CN111445062B

CN111445062B - Pest location prediction method, device, computer equipment and storage medium

Info

Publication number: CN111445062B
Application number: CN202010184911.XA
Authority: CN
Inventors: 尹海波; 马啸
Original assignee: Shenzhen Shuliantianxia Intelligent Technology Co Ltd
Current assignee: Shenzhen Shuliantianxia Intelligent Technology Co Ltd
Priority date: 2020-03-17
Filing date: 2020-03-17
Publication date: 2023-08-29
Anticipated expiration: 2040-03-17
Also published as: CN111445062A

Abstract

The embodiment of the invention discloses a pest position prediction method, a device, computer equipment and a storage medium, comprising the following steps: acquiring a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position; obtaining a pest reference position according to the plurality of pest positions; obtaining a category center position corresponding to a target azimuth category under each first insect pest position according to each insect pest position, a first insect pest time corresponding to each insect pest position and the insect pest reference position; and obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time. By the mode, the insect pest position can be effectively predicted.

Description

Pest location prediction method, device, computer equipment and storage medium

Technical Field

The present invention relates to the field of pest position prediction technologies, and in particular, to a pest position prediction method, a device, a computer apparatus, and a storage medium.

Background

Crop pest and disease damage monitoring is the basis for controlling pest and disease damage and ensuring high quality and high yield of crops. With the continuous development of computer technology in various industries, the computer technology is also widely applied to the field of plant protection, and a large number of databases and management systems related to plant diseases and insect pests have been developed at home and abroad.

At present, most of the systems aim at pest monitoring, and the types and the severity of the pests are identified through technologies such as computer vision and the like. For example, the pest position is obtained through a built-in GPS of the mobile phone, and the type and severity of the pest are confirmed through a pest image shot by a camera of the mobile phone.

Although these systems are capable of monitoring pest data, it is important to predict pest locations to effectively kill pests in bassinets for agricultural production.

Disclosure of Invention

Based on this, it is necessary to provide a pest position prediction method, apparatus, computer device, and storage medium that effectively predict a pest position, in view of the above-described problems.

In a first aspect, there is provided a method of pest location prediction, the method comprising: acquiring a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position; obtaining a pest reference position according to the plurality of pest positions; obtaining a category center position corresponding to a target azimuth category under each first pest position according to each pest position, a first pest time corresponding to each pest position and the pest reference position, wherein the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, the target azimuth range is any azimuth range of a plurality of preset azimuth ranges, and the azimuth ranges are overlapped to cover all azimuths; obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time.

In a second aspect, there is provided a pest position predicting device comprising: the position acquisition module is used for acquiring a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position; the reference determining module is used for obtaining insect pest reference positions according to the insect pest positions; the category determining module is used for obtaining a category center position corresponding to a target azimuth category under each first pest position according to each pest position, a first pest time corresponding to each pest position and the pest reference position, wherein the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, the target azimuth range is any azimuth range in a plurality of preset azimuth ranges, and the azimuth ranges are overlapped to cover all azimuths; the result prediction module is used for obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time.

In a third aspect, there is provided a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of: acquiring a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position; obtaining a pest reference position according to the plurality of pest positions; obtaining a category center position corresponding to a target azimuth category under each first pest position according to each pest position, a first pest time corresponding to each pest position and the pest reference position, wherein the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, the target azimuth range is any azimuth range of a plurality of preset azimuth ranges, and the azimuth ranges are overlapped to cover all azimuths; obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time.

In a fourth aspect, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of: acquiring a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position; obtaining a pest reference position according to the plurality of pest positions; obtaining a category center position corresponding to a target azimuth category under each first pest position according to each pest position, a first pest time corresponding to each pest position and the pest reference position, wherein the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, the target azimuth range is any azimuth range of a plurality of preset azimuth ranges, and the azimuth ranges are overlapped to cover all azimuths; obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time.

The implementation of the embodiment of the invention has the following beneficial effects:

the invention provides a method, a device, computer equipment and a storage medium for predicting pest positions, wherein pest reference positions are obtained according to a plurality of pest positions in a history pest set, first pest time corresponding to each pest position and the pest reference positions are obtained according to the plurality of pest positions in the history pest set, class center positions corresponding to target azimuth classes under each first pest time are obtained, and finally, the prediction of the pest positions is realized according to the pest reference positions and the class center positions corresponding to the target azimuth classes under each first pest time. In addition, the predicted result of the pest position is a predicted result in the target azimuth angle range, that is, the predicted result in the different azimuth angle ranges can be obtained in the above manner, and a finer predicted result can be obtained in the manner than in the manner that only one predicted result in the second pest time can be obtained.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Wherein:

FIG. 1 is a schematic flow chart of an implementation of a method for predicting pest locations in one embodiment;

FIG. 2 is a schematic diagram of azimuth categories and azimuth ranges in one embodiment;

FIG. 3 is a schematic diagram of a process implemented in step 106 in one embodiment;

FIG. 4 is a schematic illustration of pest reference locations and category center locations in one embodiment;

FIG. 5 is a schematic flow chart of an implementation of a method for predicting pest locations in one embodiment;

FIG. 6 is a flow diagram of the implementation of step 108 in one embodiment;

FIG. 7 is a schematic representation of the impact of stroke vectors in one embodiment;

FIG. 8 is a block diagram of a pest position predicting device according to one embodiment;

FIG. 9 is a block diagram of a computer device in one embodiment.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In one embodiment, a method for predicting a pest position is provided, and an execution subject of the method for predicting a pest position according to the embodiment of the present invention is a device capable of implementing the method for predicting a pest position according to the embodiment of the present invention, where the device may include, but is not limited to, a terminal and a server. The terminal comprises a desktop terminal and a mobile terminal, wherein the desktop terminal comprises, but is not limited to, a desktop computer and a vehicle-mounted computer; mobile terminals include, but are not limited to, cell phones, tablets, notebook computers, and smart watches. The server includes a high-performance computer and a high-performance computer cluster.

