CN115577951B - Summer corn lodging early warning algorithm based on corn growth mechanism model - Google Patents
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Abstract
The invention discloses a summer corn lodging early warning algorithm based on a corn growth mechanism model, and relates to the technical field of lodging early warning algorithms. The early warning algorithm for summer corn lodging based on the corn growth mechanism model comprises the steps of building a server cluster; storing data to a corresponding server, and constructing an application service interface; and constructing a corn lodging disaster early warning platform. The invention combines the requirements of the corn growth and development on light, temperature and water to establish a growth period prediction model, accurately predicts the growth and development stage of the corn, and establishes a growth period prediction model, and accurately predicts the growth and development stage of the corn. The method combines the growth stages of the historical corns when lodging occurs to classify, determines the growth stages of corns which are easy to lodge, judges whether the growth stages of the corns reach the stage of easy lodging or not later, considers various factors such as corn growth stage nodes, agriculture management, meteorological environment and the like for early warning of lodging, and improves the early warning accuracy of lodging disasters.
Description
Technical Field
The invention relates to the technical field of lodging early warning algorithms, in particular to a summer corn lodging early warning algorithm based on a corn growth mechanism model.
Background
When early warning is carried out on summer corn lodging, stored predicted weather data including weather data such as the highest day temperature, the lowest day temperature, the relative air humidity, the rainfall, the wind speed, the sunshine hours and the like are directly obtained from a database after the position is determined by inputting longitude and latitude points, sowing data of current corn planted by longitude and latitude points including sowing date, density and variety are additionally input, the weather data and sowing data are transmitted to a corn growth mechanism model, the growth period is simulated, then the date corresponding to the period from the 12-leaf stage of a growth period node of corn lodging to the wire-laying period is taken out, the date is input into a summer corn lodging disaster early warning algorithm, lodging resistance characteristics of the variety and weather predicted data of 15 days in the future are combined for forecasting lodging, and finally the predicted lodging occurrence degree and date are output. The corn growth simulation model comprises a photosynthetic module, a respiration module, a distribution module, a growth period module, a soil water module and the like, and needs to be optimized according to historical corn growth period data of an early warning area, and ensures that a growth period prediction error is within +/-2 days; the early warning algorithm of summer corn lodging disasters combines lodging resistance characteristics of different corn varieties, analyzes according to lodging occurrence data of the different corn varieties and current environmental data, finds relevant influence factors of corn lodging, sets an index threshold value, and builds a lodging algorithm.
In the prior art, early warning of corn lodging is basically carried out on corn lodging in different growth stages based on meteorological conditions in a fixed time stage, the resistance of the corn to wind in the different growth stages is ignored, and lodging prediction is not conveniently carried out according to the resistance of the corn to wind in the different growth stages.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a summer corn lodging early warning algorithm based on a corn growth mechanism model, which solves the problems that the prior art ignores the resistance capability of corn to wind in different growth stages and is inconvenient to predict lodging according to the resistance capability of corn to wind in different growth stages.
Technical proposal
In order to achieve the above purpose, the invention is realized by the following technical scheme: a summer corn lodging early warning algorithm based on a corn growth mechanism model comprises the following steps:
s1, building a server cluster;
s2, storing data to a corresponding server, and constructing an application service interface;
s3, constructing a corn lodging disaster early warning platform.
Further, the server cluster in the step S1 includes a big data server for storing foundation information of soil, weather and crop varieties, an operation server for deploying model operation, a deployment interface and an interface server for constructing an interface resource pool.
Further, the data in the step S2 comprise basic data and simulation algorithms, wherein the basic data comprise soil data, weather data, crop variety data and field operation data, and the simulation algorithms comprise a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm;
when the application service interface is built, the service interface is deployed through ngi nx, uWSG I and Django, wherein ngi nx is used as the forefront end of the server.
Further, the early warning platform in the step S3 includes a PC end and a mobile phone end, where the application services of the PC end and the mobile phone end adopt a front-back end separation technology.
Further, the corn growth mechanism model algorithm includes calculating a net dry matter mass and utilizing the heat accumulation to make a growth stage node calculation.
Further, calculating the net dry matter mass in the corn growth mechanism model algorithm comprises:
step one: calculating the daily assimilation dry matter mass of the corn leaves;
step two: calculating the dry matter mass consumed by respiration of the corn leaves;
step three: the net dry matter mass was calculated from the daily assimilated dry matter mass of the corn leaf and the dry matter mass consumed by the respiration of the corn leaf.
