CN118116471A - Sex pheromone receptor-based pest control and evaluation method and system - Google Patents
Sex pheromone receptor-based pest control and evaluation method and system Download PDFInfo
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Abstract
The invention discloses a sex pheromone receptor-based pest control and evaluation method and a sex pheromone receptor-based pest control and evaluation system, comprising the following steps: dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area; formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme; monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control; judging whether the expected control effect is achieved or not according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme. By analyzing the types and the degrees of the pests in each area, a control scheme conforming to the conditions of the areas is formulated, and pest control is evaluated from subjective and objective angles, so that the control effect is better known. And (3) optimizing the prevention and control scheme based on pest habit, setting a prevention and control optimization scheme conforming to a target area, and improving pest prevention and control effect and evaluation accuracy.
Description
Technical Field
The invention relates to the technical field of pest control and evaluation, in particular to a sex pheromone receptor-based pest control and evaluation method and system.
Background
With the development of biotechnology and molecular biology, prevention and control by utilizing biological characteristics of pests are a new idea. Sex pheromones are a class of chemical substances produced in the body of pests that can transfer information between pests, affecting the life habits and behaviors of the pests. Sex pheromones play an important role in the reproduction process of pests, such as guiding mating behavior of pests, inducing aggregation of pests, and the like. By simulating and utilizing sex pheromone of pests, the reproductive behavior of the pests can be interfered, the pests are guided to gather, and the pest is assisted with a killing technology, so that the control effect is achieved.
However, the method for pest control on pheromones still has defects, such as that a pest and the like cannot be better attracted due to the fact that a control device is not purposefully put in, so that an expected control effect cannot be achieved, the control cost is increased, and meanwhile, the deployment of pest control is affected due to inaccurate evaluation of pest control. Therefore, how to better utilize pheromones for pest control and to improve the accuracy of pest control evaluation is an important issue.
Disclosure of Invention
The invention overcomes the defects of the prior art, and provides a sex pheromone receptor-based pest control and evaluation method and system, which have the important purpose of improving the benefit of pest control and the accuracy of pest control evaluation.
To achieve the above object, the first aspect of the present invention provides a pest control and evaluation method based on sex pheromone receptor, comprising:
dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area to obtain pest analysis information;
formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme;
monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control to obtain pest control evaluation information;
judging whether the expected control effect is achieved or not according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme.
In this scheme, its characterized in that, divide the target area, carry out pest sampling according to the division result, analyze pest degree and kind in each district, specifically do:
acquiring target area position information, dividing a target area into a plurality of sub-areas according to the target area position information;
Presetting a classification threshold, extracting the respective taken planting information, calculating the proportion of various plants to total plants in each region, judging the proportion with the classification threshold, and classifying each sub-region according to a judging result to obtain classification information;
If the ratio of various plants to total plants in a certain subarea is smaller than the classification threshold value, defining the corresponding area as a mixed planting area;
Sampling pests in each subarea according to the category division information to obtain pest sampling information;
Constructing a pest identification model based on CNN, acquiring various pest characteristic information through big data retrieval, performing deep learning and training on the pest identification model, inputting the pest sampling information into the pest identification model for analysis, and obtaining pest identification information;
and presetting a pest degree judgment threshold, extracting various pest numbers and types in each region according to the pest identification information, and judging with the pest degree judgment threshold to obtain pest analysis information.
In this scheme, its characterized in that, according to pest analysis information formulates prevention and control scheme, carries out pest control according to prevention and control scheme, specifically does:
acquiring pest sex pheromone control examples of different types and degrees based on big data retrieval to form an example data set;
Obtaining pest analysis information, performing similar calculation on the pest analysis information and the example data set, obtaining a similarity value, and judging with a preset threshold value to obtain candidate prevention and control scheme information;
Extracting the control effect of each candidate control scheme as weight, carrying out weighted calculation on each candidate control scheme, and sequencing according to the weighted calculation result;
and selecting a final prevention and control scheme according to the sequencing result, and performing pest control according to the final prevention and control scheme.
