Method and system for preventing sunscald of fruits

Abstract

The present disclosure provides a fruit sunscald prevention method and system, comprising: determining relevant environmental parameters affecting sunscald decision variables, and deploying sensors inside the orchard according to the relevant environmental parameters, and determining the measurement scheme of the sensors, wherein the sunscald decision variables include primary decision variables and secondary decision variables, and a mathematical model between the sunscald decision variables and the relevant environmental parameter data is established according to the relevant environmental parameter data of the local area of the orchard, the mathematical model is used to determine the simulation value of the sunscald decision variables corresponding to the relevant environmental parameter data, the fruit sunscald prevention operation task is determined according to the simulation value, the fruit sunscald prevention operation task is adjusted according to the operation effect corresponding to the fruit sunscald prevention operation task, and the fruit sunscald prevention operation task is executed in advance according to the future change trend of the relevant environmental parameters, so as to improve the fruit sunscald prevention efficiency.

Description

Technical Field

[0001] The present disclosure relates to the field of agricultural disease prevention and control, and in particular to a method and system for preventing fruit sunburn. Background Art

[0002] Fruit sunscald is a common physiological disease in agricultural production. Long-term exposure of fruits to high temperature and strong light is the main cause of this disease.

[0003] In recent years, global warming has made sunburn more common and severe. For example, sunburn in citrus can cause the entire peel to thicken and even become deformed. The sunburned area turns yellow, or in severe cases, dark brown, and becomes rough and hard. The fruit also loses significant moisture, ultimately leading to a significant decrease in appearance and quality, severely impacting or even completely destroying its economic value, and causing significant financial losses to fruit farmers.

[0004] At present, the main operational measures generally used to prevent sunburn include: bagging, spraying protective agents (films), and retaining summer shoots. Although these measures can effectively prevent fruit sunburn, they cannot be operated mechanically for the time being, require a lot of manpower costs, and may affect the appearance of the fruit, resulting in low preventive benefits of these operational measures.

[0005] The content of the background technology section is merely information known to the inventor personally, and does not mean that the above information has entered the public domain before the application date of this disclosure, nor does it mean that it can become the prior art of the present disclosure. Summary of the Invention

[0006] The present disclosure provides a method and system for preventing fruit sunscald disease, so as to avoid at least one of the above-mentioned technical problems.

[0007] In a first aspect, the present disclosure provides a method for preventing fruit sunscald, comprising:

[0008] Determining relevant environmental parameters that affect sunburn determining variables, deploying sensors in the orchard based on the relevant environmental parameters, and determining a measurement scheme for the sensors, wherein the sunburn determining variables include primary determining variables and secondary determining variables;

[0009] Establishing a mathematical model between the sunburn determining variable and the relevant environmental parameter data based on the relevant environmental parameter data of the local area of ​​the orchard, wherein the mathematical model is used to determine a simulated value of the sunburn determining variable corresponding to the relevant environmental parameter data;

[0010] determining a fruit sunburn prevention task according to the simulation value, and performing feedback adjustment on the fruit sunburn prevention task according to an operation effect corresponding to the fruit sunburn prevention task;

[0011] According to the future change trend of the relevant environmental parameters, the fruit sunburn prevention task is performed in advance.

[0012] In some embodiments, determining relevant environmental parameters that affect sunburn determination variables includes:

[0013] Dividing the orchard into regions to obtain local regions of the orchard, wherein the fluctuation of the internal environmental parameters of each local region of the orchard approaches 0;

[0014] Determining a weather system standard local area corresponding to orchard weather data, wherein the orchard weather data is weather data corresponding to the orchard by the weather system, and the weather system standard local area is an area in each orchard local area having an environmental parameter that is most highly correlated with the orchard weather data;

[0015] determining the relevant environmental parameters according to the standard local area of ​​the weather system;

[0016] and, deploying sensors in the orchard according to the relevant environmental parameters, including:

[0017] deploying the sensors for measuring the relevant environmental parameters in the orchard;

[0018] And, also includes:

[0019] The data collected by the sensor is marked and sent back to the orchard environmental data collection and management center for aggregation and classification.

[0020] In some embodiments, the measurement scheme includes a mobile measurement scheme, the mobile measurement scheme includes a data collection frequency; the method further includes: adjusting the data collection frequency;

[0021] Wherein, adjusting the data collection frequency includes:

[0022] A scoring system is configured to evaluate the severity of data changes in the data collected based on the data collection frequency;

[0023] A score range corresponding to the severity of data changes, wherein the score range is used to represent the variation interval of the severity of data changes;

[0024] Obtain the severity of the current data change, and determine the score of the severity of the current data change based on the scoring system, wherein the scoring system is a function O=f(x d ), x d is the degree of change of the current data, O is x d the score of

[0025] Based on the score range and the score, determine whether the current data change intensity is reasonable, and if it is unreasonable, adjust the data collection frequency until the data change intensity score corresponding to the adjusted data collection frequency falls within the score range.

[0026] In some embodiments, determining the relevant environmental parameters based on the weather system standard local area includes:

[0027] Collecting and obtaining data of sunburn determining variables in the standard local area of ​​the weather system, and obtaining orchard weather data corresponding to the collection time of the obtained data of the sunburn determining variables in the local weather system;

[0028] The correlation analysis of the sunburn determining variables and all orchard weather data was performed to obtain the correlation coefficient r of all sunburn determining variables and the orchard weather parameters. ij and the test value P ij ;

[0029] According to the correlation coefficient r ij and the test value P ij , determine the relevant environmental parameters.

[0030] In some embodiments, the measurement scheme includes a mobile measurement mode and a fixed measurement mode;

[0031] The mobile measurement refers to deploying the sensor on a movable device to measure the environmental parameters in the orchard when the movable device performs a patrol mission in the orchard. The movable device includes a drone and / or an unmanned vehicle.

[0032] The fixed measurement refers to measuring the environmental parameters within a preset range of the orchard by installing a fixed measurement station in the orchard and installing fixed distributed sensors in a local area of ​​the orchard;

[0033] The measurement scheme includes: a combined measurement scheme in which the mobile measurement method is primary and the fixed measurement method is auxiliary.

[0034] In some embodiments, the establishing of a mathematical model between the sunburn determining variable and the relevant environmental parameter data based on the relevant environmental parameter data of the local area of ​​the orchard includes:

[0035] Collecting data of sunburn determining variables in a local area of ​​the orchard, and giving a regional label and a collection time label to the local area of ​​the orchard;

[0036] The mathematical model is obtained by performing a correlation analysis on the main determining variables of sunburn and related environmental parameters.

[0037] In some embodiments, the fruit sunburn prevention task includes spraying water mist and setting up sunshade nets;

[0038] The water mist spraying is performed in a combination of dynamic and static operation modes. The water mist spraying is performed based on a drone or unmanned vehicle in the orchard, or by installing fixed nozzles in the orchard.

[0039] In some embodiments, the number of the primary decision variables is multiple, the number of the secondary decision variables is multiple; determining the fruit anti-sunburn task according to the simulation value, and performing feedback adjustment on the fruit anti-sunburn task according to the operation effect corresponding to the fruit anti-sunburn task, includes:

[0040] S3-1: Divide each major determining variable into different levels of operations to be performed based on its simulated value, and formulate a corresponding sunburn prevention operation plan G for each level of operations to be performed;

[0041] S3-2: synthesize the sunburn prevention operation plans G to obtain a comprehensive sunburn prevention operation plan L;

[0042] S3-3: For each secondary decision variable, divide it into different impact levels according to its simulated value, and formulate corresponding operation adjustment plan D for each impact level;

[0043] S3-4: synthesize the operation adjustment plans D to obtain the comprehensive operation adjustment plan A;

[0044] S3-5: Adjusting the comprehensive anti-sunburn operation plan L according to the comprehensive operation adjustment plan A to obtain a proposed anti-sunburn operation task, wherein the fruit anti-sunburn operation task includes the proposed anti-sunburn operation task;

[0045] S3-6: Control relevant operating facilities or equipment to perform sunburn prevention operations, and obtain and evaluate the results of the operations;

[0046] S3-7: Based on the operation results, the hierarchical division, scheme formulation, and scheme synthesis methods of steps S3-1 to S3-4 are adjusted through a hierarchical adjustment algorithm or a scheme adjustment algorithm;

[0047] The method of dividing different levels of work to be done in S3-1 is a compression characteristic function y=f(x), and the compression characteristic function is expressed by y=T th +k·ln[α(t―T th )+1] means, t>T th , t is the simulation value, T this the preset threshold, k is the proportional coefficient, α is the compression adjustment factor, α>0, the smaller α is, the smaller the compression is, and the more the division result tends to be uniform; vice versa;

[0048] Among them, in S3-2, the anti-sunburn operation plan G of the operation measure j corresponding to the main decision variable i ig , the implementation degree of j operation measures is V ij , the composite coefficient of operation measure j is coefficient k ij In the case of ij Synthesize the operational measures and their implementation level in the comprehensive sunburn prevention operation plan j , and L j =∑ i k ij V ij , coefficient k ij By execution level V ij The calculation weight R ij Determine: If and only if j operation measure exists or is implemented to the degree V ij When it is not 0, R ij =C i Otherwise, if V ij =0, then R ij =0, C i is the weight coefficient of the main decision variable i, and is composed of the subjective weight M i and objective weight S i Composition, C i =M i +S i , coefficient k ij pass Indicates that ∑ i k ij =1;

[0049] Among them, in S3-7, the contents of S3-1 to S3-4 are adjusted according to the operation results and through the operation level adjustment algorithm or the scheme adjustment algorithm. The operation level adjustment algorithm or the scheme adjustment algorithm includes adjusting the level division of the operation to be performed, adjusting the threshold, formulating the adjustment scheme, and synthesizing the adjustment scheme.

[0050] In some embodiments, when the simulated value of the main determining variable exceeds a preset main determining variable threshold, sunburn occurs in the fruit. The main determining variable threshold is a numerical value or numerical range obtained based on the local climatic conditions, fruit variety, plant protection experience, and experiments of the fruit, and the simulated value of the main determining variable is a value that avoids sunburn in the fruit or reduces the incidence and degree of sunburn in the fruit.

[0051] In a second aspect, the present disclosure provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the method described in the first aspect above.

