Pesticide Detection and Early Warning System Based on Data Analysis

Abstract

The present invention relates to the technical field of agricultural environment monitoring, specifically a pesticide detection and early warning system based on data analysis. The system includes: a data reconstruction module, an adaptation determination module, a rate mapping module, an early warning screening module, and a risk analysis output module. In the present invention, through the normalization processing of environmental factors at the pesticide application point and the matching of spatial coordinates, combined with terrain data such as climate humidity and slope for joint analysis, the accurate characterization of the suitability of the pesticide application environment is realized. Based on the calculation of the difference in the spatial distance relationship between adjacent points and the environmental suitability value, a direction consistency judgment mechanism is constructed to identify the environmental trend partition, effectively revealing the correlation of pesticide application behaviors in space. By introducing the superposition analysis of the evaporation response value and the change of the climate time series, through the mapping of the influence of the temperature and humidity change rate on the dose, the quantitative expression of the influence degree of environmental change on the pesticide application effect is realized, enhancing the dynamic perception ability of the pesticide residue risk under extreme weather conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural environment monitoring, and particularly to a pesticide detection and early warning system based on data analysis. Background Art

[0002] The technical field of agricultural environment monitoring includes technical contents such as dynamic monitoring, data collection, analysis, and management of environmental factors in the agricultural production process. The core content of this technical field includes the identification and tracking of harmful substances in agricultural ecosystems such as air, water bodies, and soil, the real-time acquisition of pollutant indicators through sensors and information processing devices, and the integration of data, anomaly identification, and trend judgment through a data platform.

[0003] Among them, the pesticide detection and early warning system based on data analysis refers to a monitoring system that takes data analysis as the core means to detect the pesticide residue situation in agricultural production and provide early warnings, including the acquisition of pesticide component data, the setting of judgment criteria for pollution characteristics, the quantitative analysis of residue levels, and the construction of early warning rules. Specifically, based on the data of crop samples collected from multiple sources, by setting the detection threshold of pesticide chemical components, comparing historical residue records, and combining regional planting environment difference factors, and generating pesticide risk level early warning information according to matching conditions.

[0004] Although the existing technology has the ability to detect and early warn of pesticide residues, its core judgment standard is a static threshold, lacking the ability to standardize the measurement of the suitability of the pesticide application environment, and unable to accurately describe the dynamic changes in the influence of terrain and climate conditions on pesticide application behavior. In the spatial dimension, no correlation analysis means has been established between pesticide application points, making it difficult to discover risk changes caused by opposite environmental trends in adjacent areas, resulting in insufficient perception of local anomalies. The combined effect of temperature and humidity on evaporation has not been modeled, making the ability to identify changes in pesticide effectiveness or an increase in risk levels under high-temperature and dry conditions weak. At the same time, the identification of the volatility of pesticide application behavior only relies on the dosage and residue values at a single time node, ignoring the promoting effect of continuous fluctuations and meteorological disturbances in the time series on risk formation, limiting the ability to identify potential abnormal behaviors. For example, when the climate changes drastically, there are phenomena such as dose mutations or abnormal area adjustments in some areas. If there is no time and space correlation judgment, it is often misjudged as normal pesticide application, thus delaying the early warning response. The absence of a joint judgment mechanism also makes it difficult to timely identify the high-risk state formed by the superposition of multiple low-risk factors, reducing the sensitivity and accuracy of the overall early warning. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a pesticide detection and early warning system based on data analysis.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The pesticide detection and early warning system based on data analysis includes:

[0007] The data reconstruction module obtains the coordinates and time of the pesticide application points, collects the slope and the humidity of the day at the corresponding positions, associates the application points and calculates the corresponding environmental suitability values, and generates an environmental suitability set of the application points;

[0008] The adaptation determination module extracts the environmental suitability values and the corresponding coordinates in the environmental suitability set of the application points, sorts the adjacent point pairs according to the spatial distance, marks the consistent and conflicting sections in the adjacent point pairs, and obtains a direction consistency partition annotation set;

[0009] The rate mapping module obtains the application points located in the direction consistent section in the direction consistency partition annotation set, extracts the temperature and humidity time series, compares the change amplitudes in combination with the application dosage, evaluates the evaporation influence intensity under environmental fluctuations, and generates an evaporation influence superposition analysis result;

[0010] The early warning screening module identifies the disturbance points in the application points where the evaporation response value in the evaporation influence superposition analysis result is greater than the average response reference value and is in the direction conflict section, and forms an application disturbance abnormal point set;

[0011] The risk analysis and output module obtains all the points and the corresponding point information in the application disturbance abnormal point set, marks the points with diffusion risks, and generates a pesticide use detection and risk early warning result.

[0012] As a further solution of the present invention, the environmental suitability set of the application points includes environmental suitability values, application point spatial coordinates, and normalized environmental factors. The direction consistency partition annotation set is specifically direction consistent section annotation, direction conflict section annotation, and the difference rate of the suitability values of adjacent application points. The evaporation influence superposition analysis result includes the influence degree of the temperature rise rate on the application, the influence degree of the humidity drop rate on the application, and the dose evaporation response comparison under each environmental fluctuation condition. The application disturbance abnormal point set includes the spatial position of the disturbance point, the wind direction and humidity variation characteristics of the disturbance point, and the dose and area fluctuation ratio of the disturbance point. The pesticide use detection and risk early warning result includes a list of detected abnormal points and a combined determination label for the three indicators of the abnormal points.

