximum dry matter is affected by many factors, including but not limited to light, water, environment, temperature, etc. The maximum dry matter is often used for crop yield estimation and production management, which can help agricultural producers make reasonable cultivation decisions.

[0050] Effective accumulated temperature is an important concept in agricultural meteorology, which is usually used to measure the temperature accumulation of a region or a crop during the growth period. Effective accumulated temperature is obtained by weighting the daily average temperature, removing the low temperature part below a certain threshold, and only considering the temperature range that promotes crop growth.

[0051] Ozone, as an air pollutant, can damage the chloroplasts of rice, reduce the efficiency of photosynthesis, and thus affect dry matter accumulation. When the ozone concentration is too high, the dry matter accumulation rate of rice will slow down, and even reach a certain concentration, which may cause growth stagnation or death.

[0052] In the embodiments of the present application, the quantitative response relationship of the maximum dry matter in the rice growth period to ozone and relative effective accumulated temperature is constructed, which can quantitatively describe the sensitivity and response characteristics of the dry matter accumulation process of rice to environmental factors, and reveal the crop growth mechanism under the synergistic action of ozone stress and heat resources, providing basis and parameter support for the following dynamic simulation of dry matter and yield prediction.

[0053] Optionally, in an embodiment of the present application, the quantitative response relationship can be expressed as:

[0054] Y e,max = a1*[AOT 40 ] m *(1-GDD / GDD opt )+a2,

[0055] Where: Y e,max is the maximum dry matter simulation value under different ozone concentrations, unit g / plant; e is the simulation value; max is the maximum value; [AOT 40 ] mThe cumulative value of ozone concentration greater than 40 ppb for the maximum dry matter, unit: ppb h; m is the day sequence for the maximum dry matter; GDD is the actual effective accumulated temperature, ℃ d; GDD opt The optimal effective accumulated temperature; unit: ℃ d; a1, a2 are regression coefficients.

[0056] It should be noted that the unit of ppb h can represent the product of pollutant concentration (in ppb) and exposure time (in hours). For example, 100 ppb h represents 100 ppb concentration under 1 hour of exposure.

[0057] The unit of ℃ d can represent the product of temperature (in Celsius) and exposure time (in days), which is used to describe the heat accumulated by a crop in a certain time under a certain temperature condition. For example, the accumulated effective accumulated temperature of a crop grown at 25 ℃ for 10 days can be 250 ℃ d.

[0058] It can be understood that a1 and a2 are both regression coefficients, which can be obtained by statistical analysis and regression calculation of a large amount of experimental data. In the embodiments of the present application, a1 and a2 can be-0.02 and 106.01 respectively, which are used to reflect the influence degree of the corresponding independent variable on the simulation value.

[0059] In step S102, based on the quantitative response relationship, the key threshold value when the dry matter reduction target percentage is determined, and combined with the influence equation of ozone on dry matter distribution, the coupling influence factor of ozone stress and effective accumulated temperature is determined.

[0060] The key threshold value refers to the value that when a parameter value exceeds (or is lower than) the value, the system behavior will change significantly under certain environment or conditions.

[0061] For example, taking 10% reduction of dry matter as the target percentage, setting the total amount of dry matter of the control group (without ozone treatment) as 100 g, if the dry matter of a treatment group is measured as 90 g, it is considered to have reached the “10% dry matter reduction” judgment standard. Through regression analysis, when the ozone exposure amount reaches 5000 ppb h, the average dry matter reduction ratio is 10%, and 5000 ppb h can be considered as the key threshold value of 10% reduction of dry matter.

[0062] It can be understood that the coupling influence factor of ozone stress and effective accumulated temperature cannot be simply regarded as the linear superposition of the effects of the two, and the interaction between the two needs to be considered, including but not limited to whether the inhibition degree of ozone concentration on dry matter changes under different accumulated temperature conditions, whether the temperature condition amplifies or alleviates the negative effect of ozone under different ozone exposure levels, etc.

[0063] Optionally, in one embodiment of the present application, before determining the coupling impact factor of the ozone stress and the effective accumulated temperature, the method further comprises: establishing an influence equation of the ozone stress during the growth period according to the average effective accumulated temperature and the response relationship in the region for many years, wherein the calculation formula of the influence equation can be represented as:

[0064]

[0065] wherein: is a correction coefficient of the ozone to the effective accumulated temperature; O3 is the ozone; is a cumulative value of the ozone concentration greater than 40 ppb at the end of the tillering stage of the rice, in units of ppb·h; t1 is the last day of the end of the tillering stage; t is the day sequence; a3 is a parameter; [AOT 40 ] 50 is a critical [AOT 40 ] when the dry matter is reduced by 50% under the temperature conditions in the region, in units of ppb·h.