[0052] In a third aspect, the present disclosure provides a computer program product, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0053] In a fourth aspect, the present disclosure provides a fruit sunburn disease prevention system, comprising: a processor, and a memory communicatively connected to the processor;

[0054] The memory stores computer-executable instructions;

[0055] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0056] The present disclosure provides a method and system for preventing fruit sunburn diseases, comprising: determining relevant environmental parameters that affect sunburn determining variables, deploying sensors in an orchard based on the relevant environmental parameters, and determining a measurement scheme for the sensors, wherein the sunburn determining variables include primary determining variables and secondary determining variables, establishing a mathematical model between the sunburn determining variables and the relevant environmental parameter data based on the relevant environmental parameter data of a local area of ​​the orchard, the mathematical model being used to determine a simulation value of the sunburn determining variable corresponding to the relevant environmental parameter data, determining a fruit sunburn prevention task based on the simulation value, and performing feedback adjustment on the fruit sunburn prevention task based on the operation effect corresponding to the fruit sunburn prevention task, and executing the fruit sunburn prevention task in advance based on the future change trend of the relevant environmental parameters to improve the efficiency of fruit sunburn prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0058] Figure 1 Schematic diagram of a method for preventing fruit sunburn according to an embodiment of the present disclosure;

[0059] Figure 2 A schematic diagram of adjusting the data collection frequency according to an embodiment of the present disclosure;

[0060] Figure 3 The scoring system in the adaptive acquisition frequency adjustment method of the embodiment of the present disclosure corresponds to the function image embodied in the embodiment;

[0061] Figure 4 A block diagram of a method for formulating, generating, and adaptively adjusting sunburn prevention tasks according to an embodiment of the present disclosure;

[0062] Figure 5 This is a schematic diagram of the division of the peel surface temperature to be processed levels according to an embodiment of the present disclosure;

[0063] Figure 6 Schematic diagram of the uneven division of the peel surface temperature into the waiting operation levels according to the embodiment of the present disclosure;

[0064] Figure 7 A schematic diagram comparing the uneven division of the waiting level under different compression levels according to an embodiment of the present disclosure;

[0065] Figure 8 A technical roadmap for implementing the fruit sunburn prevention method according to an embodiment of the present disclosure;

[0066] Figure 9 This is a block diagram of the composition of the fruit sunburn disease prevention system according to an embodiment of the present disclosure.

[0067] The above drawings illustrate specific embodiments of the present disclosure, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of the present disclosure to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0068] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0069] It should be understood that the terms "including" and "having" and any variations thereof in the embodiments of the present disclosure are intended to cover but not exclude inclusion. For example, a product or device that includes a series of components is not necessarily limited to those components explicitly listed, but may include other components not explicitly listed or inherent to these products or devices.

[0070] In the embodiments of the present disclosure, the term "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0071] In the embodiments of the present disclosure, the term "plurality" refers to two or more than two, and other quantifiers are similar thereto.

[0072] The terms "first," "second," "third," and the like in this disclosure are used to distinguish between similar or similar objects or entities and are not necessarily intended to limit a particular order or precedence, unless otherwise indicated. It should be understood that the terms used in this manner are interchangeable where appropriate, e.g., capable of being implemented in an order other than that given in the illustrations or descriptions of the embodiments of this disclosure.

[0073] The term "unit / module" as used in this disclosure refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0074] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure and not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0075] According to one aspect of the embodiments of the present disclosure, the present disclosure provides a method for preventing fruit sunburn, such as Figure 1 As shown, the method includes the following S1-S4:

[0076] S1: Determine the relevant environmental parameters that affect the sunburn determination variables, deploy sensors in the orchard based on the relevant environmental parameters, and determine the sensor measurement scheme.

[0077] In some embodiments, S1 may include the following S1-1 to S1-7:

[0078] S1-1: Divide the area within the orchard to obtain the local area of ​​the orchard.

[0079] For example, regional division is carried out within the orchard to obtain multiple local areas of the orchard, and the regional division can be guided by factors such as the type of fruit trees in the orchard and the concentration of fruit tree planting. However, it is necessary to ensure that the fluctuations in the internal environmental parameters (such as air temperature, light intensity, etc.) of each local area obtained by division are negligible, that is, the environmental parameters within each local area of ​​the orchard can be regarded as basically consistent, so that "the average of the environmental parameters measured at any position in any local area of ​​the orchard can approximately represent the current environmental parameters of the area" is rationalized.

[0080] In some embodiments, for the case where the orchard is large and different varieties are planted in a scattered manner, if there are fruit tree varieties such as mandarin oranges, ponkan oranges, and mandarin oranges in the orchard, and the different varieties are planted in a scattered and small area (the fluctuation of environmental parameters within the planting range is negligible), in this case, the single planting range of different fruit tree varieties can be directly divided into one local area, and finally multiple local orchard areas are obtained in the orchard.

[0081] In other embodiments, for the case where the orchard is large and the fruit trees are planted in concentrated and continuous areas, if there are multiple varieties concentrated in the orchard and planted in large-scale continuous areas (the environmental parameters within the planting range vary greatly and cannot be ignored), in this case, the large-scale continuous planting area can be divided according to a certain space (the fluctuation of environmental parameters within the spatial range can be ignored, and the specific spatial range is determined by experience or experimental methods), such as dividing a large-scale continuous planting area into multiple local orchard areas.

[0082] In some other embodiments, for a small-scale orchard, the fluctuation of environmental parameters in the orchard can be ignored, and the entire orchard can be divided into one local area of ​​the orchard.

[0083] The above embodiments are for illustration only and should not be understood as limiting regional divisions. For example, regional divisions can be carried out in a targeted manner based on other factors such as the climatic conditions in orchards or other plantations, the planting conditions of different crops, etc. However, it should be ensured as much as possible that the fluctuations in environmental parameters within any local area of ​​the orchard obtained through division are negligible, that is, that the environmental parameters within any local area of ​​the orchard can be considered to be basically consistent.

[0084] S1-2: Determine a standard local area of ​​a weather system corresponding to orchard weather data, wherein the orchard weather data is weather data of the weather system corresponding to the orchard.

[0085] For example, within an orchard, environmental parameter data for different local areas is collected using portable instruments and equipment. The data is annotated with the corresponding collection time and local area number and recorded. Subsequently, orchard weather data (also known as weather forecast data) corresponding to the local weather system and the aforementioned environmental parameters and data collection time is obtained. Finally, based on a correlation analysis between the data collected from each area and the orchard weather data, the orchard local area with the strongest correlation with the orchard weather data is selected as the standard local area for the weather system.

[0086] For example, multiple environmental parameters were collected several times at different local orchard locations and times, and the average values ​​were calculated. Orchard weather data for the corresponding time periods was then obtained for data comparison and correlation analysis. Assuming that the environmental parameters that the portable instrument can collect are light intensity, air temperature, and air humidity, the orchard weather data and calculations are shown in Table 1.

[0087] Table 1

[0088]

[0089] With reference to Table 1, taking the correlation analysis of the light intensity of the orchard local area 1 (referred to as local area 1) as an example, the correlation analysis (preferably, the bivariate Person test is generally used) is performed on the average value of the light intensity collection data (vdl, vhl, vm1) of the local area 1 T and the orchard weather data (dl, hl, ml) T to obtain the correlation coefficient r and the P value, that is, r1 and P1 (not shown in Table 1).

[0090] r represents the correlation coefficient between variables in a sample, indicating the size of the correlation, and the value range is [-1, 1]. The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the variables is; the closer the absolute value is to 0, the weaker the correlation between the variables is. If the correlation coefficient is greater than 0, it indicates that there is a positive correlation between the two; if the correlation coefficient is less than 0, it indicates that there is a negative correlation between the two.

[0091] The P value is a test value for testing the significant level of the correlation between two variables. Generally, P < 0.05 indicates that the correlation reaches a significant level, and P < 0.01 indicates that the correlation reaches an extremely significant level.

[0092] The absolute values of the correlation coefficients of different environmental parameters of each orchard local area are averaged to obtain the regional average correlation coefficient, as shown in Table 1. The maximum value is found from the regional average correlation coefficient. If the data correlation of a local area of an orchard is generally significant, the local area of the orchard corresponding to the maximum value is selected as the most relevant area to the weather data of the orchard, and it can be called the weather system standard local area; otherwise, the area with the second largest regional average correlation coefficient value is selected, and the correlation significant level is analyzed in the same way, and so on, until a suitable area is selected as the weather system standard local area.

[0093] In the weather system standard local area, the environmental parameters collected by the portable instrument and the weather data of the orchard will show a significant correlation, indicating that the actual environmental parameters of the area can be approximately represented by the weather data of the orchard.

[0094] The above examples are only used for illustration, and in actual implementation, the measured local areas of the orchard, environmental parameters, collection and measurement time, and collection and measurement times may be more, and the data collection and measurement, data statistics, and correlation analysis methods may be different, but as long as the collection and analysis methods are reasonable and can ensure that the environmental parameters of the weather system standard local area have a significant correlation with the weather data of the orchard, that is, the environmental parameters of the weather system standard local area can be approximately represented by the weather data of the orchard.

[0095] S1-3: Identify relevant environmental parameters that influence sunburn determination variables.

[0096] The sunburn determining variables include primary and secondary variables. The primary variable may include at least one of the following: peel surface temperature, peel surface light intensity, peel surface UV intensity, and fruit moisture content. The secondary variables may be variables other than the primary variables that affect sunburn. This embodiment primarily uses the primary variable as an example for illustrative purposes. The technical principles of the secondary variables can be found in the description of the primary variables. To avoid tedious details, this embodiment will not elaborate further.

[0097] For example, the prevention system can collect data on certain main determining variables (which can be called main determining variable data, or collected data of main determining variables) in the standard local area of ​​the weather system, and obtain weather data corresponding to the time when the main determining variable data is obtained. The relevant information is shown in Table 2.

[0098] Table 2

[0099]

[0100] Combined with Table 2, and taking the surface temperature of the peel as an example, the surface temperature and weather data of the peel in Table 2 are moved to Table 3, and based on the data (A1, A2, A3, A4, A5, ...) T and (a1,a2,a3,a4,a5,…) T Correlation analysis was performed to obtain the correlation coefficient r 11 and P 11 Value; based on data (A1, A2, A3, A4, A5, ...) T and (b1,b2,b3,b4,b5,…) T Correlation analysis was performed to obtain the correlation coefficient r 12 and P 12 The correlation analysis of other main determining variables and weather parameters is similar, and the correlation coefficients r of all main determining variables and weather parameters can be obtained. ij and P ij Value (i=1,2,3,4,… represents the serial number of the main determining variable; j=1,2,3,… represents the serial number of the weather parameter). ij and P ij values, and determine the relevant environmental parameters that affect the main determining variables of sunburn.

[0101] Table 3

[0102]

[0103] S1-4: Deploy sensors in the orchard to measure relevant environmental parameters that affect sunburn determination variables.

