A stage-based insect pest population dynamics modeling method and system

Abstract

This invention proposes a method and system for modeling insect population dynamics based on stage-based development, belonging to the field of agricultural insect pest monitoring and prediction technology. The key technical points of this invention include: acquiring daily meteorological data of the target area, including daily average temperature, daily relative humidity, sunshine duration, daily average wind speed, and daily precipitation; inputting the meteorological data into an insect population dynamic model, which includes an egg stage development sub-model, a larval stage development sub-model, a pupal stage development sub-model, an adult oviposition model, and a population renewal sub-model; the sub-models collaboratively calculate and output the daily population survival rate; further, based on the historical population survival rate sequence output by the population renewal sub-model, the moment estimation method is used to fit the Gamma distribution to calculate and output the disease probability corresponding to the current population survival rate. This invention is mainly applied to early warning and population dynamic monitoring of insect pests in agricultural scenarios such as farmland, orchards, and tea gardens.

Description

Technical Field

[0001] This invention belongs to the field of agricultural pest monitoring and prediction technology, specifically involving a method and system for modeling pest population dynamics based on stage development. Background Technology

[0002] Agricultural pests are one of the core factors affecting crop yield and quality. Timely and accurate pest identification and risk prediction are crucial for scientific control, reducing pesticide overuse, and minimizing agricultural losses. Currently, pest monitoring and prediction in agricultural production still relies primarily on traditional manual identification methods. Plant protection personnel regularly go into the fields to identify pest types and count populations through visual observation and manual counting, combining their experience with historical observation records to assess the risk of pest outbreaks. In some scenarios, simple meteorological factors are used for empirical judgment, but a systematic modeling method has not yet been developed. Relevant technical specifications can be found in the "Technical Specifications for Monitoring Agricultural Plant Pests" (GB / T 33122-2016).

[0003] The existing technology has the following defects: (1) Manual identification relies on professional experience and is affected by subjective judgment and visual errors. It is difficult to accurately distinguish pests with similar morphology, and the counting error is usually not less than 30%; (2) It takes 3-7 days to complete a comprehensive monitoring of a large area of ​​farmland, which cannot capture the key nodes of pest outbreaks in time; (3) The prediction of disease incidence probability relies entirely on manual experience and lacks quantitative basis. Different people have different judgments; (4) Manual monitoring is costly and cannot accurately cover the population; (5) Manual monitoring is a phased snapshot monitoring and cannot realize the chain-like dynamic update of population survival; (6) There is a lack of standardized data format, making it difficult to integrate historical data to optimize the prediction logic.

[0004] Existing patents, such as CN110110945B, disclose a pest prediction method based on a population model. This method uses temperature as a single factor to drive the development of each insect stage, without considering the combined effects of multiple environmental factors such as humidity and light duration. It also does not include a multi-factor bottleneck effect model for mortality and a probability prediction mechanism based on historical data. Summary of the Invention

[0005] The present invention aims to provide a method and system for modeling the dynamics of pest populations based on stage development, in order to solve the technical problems of low accuracy, poor efficiency, strong subjectivity in prediction, and insufficient consideration of environmental factors in existing models.

[0006] According to one aspect of the present invention, a method for modeling the dynamics of pest populations based on stage development is proposed, the method comprising the following steps: S1. Obtain daily meteorological data for the target area, including daily average temperature, daily relative humidity, sunshine duration, daily average wind speed, and daily precipitation; S2. Input the meteorological data into the pest population dynamics model, which includes an egg stage development sub-model, a larval stage development sub-model, a pupal stage development sub-model, an adult oviposition model, and a population renewal sub-model; the sub-models work together to calculate and output the population survival rate daily; wherein... The egg development sub-model calculates the daily development amount during the egg period based on the daily average temperature and daily relative humidity, and determines hatching when the cumulative egg development amount reaches the cumulative threshold during the egg period. The larval development sub-model calculates the daily development amount of the larvae based on the daily average temperature, daily relative humidity, and daylight duration, and calculates the daily feeding amount based on the daily average temperature and daily relative humidity. When the cumulative daily development amount and cumulative feeding amount of the larvae both reach the threshold corresponding to the current instar, the larvae enter the next instar. The pupal development sub-model calculates the daily pupal development amount based on the daily average temperature. After the cumulative pupal development amount reaches the pupal cumulative threshold, the success probability of emergence is determined based on the daily relative humidity. If the success probability is greater than the preset threshold, the pupal emerges as an adult. The adult insect oviposition model calculates the daily oviposition amount based on the daily average temperature and daily relative humidity, and outputs the daily oviposition amount each day during the oviposition period; The population renewal sub-model determines the daily mortality rate by taking the maximum value of five mortality factors: temperature, humidity, wind speed, precipitation, and crop resistance, and then determines the daily population survival rate.