[0013] As a further solution of the present invention, the data reconstruction module includes:

[0014] The application information collection sub-module obtains the coordinate position and application time of the pesticide application points, collects the slope value corresponding to the coordinate position and the climate humidity data of the day, records the collection results as two environmental factors, namely the slope factor and the humidity factor, and obtains an environmental factor data group of the application points;

[0015] The environmental factor normalization sub-module performs normalization processing on the slope factor and humidity factor data in the environmental factor data set of the application point respectively, establishes a corresponding relationship between the results after normalization processing and the coordinate position of the application point, calculates the average value of the normalized slope value and the normalized humidity value as the environmental suitability value, and generates an environmental suitability set for the application point.

[0016] As a further solution of the present invention, the adaptation determination module includes:

[0017] The suitability value extraction sub-module obtains the environmental suitability value and the corresponding coordinate data in the environmental suitability set of the application point, identifies the positional relationship of all application points in space according to the coordinate information, calls the application point coordinate set, measures and sorts the distances of the application points in space based on the adjacent distance threshold, and generates an adjacent application point distance sorting sequence;

[0018] The difference rate calculation sub-module is based on the adjacent application point distance sorting sequence and uses the formula:

[0019] ;

[0020] Calculate the suitability value difference rate between the i-th and j-th adjacent application points , and integrate and generate a suitability value difference rate sequence;

[0021] Among them, represents the environmental suitability value of the i-th application point, represents the environmental suitability value of the j-th application point, represents the humidity value difference between the i-th and j-th application points, represents the average humidity value calculated among all application points, represents the slope value difference between the i-th and j-th application points, represents the average slope value calculated among all application points;

[0022] The direction consistency recognition sub-module is based on the suitability value difference rate sequence, extracts the slope change direction and humidity change direction between adjacent application points, classifies and labels according to whether the change trends of each pair of application points in the two directions are consistent, records and groups the sections with consistent directions and conflicting directions respectively, and obtains a direction consistency partition annotation set.

[0023] As a further solution of the present invention, the rate mapping module includes:

[0024] The environmental sequence extraction sub-module filters the sections marked as having consistent directions according to the direction consistency partition annotation set, detects the air temperature data and relative humidity data during the application time period of each application point, arranges them in chronological order to form an air temperature time series and a humidity time series, and generates an environmental change time series set;

[0025] Based on the environmental change time series set, the evaporation superposition calculation sub-module calculates the air temperature rise rate and humidity drop rate between consecutive time nodes in the time series of each application point, makes a side-by-side comparison of the air temperature rise rate and humidity drop rate under the same dosage condition, identifies the numerical relationship of the change amplitude under the dosage through a joint analysis of the two types of rate indicators, integrates the influence value sequences of each application point, and establishes the evaporation influence superposition analysis result.

[0026] As a further solution of the present invention, the early warning screening module includes:

[0027] The record extraction sub-module screens the application points with evaporation response values greater than the average response reference value and the application points in the direction conflict section according to the evaporation influence superposition analysis result, extracts the continuous application records of the application points in chronological order, collects the application dosage and application area data corresponding to each time node, and generates a continuous application record set;

[0028] The dosage-area ratio calculation sub-module calls the continuous application record set, extracts the dosage value and area of the application point at two consecutive time nodes respectively, calculates the dosage change ratio and application area change ratio respectively, integrates them into a dosage change ratio sequence and an application area change ratio sequence, and establishes a application fluctuation change data set;

[0029] The perturbation identification sub-module extracts the wind direction change amplitude data and humidity fluctuation data in the corresponding time period based on the application fluctuation change data set, determines whether both the dosage change ratio and the area change ratio exceed the set fluctuation identification threshold, determines whether the wind direction change amplitude and the humidity fluctuation exceed the perturbation determination threshold at the same time, marks the time nodes that meet the conditions as perturbation points, and generates an application perturbation abnormal point set.

[0030] As a further solution of the present invention, the risk analysis output module includes:

[0031] The index joint determination sub-module obtains all the points and the corresponding coordinate and identification information in the application perturbation abnormal point set, and uses the formula:

[0032] ;

[0033] Calculate the joint risk determination value of the k-th application point , and establish a joint risk determination value sequence;

[0034] Among them, represents the evaporation response value of the k-th point, is the evaporation response risk threshold, represents the environmental suitability value of the k-th point, which is a normalized environmental score value, is the suitability reference value, and is also a constant threshold within the normalized score range.

[0035] Based on the joint risk determination value sequence, the abnormal output sorting sub-module screens out the points where the evaporation response value is greater than the evaporation response risk threshold, the environmental suitability value is lower than the suitability reference value, and the direction consistency label is the conflict section, extracts the corresponding point numbers, position identifiers, and the sub-regions to which they belong, marks them as detected abnormalities with diffusion risks, and outputs the points that meet the joint conditions in a structured format by region to generate the pesticide use detection and risk warning results.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0037] In the present invention, through the normalization processing of the environmental factors at the pesticide application points and the matching of spatial coordinates, combined with the joint analysis of topographic data such as climate humidity and slope, the accurate characterization of the suitability of the pesticide application environment is realized. Based on the calculation of the spatial distance relationship between adjacent points and the difference in environmental suitability values, a direction consistency judgment mechanism is constructed to identify the environmental trend sub-regions, effectively revealing the correlation of pesticide application behaviors in space. The superposition analysis of the evaporation response value and the climate time series change is introduced, and through the mapping of the influence of the temperature and humidity change rates on the dosage, the quantitative expression of the influence degree of environmental changes on the pesticide application effect is realized, enhancing the dynamic perception ability of the pesticide residue risk under extreme weather conditions. The disturbance identification of the pesticide application behavior not only integrates the fluctuation trends of continuous dosage and area, but also introduces the drastic change indicators of wind direction and humidity to identify the abnormal points brought by non-linear disturbances, expanding the adaptation scenarios for risk warning identification. A joint risk determination mechanism is established under multi-factor conditions, integrating the evaporation response intensity, environmental suitability score, and direction conflict identifier, and outputting the diffusible risk points in a structured manner to achieve precise warning driven by multi-dimensional information. Description of the Drawings