[0104] Sensor measurement solutions can be divided into two types: mobile measurement and fixed measurement. Mobile measurement involves deploying sensors on mobile devices such as drones and unmanned vehicles to measure relevant environmental parameters while they are patrolling the orchard. Fixed measurement involves installing fixed measurement stations or distributed sensors in localized areas of the orchard to measure environmental parameters within a fixed area of ​​the orchard.

[0105] Relatively speaking, mobile measurement solutions have the characteristics of flexible measurement and a wider range. For example, environmental parameters can be measured at any location in the orchard that can be reached by mobile equipment. In addition, they have the characteristics of more cost-effective sensor deployment. For example, compared with fixed distributed sensors, mobile measurement solutions require fewer sensors and have flexible measurement locations. Compared with fixed measurement stations, mobile measurement solutions can measure a wider dynamic range.

[0106] The fixed measurement scheme has the characteristic of stronger environmental adaptability. If there may be extreme areas in the orchard where mobile equipment such as drones and unmanned vehicles cannot work, the fixed measurement scheme can be used instead in this area. The fixed measurement scheme has the characteristic of continuous data collection. For example, for a local area of ​​the orchard, the temporal continuity of the data of the local area of ​​the orchard obtained by the mobile measurement scheme depends on the frequency of the mobile equipment performing data collection tasks in the local area of ​​the orchard (that is, the temporal frequency of data collection in the local area of ​​the orchard), which is a discrete value. In contrast, the fixed measurement scheme can stably and continuously measure at a fixed position in the area, and the data obtained is continuous.

[0107] S1-5: Determine the measurement scheme of the sensor.

[0108] For example, in general areas where both of the above-mentioned measurement schemes are feasible (such as flat land), it is generally preferred to use a mobile measurement scheme for data collection due to the consideration of efficient resource utilization and cost issues. In extreme areas, considering the impact of weather and special terrain on the normal operation of mobile equipment such as drones and unmanned vehicles (for example, strong winds in mountaintop areas make it impossible for drones to fly; rugged hillside areas are prone to cause accidents for unmanned vehicles), it is preferred to use a fixed measurement scheme for data collection. In addition, determining the measurement scheme should also take into account the different scopes of local areas of the orchard obtained by dividing the orchard at different locations, and the different degrees of changes in climate and environmental parameters.

[0109] Among them, the prevention system can determine the measurement scheme based on actual needs. The measurement scheme is a mobile measurement scheme, a fixed measurement scheme, or a measurement scheme with a mobile measurement scheme as the main and a fixed measurement scheme as the auxiliary (also called a combined measurement scheme).

[0110] In the case of mobile measurement solutions,

[0111] S1-6: Data acquisition frequency f in the measurement plan m .

[0112] Since the data of the local area of ​​the orchard obtained by the mobile measurement method are discrete values, the time interval between discrete values ​​depends on the data collection frequency f of the mobile device in the local area of ​​the orchard. m In order to ensure that discrete data can better reflect the changes in environmental parameters and prevent excessive consumption of equipment resources due to too frequent data collection, a reasonable data sampling time interval should be set, that is, a reasonable data sampling frequency f should be set. m .

[0113] In some embodiments, as Figure 2 As shown, S1-6 may include the following S1-6-1 to S1-6-4:

[0114] S1-6-1: Set up a scoring system for evaluating the severity of data changes. The scoring system is used to output a corresponding score based on the severity of the input data changes.

[0115] S1-6-2: Set a reasonable score range for the severity of data changes. By defining the reasonable severity of data changes, a score range for judging the severity of data changes is obtained.

[0116] S1-6-3: Get the score of the degree of drastic change of the current data. By obtaining the current data collection frequency f of a local area in an orchard m The degree of data change under the current environment parameters is input into the scoring system to obtain its score, which is used to describe the f m rationality.

[0117] S1-6-4: Based on the reasonable score range and score, determine whether the current data change intensity is reasonable and implement corresponding measures. Determine whether the score is within the reasonable score range: If yes, it means that the current data change intensity is normal, that is, the current data collection frequency f m Reasonable, so as to continue to maintain the frequency f m If not, it means that the current data changes too drastically or too slowly, that is, the current data collection frequency f m Unreasonable, so take corresponding measures to adjust the data collection frequency f m, until the corresponding score of the intensity of the change in the collected data falls into a reasonable score range, that is, the data collection frequency f of the local area m Reasonable. Among them, the data sampling frequency f corresponding to the reasonable score range m It should be ensured that: at this frequency f m In this case, the collected data can better reflect the actual environmental parameter changes in the local area, that is, the loss of environmental parameter information between data collection intervals is small.

[0118] For example, the scoring system is a function with the data difference as the independent variable, and is represented by Formula 1:

[0119] 0=f(x d )

[0120] Among them, x d Indicates the difference between the current collected data and the last collected data, that is, the degree of change. O represents the data difference x d The score of . Formula 1 can be obtained by Figure 3 express, Figure 3 O on the middle vertical axis fmax , O fmin Represents the maximum and minimum values ​​of the score range, respectively. -fmin 、x d-fit 、x d-fmax They represent the minimum normal data change intensity, the best normal data change intensity, and the maximum normal data change intensity corresponding to the score range. m Improve, that is, the data collection time interval is shortened, then the data change intensity is reduced, corresponding to x di and fraction O i The change process is from 6 to 1; and vice versa. mi 、x di , O i The three correspond to each other (i=1, 2, 3, 4, 5, 6).

[0121] For example, based on the data x currently obtained by the device n and the previous data x n-1 Get x d , input O=f(x d ), get the corresponding score O; determine whether O is in [O fmin ,O fmax ], if so, it means the data collection frequency f m If the change in intensity is normal, the device maintains the data collection frequency. Otherwise, it is determined that x d <x d-fmin or x d >x d-fmax .

[0122] If x d <x d-fmin , indicating that the degree of change is too small, that is, for the changes in relevant environmental parameters, the data collection frequency f m If the value is too large, the collected data can reflect the changes in the environmental parameters of the local area of ​​the orchard, but it causes a waste of collection equipment resources. At this time, the data collection frequency should be appropriately reduced. m .

[0123] like Figure 3 As shown, assuming that the data acquisition frequency of the current device is f m1 (not marked in the figure), the current data difference is x d1 , corresponding score O1(O1<O fmin ), at this time, the data collection time interval can be gradually and iteratively increased according to a certain ratio, and the current x d1 The x corresponding to the sampling time interval after each iteration of the benchmark calculation di and score O i , stop the iterative calculation when the score starts to decrease.

[0124] If x di From x d1 Change to x d4 (x d3 Change to x d4 When the corresponding score drops, the iteration stops), the corresponding score is O i The change process is from O1 to O4. At this time, the x corresponding to O3, which is the highest before the score starts to decrease, is taken. d3 Data acquisition frequency f m3 As the subsequent data collection frequency for the local area of ​​the orchard, at this time f m3 Relatively m1 Lower, and the adjustment is complete.

[0125] If x d >x d-fmax , indicating that the data changes too drastically, that is, the data collection frequency f is too high for the changes in the relevant environmental parameters. m If the value is too small, it will easily lead to the loss of relevant environmental parameter change information between two data collection intervals, thereby affecting the sensitivity of the prevention system to environmental changes. In this case, the data collection frequency f should be appropriately increased. m .

[0126] like Figure 3 As shown, assuming that the data acquisition frequency of the current device is f m6 (not marked in the figure), the current data difference is x d6 , corresponding score O6 (O6<O fmin), at this time, the data collection time interval can be gradually and iteratively reduced according to a certain ratio, and the current x d6 The x corresponding to the sampling time interval after each iteration of the benchmark calculation di and score O i , stop the iterative calculation when the score starts to decrease.

[0127] Such as x di The iterative process is x d6 to x d2 (x d3 Change to x d2 When the corresponding score drops, the iteration stops), then the score is O i The corresponding change process is from O6 to O2. At this time, the x value corresponding to O3, which was the highest before the score started to decrease, is taken. d3 Data acquisition frequency f m3 As the subsequent data collection frequency for the local area of ​​the orchard, at this time f m3 Relatively m6 Improve, and the adjustment is completed.

[0128] From the above embodiment, it can be seen that the data acquisition frequency f m The adjustment can be achieved through iterative calculation. When the prevention system determines the data collection frequency f for a local area of ​​an orchard, m If it is too high, you can use this method to adjust f m Automatically adjust to a suitable value; when the prevention system determines the data collection frequency f for a local area of ​​an orchard m If it is too low, you can use this method to adjust f m Automatically adjust to the appropriate value. At the same time, thanks to the definition of the range of reasonable data changes (such as Figure 3 [x d-fmin ,x d-fmax ]), when the data acquisition frequency f m After adjusting to the appropriate value, even if the changes in the relevant environmental parameters have changed, the data collection frequency f m It can also maintain its adaptability over a period of time, avoiding the need for frequent calculations and adjustments to data collection frequency due to environmental changes. m Based on the above characteristics, the method disclosed in this disclosure can realize the data acquisition frequency f of the mobile measurement mode. m Compared with the fixed data acquisition frequency f m ,The prevention system can dynamically allocate the collection equipment resources to further improve the ,utilization rate of the orchard’s resources.

[0129] At this point, assuming that through the above steps S1-4 to S1-6, the sensors for the relevant environmental parameters that affect the sunburn determining variables in the orchard have been deployed and a reasonable measurement plan has been formulated, then the relevant environmental parameters of all local areas in the orchard can be collected and obtained by these local sensors within the orchard.

[0130] In order to facilitate the management of orchard data, an orchard environmental data collection and management center is established to collect, summarize, and classify the relevant environmental parameter data collected from each local area.

[0131] S1-7: Collect, summarize, and categorize the data on relevant environmental parameters collected from local areas of each orchard.

[0132] For example, the data collected by the sensor is marked and sent back to the orchard environmental data collection and management center for aggregation and classification. The data collected by the sensor in each local area of ​​the orchard is marked with the region and collection time, and sent back to the orchard environmental data collection and management center in real time for aggregation. The orchard environmental data collection and management center receives the sensor data in real time and classifies it into the corresponding orchard local area environmental parameter data management partition for storage based on the regional mark of the data. The data of each relevant environmental parameter can be known by its collection time mark. Its specific data collection time. That is, the orchard environmental data collection and management center manages the data of the relevant environmental parameters of each local area of ​​the orchard in a partitioned manner. Orchard staff can use the orchard environmental data collection and management center to view and obtain the real-time or historical relevant environmental parameter data of all local areas of the orchard.