Further, the calculation formula of the daily assimilated dry matter mass of the corn leaf is as follows:
wherein A is CO per square meter per day 2 Assimilation amount, LAI is leaf area index, A max For maximum assimilation rate, carbon gross For the dry matter mass per square meter per day, P is the number of plants per square meter, L is the depth of canopy of the maize plant, PAR i，L The effective solar radiation quantity of different canopy depths is epsilon, and the effective utilization rate of initial light energy is epsilon;
the calculation formula of the dry matter mass consumed by the respiration of the corn leaves is as follows:
mResp＝DM live *Coef resp *Coef T
wherein, coef T T is the coefficient of influence of temperature on respiratory consumption mean T is the daily average temperature ref For reference temperature, mResp maintains respiratory consumption, DM, for different tissues and organs live For the weight of the organ, coef T Maintaining a respiratory coefficient specific to the organ;
the calculation formula of the net dry matter mass is as follows:
Carbo＝Carbon gross -∑mResp。
further, the calculation formula for calculating the growth period node by using the accumulated temperature is as follows:
when the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is higher than the upper limit of the corn growth temperature,
when the lowest daily temperature is within the upper and lower limits of the corn growth temperature and the highest daily temperature is higher than the upper limit of the corn growth temperature,
when the lowest daily temperature and the highest daily temperature are within the upper and lower limit ranges of the corn growth temperature,
GDD＝T mean -T L ；
when the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is within the upper and lower limits of the corn growth temperature,
when the lowest daily temperature and the highest daily temperature are both higher than the lower limit of the corn growth temperature,
GDD＝T U -T L ；
when the lowest daily temperature and the highest daily temperature are all lower than the lower limit of the corn growth temperature,
GDD＝0。
further, the growth period node comprises a seedling emergence period, a jointing period, a 12-leaf period, a wire laying period, a grouting period, a milk maturation period and a maturation period.
Further, the calculation formula of the summer corn lodging early warning model algorithm is as follows:
wherein a1, a2, a3, b1, b2, b3, C1, C2, C3, D1, D2, D3, e1, e2, e3 are system parameters, Y is lodging occurrence degree, P is growth period stage, W is wind speed, R is rainfall, F is crop variety lodging resistance coefficient, C is agriculture chemical control management, and D is density.
Advantageous effects
The invention has the following beneficial effects:
the summer corn lodging early warning algorithm based on the corn growth mechanism model combines the requirements of the corn growth and development on light, temperature and water to establish a growth period prediction model, and accurately predicts the growth and development stage of the corn. The method comprises the steps of classifying the growth stages of the corns when the lodging of the historical corns occurs, determining the growth stages of the corns which are easy to lodge, judging whether the growth stages of the corns reach the stages which are easy to lodge or not by using the follow-up, taking into consideration various factors such as nodes of the growth stages of the corns, agriculture management, meteorological environment and the like for early warning, and most of the prior art carries out early warning based on the influence of the meteorological environment, ignoring the characteristics that the same climatic environment cannot cause lodging when the corn seedling stage occurs, but can cause lodging under the high probability after the large bell mouth stage; the influence of the same strong wind and rainfall environment on corns planted in different densities is different, and the higher the density is, the greater the probability of lodging is; if the maize plants are subjected to chemical control to cause dwarf, the same heavy wind rainfall has different lodging degrees; the lodging resistance of the variety needs to be considered, most factors affecting lodging of corn plants are considered, and the early warning accuracy of lodging disasters is improved.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
FIG. 1 is a summer corn lodging early warning flow chart of the invention;
fig. 2 is a flow chart of early warning of lodging of summer corns in the prior art.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "open," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like indicate orientation or positional relationships, merely for convenience in describing the present invention and to simplify the description, and do not indicate or imply that the components or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
Referring to fig. 1, the embodiment of the invention provides a technical scheme: a summer corn lodging early warning algorithm based on a corn growth mechanism model comprises the following steps:
s1, building a server cluster;
s2, storing data to a corresponding server, and constructing an application service interface;
s3, constructing a corn lodging disaster early warning platform.
Specifically, the server cluster in the step S1 includes a big data server for storing basic information of soil, weather and crop varieties, an operation server for deploying model operation, a deployment interface and an interface server for constructing an interface resource pool.
Specifically, the data in the step S2 comprises basic data and a simulation algorithm, wherein the basic data comprises soil data, weather data, crop variety data and field operation data, and the simulation algorithm comprises a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm;
when the application service interface is built, the service interface is deployed through ngi nx, uWSG I and Django, wherein ngi nx is used as the forefront end of the server.