The scheme is characterized in that pest control evaluation is performed to obtain pest control evaluation information, and the pest control evaluation information specifically comprises:
based on big data retrieval, obtaining various pest control evaluation indexes, calculating pearson correlation coefficients between each evaluation index and control effect, and judging with a preset threshold value to obtain evaluation index information, wherein the pearson correlation coefficients are used as correlation analysis indexes;
based on an expert knowledge method, obtaining influence degree scores of all evaluation indexes according to the evaluation index information, introducing an intuitionistic fuzzy analysis method to perform subjective weight distribution on all the evaluation indexes, and constructing an intuitionistic fuzzy evaluation matrix according to the influence degree scores;
Calculating the intuitionistic fuzzy entropy of each evaluation index according to the intuitionistic fuzzy evaluation matrix, calculating the intuitionistic fuzzy number of each evaluation index through the intuitionistic fuzzy entropy, and carrying out subjective weight distribution on each evaluation index to obtain subjective weight information;
Introducing CRITIC method to perform objective weight distribution, constructing a matrix to be evaluated according to the influence degree score of each evaluation index, normalizing the matrix to be evaluated, calculating the standard deviation and the correlation coefficient of each evaluation index, and performing ascending order sequencing on each evaluation index through the correlation coefficient obtained by calculation;
Constructing an ordered vector according to the ordering result, calculating the difference coefficient of each evaluation index through the constructed ordered vector, and carrying out objective weight distribution by combining the standard deviation and the correlation coefficient of each evaluation index to obtain objective weight information;
performing weight combination on each index based on a game theory method by combining subjective weight information and objective weight information to obtain evaluation index weight information;
And (3) monitoring pest control in the target area, acquiring pest control monitoring information, and evaluating pest control by combining the evaluation index weight information to obtain pest control evaluation information.
In this scheme, its characterized in that, whether according to pest control evaluation information judge reaches the expected prevention and cure effect, carry out pest habit analysis, specifically do:
Presetting a control effect evaluation rule, acquiring pest control evaluation information, judging with the control effect evaluation rule, and analyzing whether an expected control effect is achieved;
If the expected control effect is not achieved, analyzing pest habit, and optimizing a control scheme;
acquiring pest analysis information, carrying out characteristic areas on the pest analysis information, and extracting the type characteristics and the degree characteristics of pests in each area to obtain regional pest characteristic information;
And carrying out pest habit analysis based on expert analysis method in combination with the regional pest characteristic information, and analyzing the presence preference, survival preference and mating preference of different types of pests to obtain pest habit analysis information.
In this scheme, its characterized in that, optimize prevention and control scheme, specifically do:
acquiring regional geographic information and regional meteorological information, analyzing whether pest adaptive areas exist in all the subareas by combining the pest habit analysis information, and calculating the similarity between the pest habit analysis information and the regional geographic information to be used as an analysis index;
judging the calculated similarity value with a preset threshold value, and analyzing whether a pest suitable area exists according to a judging result to obtain pest suitable area information;
Extracting pest emergence preference and mating preference according to the pest habit analysis information, calculating pearson correlation coefficient with the pest adaptive region information as breeding region analysis index, and analyzing whether each adaptive region is a pest breeding region or not to obtain breeding region analysis information;
Constructing a pest activity prediction model, inputting the pest habit analysis information, the regional weather information, the breeding regional information and the pest adaptation regional information into the pest activity prediction model for analysis, and predicting pest activity conditions of all regions in a future time period to obtain pest activity prediction information;
Taking the activity degree of the pests as an evaluation index of the regional prevention and control force, presetting an activity degree judgment threshold, judging the activity prediction information of the pests and the activity degree judgment threshold, and classifying the prevention and control grades of the regions to obtain prevention and control force evaluation information;
optimizing the prevention and control scheme according to the prevention and control force evaluation information and the pest activity prediction information, and optimizing the prevention and control scheme according to the prevention and control force and pest activity characteristics of each area to obtain initial optimization scheme information;
And calculating the cost of each initial optimization scheme according to the initial optimization scheme information, carrying out weighted calculation on each initial optimization scheme as a weight, sequencing, and selecting a final optimization scheme according to a sequencing result to control pests.
In a second aspect, the present invention provides a sex pheromone receptor-based pest control and evaluation system, comprising: the system comprises a memory and a processor, wherein the memory contains a sex pheromone receptor-based pest control and evaluation method program, and the sex pheromone receptor-based pest control and evaluation method program realizes the following steps when being executed by the processor:
dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area to obtain pest analysis information;
formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme;
monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control to obtain pest control evaluation information;
judging whether the expected control effect is achieved or not according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme.
The invention discloses a sex pheromone receptor-based pest control and evaluation method and a sex pheromone receptor-based pest control and evaluation system, comprising the following steps: dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area; formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme; monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control; judging whether the expected control effect is achieved or not according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme. By analyzing the types and the degrees of the pests in each area, a control scheme conforming to the conditions of the areas is formulated, and pest control is evaluated from subjective and objective angles, so that the control effect is better known. And (3) optimizing the prevention and control scheme based on pest habit, setting a prevention and control optimization scheme conforming to a target area, and improving pest prevention and control effect and evaluation accuracy.