[0007] Furthermore, it also includes: based on the historical population survival sequence output by the population update sub-model, using the moment estimation method to fit the Gamma distribution, calculating and outputting the disease probability corresponding to the current population survival.

[0008] Furthermore, the daily developmental rate during the oviposition period described in S2 is calculated using a piecewise linear interpolation method, with the following formula: R_egg = interp(T, C_temp_egg) × interp(RH, C_humi_egg); Where R_egg represents the daily development during the egg stage; interp is a one-dimensional piecewise linear interpolation function; C_temp_egg and C_humi_egg are the egg stage temperature development coefficient and egg stage humidity development coefficient, respectively; T represents the daily average air temperature; and RH represents the daily relative humidity.

[0009] Furthermore, the daily larval development rate described in S2 is calculated using a piecewise linear interpolation method, as shown in the formula: R_larva=interp(T,C_temp_larva)×interp(RH,C_humi_larva)×interp(I,C_light); Where R_larva represents the daily development amount during the larval stage, C_temp_larva, C_humi_larva and C_light represent the temperature development coefficient, humidity development coefficient, and light duration development coefficient during the larval stage, respectively; I represents the duration of sunlight. The daily food intake is calculated using a piecewise linear interpolation method, with the following formula: F_intake = F_max_i × interp(T, C_temp_feed) × interp(RH, C_humi_feed); Where F_intake represents the daily food intake; F_max_i represents the maximum daily food intake of the i-th instar larva; C_temp_feed and C_humi_feed represent the larval feeding temperature coefficient and the larval feeding humidity coefficient, respectively.

[0010] Furthermore, the daily pupal development rate described in S2 is calculated using a piecewise linear interpolation method, as shown in the formula: R_pupa = interp(T, C_temp_pupa); Where R_pupa represents the daily developmental rate during the pupal stage; C_temp_pupa represents the temperature-dependent developmental coefficient during the pupal stage. The formula for calculating daily egg production is: E_daily = (E_total / D_oviposit) × interp(T, rtluan) × interp(RH,rwluan); Where E_daily represents the daily egg production; E_total represents the total egg production per female; D_oviposit represents the number of days in the egg-laying period; rtluan and rwluan represent the temperature-induced egg-laying coefficient and the humidity-induced egg-laying coefficient, respectively.

[0011] Furthermore, the formula for calculating the daily population survival rate described in S2 is as follows: N_t = N_{t-1} ×B_t; Where N_t represents the population survival rate on day t; N_{t-1} represents the population survival rate on day t-1; B_t represents the survival coefficient, B_t = 1-M_t, and M_t represents the mortality rate.

[0012] Furthermore, the formula for calculating the mortality rate M_t is: M_t=max(m_T(T),m_RH(RH),m_W(wind_t),m_P(rain_t),m_R(resistance)); Where m_T(T), m_RH(RH), m_W(wind_t), m_P(rain_t), and m_R(resistance) represent the temperature mortality factor, humidity mortality factor, wind speed mortality factor, precipitation mortality factor, and crop resistance mortality factor, respectively; max indicates taking the maximum value; wind_t represents the daily average wind speed; rain_t represents the daily precipitation; resistance represents the crop resistance coefficient; and the crop resistance mortality factor m_R(resistance) = 1 - resistance × 0.1.

[0013] Furthermore, based on the historical population survival sequence output by the population update sub-model, the moment estimation method is used to fit the Gamma distribution, calculate the disease probability corresponding to the current population survival, and output the following: Obtain the historical population survival sequence of the target area at the same time, and calculate the sample mean μ and variance σ²; Calculate the moment estimate shape parameter α = μ² / σ², and the moment estimate scale parameter β = μ / σ²; Once the cumulative distribution function of the Gamma distribution is determined, the formula for calculating the probability of disease incidence is: p_risk = 1 - F_Γ(μ; shape = α + N_cur / 10, scale = β + 1); Where p_risk represents the probability of disease onset; F_Γ is the cumulative distribution function of the Gamma distribution; shape represents the shape parameter of the Gamma distribution; scale represents the scale parameter of the Gamma distribution; and N_cur represents the current population survival rate.