As shown in fig. 1, the pest position prediction method according to the embodiment of the present invention specifically includes:

step 102, obtaining a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position.

The insect pest position is the position where the insect pest is located and can be expressed in terms of longitude and latitude; to simplify the representation and facilitate subsequent calculations, the pest location may also be represented in pi form, e.g., by equations (1) and (2), where a _i Longitude or latitude corresponding to the ith pest location in the historical pest set.

。

The first pest time is obtained according to a preset rule based on a third pest time, wherein the third pest time is the time for detecting the pest position. It should be noted that one first pest time corresponds to at least one pest location.

For example, when the time range corresponding to the third pest time is relatively large, for example, when the time range corresponding to the third pest time is one week, the preset rule may be: segmenting the third pest time to obtain a plurality of first pest times, for example, segmenting one third pest time to obtain 7 first pest times; for example, when the time range corresponding to the third pest time is smaller or when the pest position corresponding to each third pest time is smaller, for example, the time range corresponding to the third pest time is one day, the preset rule may be: combining the plurality of third pest times to obtain a first pest time. The purpose of combining or segmenting the third pest time is primarily to make the number of pest locations in the first pest time ultimately determined reasonable. The specific preset rules are not specifically limited herein. Table 1 shows the pest locations for the third pest time, and it can be seen that one third pest time may also correspond to multiple pest locations, i.e., multiple pest locations are detected on the day.

TABLE 1

Two examples of pest locations are provided: 1. acquiring pest positions through a terminal with a GPS; 2. the method comprises the steps of setting pest monitoring points at a plurality of different positions in advance, recording longitude and latitude corresponding to the pest monitoring points in advance, and taking the longitude and latitude corresponding to the pest monitoring points as pest positions when the pest is monitored at a certain pest monitoring point.

And 104, obtaining a pest reference position according to the plurality of pest positions.

The pest reference position refers to a reference position determined for obtaining a target pest position prediction result, that is, the pest position is predicted according to the relative position relationship between the pest reference position and the class center position corresponding to the target azimuth class at each first pest time, and the finally obtained target pest position prediction result takes the pest reference position as a reference.

An example of obtaining pest reference locations is provided, comprising: selecting a portion of the pest locations from the plurality of pest locations; calculating according to the selected part of insect pest positions to obtain the center positions corresponding to the part of insect pest positions; and taking the central position corresponding to the part of insect pest positions as an insect pest reference position.

For example, the selected partial pest locations are: {150 °,60 ° }, {153 °,62 ° }, {161 °,70 ° }, the center position (pest reference position) corresponding to the part of pest positions is: { (150++153°) +161°)/3, (60 +62 + 70)/3 }.

Illustratively, a method of selecting a portion of a pest location from the plurality of pest locations is provided: arranging a plurality of first insect pest time according to time sequence to obtain first insect pest time with earliest time; and selecting the pest positions corresponding to the first pest time with the earliest time as the partial pest positions.

For example, the three first pest times in the historical pest set are 2000.10.01, 2000.10.02 and 2000.10.03, so that the earliest first pest time is 2000.10.01, and the respective pest positions corresponding to 2000.10.01 are selected as part of the pest positions.

Step 106, obtaining a category center position corresponding to a target azimuth category under each first pest position according to each pest position, a first pest time corresponding to each pest position and the pest reference position, wherein the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, the target azimuth range is any azimuth range of a plurality of preset azimuth ranges, and the azimuth ranges are overlapped to cover all azimuths.

The azimuth category refers to a category of azimuth preset for distinguishing insect pests in different azimuth so that the subsequent prediction of the insect pest position is finer. The target azimuth category is the azimuth category corresponding to the target azimuth range.

As shown in fig. 2, all orientations refer to orientations comprising 0 ° to 360 °, and a total of 8 azimuth ranges are included in fig. 2: [0 °,45 °), [45 °,90 °), [90 °,135 °), [135 °,180 °), [180 °,225 °), [225 °,270 °), [270 °,315 ° ] and [315 °,360 ° ], the corresponding azimuth categories are category 1, category 2, …, category 8, and if the target azimuth range is [270 °,315 °), the target azimuth category is category 7, and the center of the pest position within the target azimuth range is [270 °,315 °) is the solid small triangle in fig. 2, that is, the solid small triangle is the category center position of category 7.

In one embodiment, as shown in fig. 3, in step 106, obtaining the category center position corresponding to the target azimuth category at each of the first pest positions according to each of the pest positions, the first pest time corresponding to each of the pest positions, and the pest reference position includes:

Step 106A, determining azimuth angles of the insect pest positions relative to the insect pest reference positions according to the insect pest positions and the insect pest reference positions.

The azimuth angle is used for reflecting the relative position relation between the insect pest position and the insect pest reference position, and the relative position relation is specifically an angle position relation. Since longitude and latitude are relative to spherical earth, the azimuth is calculated using equations (3) through (5).