Drawings
In order to more clearly illustrate the technical solutions of embodiments or examples of the present invention, the drawings that are required to be used in the embodiments or examples of the present invention will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive efforts for those skilled in the art.
FIG. 1 is a flow chart of a method for pest control and evaluation based on sex pheromone receptors according to an embodiment of the present invention;
FIG. 2 is a flow chart of pest control based on a pheromone receptor according to an embodiment of the present invention;
FIG. 3 is a block diagram of a sex pheromone receptor-based pest control and evaluation system according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 is a flow chart of a method for pest control and evaluation based on sex pheromone receptors according to an embodiment of the present invention;
As shown in fig. 1, the present invention provides a pest control and evaluation method based on sex pheromone receptor, comprising:
s102, dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area to obtain pest analysis information;
S104, formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme;
S106, monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control to obtain pest control evaluation information;
s108, judging whether the expected control effect is achieved according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme.
The invention provides a method and a system for pest control and evaluation based on a pheromone receptor, which are characterized in that a target control area is divided, the planting composition of each area is known, pest sampling is carried out on each area, the types and the degrees of pests in each area are analyzed, so that a control strategy conforming to the actual conditions of each area is formulated, and sex pheromones conforming to the types of the pests in the target area are selected. Each region after pest control is monitored and evaluated to judge whether an expected control effect is achieved, whether a proper growth region and a breeding region exist in the control region is judged by analyzing pest habits in the target region, and a control strategy is optimized by combining pest habits, so that the control effect is improved.
Further, in a preferred embodiment of the present invention, the dividing the target area, sampling the pests according to the division result, and analyzing the extent and the type of the pests in each area specifically includes:
acquiring target area position information, dividing a target area into a plurality of sub-areas according to the target area position information;
Presetting a classification threshold, extracting the respective taken planting information, calculating the proportion of various plants to total plants in each region, judging the proportion with the classification threshold, and classifying each sub-region according to a judging result to obtain classification information;
If the ratio of various plants to total plants in a certain subarea is smaller than the classification threshold value, defining the corresponding area as a mixed planting area;
Sampling pests in each subarea according to the category division information to obtain pest sampling information;
Constructing a pest identification model based on CNN, acquiring various pest characteristic information through big data retrieval, performing deep learning and training on the pest identification model, inputting the pest sampling information into the pest identification model for analysis, and obtaining pest identification information;
and presetting a pest degree judgment threshold, extracting various pest numbers and types in each region according to the pest identification information, and judging with the pest degree judgment threshold to obtain pest analysis information.
Before pest control, the target area needs to be known, the planting composition of the target area is analyzed, the areas are divided according to the proportion of different kinds of plants and total kinds of plants in the areas, and the areas where the plants in the areas are smaller than a preset threshold value are defined as mixed planting areas. Through regional division, the planting composition in the region can be better understood, so that the pests which easily appear in the region can be known. And then, sampling the pests in each area, and analyzing the pests in detail by a sampling means to judge the types and the amounts of the pests in each area in the current time period, thereby providing a basis for the subsequent formulation of prevention and control schemes.
Further, in a preferred embodiment of the present invention, the controlling scheme is formulated according to the pest analysis information, and pest control is performed according to the controlling scheme, which specifically includes:
acquiring pest sex pheromone control examples of different types and degrees based on big data retrieval to form an example data set;
Obtaining pest analysis information, performing similar calculation on the pest analysis information and the example data set, obtaining a similarity value, and judging with a preset threshold value to obtain candidate prevention and control scheme information;
Extracting the control effect of each candidate control scheme as weight, carrying out weighted calculation on each candidate control scheme, and sequencing according to the weighted calculation result;
and selecting a final prevention and control scheme according to the sequencing result, and performing pest control according to the final prevention and control scheme.
After the target area is known in detail, according to the pest degree and the species of each area, a pest control scheme needs to be formulated, at this time, the historical control scheme of various pests is obtained through big data retrieval, and whether the target scheme is suitable for the area is judged by calculating the similarity of the species and the degree of the pests in each area and the scene when the historical control scheme is adopted, and at this time, a candidate control scheme is obtained. For the adoption of the candidate prevention and control schemes, attention is paid to the prevention and control effects of each scheme, and the prevention and control effects of each candidate prevention and control scheme are used as weights for weighted calculation, so that a scheme with good prevention and control effects is selected for pest prevention and control. Meanwhile, aiming at different pest types and conditions in different areas, the pest control scheme of each area can adopt a pheromone for pest control.