[0014] According to another aspect of the present invention, a pest population dynamics modeling system based on stage development is proposed, the system being used to implement the above-described pest population dynamics modeling method based on stage development; the system includes: Data acquisition module: configured to acquire daily meteorological data of the target area; the meteorological data includes daily average temperature, daily relative humidity, sunshine duration, daily average wind speed, and daily precipitation; Population survival calculation module: configured to input the meteorological data into the pest population dynamic model, which includes an egg stage development sub-model, a larval stage development sub-model, a pupal stage development sub-model, an adult oviposition model, and a population renewal sub-model; the population survival rate is calculated daily by the collaborative calculation of each sub-model; wherein... The egg development sub-model calculates the daily development amount during the egg period based on the daily average temperature and daily relative humidity, and determines hatching when the cumulative egg development amount reaches the cumulative threshold during the egg period. The larval development sub-model calculates the daily development amount of the larvae based on the daily average temperature, daily relative humidity, and daylight duration, and calculates the daily feeding amount based on the daily average temperature and daily relative humidity. When the cumulative daily development amount and cumulative feeding amount of the larvae both reach the threshold corresponding to the current instar, the larvae enter the next instar. The pupal development sub-model calculates the daily pupal development amount based on the daily average temperature. After the cumulative pupal development amount reaches the pupal cumulative threshold, the success probability of emergence is determined based on the daily relative humidity. If the success probability is greater than the preset threshold, the pupal emerges as an adult. The adult insect oviposition model calculates the daily oviposition amount based on the daily average temperature and daily relative humidity, and outputs the daily oviposition amount each day during the oviposition period; The population renewal sub-model determines the daily mortality rate by taking the maximum value of five mortality factors: temperature, humidity, wind speed, precipitation, and crop resistance, and then determines the daily population survival rate.

[0015] The present invention has the following advantages over the prior art: (1) Improved accuracy: By using multi-environmental factor-driven stage development modeling, subjective human error is avoided, and the accuracy of population survival calculation is significantly higher than that of manual counting.

[0016] (2) Improved efficiency: The automated monitoring and prediction of thousands of acres of farmland can be completed in just 1-2 minutes, which is more than 90% more efficient than manual monitoring.

[0017] (3) Prediction standardization: Based on historical data, the probability prediction of Gamma distribution is used to quantify the risk of disease and avoid subjective differences in experience judgment.

[0018] (4) Reduce labor costs: The entire process is automated, which can reduce labor input by more than 80%.

[0019] (5) Data reusability: Standardized data output can be connected to prevention and control equipment to achieve integrated "prediction-prevention". Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for modeling the dynamics of pest populations based on stage development, as described in this invention.

[0021] Figure 2 This is a schematic diagram of the structure of a pest population dynamics modeling system based on stage development, as described in this invention. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0023] This invention divides the developmental process of pests into stages: egg stage, larval stage (including multiple instars), pupal stage, and adult stage. It quantifies and models the environmental influencing factors at each stage, enabling chain-like extrapolation of population survival and quantitative prediction of disease probability. This invention is primarily applied to early pest warning and population dynamic monitoring in agricultural settings such as farmland, orchards, and tea gardens.

[0024] This invention proposes a method for modeling the dynamics of pest populations based on stage development, such as... Figure 1 As shown, the method includes the following steps: S1. Obtain daily meteorological data for the target area, including daily average temperature, daily relative humidity, sunshine duration, daily average wind speed, and daily precipitation; S2. Input the meteorological data into the pest population dynamics model, which includes an egg stage development sub-model, a larval stage development sub-model, a pupal stage development sub-model, an adult oviposition model, and a population renewal sub-model; the sub-models work together to calculate and output the population survival rate daily; wherein... The egg development sub-model calculates the daily development amount during the egg period based on the daily average temperature and daily relative humidity, and determines hatching when the cumulative egg development amount reaches the cumulative threshold during the egg period. The larval development sub-model calculates the daily development amount of the larvae based on the daily average temperature, daily relative humidity, and daylight duration, and calculates the daily feeding amount based on the daily average temperature and daily relative humidity. When the cumulative daily development amount and cumulative feeding amount of the larvae both reach the threshold corresponding to the current instar, the larvae enter the next instar. The pupal development sub-model calculates the daily pupal development amount based on the daily average temperature. After the cumulative pupal development amount reaches the pupal cumulative threshold, the success probability of emergence is determined based on the daily relative humidity. If the success probability is greater than the preset threshold, the pupal emerges as an adult. The adult insect oviposition model calculates the daily oviposition amount based on the daily average temperature and daily relative humidity, and outputs the daily oviposition amount each day during the oviposition period; The population renewal sub-model determines the daily mortality rate by taking the maximum value of five mortality factors: temperature, humidity, wind speed, precipitation, and crop resistance, and then determines the daily population survival rate.