[0038] Figure 1 is the system flow chart of the present invention;

[0039] Figure 2 is the flow chart of the data reconstruction module of the present invention;

[0040] Figure 3 is the flow chart of the adaptation determination module of the present invention;

[0041] Figure 4 is the flow chart of the rate mapping module of the present invention;

[0042] Figure 5 is the flow chart of the warning screening module of the present invention;

[0043] Figure 6 is the flow chart of the risk analysis and output module of the present invention. Detailed Embodiments

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0046] Please refer to Figure 1 , the pesticide detection and early warning system based on data analysis includes:

[0047] The data reconstruction module obtains the coordinates and time of the pesticide application points, collects the slope and the humidity of the day at the corresponding positions, associates the application points and calculates the corresponding environmental suitability values, and generates an environmental suitability set for the application points;

[0048] The adaptation determination module extracts the environmental suitability values and the corresponding coordinates in the environmental suitability set of the application points, sorts the adjacent point pairs according to the spatial distance, and marks the consistent and conflicting sections in the adjacent point pairs to obtain a direction consistency partition annotation set;

[0049] The rate mapping module obtains the application points located in the direction consistent section in the direction consistency partition annotation set, extracts the temperature and humidity time series, compares the change amplitudes in combination with the application dose, evaluates the evaporation influence intensity under environmental fluctuations, and generates an evaporation influence superposition analysis result;

[0050] The early warning screening module identifies the disturbance points in the application points where the evaporation response value in the evaporation influence superposition analysis result is greater than the average response reference value and is in the direction conflict section, and forms a set of application disturbance abnormal points;

[0051] The risk analysis and output module obtains all the points and the corresponding point information in the set of application disturbance abnormal points, marks the points with diffusion risks, and generates a pesticide use detection and risk early warning result;

[0052] The suitable environment set for application points includes the environmental suitability value, the spatial coordinates of the application points, and the normalized environmental factors. The direction consistency partition annotation set specifically includes the direction consistent section annotation, the direction conflict section annotation, and the difference rate of the suitable value between adjacent application points. The evaporation impact superposition analysis result includes the impact degree of the air temperature rise rate on application, the impact degree of the humidity decline rate on application, and the dose evaporation response comparison under each environmental fluctuation condition. The application disturbance abnormal point set includes the spatial position of the disturbance point, the wind direction and humidity variation characteristics of the disturbance point, and the dose - area fluctuation ratio of the disturbance point. The pesticide use detection and risk warning result includes the list of detected abnormal points and the combined determination label of three indicators for abnormal points.

[0053] Please refer to Figure 2 , the data reconstruction module includes:

[0054] The application information acquisition sub - module obtains the coordinate position and application time of the pesticide application point, collects the slope value corresponding to the coordinate position and the daily climate humidity data, and records the collection results as two environmental factors, namely the slope factor and the humidity factor, to obtain the environmental factor data group of the application point;

[0055] To obtain the coordinate positions and application times of pesticide application points, it is necessary to first extract the trajectory data of the operation machinery through the operation records in the agricultural operation scheduling system or the GPS terminal. Select discrete points with an interval of 5 meters in this trajectory data as the representative coordinate positions of the application points. Subsequently, extract the corresponding application time based on the pesticide spraying task records registered in the operation scheduling system. After obtaining the coordinates and times, call the digital elevation model (DEM) data in the geographic information system (GIS) to query the grid corresponding to the application point coordinates and analyze the slope information of this grid. For example, if the coordinate of a certain application point is (117.051, 36.765), the slope of its corresponding DEM grid is 12.6 degrees, and the slope value is set to 12.6; then, capture the climate humidity data corresponding to the application time from the meteorological service platform docked with the agricultural meteorological system. The climate humidity uses relative humidity as an indicator and is obtained through the corresponding meteorological stations. For example, the humidity of the meteorological station corresponding to the application point at 14:00 on April 5th is 74%, and the humidity value is set to 74; after performing the above operations on all application points, a data set containing each coordinate point and its corresponding slope value and humidity value is formed. Among them, the slope value is divided into three intervals according to the required operation stability and terrain adaptability of the plant protection operation during implementation, namely: low slope (0° ≤ slope < 7°), medium slope (7° ≤ slope < 15°), high slope (slope ≥ 15°); the humidity value is divided into three intervals: dry (humidity < 40%), moderate (40% ≤ humidity ≤ 70%), humid (humidity > 70%). During the execution process, three fields are respectively recorded for each application point: coordinates (longitude and latitude), slope value, and humidity value. For example, application point A: (117.051, 36.765), slope 12.6, humidity 74, application point B: (117.055, 36.768), slope 9.2, humidity 66, forming an environmental factor data group of application points.