。

Wherein lon _a As the longitude value of pest reference position A, lat _a The latitude value of the insect pest reference position A; lon (lon) _b For a longitude value of a pest position B, lat _b Is the latitude value of the pest position B.

And 106B, determining the insect pest positions belonging to the target azimuth category according to the azimuth angles of the insect pest positions relative to the insect pest reference positions and the target azimuth range.

The pest locations having azimuth angles within the target azimuth range are determined as pest locations belonging to the target azimuth category. For example, the azimuth angle of the pest position P1 is 35 °, the azimuth angle of the pest position P2 is 60 °, the target azimuth angle range is [0 °,45 °), and the target azimuth category is the 1 st category, and the pest position belonging to the target azimuth category is P1.

And 106C, obtaining a class center position corresponding to the target azimuth class under each first pest time according to the pest positions belonging to the target azimuth class based on the first pest time corresponding to the pest positions.

Each first pest time includes a plurality of azimuth categories, each azimuth category includes a target azimuth category, in order to obtain a category center position corresponding to the target azimuth category under each first pest time, it is necessary to find a pest position belonging to the target azimuth category under the first pest time, then calculate a center of the pest position belonging to the target azimuth category, and the calculated center is the category center position corresponding to the target azimuth category under the first pest time.

And step 108, obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time.

Wherein the second pest time is a time after the plurality of first pest times in the historical pest set, e.g., 3 first pest times in the historical pest set are: 2000.10.01 2000.10.02 and 2000.10.03, the second pest time can be 2000.10.04.

As shown in fig. 4, the 1 st to N-1 th days are N-1 first pest time, the numbers 1 to 8 represent 8 azimuth categories, the solid small dots represent pest reference positions, the solid small triangles represent category center positions, when the solid small triangles are in a hollow circle, the solid small triangles represent category center positions corresponding to each azimuth category under a certain first pest time, and assuming that the N-th day is a second pest time, the target pest position prediction result corresponding to the target azimuth category on the N-th day can be obtained through the steps 102 to 108.

Since the target azimuth category is any one of a plurality of azimuth categories, pest position prediction results corresponding to other azimuth categories can also be obtained through steps 102 to 108.

According to the pest position prediction method, the pest reference positions are obtained according to the plurality of pest positions in the historical pest set, then the first pest time corresponding to each pest position and the pest reference positions are obtained according to the plurality of pest positions in the historical pest set, the class center positions corresponding to the target azimuth classes under each first pest time are obtained, and finally the pest position prediction is realized according to the pest reference positions and the class center positions corresponding to the target azimuth classes under each first pest time. In addition, the predicted result of the pest position is a predicted result in the target azimuth angle range, that is, the predicted result in the different azimuth angle ranges can be obtained in the above manner, and a finer predicted result can be obtained in the manner than in the manner that only one predicted result in the second pest time can be obtained.

In one embodiment, more refined azimuth classification is required when the number of insect pests is high, and not too fine azimuth classification is required when the number of insect pests is low, so, in order to obtain a classification result with higher accuracy, as shown in fig. 5, before obtaining the historical insect pest set in step 102, the method further includes:

step 110, obtaining a historical pest number in a preset time period.

The preset time period is a preset time period, and can be set according to actual requirements, for example, the preset time period is set to be half a month or one month or even half a year; and the historical insect pest number is the counted insect pest number in a preset time period.

And step 112, determining the azimuth angle ranges according to the historical insect pest quantity in the preset time period.

When the number of the historical insect pests is large, the number of azimuth angle ranges is larger, and correspondingly, the azimuth angle included in each azimuth angle range is smaller, so that finer azimuth classification is performed, and a more accurate prediction result is obtained; when the number of historical insect pests is smaller, the number of azimuth angle ranges is smaller, and correspondingly, the azimuth angles contained in each azimuth angle range are larger, so that more insect pest positions are contained through more azimuth angles, and more accurate prediction results are obtained.

Exemplary, a standard quantity range [ P1, P2 ] is obtained]The standard quantity of the azimuth angle range is M, and when the number of the historical insect pests in the preset time period is larger than a large value P2 in the standard quantity range, the number of the azimuth angle range is larger than M; when the number of the historical insect pests in the preset time period is smaller than a small value P1 in the standard number range, the number of the azimuth angle ranges is smaller than M; when the number of the historical insect pests in the preset time period is within the standard number range [ P1, P2 ]]In which the number of azimuth ranges is equal to M. Since the number of azimuth ranges is determined, each azimuth range at that number can be determined. For example, the number of azimuth ranges is m, and determining the plurality of azimuth ranges is:wherein, the method comprises the steps of, wherein,wherein, the gradient _i Is the i-th azimuth range.

Step 114, determining the azimuth category corresponding to each azimuth range.

The azimuth category corresponding to the ith azimuth range is illustratively determined as the ith category, and of course, the azimuth category corresponding to the ith azimuth range may also be determined as the mth category, where,。

and step 116, determining a target azimuth category corresponding to the target azimuth range according to the azimuth category corresponding to each azimuth range and the target azimuth range.

Comparing a target azimuth angle range with each azimuth angle range, and determining an azimuth angle range which is the same as the target azimuth angle range in each azimuth angle range; and acquiring azimuth categories corresponding to azimuth ranges which are the same as the target azimuth range in each azimuth range, and taking the azimuth categories as target azimuth categories corresponding to the target azimuth range.