[0133] To avoid misunderstandings, the subsequent steps will be discussed based on a local area in the orchard as an example. The same applies to other local areas in the orchard, which will remain consistent with this discussion or be modified according to special actual circumstances.

[0134] S2: Based on the relevant environmental parameter data of the local area of ​​the orchard, a mathematical model is established between the sunburn determining variables and the relevant environmental parameters.

[0135] For example, S2 may include the following S2-1 and S2-2:

[0136] S2-1: Collect data on sunburn determining variables in a local area of ​​the orchard (referred to as collected data). Data on sunburn determining variables in a local area of ​​the orchard are collected using a certain measurement method (generally manual measurement) and are labeled with the region and collection time.

[0137] S2-2: Organize Data and Build a Mathematical Model. Based on the region and collection time stamps for the sunburn determining variable data, obtain data on relevant environmental parameters affecting the sunburn determining variable corresponding to the local orchard area and collection time from the orchard environmental data collection and management center. After aggregating and organizing these two types of data, perform a correlation analysis between the sunburn determining variable and its related environmental parameters and build a mathematical model. This mathematical model can be used to infer the sunburn determining variable from its related environmental parameters.

[0138] Specifically, the collected data and relevant environmental parameter data for a local area of ​​the orchard were summarized and collated to form Table 4. Correlation analysis was then performed based on this data. The principles of correlation analysis can be found in the examples above and will not be further elaborated here. After the correlation analysis, a multivariate linear regression model (i.e., a mathematical model) was fitted between each collected data point and the relevant environmental parameter data.

[0139] Table 4

[0140]

[0141] In fact, the relevant environmental parameter data in the modeling process of the mathematical model are not limited to being data collected by sensors, but can also be other data sources (for example, local weather systems, etc.), depending on the specific conditions of the orchard; the final mathematical model does not have to be an equation with a clear functional relationship, but can also be other intelligent models obtained through data training with implicit functional relationships (for example, neural networks, etc.). The specific model selection should be based on the results of the model's adaptability analysis.

[0142] Generally speaking, collecting and measuring environmental parameters is simpler than sunburn-determining variables (the former can be obtained through sensors or online networking). Once the mathematical model of the impact is established as described above, the simulated values ​​of the sunburn-determining variables can be inferred by obtaining relevant environmental parameter data. This indirect method effectively reduces the difficulty of obtaining simulated values ​​of the sunburn-determining variables.

[0143] It should be noted that the above-mentioned “analog value” in this disclosure means a continuous quantity relative to a discrete quantity.

[0144] In theory, the prevention system can obtain relevant environmental parameter data in real time from the orchard environmental data collection and management center, and obtain the simulated values ​​of sunburn determining variables in real time based on mathematical models.

[0145] Combined with the above analysis, it can be seen that sunburn determining variables include primary determining variables and secondary determining variables. The above-mentioned construction process of the mathematical model of sunburn determining variables can be the construction process of primary determining variables or the construction process of secondary determining variables. The specific types of sunburn determining variables will not be elaborated here.

[0146] S3: Develop tasks for preventing fruit from sunburn and make adjustments based on feedback from the results of the tasks.

[0147] For example, taking the main determining variables as an example, the main determining variables that generally cause sunburn on fruits are the surface temperature of the peel, the light intensity on the peel, the ultraviolet intensity on the peel, and the moisture content of the fruit. In order for the fruit to experience sunburn, the simulated value of the main determining variable must exceed a certain threshold (for example, the threshold for sunburn on a certain fruit in a certain city is 41.0°C for the surface temperature of the peel, 1599 μmol·m for the light intensity of the peel, and 1599 μmol·m for the ultraviolet intensity of the peel). -2 ·s -1 ).

[0148] In fact, there must be some kind of influence relationship between the main determining variables rather than mutual independence. Moreover, due to differences in climatic and environmental conditions, the threshold of the main determining variable is not a fixed value, but a fuzzy value (compared to the "specific value" such as 41.0℃, 1599umol·m -2 ·s -1 ).

[0149] Therefore, the present disclosure defines the threshold value or threshold range of the main determining variable as: the value or value range of the main determining variable obtained based on the local climatic conditions, fruit varieties, plant protection experience or experiments of the fruit. When the simulated value of the main determining variable can meet the corresponding conditions (such as size relationship, subordination) based on the value, it can effectively avoid sunburn of the fruit or effectively reduce the incidence and degree of sunburn of the fruit. This value is the threshold value or threshold range of the main determining variable for sunburn. For example, in combination with the above example, the surface temperature of the peel of a certain fruit in a certain place does not exceed 41°C and the light intensity on the peel surface does not exceed 1599umol·m -2 ·s -1 At this time, the fruit in this area can effectively prevent sunburn or effectively reduce the incidence and severity of sunburn.

[0150] It is worth noting that: on the one hand, since the main determining variables are not independent of each other, and it is difficult to clarify the "or" and "and" relationship between the main determining variables that cause sunburn on fruits, it is impossible to determine whether the sunburn on fruits is caused by the simulated value of a certain main determining variable exceeding the threshold, or because the simulated values ​​of multiple main determining variables exceed the threshold at the same time; on the other hand, in order to avoid ambiguity and to avoid sunburn on fruits or reduce the sunburn rate and degree of fruits as much as possible, this disclosure will determine in subsequent discussions that "the thresholds of the main determining variables that cause sunburn on fruits are in an 'or' relationship", that is, as long as the simulated value of a certain main determining variable exceeds the threshold, the corresponding fruit sunburn prevention measures will be implemented.

[0151] Generally, the main determinants of sunburn on fruits are the surface temperature of the peel, the intensity of light on the peel, the intensity of ultraviolet rays on the peel, and the moisture content of the fruit. Therefore, the operational measures to prevent sunburn on fruits should be considered from the aspects of cooling, shading, irrigation, and humidification. In the relevant technology, the main operational measures generally used to prevent sunburn include: spraying, setting up sunshade nets, cold water irrigation, spraying protective agents (films), enhancing ventilation and heat dissipation in orchards, retaining summer shoots, bagging, etc. For the convenience of problem description, the present disclosure preferably uses the two operational measures of spraying water mist and setting up sunshade nets as examples for subsequent discussion.

[0152] Different operational measures and the degree of implementation of these measures result in different effects. For example, regarding spraying water mist, preferably, the present disclosure adopts the same dynamic and static combined operation scheme as that for data collection, i.e., in local areas of general orchards, this function is performed by drones or unmanned vehicles, and in local areas of extreme orchards, this function is performed by installing fixed nozzles between gardens. This function mainly plays a cooling role, and different spraying doses produce different cooling degrees; a sunshade net is set up above the fruit trees in each local area of ​​the orchard, and its shading degree can be changed through a certain mechanical structure or control method, thereby controlling the light intensity received by the local area of ​​the orchard or the fruit trees below it, mainly playing the role of shading and cooling. Different shading degrees of sunshade nets have different shading effects and cooling degrees.

[0153] Specifically, the anti-sunburn operation plan can combine different operation measures or adjust the execution level of different operation measures. For example, Plan 1: Control the shade net to 100% shading; Plan 2: Control the shade net to 25% shading and apply water mist once; Plan 3: Control the shade net to 25% shading and apply water mist twice, and so on.

[0154] Furthermore, after developing a sunburn prevention plan based on the primary determinants, the actual implementation requires integrating the sunburn prevention plans for each primary determinant and considering secondary influencing variables (e.g., fruit tree canopy parameters). Based on this, adjustments should be made to the integrated sunburn prevention plan before implementation. For example, if the canopy density of fruit trees is high, the water mist spray rate may need to be increased. After implementation, feedback should be provided based on the effectiveness of the plan, and appropriate adjustments should be made to the original plan if necessary.

[0155] In some embodiments, combined Figure 4 It can be seen that S3 may include the following S3-1 to S3-7:

[0156] S3-1: For each major determining variable, according to its simulated value, different levels of work to be done are divided according to a certain method, and a corresponding sunburn prevention work plan G is formulated for each level of work to be done;

[0157] S3-2: Each major determining variable obtains a corresponding sunburn prevention operation plan G according to its simulation value. The sunburn prevention operation plans of each major determining variable are synthesized into a comprehensive sunburn prevention operation plan L according to a certain method;

[0158] S3-3: For each secondary decision variable, according to its simulated value, different impact levels are divided according to a certain method, and corresponding operation adjustment plan D is formulated for each impact level;

[0159] S3-4: Each secondary determining variable obtains a corresponding operation adjustment plan D according to its simulation value. The sunburn prevention operation plans of each secondary determining variable are synthesized into a comprehensive operation adjustment plan A according to a certain method;

[0160] S3-5: After the comprehensive anti-sunburn operation plan L is adjusted according to the comprehensive operation adjustment plan A, the proposed anti-sunburn operation task is obtained;

[0161] S3-6: Relevant operating facilities or equipment shall perform sunburn prevention operations and obtain and evaluate the operation results;

[0162] S3-7: Based on the operation results, the hierarchical division, scheme formulation and scheme synthesis methods of S3-1 to S3-4 are adjusted through the hierarchical adjustment algorithm or the scheme adjustment algorithm.

[0163] Preferably, in an execution process of S3-1, taking the surface temperature of the peel as an example, firstly, the surface temperature of the peel is divided into different levels to be processed according to a certain method: according to the surface temperature threshold T th , the temperature range below the threshold is divided into the non-operation level G0, under which the system does not need to perform the anti-sunburn task; the temperature range above the threshold T thThe temperature range is divided into the first level of operation G1~G n , the system needs to perform the corresponding anti-sunburn task at this level. Further, the first level of pending operations is divided into multiple second levels of pending operations G1, G2, ..., G n There is a separation value T between each secondary level of the pending job th1 、T th2 ,…,T th(n-1) ,like Figure 5 Different levels will have corresponding sunburn prevention work plans.

[0164] Since the surface temperature of the peel usually exceeds the threshold value when the threshold value T th near the threshold T th Therefore, when the number of anti-sunburn operation plans is the same, it is necessary to concentrate more anti-sunburn operation plan resources on T when dividing the secondary level of waiting operations. th Near the peel surface temperature threshold T th There are more secondary layers to be operated nearby, so as to improve the prevention system's response to the peel surface temperature exceeding the threshold T th Operation response sensitivity.

[0165] The above-mentioned division of the first level of the work to be done is a non-uniform division. To achieve this division, preferably, a function y=f(t) with compression characteristics can be used to calculate the super-threshold range of the peel surface temperature [T th ,T max ] is compressed first (the range can be obtained by statistics of historical data), where T max Then, the range obtained after the super-threshold range is compressed is divided evenly, and the non-uniform division of the super-threshold range is obtained, that is, the first level G1 to G2 to be processed is completed. n Non-uniform division.