Further, in a preferred embodiment of the present invention, the pest control evaluation is performed to obtain pest control evaluation information, which specifically includes:
based on big data retrieval, obtaining various pest control evaluation indexes, calculating pearson correlation coefficients between each evaluation index and control effect, and judging with a preset threshold value to obtain evaluation index information, wherein the pearson correlation coefficients are used as correlation analysis indexes;
based on an expert knowledge method, obtaining influence degree scores of all evaluation indexes according to the evaluation index information, introducing an intuitionistic fuzzy analysis method to perform subjective weight distribution on all the evaluation indexes, and constructing an intuitionistic fuzzy evaluation matrix according to the influence degree scores;
Calculating the intuitionistic fuzzy entropy of each evaluation index according to the intuitionistic fuzzy evaluation matrix, calculating the intuitionistic fuzzy number of each evaluation index through the intuitionistic fuzzy entropy, and carrying out subjective weight distribution on each evaluation index to obtain subjective weight information;
Introducing CRITIC method to perform objective weight distribution, constructing a matrix to be evaluated according to the influence degree score of each evaluation index, normalizing the matrix to be evaluated, calculating the standard deviation and the correlation coefficient of each evaluation index, and performing ascending order sequencing on each evaluation index through the correlation coefficient obtained by calculation;
Constructing an ordered vector according to the ordering result, calculating the difference coefficient of each evaluation index through the constructed ordered vector, and carrying out objective weight distribution by combining the standard deviation and the correlation coefficient of each evaluation index to obtain objective weight information;
performing weight combination on each index based on a game theory method by combining subjective weight information and objective weight information to obtain evaluation index weight information;
And (3) monitoring pest control in the target area, acquiring pest control monitoring information, and evaluating pest control by combining the evaluation index weight information to obtain pest control evaluation information.
In the pest control evaluation process, a plurality of indexes are often required to be comprehensively considered so as to comprehensively evaluate pest control. First, various evaluation indexes related to pest control, such as the number of pests, the trend of pest variation, etc., are obtained by large data retrieval, and representative evaluation indexes are screened from the viewpoint of correlation by calculating pearson correlation coefficients between them and control effects. And then, obtaining the influence degree score of each evaluation index by an expert knowledge method, wherein the influence degree score is the score of a professional in the related field affecting a certain evaluation object on a certain index, and introducing an intuitionistic fuzzy analysis method to perform subjective weight analysis, constructing an intuitionistic fuzzy matrix, calculating intuitionistic fuzzy entropy and intuitionistic fuzzy number of each evaluation index, and further distributing main weight. Then, objective weight analysis is carried out through CRITIC method, matrix to be evaluated is constructed, standardization is carried out on the matrix to be evaluated, and standard deviation and correlation coefficient of each evaluation index are calculated. And (3) carrying out ascending order sequencing on each evaluation index according to the calculated correlation coefficient, constructing an ordered vector according to the sequencing result, calculating the difference coefficient of each evaluation index, and balancing the specific gravity between index conflict and contrast through the difference coefficient so as to determine more reasonable evaluation weight. Finally, the final weight is obtained by combining weights through a game theory method, and the subjectivity and objectivity of the evaluation of each index are comprehensively considered, so that the evaluation result is more accurate.
The CRITIC method is an evaluation method for objective weighting. The core idea of this approach is to use the contrast intensity (also called volatility) and the conflict (also called correlation) of the evaluation index to integrate the objective weight of the evaluation index. The contrast intensity is measured by the difference in value between the different evaluation schemes of the index, and usually occurs in the form of standard deviation. The larger the standard deviation, the larger the fluctuation of the index, and thus the weight will generally be higher. Conversely, if the correlation coefficients of the two indices are large, they exhibit strong positive or negative correlations, which indicates that they are less conflicting and thus relatively low in weight. The method is helpful for reducing unnecessary repeated calculation caused by strong correlation of certain indexes, considers the variability of the indexes and simultaneously considers the correlation among the indexes, and does not depend on the size of the index numerical value to determine the weight, thereby improving the accuracy of the evaluation result.
Further, in a preferred embodiment of the present invention, the determining whether the expected controlling effect is achieved according to the pest controlling evaluation information, and performing pest habit analysis specifically includes:
Presetting a control effect evaluation rule, acquiring pest control evaluation information, judging with the control effect evaluation rule, and analyzing whether an expected control effect is achieved;
If the expected control effect is not achieved, analyzing pest habit, and optimizing a control scheme;
acquiring pest analysis information, carrying out characteristic areas on the pest analysis information, and extracting the type characteristics and the degree characteristics of pests in each area to obtain regional pest characteristic information;
And carrying out pest habit analysis based on expert analysis method in combination with the regional pest characteristic information, and analyzing the presence preference, survival preference and mating preference of different types of pests to obtain pest habit analysis information.
