Method, device and equipment for analyzing intrusion trend of alien species and storage medium

Abstract

The invention discloses an alien species intrusion trend analysis method, device and equipment and a storage medium, relates to the technical field of biological control, and discloses an alien species intrusion trend analysis method comprising the following steps: obtaining alien species epidemic area historical data; performing data preprocessing on the alien species epidemic area historical data to obtain preprocessed epidemic area data; determining alien species diffusion information and a potential risk area according to the preprocessed epidemic area data; obtaining alien species epidemic area prediction data according to the preprocessed epidemic area data and a long-short-term memory model; and determining alien species invasion trend information according to the alien species diffusion information, the potential risk area and alien species epidemic area prediction data. The invasion trend of foreign species can be accurately predicted, so that early warning and scientific prevention and control of foreign species are realized.

Description

Technical Field

[0001] This application relates to the technical field of biological control, and particularly to a method, device, equipment and storage medium for analyzing the trend of alien species invasion. Background Art

[0002] Alien species are biological populations that have been introduced from their native regions into new ecosystems under the influence of human activities. These species may have negative impacts on the local ecosystem, such as competition, reproduction, disease transmission, etc.

[0003] Existing solutions often focus on the identification and control of current alien species invasions, without considering the analysis and early warning of future alien species invasion trends. How to accurately predict the trend of alien species invasion to achieve early warning and scientific prevention and control of alien species has become a problem to be solved.

[0004] The above content is only used to assist in understanding the technical solution of this application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device, equipment and storage medium for analyzing the trend of alien species invasion, aiming to solve the technical problem of how to accurately predict the trend of alien species invasion to achieve early warning and scientific prevention and control of alien species.

[0006] To achieve the above purpose, this application proposes a method for analyzing the trend of alien species invasion, and the method includes:

[0007] Obtain historical data of alien species epidemic areas;

[0008] Perform data preprocessing on the historical data of the alien species epidemic area to obtain preprocessed epidemic area data;

[0009] Determine the alien species diffusion information and potential risk areas according to the preprocessed epidemic area data;

[0010] Obtain the predicted data of the alien species epidemic area according to the preprocessed epidemic area data and the long short-term memory model;

[0011] Determine the alien species invasion trend information according to the alien species diffusion information, the potential risk areas and the predicted data of the alien species epidemic area.

[0012] In one embodiment, the step of determining the alien species diffusion information and potential risk areas according to the preprocessed epidemic area data includes:

[0013] Determine the time distribution data of the epidemic area according to the preprocessed epidemic area data;

[0014] Determine the spatial distribution data of the epidemic area based on the preprocessed epidemic area data;

[0015] Determine the spread information of invasive species and potential risk areas based on the time distribution data and the spatial distribution data of the epidemic area.

[0016] In one embodiment, the step of determining the time distribution data of the epidemic area according to the preprocessed epidemic area data includes:

[0017] Determine the number of newly added epidemic areas over the years, the number of epidemic areas lifted from prevention and control over the years, the epidemic area information of provinces and cities over the years, and the epidemic area information of districts and counties over the years according to the preprocessed epidemic area data;

[0018] Determine the time distribution data of the epidemic area according to the number of newly added epidemic areas over the years, the number of epidemic areas lifted from prevention and control over the years, the epidemic area information of provinces and cities over the years, and the epidemic area information of districts and counties over the years.

[0019] In one embodiment, the step of determining the spatial distribution data of the epidemic area according to the preprocessed epidemic area data includes:

[0020] Determine the number of epidemic areas in each district and county according to the preprocessed epidemic area data;

[0021] Draw a heat map of the distribution of invasive species based on the number of epidemic areas in each district and county;

[0022] Determine the spatial distribution data of the epidemic area according to the heat map of the epidemic area.

[0023] In one embodiment, the step of obtaining the prediction data of the invasive species epidemic area according to the preprocessed epidemic area data and the long short-term memory model includes:

[0024] Divide the preprocessed epidemic area data into input sequences and output sequences based on time series information;

[0025] Learn the long short-term memory model based on the input sequences and output sequences to obtain a prediction model for the invasive species epidemic area;

[0026] Obtain the prediction data of the invasive species epidemic area according to the current data of the invasive species epidemic area through the prediction model of the invasive species epidemic area.

[0027] In one embodiment, the step of learning the long short-term memory model based on the input sequences and output sequences to obtain a prediction model for the invasive species epidemic area includes:

[0028] Construct a training set and a test set based on the input sequences and output sequences;

[0029] Obtain the initial prediction data of the epidemic area of the long short-term memory model for the training set;

[0030] Calculate the mean square error based on the initial epidemic area prediction data and the actual epidemic area data of the test set;

[0031] Optimize the long short - term memory model based on the mean square error to obtain a foreign species epidemic area prediction model.

[0032] In one embodiment, the step of performing data pre - processing on the historical data of the foreign species epidemic area to obtain pre - processed epidemic area data includes:

[0033] Perform data cleaning on the historical data of the foreign species epidemic area to obtain cleaned data;

[0034] Perform missing value processing on the cleaned data to obtain missing - value - processed data;

[0035] Normalize the data after the missing - value processing to obtain pre - processed epidemic area data.