[0066] wherein the end of the tillering stage refers to a period when the number of tillers tends to be stable and new tillers no longer obviously increase, which is an important turning point of growth and development.

[0067] Further, [AOT 40 ] 50 The calculation can be as follows:

[0068]

[0069] wherein: GDD n is the average effective accumulated temperature of the rice during the whole growth period (from June to September) in the region for 50 years, in units of ℃·d; GDD opt is the optimal effective accumulated temperature of the rice during the whole growth period (from June to September), in units of ℃·d; n is a long time sequence, which is 50 years.

[0070] In step S103, a Logistic growth model of the standardized dry matter and the coupling impact factor is established, and an empirical coefficient suitable for evaluating the influence of the ozone concentration is optimized.

[0071] In one embodiment of the present application, the Logistic growth model can be represented as:

[0072]

[0073] wherein: a4, a5, a6 and a7 are all generalized empirical coefficients of the Logistic equation; y t is the standardized data of the dry matter at day sequence t; x t is the standardized data of the effective accumulated temperature after the correction of the ozone stress at day sequence t; t is the day sequence.

[0074] Specifically, based on the observed air temperature, ozone concentration and dry matter data in 2023, the application embodiment can calculate that a4, a5, a6 and a7 are 1.0247, 4.3722, -10.6090 and 1.2881 respectively.

[0075] Further, x t The calculation formula can be represented as:

[0076]

[0077] Further, y t is the dry matter standardized data at day sequence t, and the calculation formula can be represented as:

[0078] y t = Y t / Y o,max ,

[0079] wherein Y o,max is the maximum dry matter observation value under different ozone concentrations, with the unit of g / plant; Y t is the dry matter at day sequence t, with the unit of g / plant; and O is the observation value.

[0080] In step S104, the maximum dry matter is quantitatively responded to the ozone and the relative effective accumulated temperature equation and the Logistic growth model, so as to dynamically predict the dry matter based on the air temperature and ozone data in the growth period.

[0081] The application embodiment can dynamically predict the dry matter by establishing the ozone stress and effective accumulated temperature response equation and combining the Logistic growth model, can effectively compensate the estimation error caused by not considering the ozone stress factor in the traditional dry matter prediction model, so as to improve the prediction accuracy of the dry matter.

[0082] Alternatively, in an embodiment of the application, the calculation formula of the dry matter can be represented as:

[0083]

[0084] wherein Y is the dry matter prediction value, with the unit of g / plant.

[0085] As a specific example, as Figure 2 shown, the application embodiment uses the improved method to predict the rice dry matter in 2023. It can be seen that the difference between the prediction value and the actual observation value is small, and the improved method of the rice dry matter prediction under the influence of ozone provided by the application embodiment can more accurately reflect the change trend and actual level of the maximum dry matter of rice under the influence of ozone, which verifies the reliability and practicability of the method.

[0086] As a possible implementation manner, the embodiment of the present application can compare the measured data with the predicted data, and the evaluation index can be calculated as follows:

[0087]

[0088] wherein, O i is the observation value; R s is the estimated value; when b is close to 1, it means that the estimated value is close to the observation value, and b>1 or b<1 means that the model overestimates or underestimates, respectively; is the average value of R s observation value; is the average value of the estimated value; n is the total number of observation values; when R 2 is close to 1, it indicates that the model can explain most of the changes in the observation value.

[0089] Through the above evaluation index, the accuracy and reliability of the method provided by the embodiment of the present application in dry matter prediction can be quantitatively analyzed, which provides a reference basis for subsequent method optimization.

[0090] According to the improved method for predicting dry matter of rice under the influence of ozone provided by the embodiment of the present application, the maximum dry matter is quantitatively responsive to the equation of ozone and relative effective accumulated temperature, and the Logistic growth model is used to dynamically predict the dry matter based on the air temperature and ozone data during the growth period, which can accurately depict the change rule of the dry matter of rice under the stress of ozone, effectively improve the adaptability and precision of the dry matter prediction under the high ozone scenario, and provide scientific and reliable technical support for the risk assessment and adaptive management of agricultural production under the background of climate change and atmospheric pollution combined stress.

[0091] Secondly, the ozone-influenced rice dry matter prediction improvement device according to the embodiment of the present application is described with reference to the accompanying drawings.

[0092] Figure 3 is a block schematic diagram of the ozone-influenced rice dry matter prediction improvement device according to the embodiment of the present application.

[0093] As Figure 3 shown, the ozone-influenced rice dry matter prediction improvement device 10 includes a construction module 100, a determination module 200, an optimization module 300 and a prediction module 400.

[0094] The construction module 100 is configured to construct a quantitative response relationship between the maximum dry matter and the influence of ozone and relative effective accumulated temperature during the growth period of rice.