After evaluating pest control, it is necessary to determine whether the current control effect reaches the expected effect, for example, whether the pest control degree is effectively suppressed. And judging pest control evaluation information obtained after evaluation with the pest control evaluation information through presetting a control effect evaluation rule, so as to analyze whether the expected control effect is achieved. If the control effect is not achieved, the control scheme needs to be optimized. The habits, the presence preferences, the location, the climate, etc. of the pests, which are the pest presence time, the place, the climate, etc. of the various pests present in the target area, are analyzed, such as that some pests prefer to live in a wet area, and the mating preferences, which are preferences when pests mate, such as at night or in some environmental areas, etc. Through analyzing pest habit, pest information can be further known, and a foundation is provided for optimizing a follow-up prevention and control scheme.
Further, in a preferred embodiment of the present invention, the optimizing the prevention and control scheme specifically includes:
acquiring regional geographic information and regional meteorological information, analyzing whether pest adaptive areas exist in all the subareas by combining the pest habit analysis information, and calculating the similarity between the pest habit analysis information and the regional geographic information to be used as an analysis index;
judging the calculated similarity value with a preset threshold value, and analyzing whether a pest suitable area exists according to a judging result to obtain pest suitable area information;
Extracting pest emergence preference and mating preference according to the pest habit analysis information, calculating pearson correlation coefficient with the pest adaptive region information as breeding region analysis index, and analyzing whether each adaptive region is a pest breeding region or not to obtain breeding region analysis information;
Constructing a pest activity prediction model, inputting the pest habit analysis information, the regional weather information, the breeding regional information and the pest adaptation regional information into the pest activity prediction model for analysis, and predicting pest activity conditions of all regions in a future time period to obtain pest activity prediction information;
Taking the activity degree of the pests as an evaluation index of the regional prevention and control force, presetting an activity degree judgment threshold, judging the activity prediction information of the pests and the activity degree judgment threshold, and classifying the prevention and control grades of the regions to obtain prevention and control force evaluation information;
optimizing the prevention and control scheme according to the prevention and control force evaluation information and the pest activity prediction information, and optimizing the prevention and control scheme according to the prevention and control force and pest activity characteristics of each area to obtain initial optimization scheme information;
And calculating the cost of each initial optimization scheme according to the initial optimization scheme information, carrying out weighted calculation on each initial optimization scheme as a weight, sequencing, and selecting a final optimization scheme according to a sequencing result to control pests.
After the pest habit analysis is performed, various pests in the target area are known in detail, at this time, whether the pest-suitable area exists or not is analyzed based on the geographical environment information in the target area, whether the geographical environment similar to the pest survival habit exists in each sub-area in the target area or not is analyzed by calculating the similarity of the pest habit analysis information and the geographical information of the area, and pest-suitable area information is obtained, and for the pest-suitable area, the pest-suitable area can be indicated to be a pest emergence area, namely, emergence preference and survival preference, so that the pest can be prevented and controlled from the survival position, and further, the pest can be accurately controlled. And then, extracting the play preference and mating preference of the pests, and analyzing the breeding areas of the pests by combining the information of the suitable breeding areas of the pests, so that the analysis of the breeding behaviors of the pests possibly performed in certain areas can be performed, and the information element equipment can be put in more specifically. Then, constructing a pest activity prediction model, constructing a model through CNN, acquiring historical data according to big data retrieval, and training, wherein the pest activity prediction model comprises an input layer, an output layer and a convolution layer, predicting through pest habit information, regional weather information, breeding region information and pest adaptive region information, and analyzing the activity degree of pests, namely the probability of occurrence degree of the pests in each region under the current climate. Finally, optimizing the prevention and control scheme according to the pest activity degree prediction information, and adopting an optimization strategy, such as increasing the input amount of the information element trapping device, for the region with high activity degree, so that the targeted prevention and control can be realized, resources are prevented from being wasted due to blind input of the prevention and control device, and the pest prevention and control effect is improved.