[0166] In some embodiments, the function of the compression characteristic can be expressed by Equation 2:

[0167] y=T th +k·ln[a(tT th )+1]

[0168] Where t>T th , t is the simulation value, T th is the preset threshold, k is a proportional coefficient, α is the compression adjustment factor, α>0, the smaller α is, the smaller the compression is, and the more the division result tends to be uniform; vice versa. Figure 6 As shown, [T th ,T max]After compression, we get [Y th ,Y max ], [Y th ,Y max ] is evenly divided into 4 levels, which is equivalent to dividing the scope of the first level of the work to be done [T th ,T max ] is unevenly divided into four secondary levels of waiting operations G1, G2, G3, and G4. At this time, it can be seen that G1, G2, and G3 are concentrated on T th Nearby, the requirements of the hierarchical division of pending jobs in this embodiment are met, and when α decreases (α>0), [T th ,T max ] is less unevenly divided, e.g. Figure 7 As shown (α1>α2).

[0169] After the operation levels are divided, appropriate sunburn prevention operation plans can be formulated for each level based on experience or experiments. Since each operation level corresponds to an operation plan, G can be used to represent the level or the plan corresponding to the level. For example:

[0170] (1) Anti-sunburn operation plan for the G0 operating level: control the shading degree of the sunshade net to 0%, and do not spray water mist;

[0171] (2) Anti-sunburn operation plan for the G1 operating level: control the shading degree of the sunshade net to 50%, and do not spray water mist;

[0172] (3) Anti-sunburn operation plan for the operation level G2: control the shading degree of the sunshade net to 0% and perform the water mist spraying task once;

[0173] (4) Anti-sunburn operation plan for the G3 operating level: control the shading degree of the sunshade net to 50%, and perform the water mist spraying task once;

[0174] (5) Anti-sunburn operation plan for the operation level G4: control the shading degree of the sunshade net to 50% and perform the water mist spraying task twice.

[0175] In the following, for the convenience of distinguishing other main decision variables i, the waiting operation level and its corresponding sunburn prevention operation plan are represented by Gi g Indicates (g indicates the number of the level to be processed), for example, the surface temperature of the fruit (i=1) is the level to be processed G2, which is expressed as G 12 .

[0176] The above is an example of the execution process of S3-1 using fruit peel surface temperature as an example. The execution process for other key determinants in S3-1 is similar. The specific thresholds for each key determinant, the division of the target tiers, and the development of sunburn prevention plans should be based on the specific fruit sunburn prevention needs.

[0177] At this point, we can obtain the level of work to be done for each major determining variable and the corresponding anti-sunburn work plan. Next, in a possible execution process of S3-2, we continue to discuss the possible execution process of S3-1 above:

[0178] Since the main determining variables are not independent of each other, a main determining variable may be determined by or affected by another main determining variable, that is, the former has more repeated information and its importance should decrease accordingly.

[0179] To indicate the importance of each key determinant variable in determining the comprehensive sunburn prevention solution, this disclosure proposes using a weight coefficient C to represent this importance. A larger weight coefficient indicates a greater contribution of the corresponding key determinant variable to the final, comprehensive sunburn prevention solution, and vice versa. The weight coefficient C for each key determinant variable consists of two components: an objective weight S and a subjective weight M.

[0180] The objective weight S can be determined by analyzing the relationships between the key determinants using a primary-secondary analysis method. For example, the independence weight method can be used to determine this using historical data on the key determinants. Using this historical data, the multiple correlation coefficient between each key determinant and the other key determinants is calculated. The larger the multiple correlation coefficient, the more overlap there is between that key determinant and the other key determinants, and therefore the smaller its weight should be, and vice versa. Ultimately, the objective weight S can be determined by taking the inverse of the multiple correlation coefficients for each key determinant and distributing them proportionally. The subjective weight M can be set by humans.

[0181] After the weight coefficient C of each major determining variable is finally determined, the prevention system synthesizes the sunburn prevention operation plan of each variable into a comprehensive sunburn prevention operation plan based on the weight. The synthesis method is as follows:

[0182] First, suppose

[0183] (1) i represents the serial number of the main determining variable, and j represents the serial number of the operation measure (such as sunshade net, water mist spraying);

[0184] (2)V j V represents the degree of implementation of the j operation measure in the anti-sunburn operation plan, ij Indicates the sunburn prevention operation plan G corresponding to the main determining variable of sequence number i igThe execution degree of the j operation measure (if the variable corresponds to the scheme G ig If there is no operation measure or the execution degree of the operation measure is 0, V ij = 0);

[0185] (3) R ij represents the calculation weight of V ij , k ij represents the j operation measure synthesis coefficient of the primary decision variable of the sequence number i.

[0186] (4) L j represents the execution degree of the j operation measure in the comprehensive anti-sunburn operation scheme, and L represents the comprehensive anti-sunburn operation scheme.

[0187] The relationship between the symbols is,

[0188] Ⅰ, all the anti-sunburn operation schemes G ig corresponding to the primary decision variable are determined according to the execution degree V ij of the j operation measure, and the j operation measure and the execution degree L ij of the j operation measure in the comprehensive anti-sunburn operation scheme are synthesized according to the coefficient k j , so that the comprehensive anti-sunburn operation scheme is determined according to the execution degree L j . The execution degree L

[0189]

[0190] Wherein k ij represents the j operation measure synthesis coefficient of the primary decision variable of the sequence number i, which is determined by the calculation weight R ij of the execution degree V ij of the j operation measure of the primary decision variable of the sequence number i: R ij = C ij only if the j operation measure exists or the execution degree of the operation measure is not 0 (that is, V i ≠ 0), otherwise, if V ij = 0, R ij = 0. Wherein Ci represents the weight coefficient of the primary decision variable of the sequence number i (∑C i = 1), which is composed of subjective weight M i and objective weight S i , and can be represented by formula 4, formula 4:

[0191] C i = M i +S i

[0192] The j operation measure synthesis coefficient k ij can be represented by formula 5, formula 5:

[0193]

[0194] Among them, ∑ i k ij =1.

[0195] II. Operational measures and their implementation level in the comprehensive sunburn prevention operation plan j Synthetic comprehensive sunburn prevention operation plan L.

[0196] As for the hierarchical division of the influence degree of secondary determining variables, as well as the formulation and integration of work adjustment plans, you can refer to the above-mentioned execution process of the primary determining variables and will not repeat them here.

[0197] The following examples further illustrate the execution process of determining the outdated anti-sunburn operation task. These examples are based on the execution process of determining the comprehensive anti-sunburn operation plan and the comprehensive operation adjustment plan.

[0198] For ease of understanding, assume that the number of waiting levels for each sunburn-related variable i and the corresponding sunburn prevention work plan are the same:

[0199] (1) Level G of work to be done i0 Anti-sunburn operation plan: control the shading degree of the sunshade net to 0% (V i1 =0), no water mist spraying task (V i2 =0);

[0200] (2) Level G of work to be done i1 Anti-sunburn operation plan: control the shading degree of the sunshade net to 50% (V i1 =50%), no water mist spraying task (V i2 =0);

[0201] (3) Level G of work to be done i2 Anti-sunburn operation plan: control the shading degree of the sunshade net to 0% (V i1 =0), execute the water mist spraying task once (V i2 =1);

[0202] (4) Level G of work to be done i3 Anti-sunburn operation plan: control the shading degree of the sunshade net to 50% (V i1 =50%), execute the water mist spraying task once (V i2 =1);

[0203] (5) Level G of work to be done i4 Anti-sunburn operation plan: control the shading degree of the sunshade net to 50% (V i1 =50%), execute the water mist spraying task 2 times (V i2 =2).

[0204] The weight coefficient C of each major determining variable i They are:

[0205] (1) The weight coefficient of peel surface temperature (i = 1) is C1 = 0.4;

[0206] (2) The weight coefficient of light intensity on the peel surface (i = 2) is C2 = 0.3;

[0207] (3) The weight coefficient of UV intensity on the peel surface (i = 3) is C3 = 0.2;

[0208] (4) The weight coefficient of fruit moisture content (i=4) is C4=0.1.

[0209] In some embodiments (which may be referred to as embodiment 1), only one primary decision variable exceeds the threshold, and there are secondary decision variables to adjust the solution. The level of work to be done for the peel surface temperature (i=1) is G 13 , the anti-sunburn operation plan is: control the shading degree of the sunshade net (j=1) to 50% (V 11 =50%), execute the spraying water mist (j=2) task once (V 12 =1). At this time, only V 11 and V 12 Has calculation weight R 11 、R 12 :R 11 =R 12 =C1=0.4, other V ij Since V ij =0, so the corresponding calculation weight R ij = 0. Therefore, the combined coefficient of the peel surface temperature (i = 1) The composite coefficient k of other main determining variables ij = 0, the operation measures and their implementation level in the comprehensive anti-sunburn operation plan are obtained. j It can be expressed by formula 6:

[0210]

[0211]

[0212] Based on this, the comprehensive anti-sunburn operation plan L is: control the shading degree of the sunshade net (j=1) to 50% (V 11 =50%), execute the spraying water mist (j=2) task once (V 12 =1).

[0213] The secondary determining variables were obtained through a similar process to obtain the comprehensive operation adjustment plan A: based on the comprehensive anti-sunburn operation plan L, the shading degree of the sunshade net is increased by 50%, and the number of water mist spraying tasks is increased by 100%.

[0214] Therefore, in the task of anti-sunburn operation

[0215]

[0216] That is, after the final comprehensive anti-sunburn operation plan L is adjusted according to the comprehensive operation adjustment plan S, the proposed anti-sunburn operation tasks are: controlling the shading degree of the sunshade net to 75% and performing the water mist spraying task twice.

[0217] In other embodiments (which may be referred to as embodiment 2), multiple primary decision variables exceed thresholds, and secondary decision variables are used to adjust the plan.