[0095] The determination module 200 is configured to determine a key threshold when the dry matter reduction target percentage is based on the quantitative response relationship, and determine a coupling influence factor of ozone stress and effective accumulated temperature by combining the influence equation of ozone on dry matter distribution.

[0096] The optimization module 300 is used to establish a Logistic growth model of standardized dry matter and coupling impact factors, and to optimize empirical coefficients suitable for evaluating the impact of ozone concentration.

[0097] The prediction module 400 is used to dynamically predict dry matter based on the growth period temperature and ozone data through the quantitative response equation of the maximum dry matter to ozone and relative effective accumulated temperature and the Logistic growth model.

[0098] Optionally, in an embodiment of the present application, the quantitative response relationship can be expressed as:

[0099] Y e,max = a1*[AOT 40 ] m *(1-GDD / GDD opt )+a2,

[0100] Wherein: Y e,max is the simulated value of the maximum dry matter under different ozone concentrations; e is the simulated value; max is the maximum value; [AOT 40 ] m is the cumulative value of ozone concentration greater than 40 ppb when the dry matter is maximum; m is the day sequence when the dry matter is maximum; GDD is the actual effective accumulated temperature; GDD opt is the optimal effective accumulated temperature; opt is the optimal value; a1 and a2 are regression coefficients.

[0101] Optionally, in an embodiment of the present application, the determination module 200 comprises an establishment unit.

[0102] The establishment unit is configured to establish an influence equation of ozone stress during the growth period according to the average effective accumulated temperature of the region in multiple years and the response relationship, and the calculation formula of the influence equation can be expressed as:

[0103]

[0104] Wherein: is the correction coefficient of ozone to effective accumulated temperature; O3 is ozone; is the cumulative value of ozone concentration greater than 40 ppb at the end of tillering of rice; t1 is the last day of the end of tillering; t is the day sequence; a3 is a parameter; [AOT 40 ] 50 is the critical [AOT 40 ] when the dry matter is reduced by 50% under the temperature conditions in the region.

[0105] Optionally, in an embodiment of the present application, the calculation formula of the dry matter can be expressed as:

[0106]

[0107] wherein Y is a dry matter forecast value; Y e,max is a dry matter simulation value based on a maximum dry matter response relationship; e is a simulation value; max is a maximum value; x t is a standardization data of effective accumulated temperature after ozone stress correction; t is a day sequence.

[0108] It should be noted that the foregoing explanation of the method for improving the dry matter forecast of rice under the influence of ozone also applies to the device for improving the dry matter forecast of rice under the influence of ozone, which will not be repeated here.

[0109] The device for improving the dry matter forecast of rice under the influence of ozone provided by the embodiments of the present application can accurately depict the change rule of the dry matter of rice under the influence of ozone by using the quantitative response equation of the maximum dry matter to ozone and relative effective accumulated temperature and the Logistic growth model to dynamically forecast the dry matter based on the air temperature and ozone data during the growth period, effectively improves the adaptability and accuracy of the dry matter forecast under the high ozone scenario, and provides scientific and reliable technical support for the risk assessment and adaptive management of agricultural production under the background of climate change and atmospheric pollution combined stress.

[0110] Figure 4 The structure schematic diagram of the electronic device provided by the embodiments of the present application is shown. The electronic device can include:

[0111] The memory 401, the processor 402 and the computer program stored in the memory 401 and executable on the processor 402.

[0112] The processor 402 implements the method for improving the dry matter forecast of rice under the influence of ozone provided in the above embodiments when executing the program.

[0113] Further, the electronic device further includes:

[0114] The communication interface 403 is used for communication between the memory 401 and the processor 402.

[0115] The memory 401 is used to store the computer program executable on the processor 402.

[0116] The memory 401 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0117] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 4 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0118] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can complete communication between each other through an internal interface.

[0119] The processor 402 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0120] The embodiment further provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the above method for improving prediction of dry matter of rice under ozone influence.

[0121] The embodiment of the present application further provides a computer program product, which includes a computer program, and the computer program can run computer instructions, and the computer instructions are executed by a processor to implement the method for improving prediction of dry matter of rice under ozone influence provided by the embodiment of the present application.

[0122] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0123] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features of the application, and do not imply or connote relative importance or a specific order of precedence. Thus, features defined with "first", "second", etc. can include at least one of the features, either explicitly or implicitly.

[0124] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments of modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts or otherwise described herein are not necessarily performed in the order shown or discussed, including, for example, performing or depending from other operations or stages, in parallel, in reverse order, or in other orders.

[0125] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of the above. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium. The computer readable signal medium can include, but is not limited to, a computer readable medium that facilitates transfer of the program from one place to another. A specific example of a computer readable medium is a non-transitory computer-readable storage medium. A specific example of a computer readable signal medium is a source or destination of the computer readable medium. Another specific example of a computer readable signal medium is a computer readable signal travelling through space. Thus, a computer readable medium can take many forms of hardware to carry out the program for use by or in connection with the instruction execution system, apparatus or device.