[0025] The method begins with S1, acquiring daily meteorological data for the target area, including daily average temperature, daily relative humidity, sunshine duration, daily average wind speed, and daily precipitation.

[0026] According to an embodiment of the present invention, the geographical location of the target area is first determined and meteorological data is read in daily increments. 1) Daily average temperature: The average of the temperature records at 24 hours from 00:00 to 23:00 on a given day is taken, in °C. For example, the hourly temperature records for a certain day are: 12°C at 02:00, 18°C ​​at 08:00, 25°C at 14:00, 16°C at 20:00, etc. The average of the 24 values ​​is 17.8°C. 2) Daily relative humidity: The average of the relative humidity records at all hours on a given day is taken, in %. Relative humidity reflects the water vapor content in the air and directly affects the water balance in pests and the hatching rate of eggs. 3) Sunshine duration: The cumulative value of the sunshine duration records at all hours on a given day is taken, in hr·d. -¹ The duration of sunlight affects the field microclimate and the activity rhythm of pests; the daily cumulative value reflects the total solar energy of the day. 4) Daily average wind speed: The average of all hourly wind speed records for the day, in m / s. -¹ Wind speed affects the spread, migration, and field distribution of pests. 5) Daily precipitation: The cumulative value of all hourly precipitation records for the day, in mm·d. -¹ Rainfall has a direct scouring and lethal effect on pest populations, and also affects field humidity.

[0027] Then, execute S2 to input the meteorological data into the pest population dynamic model, which includes an egg stage development sub-model, a larval stage development sub-model, a pupal stage development sub-model, an adult oviposition model, and a population renewal sub-model; the population survival rate is output daily by the collaborative calculation of each sub-model.

[0028] According to an embodiment of the present invention, the calculations for each sub-model are as follows.

[0029] 1. Ovary developmental model Egg development is driven by both temperature and humidity. The formula for calculating the daily development rate during the egg stage is: R_egg = interp(T, C_temp_egg) × interp(RH, C_humi_egg); Where interp is a one-dimensional piecewise linear interpolation function, C_temp_egg and C_humi_egg are the egg stage temperature development coefficient and egg stage humidity development coefficient, respectively; T represents the daily average air temperature; RH represents the daily relative humidity.

[0030] Hatching is considered complete when the cumulative development amount ∑R_egg during the egg stage reaches the cumulative threshold T_sum_egg during the egg stage.

[0031] In this embodiment of the invention, the developmental coefficient (egg stage temperature developmental coefficient, egg stage humidity developmental coefficient, etc.) refers to the relative coefficient of pest development rate under different environmental factor levels, used to quantify the driving or inhibiting effect of environmental factors on development. Its biological basis lies in the fact that pest development has a basic threshold and an upper threshold; only when environmental factors (temperature, humidity, etc.) are between these two thresholds do they make an effective contribution. Below the basic threshold, development stagnates; above the upper threshold, development is inhibited or produces harmful effects. Taking the egg stage temperature developmental coefficient as an example, the suitable temperature range for the egg stage of a certain pest is 10-25℃. When the daily temperature is 15℃, the effective contribution is equivalent to a developmental effect of 5℃ / day; when the daily temperature is 28℃, its developmental effect is equivalent to the value at 25℃. The developmental coefficient was obtained through an indoor constant temperature and humidity experiment: different temperature (or humidity) gradients were set, the developmental duration of the egg stage under each condition was measured, the developmental rate (1 / developmental days) was calculated, and discrete experimental data points were obtained; then, through nonlinear regression fitting (such as Logan model, Briere model, etc.), a continuous environmental factor-developmental coefficient response curve was obtained, which was stored in a database table in the form of node pairs for the model to call through piecewise linear interpolation.

[0032] 2. Larval developmental model Larval development was determined using a dual mechanism of "accumulated temperature drive and nutritional constraint". The daily larval development rate (R_larva) and daily larval food intake (F_intake) were calculated as follows: R_larva=interp(T,C_temp_larva)×interp(RH,C_humi_larva)×interp(I,C_light); F_intake = F_max_i × interp(T, C_temp_feed) × interp(RH, C_humi_feed); Where R_larva represents the daily growth rate of the larva, C_temp_larva, C_humi_larva and C_light represent the temperature growth coefficient, humidity growth coefficient and light duration growth coefficient of the larva, respectively; I represents the light duration; F_intake represents the daily food intake; F_max_i represents the maximum daily food intake of the i-th instar larva; C_temp_feed and C_humi_feed represent the temperature growth coefficient and humidity growth coefficient of the larva, respectively.