Specifically, the early warning platform in step S3 includes a PC end and a mobile phone end, where the application services of the PC end and the mobile phone end adopt a front-back end separation technology.
In the embodiment, a big data server for storing basic information of soil, weather and crop varieties is built, an operation server for deploying model operation, an interface server for deploying an interface and constructing an interface resource pool is built, then basic data comprising soil data, weather data, crop variety data, field operation data and the like are stored in the big data server, a simulation algorithm comprising a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm is stored in the operation server, meanwhile, an application service interface is built, service interfaces are deployed through ngi nx, uWSG I and Django, wherein the ngi nx is used as the forefront end of the server, all requests of web are received, unified management requests are built, a corn lodging disaster early warning platform comprising a PC end and a mobile phone end is built, APP application service adopts a front-rear end separation technology, when the content needs to be displayed by a PC end page and APP, the interface server is logged in to acquire token information, and when the interface service is requested, the service is accessed together with token; the server receives the request verification token information, processes the request and returns result data.
Specifically, the corn growth mechanism model algorithm comprises the steps of calculating the quality of net dry matters and calculating a growth period node by utilizing the accumulated temperature;
the calculated net dry matter mass in the corn growth mechanism model algorithm includes:
step one: calculating the daily assimilation dry matter mass of the corn leaves;
step two: calculating the dry matter mass consumed by respiration of the corn leaves;
step three: the net dry matter mass was calculated from the daily assimilated dry matter mass of the corn leaf and the dry matter mass consumed by the respiration of the corn leaf.
Specifically, the calculation formula of the daily assimilated dry matter mass of corn leaves is as follows:
wherein A is CO per square meter per day 2 Assimilation amount, LAI is leaf area index, A max For maximum assimilation rate, carbon gross For a dry matter mass per square meter per day, P is per square meterSquare rice plant number, L is maize plant canopy depth, PAR i，L The effective solar radiation quantity of different canopy depths is epsilon, and the effective utilization rate of initial light energy is epsilon;
the calculation formula of the dry matter mass consumed by the respiration of the corn leaves is as follows:
mResp＝DM live *Coef resp *Coef T
wherein, coef T T is the coefficient of influence of temperature on respiratory consumption mean T is the daily average temperature ref For reference temperature, mResp maintains respiratory consumption, DM, for different tissues and organs live For the weight of the organ, coef T Maintaining a respiratory coefficient specific to the organ;
the calculation formula of the net dry matter mass is as follows:
Carbo＝Carbon gross -∑mResp。
specifically, the calculation formula for calculating the growth period node by using the temperature is as follows:
when the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is higher than the upper limit of the corn growth temperature,
when the lowest daily temperature is within the upper and lower limits of the corn growth temperature and the highest daily temperature is higher than the upper limit of the corn growth temperature,
when the lowest daily temperature and the highest daily temperature are within the upper and lower limit ranges of the corn growth temperature,
GDD＝T mean -T L ；
when the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is within the upper and lower limits of the corn growth temperature,
when the lowest daily temperature and the highest daily temperature are both higher than the lower limit of the corn growth temperature,
GDD＝T U -T L ；
when the lowest daily temperature and the highest daily temperature are all lower than the lower limit of the corn growth temperature,
GDD＝0。
specifically, the growth stage nodes comprise a seedling stage, a jointing stage, a 12-leaf stage, a spinning stage, a grouting stage, a milk maturation stage and a maturation stage.
In the embodiment, the corn growth and development model dynamically simulates the growth state of corn based on climate data (temperature, rainfall, humidity, wind speed, sunshine hours) and by combining physiological processes of photosynthesis, respiration, growth and development of crops.
Specifically, the calculation formula of the summer corn lodging early warning model algorithm is as follows:
wherein a1, a2, a3, b1, b2, b3, C1, C2, C3, D1, D2, D3, e1, e2, e3 are system parameters, Y is lodging occurrence degree, P is growth period stage, W is wind speed, R is rainfall, F is crop variety lodging resistance coefficient, C is agriculture chemical control management, and D is density.
In the embodiment, under different growth period stages, the lodging degree of plants caused by the same strong wind, rainfall and agriculture management environment is different, which indicates that the lodging resistance of plants in different stages is different, so that three stages, namely a 12-leaf stage-male-pulling stage, a male-pulling stage-grouting stage and a grouting stage-mature stage, are divided.