[0036] In addition, to achieve the above - mentioned purpose, the present application also proposes a foreign species invasion trend analysis device, which includes:

[0037] A data acquisition module, configured to acquire historical data of the foreign species epidemic area;

[0038] A data processing module, configured to perform data pre - processing on the historical data of the foreign species epidemic area to obtain pre - processed epidemic area data;

[0039] The data processing module is further configured to determine the foreign species diffusion information and potential risk areas according to the pre - processed epidemic area data;

[0040] A data prediction module, configured to obtain foreign species epidemic area prediction data according to the pre - processed epidemic area data and the long short - term memory model;

[0041] A data analysis module, configured to determine the foreign species invasion trend information according to the foreign species diffusion information, the potential risk areas, and the foreign species epidemic area prediction data.

[0042] In addition, to achieve the above - mentioned purpose, the present application also proposes a foreign species invasion trend analysis device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the foreign species invasion trend analysis method as described above.

[0043] In addition, to achieve the above - mentioned purpose, the present application also proposes a storage medium, which is a computer - readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the foreign species invasion trend analysis method as described above.

[0044] In addition, to achieve the above object, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the alien species invasion trend analysis method described above.

[0045] One or more technical solutions proposed by the present application have at least the following technical effects:

[0046] By obtaining historical data of alien species epidemic areas; performing data preprocessing on the historical data of alien species epidemic areas to obtain preprocessed epidemic area data; determining alien species diffusion information and potential risk areas according to the preprocessed epidemic area data; obtaining predicted data of alien species epidemic areas according to the preprocessed epidemic area data and a long short-term memory model; and determining alien species invasion trend information according to the alien species diffusion information, the potential risk areas, and the predicted data of alien species epidemic areas. By obtaining the historical data of alien species epidemic areas and performing data preprocessing, cleaning and standardizing the data to obtain preprocessed epidemic area data, identifying alien species diffusion information and potential risk areas based on the preprocessed epidemic area data, providing key background information for subsequent prediction, training the preprocessed epidemic area data using a long short-term memory model to capture the dependencies in time series data, effectively predicting the future distribution of alien species and the changes in epidemic areas, obtaining predicted data of alien species epidemic areas, and by combining alien species diffusion information, potential risk areas, and predicted data of alien species epidemic areas, potential invasion trends can be accurately identified, providing reliable early warning information for biological control, thereby realizing early warning and prevention and control of alien species. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0048] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the accompanying drawings required for describing the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the alien species invasion trend analysis method of the present application;

[0050] Figure 2 It is a schematic diagram of the LSTM memory unit structure provided for Embodiment 1 of the alien species invasion trend analysis method of the present application;

[0051] Figure 3The flow diagram provided for the second embodiment of the method for analyzing the trend of alien species invasion in this application;

[0052] Figure 4 The heat map of the distribution of the fall webworm provided for the second embodiment of the method for analyzing the trend of alien species invasion in this application;

[0053] Figure 5 The module structure diagram of the alien species invasion trend analysis device in the embodiment of this application;

[0054] Figure 6 The device structure diagram of the hardware operating environment involved in the method for analyzing the trend of alien species invasion in the embodiment of this application.

[0055] The realization of the purpose, functional characteristics and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0056] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0057] In order to better understand the technical solutions of this application, the following will be described in detail in combination with the drawings in the specification and the specific implementation manners.

[0058] The main solution of the embodiment of this application is: obtaining historical data of alien species epidemic areas; performing data preprocessing on the historical data of alien species epidemic areas to obtain preprocessed epidemic area data; determining alien species diffusion information and potential risk areas according to the preprocessed epidemic area data; obtaining predicted data of alien species epidemic areas according to the preprocessed epidemic area data and the long short-term memory model; determining alien species invasion trend information according to the alien species diffusion information, the potential risk areas and the predicted data of alien species epidemic areas.

[0059] In this embodiment, for the convenience of description, the following will be described with the device for analyzing the trend of alien species invasion as the execution subject.

[0060] Since the existing solutions often focus on the identification and control of current alien species invasions and do not consider the analysis and early warning of future alien species invasion trends. How to accurately predict the trend of alien species invasion to achieve early warning and scientific prevention and control of alien species has become a problem to be solved.

[0061] The present application provides a solution, which includes obtaining historical data of quarantine areas for alien species; performing data preprocessing on the historical data of quarantine areas for alien species to obtain preprocessed quarantine area data; determining alien species spread information and potential risk areas based on the preprocessed quarantine area data; obtaining predicted data for quarantine areas of alien species based on the preprocessed quarantine area data and a long short-term memory model; and determining alien species invasion trend information based on the alien species spread information, the potential risk areas, and the predicted data for quarantine areas of alien species. By obtaining the historical data of quarantine areas for alien species and performing data preprocessing, cleaning and standardizing the data to obtain preprocessed quarantine area data, identifying alien species spread information and potential risk areas based on the preprocessed quarantine area data provides key background information for subsequent prediction. Using the long short-term memory model to train the preprocessed quarantine area data to capture the dependencies in time series data can effectively predict the future distribution of alien species and changes in quarantine areas, obtaining predicted data for quarantine areas of alien species. By combining the alien species spread information, potential risk areas, and predicted data for quarantine areas of alien species, potential invasion trends can be accurately identified, providing reliable early warning information for biological control, thereby realizing early warning and prevention and control of alien species.