[0056] The environmental factor normalization sub-module performs normalization processing on the slope factor and humidity factor data in the environmental factor data group of application points respectively, establishes a corresponding relationship between the results of the normalization processing and the coordinate positions of the application points, calculates the average value of the normalized slope value and the normalized humidity value as the environmental suitability value, and generates an environmental suitability set of application points;

[0057] Based on the slope factor and humidity factor data in the environmental factor dataset of the application point, normalization processing is carried out separately. First, the maximum and minimum values of the two factors need to be extracted respectively. Assume that the maximum value in the slope value dataset is 18.0 degrees and the minimum value is 2.0 degrees, and the maximum value in the humidity value dataset is 92% and the minimum value is 35%. The min-max normalization method is used to process all slope and humidity values. For example, for application point A, the normalized slope value = (12.6 - 2.0) / (18.0 - 2.0) = 10.6 / 16 = 0.6625, and the normalized humidity value = (74 - 35) / (92 - 35) = 39 / 57 ≈ 0.6842; for application point B, the normalized slope value = (9.2 - 2.0) / 16 = 7.2 / 16 = 0.45, and the normalized humidity value = (66 - 35) / 57 ≈ 0.5439; the normalized slope value and normalized humidity value of each application point are added and then averaged to obtain the environmental suitability value of the application point. For example, the environmental suitability value of application point A is (0.6625 + 0.6842) / 2 ≈ 0.6734, and that of application point B is (0.45 + 0.5439) / 2 ≈ 0.4969. This value reflects the overall suitability level under the current terrain and climate conditions. During the entire processing process, the result of each normalization process needs to establish a one-to-one correspondence with the original coordinates to generate the environmental suitability set of the application points. The set form is: {Application point A: (117.051, 36.765), suitability value: 0.6734}, {Application point B: (117.055, 36.768), suitability value: 0.4969}, and so on. Finally, the coordinates and environmental suitability values of all application points are obtained. The environmental suitability value reflects the overall environmental suitability of the application point under the current terrain (such as steepness) and climate (such as humidity) conditions. The higher the value, the more suitable the point is for pesticide application operations, and vice versa, the suitability is poorer. The role of the environmental suitability value is to provide a basis for pesticide application operations in precision agriculture, and to help judge whether each application point is in relatively ideal environmental conditions through a quantitative method, so as to optimize resource allocation, improve the efficiency and effect of pesticide application, and reduce environmental impact.

[0058] Please refer to Figure 3 , the adaptation determination module includes:

[0059] The suitability value extraction sub-module obtains the environmental suitability value and corresponding coordinate data in the environmental suitability set of the application points, identifies the proximity relationship of the spatial positions of all application points according to the coordinate information, calls the application point coordinate set, measures and sorts the distances of the spatially adjacent application points based on the adjacent distance threshold, and generates an adjacent application point distance sorting sequence;

[0060] To obtain the environmental suitability values and corresponding coordinate data in the environmental suitability set of pesticide application points, it is necessary to first retrieve the existing dataset of pesticide application point distributions and parse out the environmental suitability value field and the geographical coordinate field from it. For example, in a certain area, there are 5 pesticide application points, with their suitability values being 70, 85, 90, 60, and 75 respectively, and the corresponding longitude and latitude coordinates being (30.12, 120.34), (30.13, 120.36), (30.15, 120.38), (30.16, 120.39), and (30.17, 120.40). Subsequently, calculate the Euclidean distance between every two pesticide application points based on the coordinates. For example, the distance between the 1st and 2nd pesticide application points can be calculated as √[(30.13 - 30.12)²+(120.36 - 120.34)²]≈0.0224 degree units, which is approximately 2.5 km when converted to meters. After confirming the distances between all pairs of pesticide application points, filter them with an adjacent distance threshold d = 3 km, and retain all pairs of pesticide application points with a distance less than 3 km, such as (point 1, point 2), (point 2, point 3), (point 3, point 4), etc. Finally, sort all the pairs of pesticide application points that meet the adjacent conditions in ascending order of distance to form the final sorted sequence of adjacent pesticide application point distances, which serves as the basic data for the calculation of the difference rate in the next stage;

[0061] The difference rate calculation sub-module is based on the sorted sequence of adjacent pesticide application point distances and uses the formula:

[0062] ;

[0063] Calculate the difference rate of suitability values between the i-th and j-th adjacent pesticide application points and integrate to generate a sequence of difference rates of suitability values; the difference rate of suitability values represents the degree of difference in environmental suitability between two adjacent pesticide application points. Its calculation comprehensively considers the differences in the environmental suitability values themselves and the changes in two key factors, humidity and slope. This indicator is used to measure whether there are significant differences in environmental conditions in adjacent areas in space, thereby providing a basis for the continuity and stability of agricultural operations. If the difference rate value is small, it indicates that the environmental conditions of the two pesticide application points are relatively consistent, which is conducive to a unified pesticide application strategy; conversely, a large difference rate indicates obvious environmental changes, and it may be necessary to adjust the operation parameters or strategies to adapt to local conditions.

[0064] Among them, represents the environmental suitability value of the i-th pesticide application point, represents the environmental suitability value of the j-th pesticide application point, represents the humidity value difference between the i-th and j-th pesticide application points, represents the average humidity value calculated among all pesticide application points, represents the slope value difference between the i-th and j-th pesticide application points, Represents the average slope value calculated among all pesticide application points.

[0065] Let point 2 and point 3 be selected, and the parameter data is as follows: Environmental suitability value of pesticide application point 2: , Environmental suitability value of pesticide application point 3: , Humidity of pesticide application point 2: , Humidity of pesticide application point 3: , Humidity difference: , Average humidity: (assuming the total humidity is 190), Slope of pesticide application point 2: , Slope of pesticide application point 3: , Slope difference: , Average slope: (assuming the total slope is 24).