In one embodiment, a method is provided for deriving a target pest location prediction that combines azimuth and distance to determine the target pest location prediction, and is based on spherical earth when both azimuth and distance are calculated, which will have a higher accuracy than a two-bit plane based approach. As shown in fig. 6, in step 108, the obtaining a target pest position prediction result according to the pest reference position and the category center position corresponding to the target azimuth category under each first pest time includes:

and step 108A, calculating azimuth angles and distances of the class center positions corresponding to the target azimuth classes under each first insect pest time relative to the insect pest reference positions.

Formulas for calculating the azimuth angle are shown as formulas (3) to (5), and will not be described in detail herein. In the embodiment of the present invention, the distance is calculated with reference to formula (6).

。

Wherein R is _a R is the equatorial radius of the earth _b Distance is the calculated class center position (longitude lon _b Latitude lat _b ) With pest reference position (longitude: lon (lon) _a Latitude lat _a ) Is a distance of (3).

And step 108B, determining an azimuth target prediction model according to azimuth angles of the class center positions corresponding to the target azimuth classes under each first insect pest time relative to the insect pest reference positions, and determining a distance target prediction model according to distances of the class center positions corresponding to the target azimuth classes under each first insect pest time relative to the insect pest reference positions.

The azimuth target prediction model is an azimuth prediction model with known model coefficients; the distance target prediction model is a distance prediction model with known model coefficients. The model coefficients include variable coefficients and model constants corresponding to each independent variable in the model, for example, model y=a×x+b, x is the independent variable, y is the dependent variable, a is the variable coefficient, and b is the model constant. Model coefficients are known in the azimuth target prediction model and the range target prediction model.

In one embodiment, a method of solving model coefficients in an azimuth prediction model to obtain an azimuth target prediction model is provided. Step 108B of determining an azimuth target prediction model according to the azimuth of the category center position corresponding to the target azimuth category at each pest time relative to the pest reference position, including:

Acquiring an azimuth angle prediction model to be solved and a first loss function, wherein the azimuth angle prediction model to be solved comprises a first coefficient to be solved; obtaining a solution of the first coefficient to be solved according to azimuth angles of class center positions corresponding to the target azimuth classes under each first insect pest time relative to the insect pest reference positions and the first loss function; substituting the solution of the first coefficient to be solved into the azimuth angle prediction model to be solved to obtain the azimuth angle target prediction model.

Wherein the azimuth prediction model to be solved is an azimuth prediction model with unknown model coefficients, for example, the azimuth prediction model to be solved is，/>For the ith second pest time, the second pest times are arranged in time sequence to determine +.>Values of (e.g.)>=[1，2，3，4，5，6，…，n]Wherein i= … n, e.g. second pest time 2000.10.01 corresponds to +.>1, second pest time 2000.10.02 corresponds to +.>2, and so on. />And (5) predicting an azimuth angle corresponding to the ith second pest time.

The first loss function is a preset function for determining an optimal model coefficient in the azimuth prediction model to be solved, for example, the first loss function is: 。

Wherein the first coefficient to be solved is a model coefficient to be solved in the azimuth angle prediction model to be solved, for example,the first coefficients to be solved are a and b.

When loss obtains a partial derivative, obtaining an optimal solution of a when the partial derivative is 0; when loss is biased to b and the partial derivative is 0, an optimal solution of b is obtained as shown in the following formula.

。

Wherein x is _i For the ith first pest time in the historical pest set, as such,=[1，2，3，4，5，6，…，n]wherein i= … n; y is _i And n is the total number of the first pest time in the historical pest set for the azimuth angle of the class center position corresponding to the target azimuth class under the ith first pest time relative to the pest reference position.

After obtaining the optimal solution of a and the optimal solution of b in the first coefficient to be solved, substituting the optimal solution of a and the optimal solution of b into the azimuth angle prediction model to be solvedCan obtain the azimuth targetAnd (5) a prediction model.

In one embodiment, a method of solving model coefficients in a distance prediction model to obtain a distance target prediction model is provided. Step 108B of determining a target prediction model according to the distance between the center position of the category corresponding to the target azimuth category at each first pest time and the pest reference position, where the determining includes:

Obtaining a distance prediction model to be solved and a second loss function, wherein the distance prediction model to be solved comprises a second coefficient to be solved; obtaining a solution of the second coefficient to be solved according to the distance between the class center position corresponding to the target azimuth class under each first pest time and the pest reference position and the second loss function; substituting the solution of the second coefficient to be solved into a second azimuth angle prediction model to be solved to obtain the distance target prediction model.

The distance prediction model to be solved is a distance prediction model with unknown model coefficients; the second loss function is a preset function for determining the optimal model coefficient in the distance prediction model to be solved; and the second coefficient to be solved is a model coefficient to be solved in the distance prediction model to be solved. The method for determining the distance target prediction model is the same as the method for determining the azimuth target prediction model, and will not be described in detail herein.

And step 108C, obtaining an azimuth angle prediction result according to the azimuth angle target prediction model and the second pest time, and obtaining a distance prediction result according to the distance target prediction model and the second pest time, wherein the azimuth angle prediction result is the azimuth angle of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position, and the distance prediction result is the distance of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position.

Wherein the azimuth angle prediction result is an azimuth angle predicted by an azimuth angle target prediction model, and concretely, x is corresponding to the second pest time _i Substituting the value of (2) into the azimuth target prediction model to obtain azimuth prediction resultThe method comprises the steps of carrying out a first treatment on the surface of the The distance prediction result is the distance predicted by the distance target prediction model, specifically, the distance corresponding to the second pest time is x _i And substituting the value of (2) into a distance target prediction model to obtain a distance prediction result.