[0033] The feeding coefficients (C_temp_feed and C_humi_feed) are dimensionless relative coefficients obtained by nonlinear regression fitting after measuring the daily feeding amount of larvae under different temperature and humidity conditions in an indoor constant temperature and humidity experiment. They are used to quantify the driving or inhibiting effects of environmental factors on larval feeding activities.

[0034] The criteria for determining age-related progress are that both the cumulative development percentage and the cumulative food intake percentage reach 100%. (∑R_larva / T_sum_larva_i ) ≥ 1 and (∑F_intake / T_food_i) ≥ 1; Here, T_sum_larva_i and T_food_i represent the developmental accumulation threshold and total food intake threshold for the i-th larval instar, respectively. This condition ensures that the larva completes its physiological development and accumulates sufficient nutrients before entering the next instar. The maximum larval instar is directly limited by the preset species parameter agemx. Once the larval instar reaches the preset species parameter, it must end its larval stage and pupate.

[0035] 3. Pupal Development Model Pupal development is driven by temperature, and successful eclosion is affected by humidity on the day of eclosion.

[0036] Daily pupal development rate R_pupa = interp(T, C_temp_pupa); Where C_temp_pupa represents the pupal stage temperature development coefficient. When ∑R_pupa ≥ the pupal stage cumulative threshold T_sum_pupa, the emergence determination is entered. The humidity-emergence success rate response curve C_eclose(RH) is queried. If the success rate is greater than the preset threshold (usually 0.6), the insect will emerge as an adult.

[0037] The humidity-emergence success rate response curve C_eclose(RH) is stored in a database table, which predefines the emergence success probability (0-1) corresponding to 10 (or more) humidity levels (10%-100%). The specific value of this curve can be obtained by measuring the emergence success rate of pupae under different humidity conditions through indoor constant temperature and humidity experiments.

[0038] 4. Adult oviposition model Adults do not lay eggs during the oviposition preparation period (specified by the oviposition / reproduction-related parameter clnum, usually 1-3 days after emergence), and enter the oviposition period after the preparation period ends. The formula for calculating the daily egg production is: E_daily = (E_total / D_oviposit) × interp(T, rtluan) × interp(RH,rwluan); Where E_daily represents the daily egg production; E_total represents the total egg production per female; D_oviposit represents the number of days in the oviposition period; rtluan and rwluan represent the temperature-based oviposition coefficient and the humidity-based oviposition coefficient, respectively. The total egg production per female represents the total number of eggs laid by a single adult female during the entire oviposition period, and together with the number of days in the oviposition period, it determines the baseline value of the average daily egg production under ideal conditions.

[0039] When the daily average temperature exceeds the suitable temperature range, E_daily = 0. During the spawning period, spawning is uniformly distributed, and peak curves are not considered.

[0040] 5. Population Update Sub-model The daily recursive formula for population survival is: N_t = N_{t-1} × B_t; Where B_t is the survival coefficient, B_t = 1-M_t. The mortality rate M_t adopts the weakest link effect model, taking the maximum value of the five lethal factors: M_t=max(m_T(T),m_RH(RH),m_W(wind_t),m_P(rain_t),m_R(resistance)); Wherein, m_T(T), m_RH(RH), m_W(wind_t), m_P(rain_t), and m_R(resistance) represent the mortality factors of temperature, humidity, wind speed, precipitation, and crop resistance, respectively. The first four mortality factors are obtained by querying the response curves of the corresponding insect stages and lethal factors, using a piecewise linear interpolation method, with a value range limited to no more than 0.22. The crop resistance mortality factor m_R(resistance) is calculated as 1 - resistance × 0.1, where resistance represents the crop resistance coefficient, with a value range of 0-10.

[0041] The mortality rates during the egg stage—temperature mortality, humidity mortality, wind speed mortality, and precipitation mortality—are stored in separate database tables as "environmental factor level - mortality rate" node pairs. The system takes the daily average temperature T as input and uses piecewise linear interpolation to look up the temperature mortality rate table to obtain the temperature mortality rate factor m_T(T); similarly, it looks up the humidity mortality rate table using the daily relative humidity RH to obtain the humidity mortality rate m_RH(RH); it looks up the wind speed mortality rate table using the daily average wind speed wind_t to obtain the wind speed mortality rate m_W(wind_t); and it looks up the precipitation mortality rate table using the daily precipitation rain_t to obtain the precipitation mortality rate m_P(rain_t). In addition to the above four meteorological lethal factors, crop resistance is also introduced as a fifth lethal factor. The crop resistance mortality rate, m_R, is calculated based on the crop resistance coefficient, resistance. A higher resistance value indicates stronger resistance to the current pest. When resistance = 0, m_R = 1, indicating no resistance and no additional mortality. When resistance = 10, m_R = 0, indicating complete resistance, and all pests feeding on the crop will die. The configuration of the crop resistance coefficient follows a priority rule: user-level configuration takes precedence over crop-level configuration. That is, if a user (or plot) has set a resistance coefficient individually, that value is used; otherwise, the default value for the crop type is used. This ensures both flexibility and versatility.