[0066] Substitute the above parameters into the formula:

[0067] ;

[0068] The difference rate result of 0.0393 indicates that the difference in environmental suitability values between point 2 and point 3 remains in the low range after the adjustment of the control item. If the reference threshold set by the system is 0.05, this result is in the low-variation area, indicating that the suitability of the two points is stable, and the difference amplitude does not constitute a structural change in the humidity and slope directions; during the calculation process, the influence of high-value amplification is suppressed by processing the denominator of the normalization of the suitability value ontology, and a more realistic change rate reflecting the environmental coordination degree is obtained through the adjustment of the multi-factor difference ratio. This result can be used as the basis for subsequent direction consistency classification and spatial zoning.

[0069] The direction consistency recognition sub-module extracts the slope change direction and humidity change direction between adjacent pesticide application points based on the difference rate sequence of suitability values, classifies and labels according to whether the change trends of each pair of pesticide application points are consistent in the two directions, records and groups the sections with consistent directions and conflicting directions respectively, and obtains the direction consistency zoning annotation set;

[0070] Call the sequence of appropriate value difference rates to extract the slope change direction and humidity change direction between each pair of pesticide application points, and determine whether their change trends are consistent. First, a direction reference needs to be set for each pair of pesticide application points, that is, use the latter point minus the former point to judge the direction increase or decrease. For example, if the humidity at point 3 and point 4 is 46% and 41% respectively, and the slope is 4% and 5% respectively, then the humidity change is -5% and the slope change is +1%. Since their directions are inconsistent, this point pair is marked as direction conflict; when the humidity and slope change directions are both positive or negative, such as the humidity at point 2 and point 3 is 42% and 46% respectively, and the slope is 6% and 4% respectively, then the humidity change is +4% and the slope change is -2%, the directions are inconsistent; if for point 1 and point 2 they are 38% and 42% respectively, and the slope is 5% and 6% respectively, then the change directions are both positive, marked as direction consistent. According to this rule, perform direction comparison and annotation on all pesticide application point pairs, and merge and group the point pairs with consistent directions according to continuous consistency to finally generate a direction consistency partition annotation set.

[0071] Please refer to Figure 4 , the rate mapping module includes:

[0072] The environmental sequence extraction sub-module filters the sections marked as direction consistent according to the direction consistency partition annotation set, detects the air temperature data and relative humidity data within the pesticide application time period of each pesticide application point, arranges them in chronological order to form an air temperature time series and a humidity time series, and generates an environmental change time series set;

[0073] The direction consistency partition annotation set is the operation object of this paragraph. By reading the section numbers marked as direction consistent in the annotation set, the corresponding ID sequence of the application points covered is retrieved, and then the spatial positions and the corresponding numbers of all application points in this section are extracted. For example, it is set that a certain direction consistent section contains 10 application points with application point numbers from A01 to A10, and the corresponding coordinate range is from 115.10° east longitude to 115.15° east longitude, and from 39.90° north latitude to 39.93° north latitude. The application time periods of these application points are respectively associated, and the temperature and relative humidity data during their respective time periods are further detected. In the meteorological monitoring database, if the application time of point A01 is from 10:00 to 14:00 on June 10, 2024, then the hourly temperature data retrieved during this time period are: 28.3°C, 29.5°C, 30.1°C, 31.4°C, and the relative humidity data are: 64%, 60%, 55%, 52%. The above data are arranged in chronological order to construct a temperature time series {28.3, 29.5, 30.1, 31.4} and a humidity time series {64, 60, 55, 52}. For other application points, the data are obtained and arranged in the same way. If some time nodes are missing, the temporal continuity is restored by interpolation between adjacent nodes or linear filling. Two time series sets are established for each application point and stored in the time series database uniformly, aggregating to form an overall information structure containing application point numbers, time axes, temperature value sequences, and humidity value sequences. This structure serves as the basic input for subsequent rate analysis and dose response calculation, obtaining the environmental change time series set.

[0074] Based on the environmental change time series set, the evaporation superposition calculation sub-module calculates the temperature increase rate and humidity decrease rate between consecutive time nodes in the time series of each application point, makes a side-by-side comparison of the temperature increase rate and humidity decrease rate under the same dose condition, identifies the numerical relationship of the change amplitude under the dose through joint analysis of the two rate indicators, integrates the influence value sequences of each application point, and establishes the evaporation influence superposition analysis result;

[0075] Calculate the air temperature rise rate and humidity drop rate in the time series for each application point. For the air temperature series of each application point, calculate the temperature increment between adjacent time nodes and divide it by the time interval. For example, if the air temperature series of application point A01 is {28.3, 29.5, 30.1, 31.4}, its rise rates are: (29.5 - 28.3) / 1 = 1.2℃ / h, (30.1 - 29.5) / 1 = 0.6℃ / h, (31.4 - 30.1) / 1 = 1.3℃ / h. The average rate is (1.2 + 0.6 + 1.3) / 3 = 1.033℃ / h. Similarly, for the humidity series {64, 60, 55, 52}, calculate the drop rates as (64 - 60) / 1 = 4%, (60 - 55) / 1 = 5%, (55 - 52) / 1 = 3%. The average is (4 + 5 + 3) / 3 = 4% / h. At the same time, extract the corresponding application dose values from the application database. Suppose the dose at point A01 is 150mg / m². Compare with other application points, such as the dose at A02 is 148mg / m², the dose at A03 is 151mg / m², etc. Screen out the application point groups with doses within the range of ±5mg / m² that are close to each other, construct the application point set with the same dose, and compare the air temperature rise rate and humidity drop rate of each point under the same dose condition. After normalization, obtain the response variability value of the environmental fluctuation among different application points under a fixed dose. If the difference in air temperature rate in a group of application points exceeds 0.8℃ / h or the difference in humidity rate exceeds 6% / h, it can be considered that the environmental change has non-uniformity on the dose response result. Through clustering and comparison analysis, cluster the similar fluctuation trends into one group and record the influence degree, and summarize to generate the evaporation influence superposition analysis result.