The method comprises the steps of building a big data server for storing basic information of soil, weather and crop varieties, deploying an operation server and a deployment interface for model operation, building an interface resource pool, then storing basic data comprising soil data, weather data, crop variety data, field operation data and the like into the big data server, storing a simulation algorithm comprising a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm into the operation server, building an application service interface, deploying a service interface through nginx, uWSG, django, wherein, each ginx is used as the forefront end of the server, receiving all requests of web, uniformly managing the requests, building a corn lodging disaster early warning platform comprising a PC end and a mobile phone end, acquiring token information by a login interface server when the PC end page and the APP need to display content, and accessing the service together with the token when the interface service is requested; the server receives the request verification token information, processes the request and returns result data;
the corn growth mechanism model is based on climate data (temperature, rainfall, humidity, wind speed, sunshine hours), and combines the physiological processes of photosynthesis, respiration, growth and development of crops to dynamically simulate the growth state of corn, and comprises two parts of calculating the net dry matter mass and calculating a growth period node by using accumulated temperature, wherein the calculating of the net dry matter mass comprises three steps of calculating the dry matter mass assimilated by corn leaves every day, and calculating the dry matter mass consumed by the respiration of the corn leaves and the net dry matter mass;
first pass throughCalculating the C02 assimilation amount A per square meter per day, and then passing +.>Calculating daily dry matter weight of corn leaf per square meter gross (wherein LAI is leaf area index, A max For maximum assimilation rate, P is the number of plants per square meter, L is the depth of canopy of corn plants, PAR i，L For the effective solar radiation of different canopy depths, epsilon is the effective utilization rate of initial light energy), corn plants consume part of dry matters every day except for photosynthesisBy mass ofCalculating the coefficient of influence Coef of temperature on respiratory consumption T Again through mrresp=dm live *Coef resp *Coef T Can calculate the respiratory consumption mResp (T mean T is the daily average temperature ref For reference temperature, DM live For the weight of the organ, coef T Maintenance of respiratory coefficients specific to the organ), finally by carbo=carbon gros s- Σmrresp can calculate the net dry matter (where Σmrresp maintains the sum of the respiratory consumption for all tissues and organs);
the growth and development stage of the corn plants is dominated by accumulated temperature, and the calculation of the node in the growth period is required according to different conditions:
when the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is higher than the upper limit of the corn growth temperature,
when the lowest daily temperature is within the upper and lower limits of the corn growth temperature and the highest daily temperature is higher than the upper limit of the corn growth temperature,
gdd=t when the lowest and highest daily temperatures are within the upper and lower limits of the corn growth temperature mean -T L ；
When the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is within the upper and lower limits of the corn growth temperature,
gdd=t when both the lowest and highest daily temperatures are above the lower limit of the corn growth temperature U -T L ；
Gdd=0 when the lowest and highest daily temperatures are below the lower limit of the corn growth temperature
Wherein T is mean T is the average value of the highest temperature and the lowest temperature U 、T L Is the upper limit and the lower limit of the corn growth temperature, alpha is half of the difference between the highest temperature and the lowest temperature of the day, theta 1 、θ 2 The intersection angle of the daily temperature change curve and the upper limit and the lower limit of the growth temperature is shown, and alpha is an intermediate parameter;
predicting a key growth period node according to the requirements of different growth stages of corn on accumulated temperature, wherein the key growth period node comprises a seedling emergence period, a jointing period, a 12-leaf period, a spinning period, a grouting period, a milk maturation period and a maturation period;
the summer corn lodging early warning model algorithm can be combined with the corn fertility stage to perform lodging early warning; through the annual lodging data of the summer corn area, including lodging occurrence areas, varieties, densities, occurrence time, growth period stages, occurrence degrees, weather environment data during occurrence and whether chemical control agents are applied, the factors with stronger relativity to the lodging occurrence degrees are found by utilizing a sparman relativity analysis method, including growth period stages (P), wind speeds (W), rainfall (R), crop variety lodging resistance coefficients (F), agrochemicals control management (C) and densities (D). Combining the correlation of 6 main influencing factors and the occurrence disaster degree (no (0), mild (0-10%), moderate (10-30%), and severe (30-100%) of lodging, and establishing a lodging disaster early warning regression curve:
wherein a1, a2, a3, b1, b2, b3, c1, c2, c3, d1, d2, d3, e1, e2, e3 are system parameters, and Y is the occurrence degree of lodging;