[0062] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, an alien species invasion trend analysis device, etc. that can implement the above functions. Hereinafter, the alien species invasion trend analysis device will be taken as an example to illustrate this embodiment and the following embodiments.

[0063] Based on this, an embodiment of the present application provides an alien species invasion trend analysis method, referring to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the alien species invasion trend analysis method of the present application.

[0064] In this embodiment, the alien species invasion trend analysis method includes steps S10 to S50:

[0065] Step S10, obtaining historical data of quarantine areas for alien species;

[0066] It should be understood that in recent years, the invasion of alien species has shown a spreading trend. While invading the ecological environment, it will also cause serious damage to the sustainable development of agriculture, forestry, animal husbandry, and fishery and biodiversity. Representative alien species include Hyphantria cunea, Solenopsis invicta, Bursaphelenchus xylophilus, etc.

[0067] It should be noted that the historical data of the alien species epidemic area can include the distribution of alien species and the geographical location information of the epidemic area within a certain period (for example, for 6 consecutive years in the past). Specifically, it can include the provincial, municipal, district, and county information of the epidemic area location each year. This data can be collected through public channels such as public databases, scientific research institutions, or national environmental monitoring agencies.

[0068] Step S20: Perform data preprocessing on the historical data of the alien species epidemic area to obtain preprocessed epidemic area data;

[0069] It should be understood that the historical data of the alien species epidemic area collected through public channels is raw data, and raw data often has problems such as noise, missing values, or inconsistencies. It is necessary to perform data preprocessing on it to obtain standardized input data, that is, preprocessed epidemic area data, to improve the quality and accuracy of the data.

[0070] In a feasible implementation manner, step S20 may include: performing data cleaning on the historical data of the alien species epidemic area to obtain cleaned data; performing missing value processing on the cleaned data to obtain missing value processed data; and normalizing the data after missing value processing to obtain preprocessed epidemic area data.

[0071] It should be understood that the historical data of the alien species epidemic area may contain duplicate records, incorrect formats, or unreasonable values. Through data cleaning, noise, inconsistencies, and redundant information in the data can be eliminated to ensure the accuracy and reliability of the data. Specifically, when performing data cleaning on the historical data of the alien species epidemic area, duplicate removal, format correction, error value processing, and consistency checking can be performed on the historical data of the alien species epidemic area in sequence to obtain cleaned data.

[0072] It should be understood that the historical data of the alien species epidemic area may contain blank values. After performing data cleaning on the historical data of the alien species epidemic area to obtain cleaned data, the blank values in the cleaned data can be checked and deleted or filled to avoid the impact of missing values on the analysis results. Exemplarily, based on other existing features, the missing values can be predicted through a machine learning model (such as a regression model, KNN, etc.) to obtain the cleaned data after data filling, that is, missing value processed data.

[0073] It should be understood that after obtaining the missing value processed data, normalization processing can be performed on the missing value processed data to unify data with different dimensions and ranges into a standard range, so as to prevent certain features from having an excessive impact on the model during analysis or modeling due to their overly large or small numerical ranges. After performing data cleaning, missing value processing, and normalization on the historical data of the alien species epidemic area in sequence, the obtained preprocessed epidemic area data can ensure the quality and consistency of the data.

[0074] Step S30: Determine the alien species diffusion information and potential risk areas based on the preprocessed epidemic area data;

[0075] It should be noted that the alien species diffusion information includes the diffusion range of alien species in each province and city and the habitat environment parameters (such as the temperature, humidity, and soil type in the epidemic area, etc.) determined based on the preprocessed epidemic area data. The potential risk areas are the marked areas that may be affected by alien species, and these areas may have environmental conditions and ecosystem structures similar to those of historical epidemic areas. By analyzing the alien species invasion time information and geographical location information of each epidemic area in the historical epidemic area data, information such as the alien species invasion and diffusion speed (such as the number of newly added epidemic areas in a certain province or city per year) and habitat environment parameters (such as the temperature, humidity, and soil type in the epidemic area, etc.) can be obtained. Based on the alien species diffusion information, the current non-epidemic areas are evaluated to predict potential high-risk areas, and the potential risk areas are obtained.

[0076] Step S40: Obtain the alien species epidemic area prediction data based on the preprocessed epidemic area data and the long short-term memory model;

[0077] It should be noted that the Long Short-Term Memory (LSTM) model is a type of recurrent neural network that can process and predict time series data. In the study of alien species invasion, the LSTM model can be used to predict the potential distribution range and diffusion trend of alien species, as well as evaluate their potential impact on the ecosystem.

[0078] Refer to Figure 2 , Figure 2 It is the schematic diagram of the LSTM memory cell structure provided in the first embodiment of the alien species invasion trend analysis method of this application. The core unit of the LSTM consists of a memory cell c t and three gating structures (input gate i t , forget gate f t , output gate o t ), and realizes the dynamic update and transmission of information through a collaborative mechanism. Among them, the forget gate is based on the previous hidden state H t-1 and the current input x t , combines with the bias term b f to perform weighted summation, and generates a forget weight f t ranging from 0 to 1 through the Sigmoid function. This weight is multiplied element-wise with the previous memory cell C t-1 to determine how much historical information to retain or discard. The input gate then generates an update weight i t through the Sigmoid function, and combines with the candidate memory cell (formed by H t-1 and x tMultiply element - by - element with the new information generated by the tanh activation, and filter out the new content to be added to the current memory cell C t . Finally, the memory cell C t is multiplied by the Sigmoid weight of the output gate o t after being scaled by tanh to generate the current hidden state H t , which serves as the input for the subsequent time steps of C t+1 and H t+1 .