[0076] Please refer to Figure 5 , and the early warning screening module includes:

[0077] The record extraction sub-module screens out the application points with evaporation response values greater than the average response reference value and the application points in the direction conflict section according to the evaporation influence superposition analysis result, extracts the continuous application records of these application points in chronological order, and collects the application dose and application area data corresponding to each time node to generate a continuous application record set;

[0078] Based on the screening content of the evaporation response values in the evaporation impact superposition analysis results, extract the application points where the numerical values are greater than the average response reference value, and further cross-correspond to mark the conflict sections in the corresponding directions. Identify the target application points that are in the intersection of the two conditions item by item. During the execution process, first establish an index mapping list between the application points and their corresponding sections, then perform interval screening based on the response value sequence, and use the traversal mechanism in the data structure to match point by point. Each application point that meets the conditions will be registered as an object for subsequent extraction. For example, the evaporation response value of a certain application point in the analysis result is 6.82, while the average response reference value of the current sample application point is 5.47. This point meets the first screening condition. Then, according to the list of conflict sections marked by the direction consistency, perform coordinate cross-judgment. If the number of this point is in the list of conflict section numbers in the direction, it will be included in the output set. After that, obtain the application time record data of all application points in this set, traverse the table items of the continuous records in the database of each application point, and extract the application dose and application area data under their corresponding time series in turn, and arrange them in ascending order according to the time field to form continuous application records. For example, the continuous record data of application point P101 on June 21, June 24, and June 26, 2024, with dose values of 90, 100, and 95 respectively, and area values of 5.2, 5.0, and 5.4 respectively, then its application records will form three time-ordered records. After sorting out the records of all extraction objects, output them as a unified set to obtain a continuous application record set.

[0079] The dose-area ratio calculation sub-module calls the continuous application record set, extracts the dose values and areas of the application points at two consecutive time nodes respectively, calculates the dose change ratio and the application area change ratio respectively, and integrates them into a dose change ratio sequence and an application area change ratio sequence to establish an application fluctuation change data set;

[0080] For two adjacent time nodes within the time series of each application point, perform change ratio calculation processing on the dose and area data respectively. During the operation process, it is necessary to perform the operation of dividing the difference between each group by the absolute value of the previous value. Set the serial number of each time node as h, obtain any two records in the time series, and the dose values in the h-th and the previous record are respectively 、 ,then the dose change ratio can be calculated as ,For example, if the dose of a certain point on June 21, 2024 is 90 mL and the dose on June 24 is 100 mL, then ,Similarly, the calculation method of the area change ratio is ,If the areas are 5.2 m² and 5.0 m² respectively, then ,After aggregating all the calculation results according to the time nodes, a change ratio sequence of each application point can be formed. The parameter explanations are as follows: is the dose change ratio at the h-th time node, 、 is the dosing value at the h-th and the previous time nodes; is the area change ratio at the h-th time node, , is the dosing area value at the corresponding time node; such calculations are uniformly classified into the dosing fluctuation change dataset as one of the subsequent screening conditions to generate the dosing fluctuation change dataset. The advantage of the formula is that by quantifying the changes in dose and area separately, it can achieve independent variable amplitude recognition at different scales and avoid interference caused by mixed dimensions.

[0081] Based on the dosing fluctuation change dataset, the perturbation recognition sub-module extracts the wind direction amplitude change data and humidity fluctuation data for the corresponding time period, determines whether both the dose change ratio and the area change ratio exceed the set fluctuation recognition threshold, and determines whether the wind direction amplitude change and the humidity fluctuation simultaneously exceed the perturbation determination threshold. The time nodes that meet the conditions are marked as perturbation points to generate the dosing perturbation abnormal point set;

[0082] Extract the wind direction amplitude change and humidity fluctuation data for each record in the corresponding time period. The wind direction amplitude change is represented by the maximum wind direction angle difference between two dosings, in degrees, and the humidity fluctuation is the difference between the maximum and minimum humidity, in percentage. The corresponding values need to be extracted from the meteorological monitoring records. For example, for a dosing point, if the wind direction changes from 30° to 95° from June 21st to 24th, the wind direction amplitude change is 65°, and if the relative humidity fluctuates from 72% to 53%, the humidity fluctuation is 19%. Set the wind direction perturbation determination threshold to 60° and the humidity perturbation determination threshold to 15%. When and only when both the wind direction and humidity fluctuation values are greater than the corresponding thresholds, and the dose change ratio and the area change ratio at the corresponding time node both exceed the fluctuation recognition threshold of 0.10, then the time node is marked as a perturbation point. Continue to summarize and output the dosing points that meet the conditions and the corresponding time nodes to form the dosing perturbation abnormal point set.

[0083] Please refer to Figure 6 , the risk analysis output module includes:

[0084] The index joint determination sub-module obtains all the points, the corresponding coordinates and identification information in the dosing perturbation abnormal point set, and uses the formula:

[0085] ;

[0086] Calculate the joint risk determination value of the k-th dosing point , a combined risk determination value sequence is established; the combined risk determination value is an important indicator for comprehensively evaluating whether there is a potential abnormal application risk at a certain application point. It calculates the normalized differences between the evaporation response value and the environmental suitability value at this point and their respective standard thresholds, and synthesizes the two difference terms to quantify a comprehensive risk determination intensity. Its core function is to: by simultaneously examining the two aspects of the evaporation rate of the pesticide after operation (representing the application residue or diffusion situation) at the application point and the matching degree of its environmental conditions, determine whether there are problems such as improper application, unstable efficacy, or environmental risk hazards at this point. If the combined risk determination value is high, it indicates that there are both strong evaporation residue problems and an environmentally unsuitable area at this point, and the risk level is relatively large; conversely, a low value indicates relatively safety.