[0126] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented using any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0127] Those of skill in the art would understand that the steps of the methods carried out above can be carried out wholly or partly by a program instructing relevant hardware, and the program can be stored in a computer readable storage medium, and when executed, includes one or a combination of the steps of the method embodiments.

[0128] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0129] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. An improved method for forecasting rice dry matter under ozone influence, characterized in that, Includes the following steps: To construct a quantitative response relationship between the maximum dry matter content of rice during its growth period and the effects of ozone and relatively effective accumulated temperature; Based on the quantitative response relationship, the key threshold for the reduction of the target percentage of dry matter is determined, and combined with the influence equation of ozone on dry matter distribution, the coupling influence factor of ozone stress and effective accumulated temperature is determined. Establish a standardized logistic growth model for dry matter and the aforementioned coupled influencing factors, and optimize the empirical coefficients suitable for assessing the impact of ozone concentration. The quantitative response equation of maximum dry matter to ozone and relative effective accumulated temperature, along with the Logistic growth model, are used to dynamically forecast dry matter based on ambient temperature and ozone data during the growing season. Based on the region's average effective accumulated temperature over many years and the aforementioned response relationship, an impact equation for ozone stress during the reproductive period is established, wherein the calculation formula for the impact equation is: ， in: This is the correction factor for the effective accumulated temperature caused by ozone. O 3 represents ozone; This represents the cumulative value of ozone concentration greater than 40 ppb at the end of the rice tillering stage; t 1 is the last day of the tillering stage; t For the order of days; a 3 is a parameter; [AOT] 40 ] 50 The critical [AOT] value at which dry matter decreases by 50% under the temperature conditions of this region. 40 ]; The quantitative response relationship is expressed as follows: ， Among them: These are simulated dry matter values ​​based on the maximum dry matter response relationship; e These are simulated values; max It is the maximum value; This is the cumulative value of ozone concentration greater than 40 ppb when dry matter content is at its maximum. m The day sequence when dry matter is at its maximum; This represents the actual effective accumulated temperature. To achieve the optimal effective accumulated temperature; opt The optimal value; a 1, a Both 2 are regression coefficients; The formula for calculating the dry matter is: ， in, Y This is the dry matter forecast value; x t For daily order t Standardized data of effective accumulated temperature after ozone stress correction. .

2. An improved device for predicting the dry matter content of rice under ozone conditions, characterized in that, include: A module was constructed to establish the quantitative response relationship between the maximum dry matter content and the effects of ozone and relatively effective accumulated temperature during the rice growth period. The determination module is used to determine the key threshold when the dry matter decreases by the target percentage based on the quantitative response relationship, and to determine the coupling influence factor of ozone stress and effective accumulated temperature by combining the influence equation of ozone on dry matter distribution. An optimization module is used to establish a Logistic growth model of standardized dry matter and the coupled influencing factors, and to optimize the empirical coefficients suitable for assessing the impact of ozone concentration. The forecasting module is used to dynamically forecast dry matter based on the temperature and ozone data during the growing season by using the quantitative response equation of the maximum dry matter to ozone and the relative effective accumulated temperature and the Logistic growth model. The determining module includes: a establishing unit, used to establish the influence equation of ozone stress during the reproductive period based on the region's multi-year average effective accumulated temperature and the response relationship, wherein the calculation formula of the influence equation is: ， in: This is the correction factor for the effective accumulated temperature caused by ozone. O 3 represents ozone; This represents the cumulative value of ozone concentration greater than 40 ppb at the end of the rice tillering stage; t 1 is the last day of the tillering stage; t For the order of days; a 3 is a parameter; [AOT] 40 ] 50 The critical [AOT] value at which dry matter decreases by 50% under the temperature conditions of this region. 40 ]; The quantitative response relationship is expressed as follows: ， in: These are simulated dry matter values ​​based on the maximum dry matter response relationship; e These are simulated values; max It is the maximum value; This is the cumulative value of ozone concentration greater than 40 ppb when dry matter content is at its maximum. m The day sequence when dry matter is at its maximum; This represents the actual effective accumulated temperature. To achieve the optimal effective accumulated temperature; opt The optimal value; a 1, a Both 2 are regression coefficients; The formula for calculating the dry matter is: ， in, Y This is the dry matter forecast value; x t For daily order t Standardized data of effective accumulated temperature after ozone stress correction. .

3. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the improved method for forecasting rice dry matter under ozone influence as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the improved method for forecasting rice dry matter under ozone influence as described in claim 1.

5. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the improved method for forecasting rice dry matter under ozone influence as described in claim 1.

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