[0218] (1) The peel surface temperature (i=1) is the waiting level G 11 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 50% (V 11 =50%), no water mist spraying task (V 12 =0). Corresponding calculation weight R 11 =C1=0.4, R 12 =0;

[0219] (2) The light intensity on the peel surface (i=2) is the level to be operated G 22 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 0% (V 21 =0), execute the water mist spraying task once (V 22 =1); corresponding calculation weight R 21 =0, R 22 =C2=0.3;

[0220] (3) The UV intensity on the peel surface (i=3) is at the waiting level G 30 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 0% (V 31 =0), no water mist spraying task (V 32 =0); corresponding calculation weight R 31 =0, R 32 =0;

[0221] (4) The waiting level for the fruit moisture content (i=4) is G 40 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 0% (V 41 =0), no water mist spraying task (V 42 =0); corresponding calculation weight R 41 =0, R42 =0;

[0222] Therefore, the composite coefficients of the above four main sunburn determining variables are:

[0223] (1) Peel surface temperature (i=1):

[0224]

[0225]

[0226] (2) Light intensity on the peel surface (i=2):

[0227]

[0228]

[0229] (3) UV intensity on the peel surface (i=3):

[0230]

[0231] (4) Fruit moisture content (i=4):

[0232]

[0233]

[0234] Thus, the operation measures and their implementation degree L in the comprehensive anti-sunburn operation plan are obtained. j for

[0235]

[0236]

[0237] Based on this, the comprehensive anti-sunburn operation plan L is: control the shading degree of the sunshade net (j=1) to 50%, and perform the water mist spraying (j=2) task once.

[0238] Comprehensive operation adjustment plan A is: based on the comprehensive anti-sunburn operation plan L, the shading degree of the sunshade net is increased by 50%, and the number of water mist spraying tasks is increased by 100%.

[0239] Therefore, in the task of anti-sunburn operation

[0240]

[0241] That is, after the final comprehensive anti-sunburn operation plan L is adjusted according to the comprehensive operation adjustment plan S, the proposed anti-sunburn operation tasks are: controlling the shading degree of the sunshade net to 75% and performing the water mist spraying task twice.

[0242] In other embodiments (which may be referred to as embodiment 3), multiple primary decision variables exceed thresholds, and no secondary decision variables adjust the plan.

[0243] (1) The peel surface temperature (i=1) is the waiting level G 13 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 50% (V 11 =50%), execute the water mist spraying task once (V 12 =1). Corresponding calculation weight R 11 =R 12 =C1=0.4;

[0244] (2) The light intensity on the peel surface (i=2) is the level to be operated G 24 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 50% (V 21 =50%), execute the water mist spraying task 2 times (V 22 =2); corresponding calculation weight R 21 =R 22 =C2=0.3;

[0245] (3) The UV intensity on the peel surface (i=3) is at the waiting level G 30 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 0% (V 31 =0), no water mist spraying task (V 32 =0); corresponding calculation weight R 31 =0, R 32 =0;

[0246] (4) The waiting level for the fruit moisture content (i=4) is G 40 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 0% (V 41 =0), no water mist spraying task (V 42 =0); corresponding calculation weight R 41 =0, R 42 =0;

[0247] Therefore, the composite coefficients of the above four main determining variables are:

[0248] (1) Peel surface temperature (i=1):

[0249]

[0250]

[0251] (2) Light intensity on the peel surface (i=2):

[0252]

[0253]

[0254] (3) UV intensity on the peel surface (i=3):

[0255]

[0256]

[0257] (4) Fruit moisture content (i=4):

[0258]

[0259]

[0260] Thus, the operation measures and their implementation degree L in the comprehensive anti-sunburn operation plan are obtained. j for

[0261]

[0262]

[0263] Based on this, the comprehensive anti-sunburn operation plan L is: control the shading degree of the sunshade net (j=1) to 50%, and perform the water mist spraying (j=2) task twice (1.43>1, take 2).

[0264] Comprehensive Operation Adjustment Plan A: No adjustment is required for the comprehensive anti-sunburn operation plan. The final anti-sunburn operation tasks to be performed are: control the shade net shading degree to 50% and perform the water mist spraying task twice.

[0265] Combining the above analysis of Examples 1 to 3, it can be seen that

[0266] Composite coefficient k ij Is a dynamic coefficient, which has the following effects:

[0267] (1) When a certain operation measure only exists in the anti-sunburn scheme corresponding to one main determining variable, its composite coefficient will be k ij =1, which can ensure that in the final synthetic anti-sunburn operation plan, the operation measure has the degree of execution required by the main determining variable.

[0268] As in the above embodiment 2, "control the shade net (j=1) shading degree 50%" only the peel surface temperature (i=1) anti-sunburn solution G 11 Need to be executed, its synthesis coefficient is k 11= 1. The other main determining variables (i = 2, 3, 4) are all "controlling the shading degree of the sunshade net (j = 1) to 0%", and the corresponding composite coefficient k 21 、k 31 、k 41 All of them are 0, so the final synthetic anti-sunburn operation plan includes "controlling the shading degree of the shading net (j=1) to 50%", which ensures the surface temperature of the peel (i=1) plan G 11 The required implementation level of the measure “Control of sunshade net (j=1)” is “50%”;

[0269] If the weight R is calculated 11 After calculation, the final synthetic comprehensive anti-sunburn operation plan will have a "control of the shading degree of the shading net (j=1) 20% (50%×0.4=20%)", which cannot meet the execution level requirements of the "control of the shading net (j=1)" measure for the fruit peel surface temperature (i=1).

[0270] (2) When a certain operation measure exists in at least two or more sunburn prevention schemes corresponding to the main determining variables, the composite coefficient k ij It can ensure that in the final synthetic comprehensive anti-sunburn operation plan, the operation measures have at least the execution degree of the operation measures required by the main determining variables. At the same time, it can ensure that the same measures in the anti-sunburn operation plan are not simply linearly superimposed, thereby avoiding the problem of excessive execution degree of a certain measure in the final synthetic comprehensive anti-sunburn operation plan.

[0271] As in the above embodiment 3, for the "spraying water mist" operation measure (j=2), the anti-sunburn solution G of the peel surface temperature (i1) 13 It needs to be executed once, and the synthesis coefficient is Sunburn prevention scheme G for light intensity on the peel surface (i=2) 24 It needs to be executed twice, and the synthesis coefficient is The anti-sunburn scheme for other main determining variables (i=3,4) does not need to be implemented, and the corresponding synthesis coefficient k 32 、k 42 Both are 0, so the final synthesized comprehensive anti-sunburn operation plan includes "executing the water mist spraying (j=2) task 1.43 times (actually 2 times)", which can meet the "executing water mist spraying" measure times required by the peel surface temperature (i=1) 1 time, and also meet the "executing water mist spraying" measure times required by the peel surface light intensity (i=2) 2 times to a certain extent;

[0272] If the weight R is calculated 12 、R 22Calculation is performed, and the final synthetic anti-sunburn operation plan will include "performing spraying water mist (j=2) task 1 time (1×0.4+2×0.3=1)", which just meets the requirement of peel surface temperature (i=1), but has a large gap with the requirement of peel surface light intensity (i=2); and if the weight R is not used, 12 、R 22 If we calculate and directly perform linear superposition, the final synthetic anti-sunburn operation plan will include "executing the water mist spraying (j=2) task 3 times (1+2=3)". For the peel surface temperature (i=1), the number of execution measures is too high, and the requirement for the peel surface light intensity (i=2) is also large. ij The existence of can ensure that the final synthetic comprehensive anti-sunburn operation plan is more moderate.

[0273] As for the synthesis coefficient k ij The "dynamic" characteristics need to be compared with the calculation weight R ij In terms of ij The value can only be 0 or a fixed non-zero constant; when a certain operation measure has only one main determining variable corresponding to the anti-sunburn plan that needs to be executed, the corresponding synthesis coefficient will be k ij = 1. When there are at least two or more main decision variables corresponding to the anti-sunburn plan, different synthesis coefficients k ij The corresponding calculation weight R ij Adjust the distribution of "1" to obtain the following example: The value of

[0274] The above implementation process and examples are for reference only. In actual application, the prevention system can determine and adjust the "and" and "or" relationships between primary and secondary determining variables, relevant thresholds, selection of sunburn prevention measures, division of target levels, and formulation and synthesis of operation plans based on experience, historical records, and experiments to ensure that the disclosed method can meet the daily sunburn prevention needs of orchards. Regarding adjustments, this disclosure provides an adaptive adjustment method below to achieve this.

[0275] After determining the proposed sunburn prevention task in the above example, the prevention system can dispatch the relevant facilities or equipment to execute it. After the sunburn prevention task has been executed for a period of time and the local environment has stabilized, relevant variable data can be obtained and used to evaluate the effectiveness of the task (for example, whether the surface temperature of the fruit peel has dropped below a threshold after the task).

[0276] The prevention system can also adjust the above examples according to the operation effect and through the operation level adjustment algorithm or the scheme adjustment algorithm. Specifically, taking the fruit skin surface temperature as an example, it is assumed that the simulated value of the fruit skin surface temperature does not decrease or decreases but does not decrease below the threshold value after the operation, that is, the operation effect does not meet the standard; or the simulated value decreases below the threshold value, but decreases too much, that is, the operation effect exceeds the standard. For the two cases, the operation effect can be changed by adjusting the fruit skin surface temperature to be operated level division, scheme development or scheme synthesis method, so that the fruit skin surface temperature after the operation decreases to the normal range.

[0277] I. Adjusting the to-be-operated level division. For a fruit skin surface temperature value exceeding the threshold value, if the anti-sunburn operation scheme corresponding to the value cannot make the operation effect of the final fruit skin surface temperature meet the standard before adjustment, the to-be-operated level corresponding to the fruit skin surface temperature value should be appropriately increased to obtain an anti-sunburn operation scheme with more operation measures or higher operation measure execution degree, so as to increase the cooling effect on the fruit skin surface temperature; on the contrary, if the anti-sunburn operation scheme corresponding to the fruit skin surface temperature value makes the operation effect of the final fruit skin surface temperature exceed the standard before adjustment, the adjustment direction is opposite to the above. th th Preferably, the compression degree adjustment factor a of the compression characteristic function y=T Figure 7 + k·ln[α(t-T

[0278] I. Adjusting the to-be-operated level division. For a fruit skin surface temperature value exceeding the threshold value, if the anti-sunburn operation scheme corresponding to the value cannot make the operation effect of the final fruit skin surface temperature meet the standard before adjustment, the to-be-operated level corresponding to the fruit skin surface temperature value should be appropriately increased to obtain an anti-sunburn operation scheme with more operation measures or higher operation measure execution degree, so as to increase the cooling effect on the fruit skin surface temperature; on the contrary, if the anti-sunburn operation scheme corresponding to the fruit skin surface temperature value makes the operation effect of the final fruit skin surface temperature exceed the standard before adjustment, the adjustment direction is opposite to the above.