And step 108D, obtaining the target pest position prediction result according to the azimuth angle prediction result and the distance prediction result.

Acquiring a preset azimuth angle formula and a preset distance formula, wherein the preset azimuth angle formula records the corresponding relation between the azimuth angle and the insect pest position, and the preset distance formula records the corresponding relation between the distance and the insect pest position;

and obtaining the target pest position prediction result according to the azimuth angle prediction result, the distance prediction result, the preset azimuth angle formula and the preset distance formula.

The azimuth angle formula is preset and can be shown as formulas (3) to (5); the preset distance formula may be as shown in formula (6). Since the azimuth angle prediction result (azimuth) and the distance prediction result (distance) have already been obtained, at this time, the target pest position prediction result (longitude and latitude) can be obtained according to formulas (3) to (6).

In one embodiment, environmental impact parameters are introduced in order to improve the accuracy of the final predicted pest location, considering that the natural environment will have an impact on the direction of pest development. As shown in fig. 7, step 108D of obtaining the target pest position prediction result according to the azimuth angle prediction result and the distance prediction result includes:

step 108D1, obtaining an environmental impact parameter corresponding to the azimuth angle prediction result and the distance prediction result, where the environmental impact parameter includes at least one of an environmental coefficient, an environmental value, and an environmental value coefficient.

The environmental impact parameter is used for measuring the influence of the external environment on the pest position prediction result, such as wind, air temperature and humidity, and the like, and the environmental impact parameter comprises at least one of an environmental coefficient, an environmental value and an environmental value coefficient, i.e. the environmental impact parameter can only comprise one parameter value or a plurality of parameter values.

Wherein, the environmental coefficient is a preset constant value; an environmental value reflecting the magnitude of an external environment (e.g., wind); the environmental value coefficient can be combined with the environmental value to obtain the direct influence of the external environment on the azimuth angle prediction result and the distance prediction result, for example, the environmental value coefficient is used Representation, wherein->Is the angle between the azimuth prediction result and the environmental direction (e.g., wind direction), as shown in fig. 7.

And step 108D2, obtaining the target pest position prediction result according to the azimuth angle prediction result, the distance prediction result and the environment influence parameter.

When the environmental impact parameter only comprises one parameter value, the updated azimuth angle prediction result is obtained according to the product of the azimuth angle prediction result and the parameter value, namely the updated azimuth angle prediction result = azimuth angle prediction resultAnd w, wherein w is an environmental influence parameter, and the updated distance prediction result is obtained according to the product of the distance prediction result and the parameter value, and finally, the target pest position prediction result is obtained according to the updated azimuth angle prediction result and the updated distance prediction result.

When the environmental impact parameter includes a plurality of parameter values, for example, the environmental impact parameter includes an environmental coefficient, an environmental value, and an environmental value coefficient, the updated azimuth prediction result is obtained from the product of the azimuth prediction result and the environmental coefficient, the environmental value, and the environmental value coefficient, that is, the updated azimuth prediction result = azimuth prediction resultw/>v/> Wherein w is an environmental coefficient, v is an environmental value, < - >For the environmental value coefficient, the updated distance prediction result is also obtained from the product of the distance prediction result and the environmental coefficient, the environmental value and the environmental value coefficient, i.e. updated distance prediction result = distance prediction result +.>w/>v/> . And finally, obtaining a target pest position prediction result according to the updated azimuth angle prediction result and the updated distance prediction result.

In one embodiment, the development speed of the pest is very fast when the life cycle is in a high-rise period, at this time, the environmental coefficient can be set to be larger, and the development speed of the pest is relatively slower when the life cycle is in a low-rise period, at this time, the environmental coefficient can be set to be smaller, so that a more accurate prediction result is obtained. Step 108D1 of obtaining environmental impact parameters corresponding to the azimuth prediction result and the distance prediction result includes:

acquiring the current life cycle of the insect pest; and determining the environmental coefficients corresponding to the azimuth angle prediction result and the distance prediction result according to the current life cycle of the insect pest.

The present life cycle of the insect pest indicates the present life cycle of the insect pest, and the life cycle includes a primary period, an initial period, a high-incidence period and a death period, when the life cycle is the primary period, the development speed of the insect pest is very slow, when the life cycle is the initial period, the insect pest starts to have a certain development speed, the quantity starts to increase, when the life cycle is the high-incidence period, the development speed of the insect pest is very fast, the quantity is rapidly increased, and when the life cycle is the death period, the insect pest grows slowly and the quantity starts to gradually decrease.

A cycle coefficient table is preset, and the corresponding relation between the life cycle and the candidate environment coefficient is recorded in the cycle coefficient table. Obtaining candidate environmental coefficients corresponding to the current life cycle of the insect pest from the periodic coefficient table; and taking the candidate environmental coefficient corresponding to the current life cycle of the insect pest as the environmental coefficient in the environmental influence parameters.

In one embodiment, the application environment of the pest position prediction method is provided, and the pest position prediction method is particularly applied to prediction of pests of crops in a certain industrial park or administrative area, and meanwhile, in order to ensure the validity of the prediction result, the out-of-range pest position prediction result is also removed. Specifically, the method further comprises the following steps: acquiring a target area identifier; acquiring a latitude and longitude range corresponding to the target area identifier; and removing target pest position prediction results exceeding the longitude and latitude range from target pest position prediction results corresponding to all azimuth categories according to the longitude and latitude range corresponding to the target area identifier.