[0079] It should be understood that when the pre - processed epidemic area data is input into the LSTM model, the LSTM model will learn the diffusion pattern, change law and future trend of the exotic species epidemic area based on historical data, adjust its internal parameters during the learning process, gradually fit the historical data, so as to generate a prediction of the future spread of exotic species and obtain the predicted data of the exotic species epidemic area.

[0080] In a feasible implementation manner, step S40 may include steps S41 - S43:

[0081] Step S41, divide the pre - processed epidemic area data into an input sequence and an output sequence based on time - series information;

[0082] It should be understood that the time - series information is extracted from the pre - processed epidemic area data and can reflect the sequence of the number of epidemic areas changing over time, specifically including the number of epidemic areas at each time point (such as each year). The input sequence and the output sequence are divided from the pre - processed epidemic area data based on the time - series information. Among them, the input sequence specifically includes the number of epidemic area data at a group of consecutive time points, which is used to provide the basic data for the long - short - term memory model to help the LSTM model understand the change law of the number of epidemic areas over time; the output sequence specifically includes the number of epidemic area data at one or more time points after the input sequence, which is used as the target value for model training to help the model learn how to predict the future number of epidemic areas according to the input sequence. Exemplarily, the standardized data can be converted into the input and output of the model by means of a sliding window, such as using the data of the previous year as the input to obtain the input sequence and the data of the current year as the output to obtain the output sequence.

[0083] Step S42, learn the long - short - term memory model based on the input sequence and the output sequence to obtain an exotic species epidemic area prediction model;

[0084] It should be understood that the prediction model for the quarantine area of alien species is obtained by training a long short-term memory model based on the input sequence and the output sequence. The divided input sequence and output sequence are input into the LSTM model, which will learn the temporal relationship and trend in the data, capture the law of the spread of the quarantine area according to historical data, and adjust the internal weight parameters so that the model can effectively fit the past changes in the quarantine area, and finally form a trained prediction model for the quarantine area of alien species.

[0085] In a feasible implementation manner, step S42 may include: constructing a training set and a test set based on the input sequence and the output sequence; obtaining the initial quarantine area prediction data of the long short-term memory model for the training set; calculating the mean squared error based on the initial quarantine area prediction data and the actual quarantine area data of the test set; optimizing the long short-term memory model based on the mean squared error to obtain a prediction model for the quarantine area of alien species.

[0086] It should be understood that the training set and the test set are obtained by dividing the preprocessed quarantine area data based on the input sequence and the output sequence. The training set includes an earlier part of the data in the dataset for training the model, and the test set includes a later part of the data in the dataset for evaluating the prediction ability of the model. The data of the training set is input into the LSTM model, and the LSTM model will learn the relationship between the input sequence and the output sequence. The initial quarantine area prediction data is the predicted value of the number of quarantine areas at each time point in the training set during the training process of the LSTM model, and the actual quarantine area data is the actual value of the number of quarantine areas at each time point in the test set. The initial prediction data of the model on the training set is compared with the actual quarantine area data of the test set, and the mean squared error (MSE) between the two is calculated. The parameters of the LSTM model are adjusted and optimized according to the calculated mean squared error to minimize the error, and a prediction model for the quarantine area of alien species can be obtained.

[0087] In a specific implementation, the Sequential model can be used to sequentially add an LSTM layer and a fully connected layer, use the mean squared error as the loss function, compile the model with the Adam optimizer, and train the model 100 times. During the training process, the fit method is used to train the model, specifying the number of training epochs and the batch size batch_size to obtain a prediction model for the quarantine area of alien species. The calculation formula of the mean squared error MSE is as follows:

[0088]

[0089] where f(x) is the predicted value, y is the actual value, and n is the amount of sample data. When measuring the goodness of fit of the LSTM model, the R-squared (R2) score can also be used as an evaluation index.

[0090] Step S43: Obtain the predicted data of the alien species epidemic area based on the current data of the alien species epidemic area through the alien species epidemic area prediction model.

[0091] It should be understood that after the model training is completed, the current data of the alien species epidemic area (such as the data of the alien species epidemic area in the current year) is input into the trained alien species epidemic area prediction model. The alien species epidemic area prediction model will generate a prediction result of the epidemic area data at a future time point (such as the next three years) based on these input data, and obtain the predicted data of the alien species epidemic area. The predicted data of the alien species epidemic area can be the predicted value of the increase or decrease in the epidemic area quantity or the epidemic area quantity in a future year. Specifically, it can be the predicted value of the increase or decrease in the epidemic area quantity in each province and city in the next three years.

[0092] Step S50: Determine the alien species invasion trend information based on the alien species diffusion information, the potential risk areas, and the predicted data of the alien species epidemic area.

[0093] It should be understood that the diffusion range of alien species in each province and city in the future and the invasion possibility of alien species in potential risk areas can be evaluated according to the alien species diffusion information and the habitat environment parameters (such as the temperature, humidity, and soil type in the epidemic area). After obtaining the invasion risk scores of each potential risk area, the alien species invasion trend information can be obtained by combining the predicted data of the alien species epidemic area, providing reliable early warning information for biological control. Key attention should be paid to strengthening the prevention and control measures in high-invasion-risk areas, and at the same time strengthening the monitoring and early warning work in low-invasion-risk areas to inhibit the further spread of alien species.