[0279] I. Adjusting the to-be-operated level division. For a fruit skin surface temperature value exceeding the threshold value, if the anti-sunburn operation scheme corresponding to the value cannot make the operation effect of the final fruit skin surface temperature meet the standard before adjustment, the to-be-operated level corresponding to the fruit skin surface temperature value should be appropriately increased to obtain an anti-sunburn operation scheme with more operation measures or higher operation measure execution degree, so as to increase the cooling effect on the fruit skin surface temperature; on the contrary, if the anti-sunburn operation scheme corresponding to the fruit skin surface temperature value makes the operation effect of the final fruit skin surface temperature exceed the standard before adjustment, the adjustment direction is opposite to the above.

[0280] ​IV. Adjustment Scheme Synthesis Method. For a fruit peel surface temperature value exceeding the threshold, if, before adjustment, the anti-sunburn operation plan corresponding to this value cannot achieve the final fruit peel surface temperature operation effect that meets the standard, and the pending operation level division and anti-sunburn operation plan remain unchanged, the objective weight coefficient S1 of the fruit peel surface temperature can be appropriately increased to make the operation measures and execution degree of the anti-sunburn operation plan corresponding to the fruit peel surface temperature more important, so that the final synthesized comprehensive anti-sunburn operation plan focuses more on the cooling function of the fruit peel surface temperature; conversely, if, before adjustment, the anti-sunburn operation plan corresponding to this fruit peel surface temperature value causes the final fruit peel surface temperature operation effect to exceed the standard, then the adjustment direction is opposite to the above.

[0281] In one embodiment, the number of levels to be operated and the corresponding sunburn prevention operations for each main determining variable i are:

[0282] (1) Level G of work to be done i0 Anti-sunburn operation plan: control the shading degree of the sunshade net to 0% (V i1 =0), no water mist spraying task (V i2 =0);

[0283] (2) Level G of work to be done i1 Anti-sunburn operation plan: control the shading degree of the sunshade net to 50% (V i1 =50%), no water mist spraying task (V i2 =0);

[0284] (3) Level G of work to be done i2 Anti-sunburn operation plan: control the shading degree of the sunshade net to 50% (V i1 =50%), execute the water mist spraying task once (V i2 =1);

[0285] (4) Level G of work to be done i3 Anti-sunburn operation plan: control the shading degree of the sunshade net to 75% (V i1 =75%), execute the water mist spraying task once (V i2 =1);

[0286] (5) Level G of work to be done i4 Anti-sunburn operation plan: control the shading degree of the sunshade net to 75% (V i1 =75%), execute the water mist spraying task 2 times (V i2 =2).

[0287] The weight coefficient C of each major determining variable i They are:

[0288] (1) The weight coefficient of peel surface temperature (i = 1) is C1 = 0.4;

[0289] (2) The weight coefficient of light intensity on the peel surface (i = 2) is C2 = 0.3;

[0290] (3) The weight coefficient of UV intensity on the peel surface (i = 3) is C3 = 0.2;

[0291] (4) The weight coefficient of fruit moisture content (i=4) is C4=0.1.

[0292] Before adjustment,

[0293] (1) The peel surface temperature (i=1) is the waiting level G 13 , the anti-sunburn operation plan is: control the shading degree of the sunshade net (j=1) to 75% (V 11 =75%), execute the water mist spraying (j=2) task once (V 12 =1). Corresponding calculation weight R 11 =R 12 =C1=0.4;

[0294] (2) The light intensity on the peel surface (i=2) is the level to be operated G 21 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 50% (V 21 =50%), execute the water mist spraying task 0 times (V 22 =0); corresponding calculation weight R 21 =C2=0.3, R 22 =0;

[0295] (3) The UV intensity on the peel surface (i=3) is at the waiting level G 30 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 0% (V 31 =0), no water mist spraying task (V 32 =0); corresponding calculation weight R 31 =0, R 32 =0;

[0296] (4) The waiting level for the fruit moisture content (i=4) is G 40 , the anti-sunburn operation plan is: control the shading degree of the sunshade net to 0% (V 41 =0), no water mist spraying task (V 42 =0); corresponding calculation weight R 41 =0, R 42 =0;

[0297] Therefore, the composite coefficients of the above four main determining variables are:

[0298] (1) Peel surface temperature (i=1):

[0299]

[0300]

[0301] (2) Light intensity on the peel surface (i=2):

[0302]

[0303]

[0304] (3) UV intensity on the peel surface (i=3):

[0305]

[0306]

[0307] (4) Fruit moisture content (i=4):

[0308]

[0309]

[0310] Thus, the operation measures and their implementation degree L in the comprehensive anti-sunburn operation plan are obtained. j for

[0311]

[0312] Based on this, the synthetic comprehensive sunburn prevention plan L is: control the shade net (j=1) shading degree to 64% and perform the water mist spraying task (j=2) once. The comprehensive operation adjustment plan A is: no adjustment is required to the comprehensive sunburn prevention plan. The final proposed sunburn prevention task is: control the shade net shading degree to 64% and perform the water mist spraying task once.

[0313] Since the surface temperature of the peel after the operation did not reach the operation effect, the objective weight S1 of the peel surface temperature was adjusted upward to obtain the weight coefficient C1 = 0.5, which corresponds to the weight coefficient 0.1 extracted from the light intensity on the peel surface. The adjusted weight coefficient is:

[0314] (1) The weight coefficient of peel surface temperature (i = 1) is C1 = 0.5;

[0315] (2) The weight coefficient of light intensity on the peel surface (i = 2) is C2 = 0.2;

[0316] (3) The weight coefficient of UV intensity on the peel surface (i = 3) is C3 = 0.2;

[0317] (4) The weight coefficient of fruit moisture content (i=4) is C4=0.1.

[0318] Accordingly,

[0319] (1)R 11 =R 12 =C1=0.5;

[0320] (2)R 21 =C2=0.2, R 22 =0;

[0321] (3)R 31 =0, R 32 =0;

[0322] (4)R 41 =0, R 42 =0;

[0323] At this time, the composite coefficients of the above four main determining variables are:

[0324] (1) Peel surface temperature (i=1):

[0325]

[0326]

[0327] (2) Light intensity on the peel surface (i=2):

[0328]

[0329]

[0330] (3) UV intensity on the peel surface (i=3):

[0331]

[0332]

[0333] (4) Fruit moisture content (i=4):

[0334]

[0335]

[0336] Thus, the operation measures and their implementation degree L in the comprehensive anti-sunburn operation plan are obtained. j for

[0337]

[0338]

[0339] Based on this, the synthetic comprehensive sunburn prevention plan L is: control the shade net (j=1) to 68% shading and perform the water mist spraying task (j=2) once. Comprehensive operation adjustment plan A is: no adjustment is required for the comprehensive sunburn prevention plan. The final proposed sunburn prevention task is: control the shade net to 68% shading and perform the water mist spraying task once. Compared to the pre-adjustment plan, the adjusted comprehensive sunburn prevention plan achieves a higher shading level, which can improve the cooling effect on the fruit peel surface temperature.

[0340] The aforementioned adjustments to the scheme synthesis method are based on objective weighting adjustments. In practice, appropriate subjective weighting adjustments based on relevant planting experience can accelerate this adjustment process. Of course, it is foreseeable that the factors required to be considered in the above adjustments to the scheme synthesis method are relatively complex, so this adjustment method can be used as a secondary alternative.

[0341] The key to implementing the aforementioned adjustment algorithms I, II, III, and IV, or task-level or scenario-level adjustment algorithms, lies in defining the evaluation criteria for underachieving or exceeding task performance, as well as determining the degree of adjustment (e.g., the adjustment step size for α each time a task performance is unsatisfactory in the task-level adjustment). These adjustment algorithms can be used individually or in combination, depending on the specific situation.

[0342] Among them, the adaptive characteristics of the adjustment algorithm are mainly reflected in the reasonable evaluation definition and the degree of adjustment. When the operation effect does not meet the standard, the system further enhances the operation effect by increasing relevant operation measures or improving the execution degree of relevant operation measures; when the operation effect exceeds the standard, the system weakens the operation effect by reducing relevant operation measures or lowering the execution degree of relevant operation measures, and finally forms a closed-loop adjustment, so that the system gradually converges the operation effect to a reasonable range.

[0343] While the above adjustment method only covers the primary variables, the same principles apply to the secondary variables and will not be elaborated on here. This completes the description of the adjustment algorithm.

[0344] S4: Based on the future changing trends of relevant variable parameters, carry out the fruit sunburn prevention task in advance.

[0345] For example, historical data on relevant environmental parameters can be obtained through the orchard environmental data collection and management center and used for prediction model training (preferably, a long short-term memory recursive neural network (LSTM) multivariate prediction model can be used), or future weather forecast information can be obtained through the orchard's local weather system. In short, appropriate methods are used to obtain future prediction data on relevant environmental parameters. Based on this, the mathematical model between the main sunburn determining variables and the relevant environmental parameters established in S2 (the mathematical modeling process between the secondary sunburn influencing variables and the relevant environmental parameters can be used in the same way) is used to further infer the future change trends of the main sunburn determining variables and the secondary sunburn influencing variables.

[0346] According to a certain time limit, the future time is divided into the near future time and the far future time. For example, the next 2 hours is the near future time, and the future after 2 hours is the far future time. According to the future change trend of the variables, it is judged whether there will be a time in the future when anti-sunburn operations need to be performed (hereinafter referred to as "future operation points"). If the future operation point exists in the near future time, the system will respond at the current time, that is, according to the relevant variable parameter data of the future operation point, the operation task will be performed in advance according to the relevant anti-sunburn operation plan; if the future operation point exists in the far future time, considering the large changes in the environment, weather and climate conditions, operations that are performed too early cannot guarantee the final operation effect. Therefore, in this case, the system will not respond to the operation, but will issue relevant warning information (such as buzzer alarms, prompt information generated by the system host computer interface, etc.) to remind people to be vigilant.

[0347] In addition, the above data collection frequency f m The adaptive adjustment method can be used to know the severity of future data changes based on the future change trend of the above-mentioned related variable parameters, so as to adjust the data collection frequency f in advance. m Adjustment.

[0348] The specific process of the sunburn prevention method in the above example is as follows: Figure 8 As shown in the figure, sunburn prevention methods can include three parts: data collection, prevention decision-making, and task execution. For details, please refer to the above example description and will not be repeated here. While the above examples are mainly based on a single orchard area, the same principles apply to other orchard areas within the orchard.

[0349] The present disclosure also provides a system carrier for the above method, such as a sunburn disease prevention system (hereinafter referred to as the prevention system), Figure 9 As shown, the prevention system includes:

[0350] (1) Environmental data collection and management center. This center consists of collection facilities or equipment management modules and data management modules.