The target area identifier is used for identifying a target area, and the target area can be a certain industrial park or a certain administrative area, and is not particularly limited herein; and when the target insect pest position prediction result exceeding the longitude and latitude range exists in the target insect pest position prediction result corresponding to each azimuth category, removing the target insect pest position prediction result exceeding the longitude and latitude range, and ensuring the accuracy of the finally determined prediction result.

As shown in fig. 8, there is provided a pest position predicting apparatus 800, specifically comprising:

a location acquisition module 802 configured to acquire a historical pest set, where the historical pest set includes a plurality of pest locations and a first pest time corresponding to each of the pest locations;

a benchmark determination module 804 for deriving a pest benchmark location from the plurality of pest locations;

a category determining module 806, configured to obtain, according to each of the pest positions, the first pest time corresponding to each of the pest positions, and the pest reference position, a category center position corresponding to a target azimuth category under each of the first pest times, where the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, and the target azimuth range is any azimuth range of a preset multiple azimuth ranges, where the multiple azimuth ranges overlap and cover all azimuths;

and a result prediction module 808, configured to obtain a target pest position prediction result according to the pest reference position and a class center position corresponding to the target azimuth class under each first pest time, where the target pest position prediction result is a predicted result of a pest position in the target azimuth angle range when the pest reference position is taken as a reference position at a second pest time, and the second pest time is a time after the first pest time.

According to the pest position predicting device, the pest reference positions are obtained according to the plurality of pest positions in the historical pest set, then the first pest time corresponding to each pest position and the pest reference positions are obtained according to the plurality of pest positions in the historical pest set, the class center positions corresponding to the target azimuth classes under each first pest time are obtained, and finally the pest positions are predicted according to the pest reference positions and the class center positions corresponding to the target azimuth classes under each first pest time. In addition, the predicted result of the pest position is a predicted result in the target azimuth angle range, that is, the predicted result in the different azimuth angle ranges can be obtained in the above manner, and a finer predicted result can be obtained in the manner than in the manner that only one predicted result in the second pest time can be obtained.

In one embodiment, the category determination module 806 is specifically configured to: determining azimuth angles of the pest positions relative to the pest reference positions according to the pest positions and the pest reference positions; determining pest positions belonging to the target azimuth category according to azimuth angles of the pest positions relative to the pest reference positions and the target azimuth range; and obtaining a category center position corresponding to the target azimuth category under each first insect pest time according to the insect pest position belonging to the target azimuth category based on the first insect pest time corresponding to the insect pest position.

In one embodiment, the apparatus 800 further comprises: the quantity category module is used for acquiring the historical insect pest quantity in a preset time period; determining the azimuth angle ranges according to the historical insect pest quantity in the preset time period; determining azimuth category corresponding to each azimuth range; and determining a target azimuth category corresponding to the target azimuth range according to the azimuth category corresponding to each azimuth range and the target azimuth range.

In one embodiment, the result prediction module 808 is specifically configured to: calculating azimuth angles and distances of class center positions corresponding to the target azimuth classes under the first insect pest time relative to the insect pest reference positions; determining an azimuth target prediction model according to azimuth angles of class center positions corresponding to the target azimuth classes under the first insect attack time relative to the insect attack reference positions, and determining a distance target prediction model according to distances of class center positions corresponding to the target azimuth classes under the first insect attack time relative to the insect attack reference positions; obtaining an azimuth angle prediction result according to an azimuth angle target prediction model and the second pest time, and obtaining a distance prediction result according to a distance target prediction model and the second pest time, wherein the azimuth angle prediction result is the azimuth angle of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position, and the distance prediction result is the distance of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position; and obtaining the target pest position prediction result according to the azimuth angle prediction result and the distance prediction result.

In one embodiment, the result prediction module 808 is specifically configured to: acquiring environment influence parameters corresponding to the azimuth angle prediction result and the distance prediction result, wherein the environment influence parameters comprise at least one of environment coefficients, environment values and environment value coefficients; and obtaining the target pest position prediction result according to the azimuth angle prediction result, the distance prediction result and the environmental influence parameter.

In one embodiment, the result prediction module 808 is specifically configured to: acquiring the current life cycle of the insect pest; and determining the environmental coefficients corresponding to the azimuth angle prediction result and the distance prediction result according to the current life cycle of the insect pest.

In one embodiment, the result prediction module 808 is specifically configured to: acquiring an azimuth angle prediction model to be solved and a first loss function, wherein the azimuth angle prediction model to be solved comprises a first coefficient to be solved; obtaining a solution of the first coefficient to be solved according to azimuth angles of class center positions corresponding to the target azimuth classes under each first insect pest time relative to the insect pest reference positions and the first loss function; substituting the solution of the first coefficient to be solved into the azimuth angle prediction model to be solved to obtain the azimuth angle target prediction model.

In one embodiment, the result prediction module 808 is specifically configured to: obtaining a distance prediction model to be solved and a second loss function, wherein the distance prediction model to be solved comprises a second coefficient to be solved; obtaining a solution of the second coefficient to be solved according to the distance between the class center position corresponding to the target azimuth class under each first pest time and the pest reference position and the second loss function; substituting the solution of the second coefficient to be solved into a second azimuth angle prediction model to be solved to obtain the distance target prediction model.