[0094] In this embodiment, historical data of the alien species epidemic area is obtained; the historical data of the alien species epidemic area is preprocessed to obtain preprocessed epidemic area data; the alien species diffusion information and potential risk areas are determined according to the preprocessed epidemic area data; the predicted data of the alien species epidemic area is obtained according to the preprocessed epidemic area data and the long short-term memory model; the alien species invasion trend information is determined according to the alien species diffusion information, the potential risk areas, and the predicted data of the alien species epidemic area. By obtaining the historical data of the alien species epidemic area and performing data preprocessing, cleaning and standardizing the data to obtain preprocessed epidemic area data, identifying the alien species diffusion information and potential risk areas based on the preprocessed epidemic area data provides key background information for subsequent prediction. Using the long short-term memory model to train the preprocessed epidemic area data, capturing the dependencies in the time series data, effectively predicting the future distribution of alien species and the changes in the epidemic area, obtaining the predicted data of the alien species epidemic area. By combining the alien species diffusion information, potential risk areas, and the predicted data of the alien species epidemic area, the potential invasion trend can be accurately identified, providing reliable early warning information for biological control, thus realizing the early warning and prevention and control of alien species.

[0095] Based on the first embodiment of this application, in the second embodiment of this application, for the same or similar content as in the above-mentioned first embodiment, reference can be made to the above introduction and will not be elaborated hereinafter. On this basis, please refer to Figure 3 , step S30 may include steps S31 to S33:

[0096] Step S31, determining the time distribution data of the epidemic area according to the preprocessed epidemic area data;

[0097] It should be understood that after obtaining the preprocessed epidemic area data, the epidemic area time information, epidemic area province and city information, epidemic area district and county information, and epidemic area marking information of the preprocessed epidemic area data can be extracted to obtain an epidemic area data list. Exemplarily, as shown in Table 1, Table 1 gives a partial example of the epidemic area data list of the fall webworm in the United States from 2018 to 2023. "Year" corresponds to the epidemic area time information, "Province and City" corresponds to the epidemic area province and city information, "District and County" corresponds to the epidemic area district and county information, and "Whether it was the occurrence area of the fall webworm in the previous year" corresponds to the epidemic area marking information.

[0098] Table 1

[0099]

[0100] It should be understood that after obtaining the epidemic area data list, the number of epidemic areas, the number of newly added epidemic areas, and the number of epidemic prevention areas lifted each year can be analyzed according to the epidemic area data list to obtain the time distribution data of the epidemic area.

[0101] In a feasible implementation manner, step S31 may include: determining the number of newly added epidemic areas over the years, the number of epidemic prevention areas lifted over the years, the epidemic area information of provinces and cities over the years, and the epidemic area information of districts and counties over the years according to the preprocessed epidemic area data; determining the time distribution data of the epidemic area according to the number of newly added epidemic areas over the years, the number of epidemic prevention areas lifted over the years, the epidemic area information of provinces and cities over the years, and the epidemic area information of districts and counties over the years.

[0102] It should be noted that the number of newly added epidemic areas over the years is the number of newly emerging foreign species epidemic areas in each year, which can reflect the situation of the spread of foreign species to new areas in that year. The number of epidemic prevention areas lifted over the years is the number of areas that are no longer regarded as epidemic areas due to effective prevention and control measures in each year, which can reflect the trend of the prevention and control work. The epidemic area information of provinces and cities over the years is the total number of provinces and cities where foreign species epidemics occur and the provinces and cities marked as epidemic areas in each year. The epidemic area information of districts and counties over the years is the total number of districts and counties where foreign species epidemics occur in each province and city and the districts and counties marked as epidemic areas. The epidemic area information of provinces and cities over the years and the epidemic area information of districts and counties over the years can reflect the concentration trend and diffusion trend of epidemic areas over the years.

[0103] It should be understood that the peak year of the number of epidemic areas, the trough year of the number of epidemic areas, the annual growth rate of the increase and decrease of epidemic areas, and the annual diffusion information of epidemic areas can be determined based on the number of newly added epidemic areas in previous years, the number of epidemic prevention areas lifted in previous years, the epidemic area information of provinces and cities in previous years, and the epidemic area information of districts and counties in previous years, so as to obtain the time distribution data of epidemic areas. Among them, the peak year of the number of epidemic areas is the year when the number of newly added epidemic areas reaches the highest point within the analyzed year range, indicating that the spread of alien species is relatively rapid during that year. The trough year of the number of epidemic areas is the year when the number of newly added epidemic areas reaches the lowest point within the analyzed year range, indicating that the spread of alien species has been effectively controlled during that year. The annual growth rate of the increase and decrease of epidemic areas is the growth rate of the number of newly added epidemic areas or the growth rate of the number of epidemic prevention areas lifted between two consecutive years, which is used to evaluate the speed of the spread of alien species. The annual diffusion information of epidemic areas is used to mark the spread of alien species in different years in the geographical space. For example, "diffusion within a district or county" is marked with the first diffusion information, "diffusion within a province or city" is marked with the second diffusion information, and "diffusion outside the province or city" is marked with the third diffusion information.