[0351] The collection facility or equipment management module is divided into an information acquisition module and a control submodule. The orchard local sensors are mounted on fixed or mobile equipment or facilities. The information acquisition module mainly obtains the orchard weather data and sunburn determining variables (i.e., primary determining variables and secondary determining variables) related environmental parameter data through the local weather system and the orchard local sensors, and obtains the working status, positioning and other information of the fixed or mobile facilities or equipment; the control submodule is used to set the working parameters of the fixed or mobile facilities or equipment, and to control and schedule them for collection operations. In particular, this module is responsible for automatically adjusting the data collection frequency f of the relevant facilities or equipment in real time according to the severity of the changes in the local area environmental data. m Make feedback adjustments;

[0352] The data management module is mainly responsible for collecting and summarizing the variable-related environmental parameter data sent back by local sensors in the orchard, and classifying, partitioning, managing and storing them.

[0353] (2) Prevention Decision Center: This center consists of a data analysis and modeling module, a sunburn monitoring and task generation module, and a prediction module.

[0354] The data analysis and modeling module is responsible for performing correlation analysis on the collected data and establishing a mathematical model between sunburn determining variables and environmental parameters;

[0355] The sunburn monitoring and job task generation module is divided into a calculation and monitoring submodule, a job task generation submodule, and a job task adjustment submodule. The calculation and monitoring submodule obtains the real-time relevant environmental parameter data by accessing the environmental data acquisition management center, and uses the mathematical model obtained by the data analysis and modeling module to calculate the simulated value of the sunburn determining variable in real time and monitor it; the job task generation submodule is responsible for assigning weights to the sunburn determining variables, pending the division of job levels, and formulating and integrating anti-sunburn job plans and job adjustment plans. Based on the monitoring of sunburn variables by the calculation and monitoring submodule, decisions are made to generate corresponding anti-sunburn job tasks; the job task adjustment submodule is responsible for evaluating the system operation effect, and according to the evaluation results, assigning weights in the job task generation submodule, pending the division of job levels, and formulating and integrating anti-sunburn job plans and job adjustment plans, and automatically performing feedback adjustments;

[0356] The prediction module obtains future relevant environmental parameter data through the local environmental parameter data prediction model of the orchard or the weather forecast information of the local weather system of the orchard, and judges whether there will be a work point that needs to be protected against sunburn in the future, and judges the relationship between the time of the work point and the near and long future time, thereby forming different operation responses.

[0357] (3) Operation Control Center: This center consists of information acquisition module and control module.

[0358] The information acquisition module is used to obtain information such as the status of operating facilities or equipment, work progress, etc.

[0359] The control module is used to set the working parameters of relevant operating facilities or equipment in the orchard (for example, sunshade nets, fixed sprinklers, drones, unmanned vehicles, alarm buzzers, etc.), and to control the operation of these facilities or equipment, and to dispatch and arrange the operating tasks of the operating facilities or equipment in different areas in the orchard.

[0360] In addition, the relevant centers and modules in the above-mentioned fruit sunscald disease prevention system are also equipped with corresponding manual interaction functions, so that system operators can add, delete, adjust and modify relevant functions.

[0361] It is worth noting that when deploying the relevant methods or systems of the present disclosure, it is necessary to make corresponding adjustments according to the actual conditions of the orchard or other plantations. The above is only a possible embodiment of the present disclosure and is not intended to limit the present disclosure. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present invention.

[0362] That is, those skilled in the art may make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include such modifications and variations.

Claims

1. A method for preventing fruit sunburn, characterized in that: The method comprises: Determining relevant environmental parameters that affect sunburn determining variables, deploying sensors in the orchard based on the relevant environmental parameters, and determining a measurement scheme for the sensors, wherein the sunburn determining variables include primary determining variables and secondary determining variables, and the primary determining variables include fruit skin surface temperature, fruit skin surface light intensity, fruit skin surface ultraviolet intensity, and fruit moisture content; Establishing a mathematical model between the sunburn determining variable and the relevant environmental parameter data based on the relevant environmental parameter data of the local area of ​​the orchard, wherein the mathematical model is used to determine a simulated value of the sunburn determining variable corresponding to the relevant environmental parameter data; determining a fruit sunburn prevention task according to the simulation value, and performing feedback adjustment on the fruit sunburn prevention task according to an operation effect corresponding to the fruit sunburn prevention task; According to the future change trend of the relevant environmental parameters, the fruit sunburn prevention task is executed in advance, and the fruit sunburn prevention task includes spraying water mist and setting up sunshade nets, wherein the spraying water mist adopts a combination of dynamic and static operation modes, and the spraying water mist is performed by drones or unmanned vehicles in the orchard, or by installing fixed sprinklers in the orchard; The number of the primary determining variables is multiple, the number of the secondary determining variables is multiple; determining the fruit anti-sunburn operation task according to the simulation value, and performing feedback adjustment on the fruit anti-sunburn operation task according to the operation effect corresponding to the fruit anti-sunburn operation task, including: S3-1: Divide each major determining variable into different levels of work to be done based on its simulated value, and formulate a corresponding sunburn prevention work plan for each level of work to be done ; S3-2: Sunburn prevention operation plan Synthesize a comprehensive sunburn prevention solution ; S3-3: For each secondary decision variable, divide it into different impact levels according to its simulated value, and formulate corresponding operation adjustment plans for each impact level ; S3-4: Adjust the plan for each operation Synthesize to get comprehensive operation adjustment plan ; S3-5: Adjust the plan based on comprehensive operations Comprehensive sunburn prevention program L Adjusting to obtain a sunburn prevention task to be performed, wherein the fruit sunburn prevention task includes the sunburn prevention task to be performed; S3-6: Control the relevant operating facilities or equipment to perform the anti-sunburn operation tasks, and obtain and evaluate the operation results; S3-7: Based on the operation results, the hierarchical division, scheme formulation, and scheme synthesis methods of steps S3-1 to S3-4 are adjusted through a hierarchical adjustment algorithm or a scheme adjustment algorithm; Among them, the method of dividing different levels of pending operations in S3-1 is the compression characteristic function , and the compression characteristic function is express, , is the analog value, is the preset threshold, is the proportionality coefficient, is the compression adjustment factor, , The smaller it is, the smaller the compression is, and the more the division result tends to be uniform; vice versa; Among them, in S3-2, the main determining variables Corresponding operational measures Sunburn prevention program , The degree of implementation of operational measures is , The composite coefficient of the operation measure is the coefficient In the case of Synthetic comprehensive sunburn prevention program Operational measures and their implementation level ,and ,coefficient By degree of implementation Calculation weight of Determine: if and only if Existence or implementation of operational measures When it is not 0, Otherwise, if ,but , The main determining variable The weight coefficient is determined by the subjective weight and objective weight composition, ,coefficient pass express, ; Among them, in S3-7, the contents of S3-1 to S3-4 are adjusted according to the operation results and through the operation level adjustment algorithm or the scheme adjustment algorithm. The operation level adjustment algorithm or the scheme adjustment algorithm includes adjusting the level division of the operation to be performed, adjusting the threshold, formulating the adjustment scheme, and synthesizing the adjustment scheme.

2. The method according to claim 1, characterized in that The determination of relevant environmental parameters affecting sunburn determination variables includes: Dividing the orchard into regions to obtain local regions of the orchard, wherein the fluctuation of the internal environmental parameters of each local region of the orchard approaches 0; Determining a weather system standard local area corresponding to orchard weather data, wherein the orchard weather data is weather data corresponding to the orchard by the weather system, and the weather system standard local area is an area in each orchard local area having an environmental parameter that is most highly correlated with the orchard weather data; determining the relevant environmental parameters according to the standard local area of ​​the weather system; and, deploying sensors in the orchard according to the relevant environmental parameters, including: deploying the sensors for measuring the relevant environmental parameters in the orchard; And, also includes: The data collected by the sensor is marked and sent back to the orchard environmental data collection and management center for aggregation and classification.

3. The method according to claim 1, characterized in that The measurement scheme includes a mobile measurement scheme, and the mobile measurement scheme includes a data collection frequency; The method further includes: adjusting the data collection frequency; Wherein, adjusting the data collection frequency includes: A scoring system is configured to evaluate the severity of data changes in the data collected based on the data collection frequency; A score range corresponding to the severity of data changes, wherein the score range is used to represent the variation interval of the severity of data changes; Obtain the severity of the current data change and determine the score of the severity of the current data change based on the scoring system, wherein the scoring system is a function with the data difference as the independent variable , is the degree of change of the current data, for the score of Based on the score range and the score, determine whether the current data change intensity is reasonable, and if it is unreasonable, adjust the data collection frequency until the data change intensity score corresponding to the adjusted data collection frequency falls within the score range.

4. The method according to claim 2, characterized in that The determining of the relevant environmental parameters according to the standard local area of ​​the weather system includes: Collecting and obtaining data of sunburn determining variables in the standard local area of ​​the weather system, and obtaining orchard weather data corresponding to the collection time of the obtained data of the sunburn determining variables in the local weather system; Perform correlation analysis on the sunburn determining variables and all orchard weather data to obtain the correlation coefficients of all sunburn determining variables and the orchard weather parameters. and test value ; According to the correlation coefficient and the test value , determine the relevant environmental parameters.

5. The method according to claim 1, wherein The measurement scheme includes a mobile measurement method and a fixed measurement method; The mobile measurement refers to deploying the sensor on a movable device to measure the environmental parameters in the orchard when the movable device performs a patrol mission in the orchard. The movable device includes a drone and / or an unmanned vehicle. The fixed measurement refers to measuring the environmental parameters within a preset range of the orchard by installing a fixed measurement station in the orchard and installing fixed distributed sensors in a local area of ​​the orchard; The measurement scheme includes: a combined measurement scheme in which the mobile measurement method is primary and the fixed measurement method is auxiliary.

6. The method according to claim 1, characterized in that The method of establishing a mathematical model between the sunburn determining variable and the relevant environmental parameter data based on the relevant environmental parameter data of the local area of ​​the orchard comprises: Collecting data of sunburn determining variables in a local area of ​​the orchard, and giving a regional label and a collection time label to the local area of ​​the orchard; The mathematical model is obtained by performing a correlation analysis on the main determining variables of sunburn and related environmental parameters.

7. The method according to claim 1, characterized in that When the simulated value of the main determining variable exceeds the preset main determining variable threshold, sunburn occurs in the fruit. The main determining variable threshold is a numerical value or numerical range obtained based on the local climatic conditions, fruit variety, plant protection experience, and experiments of the fruit, and the simulated value of the main determining variable is a value that avoids sunburn in the fruit or reduces the incidence and degree of sunburn in the fruit.

8. A fruit sunburn prevention system, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

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