[0042] Furthermore, S2 is followed by S3: based on the historical population survival sequence output by the population update sub-model, the moment estimation method is used to fit the Gamma distribution, calculate the disease probability corresponding to the current population survival and output it.

[0043] According to an embodiment of the present invention, the disease incidence probability prediction adopts the Gamma distribution tail probability method based on historical population data. The population survival sequence {A_y} for the same historical period (same year, month, and day) in the target area is obtained, and the sample mean μ and variance σ² are calculated. The Gamma distribution parameters are determined using the method of moments: first, the intermediate parameters are calculated: the moment estimation shape parameter α = μ² / σ², the moment estimation scale parameter β = μ / σ², and α and β are both taken as 1 when the variance σ² is 0. Let the current population survival be N_cur, and the disease incidence probability calculation formula is: p_risk = 1 - F_Γ(μ; shape = α + N_cur / 10, scale = β + 1); Where p_risk represents the probability of disease onset; F_Γ is the cumulative distribution function of the Gamma distribution; shape represents the shape parameter of the Gamma distribution, used to control the shape of the Gamma distribution curve and determine whether the distribution is left-skewed, right-skewed, or symmetrical; and scale represents the scale parameter of the Gamma distribution. The biological significance of this formula is: with the historical mean μ as a reference, the higher the current population size, the greater the probability of tail risk.

[0044] The model framework provided by this invention is applicable to all agricultural pests for which corresponding parameters (development threshold, response curve, mortality curve) have been calibrated, including but not limited to rice stem borer, corn borer, cotton bollworm, etc.

[0045] In the above embodiments, the effects of all environmental factors (temperature, humidity, light duration) on development rate, feeding efficiency, egg-laying coefficient, and mortality rate are all implemented using the one-dimensional piecewise linear interpolation function interp. Given an ordered pair of nodes (x_k, y_k), where k = 1, 2, ..., N are node indices, the interpolation calculation logic is as follows: if x <= x_1, then y = y_1; if x >= x_N, then y = y_N; otherwise, find the interval [x_k, x_{k+1}] such that x_k <= x <= x_{k+1}, and calculate: y = y_k + (x - x_k) (y_{k+1} - y_k) / (x_{k+1} - x_k + 1e-8). Here, 1e-8 is a minimal constant to prevent division by zero errors. All response curves are stored in the database as the node pairs required for this interpolation. In the piecewise linear interpolation function, the input independent variable x represents the measured values ​​of environmental factors such as the average daily temperature, relative humidity, sunshine duration, wind speed, or precipitation. The output dependent variable y represents the corresponding dimensionless response values ​​such as the development coefficient, feeding coefficient, spawning coefficient, or mortality factor. The response curves are pre-stored as a series of ordered node pairs (x_k, y_k), where x_1 and x_N are the minimum and maximum values ​​of the independent variable, respectively, and y_1 and y_N are the corresponding response values.

[0046] This invention also proposes a pest population dynamics modeling system based on stage development, which is used to implement the pest population dynamics modeling method based on stage development described in the above embodiments; such as Figure 2 As shown, the system includes: Data acquisition module 210: configured to acquire daily meteorological data of the target area; the meteorological data includes daily average temperature, daily relative humidity, sunshine duration, daily average wind speed and daily precipitation; Population survival calculation module 220: configured to input the meteorological data into the pest population dynamic model, which includes an egg stage development sub-model 2210, a larval stage development sub-model 2220, a pupal stage development sub-model 2230, an adult oviposition model 2240, and a population renewal sub-model 2250; the population survival rate is calculated daily by the collaborative calculation of each sub-model; wherein... The egg development sub-model 2210 calculates the daily development amount during the egg period based on the daily average temperature and daily relative humidity, and determines hatching when the cumulative egg development amount reaches the cumulative threshold during the egg period. The larval development sub-model 2220 calculates the daily development amount of the larvae based on the daily average temperature, daily relative humidity, and daylight duration, and calculates the daily food intake based on the daily average temperature and daily relative humidity. When the cumulative daily development amount and cumulative food intake of the larvae both reach the threshold corresponding to the current instar, the larvae enter the next instar. The pupal development sub-model 2230 calculates the daily pupal development amount based on the daily average temperature. After the cumulative pupal development amount reaches the pupal cumulative threshold, the success probability of emergence is determined based on the daily relative humidity. If the success probability is greater than the preset threshold, the pupal emerges as an adult. The adult oviposition model 2240 calculates the daily oviposition amount based on the daily average temperature and daily relative humidity, and outputs the daily oviposition amount each day during the oviposition period; The population renewal sub-model 2250 determines the daily mortality rate by taking the maximum value of five mortality factors: temperature, humidity, wind speed, precipitation, and crop resistance, and then determines the daily population survival rate. Disease incidence probability prediction module 230: configured to use the moment estimation method to fit the Gamma distribution based on the historical population survival sequence output by the population update sub-model 2250, calculate and output the disease incidence probability corresponding to the current population survival.