[0087] Among them, represents the evaporation response value of the k-th point, is the evaporation response risk threshold, represents the environmental suitability value of the k-th point, which is a normalized environmental score value, is the suitability reference value, also a constant threshold within the normalized score range.

[0088] First, call the historical application data of each point one by one and extract the recorded evaporation response value. The evaporation response value refers to the reflection intensity of the evaporation impact at the operation point per unit time. Its numerical source can be the droplet evaporation residue rate recorded by a ground meteorological station or operation equipment. Assuming that the measured evaporation response value at a certain point is 8.6 mg / m²·h, it indicates that the evaporation rate per unit time at this point is relatively fast. Then, the environmental suitability value needs to be extracted. The environmental suitability value is often calculated through the environmental comprehensive index, and the numerical range is from 0 to 1. The closer to 1, the more suitable the environment is for pesticide retention. Assuming that the environmental suitability value of point k is 0.28. Then, read the direction consistency label attached to the point and screen the points with the label "conflict section" into the determination range. The conflict label is set as a text type through the annotation field and cannot be directly involved in the calculation, but is used as a judgment condition to limit the applicable range of the formula. When the point label is "conflict", enter the following steps, that is, use the formula:

[0089] ;

[0090] Calculate the combined risk value of this point, where , (The evaporation response risk threshold is calculated based on multi-region sampling and is usually set in the range of 5.5 - 7.5 mg / m²·h. Take the average value of 6.5 as an example), , (Based on the annual suitability statistics results, set as the neutral suitability line), substitute the above values into the formula:

[0091] Calculate the evaporation term:

[0092] ;

[0093] Calculated suitability items:

[0094] ;

[0095] The combined risk value is:

[0096] ;

[0097] This value represents the cumulative degree of risk of the deviation of the comprehensive evaporation exceeding the standard and the suitability at this point. Generally, if the combined risk value is greater than 0.5, it is regarded as a strong risk response point. When setting the judgment threshold, the standard can be set accordingly. In this implementation, if the final risk value exceeds 0.6 and the direction label is the conflict section, it is a typical target, and this point enters the subsequent structured sorting process to finally obtain the combined risk judgment value sequence;

[0098] Based on the combined risk judgment value sequence, the abnormal output sorting sub-module screens out the points where the evaporation response value is greater than the evaporation response risk threshold, the environmental suitability value is lower than the suitability benchmark value, and the direction consistency label is the conflict section, extracts the corresponding point numbers, location identifiers and the affiliated partitions, marks them as detected abnormalities with diffusion risks, and outputs the points that meet the combined conditions in a structured format to generate the pesticide use detection and risk warning results;

[0099] Based on the calculated values in the combined risk judgment value sequence, all points are screened, and the points that meet the three conditions simultaneously are selected: the evaporation response value needs to be higher than the evaporation response risk threshold , that is , the environmental suitability value needs to be lower than the suitability benchmark value , that is , and the direction consistency label must be "conflict section". Set the number of a certain test point as SP_03, and its parameter values are: evaporation response value 8.9mg / m²·h, environmental suitability value 0.24, and the direction annotation is conflict. After comparison, 8.9>6.5, 0.24<0.6, and the label is judged as the conflict section. Therefore, all conditions are met. Integrate the output fields such as the point number, coordinates, and affiliated partition of this point to form a structured data item, and the fields are set as: point number, combined risk value, evaporation response value, environmental suitability value, risk status. For example: SP_03, 0.6421, 8.9, 0.24, abnormal mark. After summarizing all the points that meet the conditions, establish the pesticide use detection and risk warning results; This result indicates that this point has shown possible deviation behaviors of pesticide diffusion at the index level and should be output as a key object to the risk zoning map for further retrospective analysis.

[0100] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A pesticide detection and early warning system based on data analysis, characterized in that, The system includes: The data reconstruction module obtains the coordinates and time of the pesticide application points, collects the slope and the humidity of the day at the corresponding positions, associates the application points and calculates the corresponding environmental suitability values, and generates an environmental suitability set for the application points; The adaptation determination module extracts the environmental suitability values and the corresponding coordinates in the environmental suitability set of the application points, sorts the adjacent point pairs according to the spatial distance, marks the consistent and conflicting sections in the adjacent point pairs, and obtains a direction consistency partition annotation set; The rate mapping module obtains the application points located in the direction consistent sections in the direction consistency partition annotation set, extracts the temperature and humidity time series, compares the change amplitudes in combination with the application dose, evaluates the evaporation influence intensity under environmental fluctuations, and generates an evaporation influence superposition analysis result; The warning screening module identifies the disturbance points among the application points where the evaporation response value in the evaporation influence superposition analysis result is greater than the average response reference value and is in the direction conflict section, and forms a set of abnormal application disturbance points; The risk analysis output module obtains all the points and the corresponding point information in the set of abnormal application disturbance points, marks the points with diffusion risks, and generates a pesticide use detection and risk warning result.