[0104] Step S32: Determine the spatial distribution data of epidemic areas according to the preprocessed epidemic area data;

[0105] It should be understood that after obtaining the epidemic area data list based on the preprocessed epidemic area data, the spatial distribution of alien species can also be analyzed according to the epidemic area data list to obtain the spatial distribution data of epidemic areas.

[0106] In a feasible implementation manner, step S32 may include: determining the number of epidemic areas in each district or county according to the preprocessed epidemic area data; drawing a heat map of the distribution of alien species based on the number of epidemic areas in each district or county; and determining the spatial distribution data of epidemic areas according to the epidemic area heat map.

[0107] It should be understood that after obtaining the epidemic area data list based on the preprocessed epidemic area data, the number of epidemic areas in each district or county within the time range of the epidemic area data list can be determined according to the epidemic area data list, the district or county name can be converted into the corresponding geographical coordinates (such as longitude and latitude coordinates), and a heat layer can be added to the map object according to the geographical coordinates and the number of epidemic areas in each district or county. Among them, the number of epidemic areas is used to determine the intensity or color of each point in the heat map, so as to obtain the heat map of the distribution of alien species.

[0108] It should be understood that according to the epidemic area heat map, information on the concentrated area of the epidemic area, the distribution density information of the epidemic area, the diffusion boundary information of the epidemic area, and the distribution pattern information of the epidemic area can be determined, and the spatial distribution data of the epidemic area can be obtained. Among them, the concentrated area of the epidemic area can be determined by identifying the area with the darkest color in the heat map. These areas represent the places with the largest number of epidemic areas, that is, the areas where alien species are most concentrated. By identifying the change in the color depth of the heat map, the distribution density information of the epidemic area in each province and city can be obtained. The darker the color, the higher the density. The diffusion boundary information of the epidemic area is the geographical boundary where the distribution density of the epidemic area begins to decrease significantly, which appears as a transition area with the color changing from deep to light on the heat map, showing the marginal area where the epidemic area spreads outward. The distribution pattern information of the epidemic area is the arrangement method of the epidemic area in the geographical space, such as dot-like, strip-like, patch-like or random distribution, etc., reflecting the regularity of the epidemic area distribution, such as environmental factors, ecological conditions or human activities, etc.

[0109] Exemplarily, refer to Figure 4 , Figure 4 which is the heat map of the distribution of Hyphantria cunea provided in the second embodiment of the method for analyzing the invasion trend of alien species in this application. As Figure 4 shown, the color depth represents the density or intensity of Hyphantria cunea. The dark areas represent a higher distribution density of Hyphantria cunea, and the light areas represent a lower distribution density. In the figure, areas A and B are high-density areas, and area C is a low-density area. On the heat map, it can be observed that the areas showing dark red represent a higher distribution density of Hyphantria cunea in these areas. These areas may be the main habitats of Hyphantria cunea or its more suitable living environment. The areas showing light colors represent a lower distribution density of Hyphantria cunea, which may be due to unsuitable environmental conditions or more effective human control measures.

[0110] Step S33, determine the alien species diffusion information and potential risk areas according to the epidemic area time distribution data and the epidemic area spatial distribution data.

[0111] It should be understood that based on the time distribution data and spatial distribution data of the epidemic areas, the peak year of the number of epidemic areas, the trough year of the number of epidemic areas, the annual increase and decrease rate of epidemic areas, the annual diffusion information of epidemic areas, the information of concentrated areas of epidemic areas, the distribution density information of epidemic areas, the diffusion boundary information of epidemic areas, and the distribution pattern information of epidemic areas can be determined. Based on the peak year of the number of epidemic areas, the trough year of the number of epidemic areas, and the annual increase and decrease rate of epidemic areas, the overall trend of the number of epidemic areas in future years, whether it is increasing or decreasing, and the intensity of the increase and decrease of the number of epidemic areas can be determined, and the prediction information of the increase and decrease of alien species can be obtained. According to the prediction information of the increase and decrease of alien species, combined with the information of concentrated areas of epidemic areas, the distribution density information of epidemic areas, the diffusion boundary information of epidemic areas, and the distribution pattern information of epidemic areas, it can be predicted which provinces and cities will have an increasing trend of epidemic areas and which provinces and cities will have a decreasing trend of epidemic areas in future years. The diffusion range of the provinces and cities with an increasing trend can be predicted, and the habitat environment parameters (such as the temperature, humidity, and soil type of the epidemic areas, etc.) of the areas within the diffusion range can be obtained, and the species diffusion information can be obtained, and the possibility of the occurrence of epidemics in the districts and counties within the diffusion range can be evaluated, and the potential risk areas can be determined from them.

[0112] In this embodiment, the time distribution data of the epidemic areas is determined according to the preprocessed epidemic area data; the spatial distribution data of the epidemic areas is determined according to the preprocessed epidemic area data; the alien species diffusion information and potential risk areas are determined according to the time distribution data and the spatial distribution data of the epidemic areas. Through the comprehensive analysis of the time and space dimensions, not only can the accuracy of the prediction be improved, but also a scientific basis can be provided for formulating targeted prevention and control measures, and the ecological harm and economic losses caused by alien species can be reduced.

[0113] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the method for analyzing the trend of alien species invasion in the present application. Based on this technical concept, more forms of simple transformations are within the protection scope of the present application.