[0047] The function of the pest population dynamic modeling system based on stage development described in this embodiment of the invention can be explained by the aforementioned pest population dynamic modeling method based on stage development. Therefore, for the parts not described in detail in the system embodiment, please refer to the above method embodiment, and they will not be repeated here.

[0048] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention. Although the spirit and principles of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; this division is merely for convenience of description. The present invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A method for modeling the dynamics of pest populations based on stage-based development, characterized in that, Includes the following steps: S1. Obtain daily meteorological data for the target area, including daily average temperature, daily relative humidity, sunshine duration, daily average wind speed, and daily precipitation; S2. Input the meteorological data into the pest population dynamics model, which includes an egg stage development sub-model, a larval stage development sub-model, a pupal stage development sub-model, an adult oviposition model, and a population renewal sub-model; the sub-models work together to calculate and output the population survival rate daily; wherein... The egg development sub-model calculates the daily development amount during the egg period based on the daily average temperature and daily relative humidity, and determines hatching when the cumulative egg development amount reaches the cumulative threshold during the egg period. The larval development sub-model calculates the daily development amount of the larvae based on the daily average temperature, daily relative humidity, and daylight duration, and calculates the daily feeding amount based on the daily average temperature and daily relative humidity. When the cumulative daily development amount and cumulative feeding amount of the larvae both reach the threshold corresponding to the current instar, the larvae enter the next instar. The pupal development sub-model calculates the daily pupal development amount based on the daily average temperature. After the cumulative pupal development amount reaches the pupal cumulative threshold, the success probability of emergence is determined based on the daily relative humidity. If the success probability is greater than the preset threshold, the pupal emerges as an adult. The adult insect oviposition model calculates the daily oviposition amount based on the daily average temperature and daily relative humidity, and outputs the daily oviposition amount each day during the oviposition period; The population renewal sub-model determines the daily mortality rate by taking the maximum value of five mortality factors: temperature, humidity, wind speed, precipitation, and crop resistance, and then determines the daily population survival rate. It also includes: based on the historical population survival sequence output by the population update sub-model, using the moment estimation method to fit the Gamma distribution, calculating and outputting the disease probability corresponding to the current population survival; The historical population survival sequence output by the population update sub-model is fitted with a Gamma distribution using the moment estimation method to calculate the disease probability corresponding to the current population survival and output the following: Obtain the historical population survival sequence of the target area at the same time, and calculate the sample mean μ and variance σ²; Calculate the moment estimate shape parameter α = μ² / σ², and the moment estimate scale parameter β = μ / σ²; Once the cumulative distribution function of the Gamma distribution is determined, the formula for calculating the probability of disease incidence is: p_risk = 1 - F_Γ(μ; shape = α + N_cur / 10, scale = β + 1); Where p_risk represents the probability of disease onset; F_Γ is the cumulative distribution function of the Gamma distribution; shape represents the shape parameter of the Gamma distribution; scale represents the scale parameter of the Gamma distribution; and N_cur represents the current population survival rate.

2. The method for modeling insect population dynamics based on stage development according to claim 1, characterized in that, The daily developmental rate during the oviparous period described in S2 is calculated using a piecewise linear interpolation method, with the following formula: R_egg = interp(T, C_temp_egg) × interp(RH, C_humi_egg); Where R_egg represents the daily development during the egg stage; interp is a one-dimensional piecewise linear interpolation function; C_temp_egg and C_humi_egg are the egg stage temperature development coefficient and egg stage humidity development coefficient, respectively; T represents the daily average air temperature; and RH represents the daily relative humidity.