2. The pesticide detection and early warning system based on data analysis according to claim 1, characterized in that, The environmental suitability set of the application points includes environmental suitability values, spatial coordinates of the application points, and normalized environmental factors. The direction consistency partition annotation set is specifically the annotation of the direction consistent section, the annotation of the direction conflict section, and the difference rate of the suitability values of adjacent application points. The evaporation influence superposition analysis result includes the influence degree of the temperature rise rate on the application, the influence degree of the humidity drop rate on the application, and the dose evaporation response comparison under each environmental fluctuation condition. The set of abnormal application disturbance points includes the spatial position of the disturbance points, the wind direction and humidity change amplitude characteristics of the disturbance points, and the dose and area fluctuation ratio of the disturbance points. The pesticide use detection and risk warning result includes a list of detected abnormal points and a combined determination label for the three indicators of the abnormal points.

3. The pesticide detection and early warning system based on data analysis according to claim 1, wherein The data reconstruction module includes: The application information collection sub-module obtains the coordinate position and application time of the pesticide application points, collects the slope value corresponding to the coordinate position and the climate humidity data of the day, records the collection results as two environmental factors, namely the slope factor and the humidity factor, and obtains an environmental factor data group for the application points; The environmental factor normalization sub-module performs normalization processing on the slope factor and humidity factor data in the environmental factor data group of the application points respectively, establishes a corresponding relationship between the normalized results and the coordinate positions of the application points, calculates the average value of the normalized slope value and the normalized humidity value as the environmental suitability value, and generates an environmental suitability set for the application points.

4. The pesticide detection and warning system based on data analysis according to claim 3, characterized in that, The adaptation determination module includes: The suitability value extraction sub-module obtains the environmental suitability values and the corresponding coordinate data in the environmental suitability set of the application points, identifies the positional relationship of all the application points in space according to the coordinate information, calls the application point coordinate set, measures and sorts the distances of the application points in space based on the adjacent distance threshold, and generates an adjacent application point distance sorting sequence; The difference rate calculation sub-module is based on the adjacent application point distance sorting sequence and uses the formula: ； Calculate the difference rate of the suitability value between two adjacent application points i and j , and integrate to generate a sequence of difference rates of suitability values; Among them, represents the environmental suitability value of the i-th application point, represents the environmental suitability value of the j-th application point, represents the humidity value difference between the i-th and j-th application points, represents the average humidity value calculated among all application points, represents the slope value difference between the i-th and j-th application points, represents the average slope value calculated among all application points; The directional consistency identification submodule extracts the slope change direction and humidity change direction between adjacent application points based on the suitability value difference rate sequence, and classifies and labels each pair of application points according to whether the change trends in the two directions are consistent. The sections with consistent directions and conflicting directions are recorded and grouped respectively to obtain a directional consistency partition labeling set.

5. The pesticide detection and early warning system based on data analysis according to claim 4, characterized in that, The rate mapping module comprises: The environmental sequence extraction submodule selects the segments marked as directionally consistent according to the directionally consistent partition annotation set, detects the temperature data and relative humidity data during the application time period of each application point, arranges them in chronological order to form a temperature time series and a humidity time series, and generates an environmental change time series set; The evaporation superposition calculation submodule calculates the temperature rise rate and humidity decrease rate between consecutive time nodes in the time series of each application point based on the environmental change time series set, compares the temperature rise rate and humidity decrease rate in parallel under the same dosage conditions, identifies the numerical relationship of the change amplitude under the dosage by jointly analyzing the two types of rate indicators, integrates the impact value sequence of each application point, and establishes the evaporation impact superposition analysis result.

6. The pesticide detection and early warning system based on data analysis according to claim 5, characterized in that The early warning screening module includes: The record extraction submodule selects the application points whose evaporation response values ​​are greater than the average response reference value and the application points in the direction conflict section according to the evaporation impact superposition analysis results, extracts the continuous application records of the application points in chronological order, collects the application dosage and application area data corresponding to each time node, and generates a continuous application record set; The dose-area ratio calculation submodule calls the continuous pesticide application record set, extracts the dose value and area of ​​the pesticide application point at two consecutive time nodes, respectively calculates the dose change ratio and the pesticide application area change ratio, respectively, integrates them into a dose change ratio sequence and a pesticide application area change ratio sequence, and establishes a pesticide application fluctuation change data set; The disturbance identification submodule extracts the wind direction variation data and humidity fluctuation data of the corresponding time period based on the pesticide application fluctuation change data set, determines whether the dosage change ratio and the area change ratio both exceed the set fluctuation identification threshold, determines whether the wind direction variation and the humidity fluctuation simultaneously exceed the disturbance judgment threshold, marks the time nodes that meet the conditions as disturbance points, and generates a pesticide application disturbance abnormal point set.

7. The pesticide detection and early warning system based on data analysis according to claim 6, characterized in that, The risk analysis output module includes: The index joint determination submodule obtains all points and corresponding coordinates and identification information in the pesticide application disturbance abnormal point concentration, using the formula: ； Calculate the combined risk determination value of the k-th application point , and establish a sequence of combined risk determination values; Among them, represents the evaporation response value at the k-th point, is the evaporation response risk threshold, represents the environmental suitability value at the k-th point, which is a normalized environmental score value, is the suitability reference value, which is also a constant threshold within the normalized score range; Based on the joint risk judgment value sequence, the abnormal output sorting submodule screens the points whose evaporation response values ​​are greater than the evaporation response risk threshold, whose environmental suitability values ​​are lower than the suitability benchmark value, and whose directional consistency labels are conflicting sections, extracts the corresponding point numbers, location identifiers, and partitions to which they belong, marks them as detected abnormalities with diffusion risks, and outputs the points that meet the joint conditions in a structured format, generating pesticide use detection and risk warning results.

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