FIG. 2 is a flow chart of pest control based on a pheromone receptor according to an embodiment of the present invention;
as shown in fig. 2, the present invention provides a pest control flow chart based on a trusted pheromone receptor, comprising:
S202, dividing each target area, sampling pests, and analyzing the types and the amounts of the pests in each area;
s204, formulating a prevention and control scheme according to the types and the quantity of pests in each subarea, and performing a pest scheme according to the formulated prevention and control scheme;
s206, pest control monitoring and evaluation are carried out, and whether the expected control effect is achieved is analyzed;
S208, if the expected control effect is not achieved, analyzing the habit of the pests, and analyzing the suitable area and the breeding area according to pest habit analysis information;
S210, predicting the activity of pests by combining regional weather information, optimizing pest prevention and control schemes according to prediction results, and accurately preventing and controlling according to the optimization schemes.
After the existence of pests in the finding area, analyzing the types and the amounts of the pests in the area, selecting corresponding sex pheromone receptors for trapping and formulating the dosage. Aiming at insect pests with different degrees, if the control effect reaches the expected effect, the insect pest control is in a successful stage, and excessive intervention is not needed, and only the supplement of the sex pheromone is needed. For the situation that the prevention and control effect cannot reach the expectation or the prevention and control time is longer after the prevention and control measures are taken, optimization is needed. In pest control, the throwing and the use of the pest control device are important factors influencing pest control effects, and the possible areas of pests and the areas suitable for breeding and reproduction of the pests are analyzed, so that the sex pheromone can be better utilized for trapping, precise control is realized, and the control effect is improved.
In addition, the sex pheromone receptor-based pest control and evaluation method further comprises the following steps:
monitoring a target area after pest prevention and control, acquiring prevention and control monitoring information, analyzing pest variation trend according to the prevention and control monitoring information, and constructing a pest variation trend graph;
calculating a pest change rate according to the pest change trend graph, and predicting the consumption time for reaching the expected control effect through the pest change rate to obtain first analysis information;
dividing pest control stages by combining pest change rate and first analysis information, dividing the consumed time reaching the expected control effect into different stages according to the pest change rate, and calculating the pest reduction quantity in each stage to obtain second analysis information;
constructing a plant influence analysis model, inputting second analysis information and pest change rate into the plant influence analysis model for analysis, and analyzing the influence degree of the plant in a target area before the expected control effect is achieved to obtain plant influence analysis information;
judging whether the plant influence analysis information is in a bearing range or not according to the plant influence analysis information and a preset threshold value, and obtaining first judgment result information;
If the first judgment result information is beyond the bearing range, generating an early warning report according to the plant influence analysis information, and prompting that the prevention and control force needs to be improved.
Fig. 3 is a diagram showing a sex pheromone receptor-based pest control and evaluation system 3 according to an embodiment of the present invention, the system includes: a memory 31 and a processor 32, wherein the memory 31 contains a sex pheromone receptor-based pest control and evaluation method program, and the sex pheromone receptor-based pest control and evaluation method program realizes the following steps when being executed by the processor 32:
dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area to obtain pest analysis information;
formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme;
monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control to obtain pest control evaluation information;
judging whether the expected control effect is achieved or not according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A sex pheromone receptor-based pest control and evaluation method, comprising:
dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area to obtain pest analysis information;
formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme;
monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control to obtain pest control evaluation information;
judging whether the expected control effect is achieved or not according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme.
2. The method for controlling and evaluating pests based on sex pheromone receptors according to claim 1, wherein the target area is divided, pest sampling is performed according to the division result, and the degree and the kind of pests in each area are analyzed, specifically comprising:
acquiring target area position information, dividing a target area into a plurality of sub-areas according to the target area position information;
Presetting a classification threshold, extracting the respective taken planting information, calculating the proportion of various plants to total plants in each region, judging the proportion with the classification threshold, and classifying each sub-region according to a judging result to obtain classification information;
If the ratio of various plants to total plants in a certain subarea is smaller than the classification threshold value, defining the corresponding area as a mixed planting area;
Sampling pests in each subarea according to the category division information to obtain pest sampling information;
Constructing a pest identification model based on CNN, acquiring various pest characteristic information through big data retrieval, performing deep learning and training on the pest identification model, inputting the pest sampling information into the pest identification model for analysis, and obtaining pest identification information;
and presetting a pest degree judgment threshold, extracting various pest numbers and types in each region according to the pest identification information, and judging with the pest degree judgment threshold to obtain pest analysis information.