and (3) inputting the corn growing period stage predicted by the mechanism model into an early warning algorithm, judging whether the three stages of lodging occur or not, and if the growing period reaches the three stages of lodging easily occurring, respectively predicting the occurrence degree of the lodging disaster according to the lodging-resistant coefficient, rainfall, wind speed, density and chemical control management data of the input variety and a lodging disaster early warning regression curve formula.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. A summer corn lodging early warning method based on a corn growth mechanism model is characterized by comprising the following steps of: the method comprises the following steps:
s1, building a server cluster;
s2, storing data to a corresponding server, and constructing an application service interface;
s3, constructing a corn lodging disaster early warning platform;
the data in the step S2 comprises basic data and a simulation algorithm, wherein the basic data comprises soil data, weather data, crop variety data and field operation data, and the simulation algorithm also comprises a summer corn lodging early warning model algorithm;
when the application service interface is built, a service interface is deployed through nginx, uWSGI, django, wherein, the nginx is used as the forefront end of the server;
the corn growth mechanism model comprises the steps of calculating the mass of net dry matters and calculating the growth period node by using the heat accumulation, wherein the calculation formula of the growth period node by using the heat accumulation is as follows:
when the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is higher than the upper limit of the corn growth temperature,
when the lowest daily temperature is within the upper and lower limits of the corn growth temperature and the highest daily temperature is higher than the upper limit of the corn growth temperature,
when the lowest daily temperature and the highest daily temperature are within the upper and lower limit ranges of the corn growth temperature,
GDD＝T mean -T L ；
when the daily minimum temperature is lower than the lower limit of the corn growth temperature and the daily maximum temperature is within the upper and lower limits of the corn growth temperature,
when the lowest daily temperature and the highest daily temperature are both higher than the upper limit of the corn growth temperature,
GDD＝T U -T L ；
when the lowest and highest daily temperatures are below the lower limit of the corn growth temperature,
GDD＝0；
wherein GDD is the accumulated temperature, T mean T is the average value of the highest temperature and the lowest temperature U 、T L Is the upper limit and the lower limit of the corn growth temperature, alpha is half of the difference between the highest temperature and the lowest temperature of the day, theta 1 、θ 2 The intersection angle of the daily temperature change curve and the upper limit and the lower limit of the growth temperature is shown, and alpha is an intermediate parameter;
the growth period node comprises a seedling emergence period, a jointing period, a 12-leaf period, a spinning period, a grouting period, a milk maturation period and a maturation period;
the calculation formula of the summer corn lodging early warning model algorithm is as follows:
wherein a1, a2, a3, b1, b2, b3, C1, C2, C3, D1, D2, D3, e1, e2, e3 are system parameters, Y is lodging occurrence degree, P is growth period stage, W is wind speed, R is rainfall, F is crop variety lodging resistance coefficient, C is agriculture chemical control management, and D is density.
2. The early warning method for lodging of summer corns based on a corn growth mechanism model according to claim 1, which is characterized in that: the server cluster in the step S1 comprises a big data server for storing foundation information of soil, weather and crop varieties, an operation server for deploying model operation, a deployment interface and an interface server for constructing an interface resource pool.
3. The early warning method for lodging of summer corns based on a corn growth mechanism model according to claim 1, which is characterized in that: the early warning platform in the step S3 comprises a PC end and a mobile phone end, and front-back end separation technology is adopted by application services of the PC end and the mobile phone end.
4. The early warning method for lodging of summer corns based on a corn growth mechanism model according to claim 1, which is characterized in that: the calculated net dry matter mass in the corn growth mechanism model comprises:
step one: calculating the daily assimilation dry matter mass of the corn leaves;
step two: calculating the dry matter mass consumed by respiration of the corn leaves;
step three: the net dry matter mass was calculated from the daily assimilated dry matter mass of the corn leaf and the dry matter mass consumed by the respiration of the corn leaf.
5. The early warning method for lodging of summer corns based on a corn growth mechanism model according to claim 4, which is characterized in that: the calculation formula of the daily assimilation dry matter mass of the corn leaves is as follows:
wherein DL is sunlight duration, A is CO per square meter per day 2 Assimilation amount, LAI is leaf area index, A max For maximum assimilation rate, carbon gross For the dry matter mass per square meter per day, P is the number of plants per square meter, L is the depth of canopy of the maize plant, PAR i，L The effective solar radiation quantity of different canopy depths is epsilon, and the effective utilization rate of initial light energy is epsilon;
the calculation formula of the dry matter mass consumed by the respiration of the corn leaves is as follows:
mResp＝DM live *oef resp *oef T
wherein, coef T T is the coefficient of influence of temperature on respiratory consumption mean T is the average value of the highest temperature and the lowest temperature ref For reference temperature, mResp maintains respiratory consumption, DM, for different tissues and organs live For the weight of the organ, coef resp Maintaining a respiratory coefficient specific to the organ;
the calculation formula of the net dry matter mass is as follows:
Carbo＝Carbon gross -∑mResp。
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