[0114] The present application also provides an apparatus for analyzing the trend of alien species invasion. Please refer to Figure 5 and the apparatus for analyzing the trend of alien species invasion includes:

[0115] A data acquisition module 10 for acquiring historical data of alien species epidemic areas;

[0116] A data processing module 20 for performing data preprocessing on the historical data of alien species epidemic areas to obtain preprocessed epidemic area data;

[0117] The data processing module 20 is further configured to determine alien species diffusion information and potential risk areas according to the preprocessed epidemic area data;

[0118] A data prediction module 30 for obtaining predicted data of alien species epidemic areas according to the preprocessed epidemic area data and a long short-term memory model;

[0119] The data analysis module 40 is configured to determine the alien species invasion trend information based on the alien species diffusion information, the potential risk areas, and the prediction data of the alien species epidemic areas.

[0120] In one embodiment, the data processing module 20 is further configured to determine the time distribution data of the epidemic areas according to the preprocessed epidemic area data; determine the spatial distribution data of the epidemic areas according to the preprocessed epidemic area data; and determine the alien species diffusion information and the potential risk areas according to the time distribution data and the spatial distribution data of the epidemic areas.

[0121] In one embodiment, the data processing module 20 is further configured to determine the number of newly added epidemic areas over the years, the number of epidemic areas lifted from prevention over the years, the epidemic area information of provinces and cities over the years, and the epidemic area information of districts and counties over the years according to the preprocessed epidemic area data; and determine the time distribution data of the epidemic areas according to the number of newly added epidemic areas over the years, the number of epidemic areas lifted from prevention over the years, the epidemic area information of provinces and cities over the years, and the epidemic area information of districts and counties over the years.

[0122] In one embodiment, the data processing module 20 is further configured to determine the number of epidemic areas in each district and county according to the preprocessed epidemic area data; draw a heat map of the alien species distribution based on the number of epidemic areas in each district and county; and determine the spatial distribution data of the epidemic areas according to the heat map of the epidemic areas.

[0123] In one embodiment, the data prediction module 30 is further configured to divide the preprocessed epidemic area data into an input sequence and an output sequence based on time series information; learn a long short-term memory model based on the input sequence and the output sequence to obtain an alien species epidemic area prediction model; and obtain the alien species epidemic area prediction data according to the current data of the alien species epidemic area through the alien species epidemic area prediction model.

[0124] In one embodiment, the data prediction module 30 is further configured to construct a training set and a test set based on the input sequence and the output sequence; obtain the initial epidemic area prediction data of the long short-term memory model for the training set; calculate the mean square error based on the initial epidemic area prediction data and the actual epidemic area data of the test set; and optimize the long short-term memory model based on the mean square error to obtain an alien species epidemic area prediction model.

[0125] In one embodiment, the data processing module 20 is further configured to perform data cleaning on the historical data of the alien species epidemic area to obtain cleaned data; perform missing value processing on the cleaned data to obtain missing value processed data; and perform normalization on the missing value processed data to obtain preprocessed epidemic area data.

[0126] The alien species invasion trend analysis device provided by this application adopts the alien species invasion trend analysis method in the above-mentioned embodiment, and can solve the technical problem of how to accurately predict the alien species invasion trend to achieve early warning and scientific prevention and control of alien species. Compared with the prior art, the beneficial effects of the alien species invasion trend analysis device provided by this application are the same as those of the alien species invasion trend analysis method provided by the above-mentioned embodiment, and other technical features in the alien species invasion trend analysis device are the same as the features disclosed in the above-mentioned embodiment method, which will not be elaborated here.

[0127] This application provides an alien species invasion trend analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the alien species invasion trend analysis method in the first embodiment above.

[0128] Refer to the following Figure 6 , which shows a schematic structural diagram of an alien species invasion trend analysis device suitable for implementing the embodiments of this application. The alien species invasion trend analysis device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The alien species invasion trend analysis device shown is only an example and should not impose any limitations on the functions and usage scopes of the embodiments of this application.

[0129] As shown in Figure 6As shown, the alien species invasion trend analysis device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the alien species invasion trend analysis device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the alien species invasion trend analysis device to communicate with other devices wirelessly or wireline to exchange data. Although the figure shows an alien species invasion trend analysis device having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0130] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0131] The alien species invasion trend analysis device provided by the present application adopts the alien species invasion trend analysis method in the above embodiments, and can solve the technical problem of how to accurately predict the alien species invasion trend to achieve early warning and scientific prevention and control of alien species. Compared with the prior art, the beneficial effects of the alien species invasion trend analysis device provided by the present application are the same as those of the alien species invasion trend analysis method provided by the above embodiments, and other technical features in the alien species invasion trend analysis device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0132] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0133] As described above, the above are only specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0134] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the alien species invasion trend analysis method in the above embodiments.

[0135] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory), or flash memory, optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0136] The above computer-readable storage medium can be included in the alien species invasion trend analysis device; or it can exist separately without being assembled into the alien species invasion trend analysis device.