3. The sex pheromone receptor-based pest control and evaluation method according to claim 1, wherein the formulation of a control scheme according to the pest analysis information, pest control according to the control scheme, specifically comprises:
acquiring pest sex pheromone control examples of different types and degrees based on big data retrieval to form an example data set;
Obtaining pest analysis information, performing similar calculation on the pest analysis information and the example data set, obtaining a similarity value, and judging with a preset threshold value to obtain candidate prevention and control scheme information;
Extracting the control effect of each candidate control scheme as weight, carrying out weighted calculation on each candidate control scheme, and sequencing according to the weighted calculation result;
and selecting a final prevention and control scheme according to the sequencing result, and performing pest control according to the final prevention and control scheme.
4. The sex pheromone receptor-based pest control and evaluation method as claimed in claim 1, wherein the pest control evaluation is performed to obtain pest control evaluation information, comprising:
based on big data retrieval, obtaining various pest control evaluation indexes, calculating pearson correlation coefficients between each evaluation index and control effect, and judging with a preset threshold value to obtain evaluation index information, wherein the pearson correlation coefficients are used as correlation analysis indexes;
based on an expert knowledge method, obtaining influence degree scores of all evaluation indexes according to the evaluation index information, introducing an intuitionistic fuzzy analysis method to perform subjective weight distribution on all the evaluation indexes, and constructing an intuitionistic fuzzy evaluation matrix according to the influence degree scores;
Calculating the intuitionistic fuzzy entropy of each evaluation index according to the intuitionistic fuzzy evaluation matrix, calculating the intuitionistic fuzzy number of each evaluation index through the intuitionistic fuzzy entropy, and carrying out subjective weight distribution on each evaluation index to obtain subjective weight information;
Introducing CRITIC method to perform objective weight distribution, constructing a matrix to be evaluated according to the influence degree score of each evaluation index, normalizing the matrix to be evaluated, calculating the standard deviation and the correlation coefficient of each evaluation index, and performing ascending order sequencing on each evaluation index through the correlation coefficient obtained by calculation;
Constructing an ordered vector according to the ordering result, calculating the difference coefficient of each evaluation index through the constructed ordered vector, and carrying out objective weight distribution by combining the standard deviation and the correlation coefficient of each evaluation index to obtain objective weight information;
performing weight combination on each index based on a game theory method by combining subjective weight information and objective weight information to obtain evaluation index weight information;
And (3) monitoring pest control in the target area, acquiring pest control monitoring information, and evaluating pest control by combining the evaluation index weight information to obtain pest control evaluation information.
5. The method for pest control and evaluation based on sex pheromone receptor according to claim 1, wherein the judging whether the expected control effect is achieved according to the pest control evaluation information, and performing pest habit analysis, specifically comprises:
Presetting a control effect evaluation rule, acquiring pest control evaluation information, judging with the control effect evaluation rule, and analyzing whether an expected control effect is achieved;
If the expected control effect is not achieved, analyzing pest habit, and optimizing a control scheme;
acquiring pest analysis information, carrying out characteristic areas on the pest analysis information, and extracting the type characteristics and the degree characteristics of pests in each area to obtain regional pest characteristic information;
And carrying out pest habit analysis based on expert analysis method in combination with the regional pest characteristic information, and analyzing the presence preference, survival preference and mating preference of different types of pests to obtain pest habit analysis information.
6. The sex pheromone receptor-based pest control and evaluation method according to claim 1, wherein the optimizing the control scheme specifically comprises:
acquiring regional geographic information and regional meteorological information, analyzing whether pest adaptive areas exist in all the subareas by combining the pest habit analysis information, and calculating the similarity between the pest habit analysis information and the regional geographic information to be used as an analysis index;
judging the calculated similarity value with a preset threshold value, and analyzing whether a pest suitable area exists according to a judging result to obtain pest suitable area information;
Extracting pest emergence preference and mating preference according to the pest habit analysis information, calculating pearson correlation coefficient with the pest adaptive region information as breeding region analysis index, and analyzing whether each adaptive region is a pest breeding region or not to obtain breeding region analysis information;
Constructing a pest activity prediction model, inputting the pest habit analysis information, the regional weather information, the breeding regional information and the pest adaptation regional information into the pest activity prediction model for analysis, and predicting pest activity conditions of all regions in a future time period to obtain pest activity prediction information;
Taking the activity degree of the pests as an evaluation index of the regional prevention and control force, presetting an activity degree judgment threshold, judging the activity prediction information of the pests and the activity degree judgment threshold, and classifying the prevention and control grades of the regions to obtain prevention and control force evaluation information;
optimizing the prevention and control scheme according to the prevention and control force evaluation information and the pest activity prediction information, and optimizing the prevention and control scheme according to the prevention and control force and pest activity characteristics of each area to obtain initial optimization scheme information;
And calculating the cost of each initial optimization scheme according to the initial optimization scheme information, carrying out weighted calculation on each initial optimization scheme as a weight, sequencing, and selecting a final optimization scheme according to a sequencing result to control pests.