3. The method for modeling insect population dynamics based on stage development according to claim 2, characterized in that, The daily larval development rate described in S2 is calculated using a piecewise linear interpolation method, with the following formula: R_larva=interp(T,C_temp_larva)×interp(RH,C_humi_larva)×interp(I, C_light); Where R_larva represents the daily development amount during the larval stage, C_temp_larva, C_humi_larva and C_light represent the temperature development coefficient, humidity development coefficient, and light duration development coefficient during the larval stage, respectively; I represents the duration of sunlight. The daily food intake is calculated using a piecewise linear interpolation method, with the following formula: F_intake = F_max_i × interp(T, C_temp_feed) × interp(RH, C_humi_feed); Where F_intake represents the daily food intake; F_max_i represents the maximum daily food intake of the i-th instar larva; C_temp_feed and C_humi_feed represent the larval feeding temperature coefficient and the larval feeding humidity coefficient, respectively.

4. The method for modeling insect population dynamics based on stage development according to claim 3, characterized in that, The daily pupal development rate described in S2 is calculated using a piecewise linear interpolation method, with the following formula: R_pupa = interp(T, C_temp_pupa); Where R_pupa represents the daily developmental rate during the pupal stage; C_temp_pupa represents the temperature-dependent developmental coefficient during the pupal stage. The formula for calculating daily egg production is: E_daily = (E_total / D_oviposit) × interp(T, rtluan) × interp(RH,rwluan); Where E_daily represents the daily egg production; E_total represents the total egg production per female; D_oviposit represents the number of days in the egg-laying period; rtluan and rwluan represent the temperature-induced egg-laying coefficient and the humidity-induced egg-laying coefficient, respectively.

5. The method for modeling insect population dynamics based on stage development according to claim 4, characterized in that, The formula for calculating the daily population survival rate described in S2 is: N_t = N_{t-1} ×B_t; Where N_t represents the population survival rate on day t; N_{t-1} represents the population survival rate on day t-1; B_t represents the survival coefficient, B_t = 1-M_t, and M_t represents the mortality rate.

6. The method for modeling insect population dynamics based on stage development according to claim 5, characterized in that, The formula for calculating the mortality rate M_t is: M_t=max(m_T(T),m_RH(RH),m_W(wind_t),m_P(rain_t),m_R(resistance)); Where m_T(T), m_RH(RH), m_W(wind_t), m_P(rain_t), and m_R(resistance) represent the temperature mortality factor, humidity mortality factor, wind speed mortality factor, precipitation mortality factor, and crop resistance mortality factor, respectively; max indicates taking the maximum value; wind_t represents the daily average wind speed; rain_t represents the daily precipitation; resistance represents the crop resistance coefficient; and the crop resistance mortality factor m_R(resistance) = 1 - resistance × 0.

1.

7. A pest population dynamics modeling system based on stage development, characterized in that, The system is used to implement the pest population dynamics modeling method based on stage development as described in any one of claims 1 to 6; the system includes: Data acquisition module: configured to acquire daily meteorological data of the target area; the meteorological data includes daily average temperature, daily relative humidity, sunshine duration, daily average wind speed, and daily precipitation; Population survival calculation module: configured to input the meteorological data into the pest population dynamic model, which includes an egg stage development sub-model, a larval stage development sub-model, a pupal stage development sub-model, an adult oviposition model, and a population renewal sub-model; the population survival rate is calculated daily by the collaborative calculation of each sub-model; wherein... The egg development sub-model calculates the daily development amount during the egg period based on the daily average temperature and daily relative humidity, and determines hatching when the cumulative egg development amount reaches the cumulative threshold during the egg period. The larval development sub-model calculates the daily development amount of the larvae based on the daily average temperature, daily relative humidity, and daylight duration, and calculates the daily feeding amount based on the daily average temperature and daily relative humidity. When the cumulative daily development amount and cumulative feeding amount of the larvae both reach the threshold corresponding to the current instar, the larvae enter the next instar. The pupal development sub-model calculates the daily pupal development amount based on the daily average temperature. After the cumulative pupal development amount reaches the pupal cumulative threshold, the success probability of emergence is determined based on the daily relative humidity. If the success probability is greater than the preset threshold, the pupal emerges as an adult. The adult insect oviposition model calculates the daily oviposition amount based on the daily average temperature and daily relative humidity, and outputs the daily oviposition amount each day during the oviposition period; The population renewal sub-model determines the daily mortality rate by taking the maximum value of five mortality factors: temperature, humidity, wind speed, precipitation, and crop resistance, and then determines the daily population survival rate.

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