[0137] The above computer-readable storage medium carries one or more programs, which, when executed by the alien species invasion trend analysis device, cause the alien species invasion trend analysis device to: obtain historical data of alien species epidemic areas; perform data preprocessing on the historical data of alien species epidemic areas to obtain preprocessed epidemic area data; determine alien species diffusion information and potential risk areas according to the preprocessed epidemic area data; obtain predicted data of alien species epidemic areas according to the preprocessed epidemic area data and the long short-term memory model; and determine alien species invasion trend information according to the alien species diffusion information, the potential risk areas, and the predicted data of alien species epidemic areas.

[0138] Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or may be connected to an external computer (for example, by connecting through an Internet service provider using the Internet).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0140] The modules involved in the embodiments of the present application can be implemented in software or in hardware. In some cases, the name of the module does not constitute a limitation on the unit itself.

[0141] The readable storage medium provided by the present application is a computer-readable storage medium, and the computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned alien species invasion trend analysis method, which can solve the technical problem of how to accurately predict the trend of alien species invasion to achieve early warning and scientific prevention and control of alien species. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the alien species invasion trend analysis method provided in the above embodiments, and will not be elaborated here.

[0142] The present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the alien species invasion trend analysis method as described above are implemented.

[0143] The computer program product provided by the present application can solve the technical problem of how to accurately predict the trend of alien species invasion to achieve early warning and scientific prevention and control of alien species. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the alien species invasion trend analysis method provided in the above embodiments, and will not be elaborated here.

[0144] The above are only some embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made by using the description and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A method for analyzing the invasion trend of alien species, characterized in that, The method for analyzing the trend of alien species invasion includes: Obtaining historical data of alien species epidemic areas; Performing data preprocessing on the historical data of alien species epidemic areas to obtain preprocessed epidemic area data; Determining alien species diffusion information and potential risk areas according to the preprocessed epidemic area data; Obtaining predicted data of alien species epidemic areas based on the preprocessed epidemic area data and a long short-term memory model; Determining alien species invasion trend information according to the alien species diffusion information, the potential risk areas, and the predicted data of alien species epidemic areas.

2. The method according to claim 1, characterized in that The step of determining alien species diffusion information and potential risk areas according to the preprocessed epidemic area data includes: Determining epidemic area time distribution data according to the preprocessed epidemic area data; Determining epidemic area spatial distribution data according to the preprocessed epidemic area data; Determining alien species diffusion information and potential risk areas according to the epidemic area time distribution data and the epidemic area spatial distribution data.

3. The method according to claim 2, characterized in that, The step of determining epidemic area time distribution data according to the preprocessed epidemic area data includes: Determining the number of newly added epidemic areas in each year, the number of epidemic areas lifted from prevention and control in each year, the epidemic area information of provinces and cities in each year, and the epidemic area information of districts and counties in each year according to the preprocessed epidemic area data; Determining epidemic area time distribution data according to the number of newly added epidemic areas in each year, the number of epidemic areas lifted from prevention and control in each year, the epidemic area information of provinces and cities in each year, and the epidemic area information of districts and counties in each year.

4. The method according to claim 2, wherein The step of determining epidemic area spatial distribution data according to the preprocessed epidemic area data includes: Determining the number of epidemic areas in each district and county according to the preprocessed epidemic area data; Drawing a heat map of alien species distribution based on the number of epidemic areas in each district and county; Determining epidemic area spatial distribution data according to the epidemic area heat map.

5. The method according to claim 1, characterized in that, The step of obtaining predicted data of alien species epidemic areas based on the preprocessed epidemic area data and a long short-term memory model includes: Dividing the preprocessed epidemic area data into an input sequence and an output sequence based on time series information; Learning the long short-term memory model based on the input sequence and the output sequence to obtain a predicted model of alien species epidemic areas; Obtaining predicted data of alien species epidemic areas according to the current data of alien species epidemic areas through the predicted model of alien species epidemic areas.

6. The method according to claim 5, wherein The step of learning the long short-term memory model based on the input sequence and the output sequence to obtain a predicted model of alien species epidemic areas includes: Constructing a training set and a test set based on the input sequence and the output sequence; Obtaining initial predicted epidemic area data of the long short-term memory model for the training set; Calculating the mean square error based on the initial predicted epidemic area data and the actual epidemic area data of the test set; Optimizing the long short-term memory model based on the mean square error to obtain a predicted model of alien species epidemic areas.

7. The method according to any one of claims 1 to 6, characterized in that, The step of performing data preprocessing on the historical data of alien species epidemic areas to obtain preprocessed epidemic area data includes: Performing data cleaning on the historical data of alien species epidemic areas to obtain cleaned data; Performing missing value processing on the cleaned data to obtain data after missing value processing; Normalizing the data after missing value processing to obtain preprocessed epidemic area data.

8. An alien species invasion trend analysis device, characterized in that, The device includes: A data acquisition module for obtaining historical data of alien species epidemic areas; A data processing module for preprocessing the historical data of the alien species epidemic area to obtain preprocessed epidemic area data; The data processing module is further configured to determine alien species diffusion information and potential risk areas based on the preprocessed epidemic area data; A data prediction module for obtaining predicted data of the alien species epidemic area according to the preprocessed epidemic area data and a long short-term memory model; A data analysis module for determining alien species invasion trend information based on the alien species diffusion information, the potential risk areas, and the predicted data of the alien species epidemic area.

9. An alien species invasion trend analysis device, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the alien species invasion trend analysis method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the alien species invasion trend analysis method according to any one of claims 1 to 7 are implemented.

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