7. A sex pheromone receptor-based pest control and evaluation system, comprising: the system comprises a memory and a processor, wherein the memory contains a sex pheromone receptor-based pest control and evaluation method program, and the sex pheromone receptor-based pest control and evaluation method program realizes the following steps when being executed by the processor:
dividing a target area, sampling pests according to a division result, and analyzing the pest degree and the species in each area to obtain pest analysis information;
formulating a prevention and control scheme according to the pest analysis information, and controlling pests according to the prevention and control scheme;
monitoring pest control, determining evaluation weight by introducing an intuitionistic fuzzy analysis method and a CRITIC method, and evaluating pest control to obtain pest control evaluation information;
judging whether the expected control effect is achieved or not according to the pest control evaluation information, analyzing pest habit, and optimizing a control scheme.
8. The sex pheromone receptor-based pest control and evaluation system according to claim 7, wherein the division of the target area, the sampling of pests according to the division result, and the analysis of the degree and kind of pests in each area specifically comprises:
acquiring target area position information, dividing a target area into a plurality of sub-areas according to the target area position information;
Presetting a classification threshold, extracting the respective taken planting information, calculating the proportion of various plants to total plants in each region, judging the proportion with the classification threshold, and classifying each sub-region according to a judging result to obtain classification information;
If the ratio of various plants to total plants in a certain subarea is smaller than the classification threshold value, defining the corresponding area as a mixed planting area;
Sampling pests in each subarea according to the category division information to obtain pest sampling information;
Constructing a pest identification model based on CNN, acquiring various pest characteristic information through big data retrieval, performing deep learning and training on the pest identification model, inputting the pest sampling information into the pest identification model for analysis, and obtaining pest identification information;
and presetting a pest degree judgment threshold, extracting various pest numbers and types in each region according to the pest identification information, and judging with the pest degree judgment threshold to obtain pest analysis information.
9. The sex pheromone receptor-based pest control and evaluation system according to claim 7, wherein the formulation of a control scheme according to the pest analysis information, pest control according to the control scheme, specifically comprises:
acquiring pest sex pheromone control examples of different types and degrees based on big data retrieval to form an example data set;
Obtaining pest analysis information, performing similar calculation on the pest analysis information and the example data set, obtaining a similarity value, and judging with a preset threshold value to obtain candidate prevention and control scheme information;
Extracting the control effect of each candidate control scheme as weight, carrying out weighted calculation on each candidate control scheme, and sequencing according to the weighted calculation result;
and selecting a final prevention and control scheme according to the sequencing result, and performing pest control according to the final prevention and control scheme.
10. The sex pheromone receptor-based pest control and evaluation system according to claim 7, wherein the pest control evaluation is performed to obtain pest control evaluation information, specifically comprising:
based on big data retrieval, obtaining various pest control evaluation indexes, calculating pearson correlation coefficients between each evaluation index and control effect, and judging with a preset threshold value to obtain evaluation index information, wherein the pearson correlation coefficients are used as correlation analysis indexes;
based on an expert knowledge method, obtaining influence degree scores of all evaluation indexes according to the evaluation index information, introducing an intuitionistic fuzzy analysis method to perform subjective weight distribution on all the evaluation indexes, and constructing an intuitionistic fuzzy evaluation matrix according to the influence degree scores;
Calculating the intuitionistic fuzzy entropy of each evaluation index according to the intuitionistic fuzzy evaluation matrix, calculating the intuitionistic fuzzy number of each evaluation index through the intuitionistic fuzzy entropy, and carrying out subjective weight distribution on each evaluation index to obtain subjective weight information;
Introducing CRITIC method to perform objective weight distribution, constructing a matrix to be evaluated according to the influence degree score of each evaluation index, normalizing the matrix to be evaluated, calculating the standard deviation and the correlation coefficient of each evaluation index, and performing ascending order sequencing on each evaluation index through the correlation coefficient obtained by calculation;
Constructing an ordered vector according to the ordering result, calculating the difference coefficient of each evaluation index through the constructed ordered vector, and carrying out objective weight distribution by combining the standard deviation and the correlation coefficient of each evaluation index to obtain objective weight information;
performing weight combination on each index based on a game theory method by combining subjective weight information and objective weight information to obtain evaluation index weight information;
And (3) monitoring pest control in the target area, acquiring pest control monitoring information, and evaluating pest control by combining the evaluation index weight information to obtain pest control evaluation information.
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