In one embodiment, the result prediction module 808 is specifically configured to: acquiring a preset azimuth angle formula and a preset distance formula, wherein the preset azimuth angle formula records the corresponding relation between the azimuth angle and the insect pest position, and the preset distance formula records the corresponding relation between the distance and the insect pest position; and obtaining the target pest position prediction result according to the azimuth angle prediction result, the distance prediction result, the preset azimuth angle formula and the preset distance formula.

FIG. 9 illustrates an internal block diagram of a computer device in one embodiment. The computer device may in particular be a terminal or a server. As shown in fig. 9, the computer device includes a processor, a memory, and a network interface connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement a pest location prediction method. The internal memory may also have stored therein a computer program which, when executed by the processor, causes the processor to perform the pest position prediction method. It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In one embodiment, the pest position prediction method provided by the present application may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 9. The memory of the computer device may store therein the various program templates that make up the pest position prediction means. Such as a location acquisition module 802, a reference determination module 804, and a category determination module 806.

A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:

acquiring a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position;

obtaining a pest reference position according to the plurality of pest positions;

obtaining a category center position corresponding to a target azimuth category under each first pest position according to each pest position, a first pest time corresponding to each pest position and the pest reference position, wherein the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, the target azimuth range is any azimuth range of a plurality of preset azimuth ranges, and the azimuth ranges are overlapped to cover all azimuths;

Obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time.

In one embodiment, the obtaining, according to each pest position, a first pest time corresponding to each pest position, and the pest reference position, a category center position corresponding to a target azimuth category under each first pest time includes: determining azimuth angles of the pest positions relative to the pest reference positions according to the pest positions and the pest reference positions; determining pest positions belonging to the target azimuth category according to azimuth angles of the pest positions relative to the pest reference positions and the target azimuth range; and obtaining a category center position corresponding to the target azimuth category under each first insect pest time according to the insect pest position belonging to the target azimuth category based on the first insect pest time corresponding to the insect pest position.

In one embodiment, prior to the obtaining the historical pest set, further comprising: acquiring the number of the historical insect pests in a preset time period; determining the azimuth angle ranges according to the historical insect pest quantity in the preset time period; determining azimuth category corresponding to each azimuth range; and determining a target azimuth category corresponding to the target azimuth range according to the azimuth category corresponding to each azimuth range and the target azimuth range.

In one embodiment, the obtaining the target pest position prediction result according to the pest reference position and the category center position corresponding to the target azimuth category under each first pest time includes: calculating azimuth angles and distances of class center positions corresponding to the target azimuth classes under the first insect pest time relative to the insect pest reference positions; determining an azimuth target prediction model according to azimuth angles of class center positions corresponding to the target azimuth classes under the first insect attack time relative to the insect attack reference positions, and determining a distance target prediction model according to distances of class center positions corresponding to the target azimuth classes under the first insect attack time relative to the insect attack reference positions; obtaining an azimuth angle prediction result according to an azimuth angle target prediction model and the second pest time, and obtaining a distance prediction result according to a distance target prediction model and the second pest time, wherein the azimuth angle prediction result is the azimuth angle of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position, and the distance prediction result is the distance of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position; and obtaining the target pest position prediction result according to the azimuth angle prediction result and the distance prediction result.

In one embodiment, the obtaining the target pest position prediction result according to the azimuth angle prediction result and the distance prediction result includes: acquiring environment influence parameters corresponding to the azimuth angle prediction result and the distance prediction result, wherein the environment influence parameters comprise at least one of environment coefficients, environment values and environment value coefficients; and obtaining the target pest position prediction result according to the azimuth angle prediction result, the distance prediction result and the environmental influence parameter.

In one embodiment, the obtaining the environmental impact parameters corresponding to the azimuth angle prediction result and the distance prediction result includes: acquiring the current life cycle of the insect pest; and determining the environmental coefficients corresponding to the azimuth angle prediction result and the distance prediction result according to the current life cycle of the insect pest.

In one embodiment, the determining the azimuth target prediction model according to the azimuth angle of the class center position corresponding to the target azimuth class at each pest time relative to the pest reference position includes: acquiring an azimuth angle prediction model to be solved and a first loss function, wherein the azimuth angle prediction model to be solved comprises a first coefficient to be solved; obtaining a solution of the first coefficient to be solved according to azimuth angles of class center positions corresponding to the target azimuth classes under each first insect pest time relative to the insect pest reference positions and the first loss function; substituting the solution of the first coefficient to be solved into the azimuth angle prediction model to be solved to obtain the azimuth angle target prediction model.

In one embodiment, determining the target prediction model according to the distance between the class center position corresponding to the target azimuth class at each first pest time and the pest reference position includes: obtaining a distance prediction model to be solved and a second loss function, wherein the distance prediction model to be solved comprises a second coefficient to be solved; obtaining a solution of the second coefficient to be solved according to the distance between the class center position corresponding to the target azimuth class under each first pest time and the pest reference position and the second loss function; substituting the solution of the second coefficient to be solved into a second azimuth angle prediction model to be solved to obtain the distance target prediction model.

In one embodiment, the obtaining the target pest position prediction result according to the azimuth angle prediction result and the distance prediction result includes: acquiring a preset azimuth angle formula and a preset distance formula, wherein the preset azimuth angle formula records the corresponding relation between the azimuth angle and the insect pest position, and the preset distance formula records the corresponding relation between the distance and the insect pest position; and obtaining the target pest position prediction result according to the azimuth angle prediction result, the distance prediction result, the preset azimuth angle formula and the preset distance formula.

In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, causes the processor to perform the steps of:

It should be noted that the above pest position prediction method, pest position prediction device, computer apparatus, and computer readable storage medium belong to one general inventive concept, and the content in the embodiments of the pest position prediction method, pest position prediction device, computer apparatus, and computer readable storage medium may be mutually applicable.

Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims

1. A method of pest location prediction comprising:

Obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time;

the method for obtaining the category center position corresponding to the target azimuth category under each first pest position according to each pest position, the first pest time corresponding to each pest position and the pest reference position comprises the following steps:

determining azimuth angles of the pest positions relative to the pest reference positions according to the pest positions and the pest reference positions;

determining pest positions belonging to the target azimuth category according to azimuth angles of the pest positions relative to the pest reference positions and the target azimuth range;

based on the first insect pest time corresponding to the insect pest position, obtaining a category center position corresponding to the target azimuth category under each first insect pest time according to the insect pest position belonging to the target azimuth category;

The method for obtaining the target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time comprises the following steps:

calculating azimuth angles and distances of class center positions corresponding to the target azimuth classes under the first insect pest time relative to the insect pest reference positions;

determining an azimuth target prediction model according to azimuth angles of class center positions corresponding to the target azimuth classes under the first insect attack time relative to the insect attack reference positions, and determining a distance target prediction model according to distances of class center positions corresponding to the target azimuth classes under the first insect attack time relative to the insect attack reference positions;

obtaining an azimuth angle prediction result according to an azimuth angle target prediction model and the second pest time, and obtaining a distance prediction result according to a distance target prediction model and the second pest time, wherein the azimuth angle prediction result is the azimuth angle of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position, and the distance prediction result is the distance of the pest position in the target azimuth angle range at the second pest time relative to the pest reference position;

Obtaining a target pest position prediction result according to the azimuth angle prediction result and the distance prediction result;

the determining an azimuth target prediction model according to the azimuth of the category center position corresponding to the target azimuth category under each insect pest time relative to the insect pest reference position comprises the following steps:

acquiring an azimuth angle prediction model to be solved and a first loss function, wherein the azimuth angle prediction model to be solved comprises a first coefficient to be solved;

obtaining a solution of the first coefficient to be solved according to azimuth angles of class center positions corresponding to the target azimuth classes under each first insect pest time relative to the insect pest reference positions and the first loss function;

substituting the solution of the first coefficient to be solved into the azimuth angle prediction model to be solved to obtain the azimuth angle target prediction model;

the determining a distance target prediction model according to the distance between the class center position corresponding to the target azimuth class under each first pest time and the pest reference position comprises the following steps:

obtaining a distance prediction model to be solved and a second loss function, wherein the distance prediction model to be solved comprises a second coefficient to be solved;

Obtaining a solution of the second coefficient to be solved according to the distance between the class center position corresponding to the target azimuth class under each first pest time and the pest reference position and the second loss function;

substituting the solution of the second coefficient to be solved into a second azimuth angle prediction model to be solved to obtain the distance target prediction model.

2. The method of claim 1, further comprising, prior to said obtaining the historical pest set:

acquiring the number of the historical insect pests in a preset time period;

determining the azimuth angle ranges according to the historical insect pest quantity in the preset time period;

determining azimuth category corresponding to each azimuth range;

and determining a target azimuth category corresponding to the target azimuth range according to the azimuth category corresponding to each azimuth range and the target azimuth range.

3. The method of claim 1, wherein said deriving said target pest location prediction result from said azimuth angle prediction result and said distance prediction result comprises:

acquiring environment influence parameters corresponding to the azimuth angle prediction result and the distance prediction result, wherein the environment influence parameters comprise at least one of environment coefficients, environment values and environment value coefficients;

And obtaining the target pest position prediction result according to the azimuth angle prediction result, the distance prediction result and the environmental influence parameter.

4. The method of claim 3, wherein the obtaining environmental impact parameters corresponding to the azimuth angle prediction result and the distance prediction result comprises:

acquiring the current life cycle of the insect pest;

and determining the environmental coefficients corresponding to the azimuth angle prediction result and the distance prediction result according to the current life cycle of the insect pest.

5. The method of any one of claims 1-4, wherein said deriving said target pest location prediction result from said azimuth angle prediction result and said distance prediction result comprises:

6. A pest position predicting device, comprising:

The position acquisition module is used for acquiring a historical pest set, wherein the historical pest set comprises a plurality of pest positions and first pest time corresponding to each pest position;

the reference determining module is used for obtaining insect pest reference positions according to the insect pest positions;

the category determining module is used for obtaining a category center position corresponding to a target azimuth category under each first pest position according to each pest position, a first pest time corresponding to each pest position and the pest reference position, wherein the category center position is a center of the pest position in a target azimuth range, the target azimuth range is an azimuth range corresponding to the target azimuth category, the target azimuth range is any azimuth range in a plurality of preset azimuth ranges, and the azimuth ranges are overlapped to cover all azimuths;

the result prediction module is used for obtaining a target pest position prediction result according to the pest reference position and the class center position corresponding to the target azimuth class under each first pest time, wherein the target pest position prediction result is a prediction result of the pest position in the target azimuth angle range when the pest reference position is taken as the reference position at a second pest time, and the second pest time is the time after the first pest time;

The category determination module is specifically configured to:

the result prediction module is specifically configured to:

The result prediction module is specifically configured to:

7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor performs the steps of the pest position prediction method of any one of claims 1 to 5 when the computer program is executed.

8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the pest position prediction method of any one of claims 1 to 5.