CN117575347A - Intelligent analysis method and system for alien species invasion risk - Google Patents
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Abstract
The invention discloses an intelligent analysis method and system for alien species invasion risk, which relate to the technical field of invasive species investigation and mainly comprise the following steps of target establishment, data retrieval, feature establishment, model import, model prediction, prediction evaluation and chart generation.
Description
Technical Field
The invention relates to the technical field of invasive species investigation, in particular to an intelligent analysis method and system for alien species invasive risk.
Background
Foreign species invasion refers to the process of a species entering, colonising and breeding from its native region to a new non-native region, commonly referred to as a foreign species, invasive species or foreign invasive species, which invades a global environmental problem with potential negative impact on biodiversity, ecosystem function and economic industry.
The current investigation of foreign invasive species information mostly adopts the following modes:
1. field investigation: this is the most common investigation mode, by field investigation and observation to determine the presence and distribution of foreign invasive species. This includes conducting species inventory surveys, spot surveys, patrol and capture, and the like.
2. Remote sensing technology: by utilizing satellite remote sensing and aerial remote sensing technologies, large-scale earth surface information including vegetation types, vegetation coverage, changes and the like can be acquired. Such information may be used to detect and monitor the distribution and spread of alien invasive species.
3. Molecular biology techniques: molecular biology techniques can be used to detect and identify foreign invasive species. For example, species can be rapidly identified by DNA barcode technology, while PCR technology can detect the presence and quantity of species.
4. Database and literature investigation: by querying existing databases and literature, distribution and record information about foreign invasive species can be obtained. These databases and literature may provide information regarding the distribution of species, intrusion history and impact.
However, there are significant limitations in the above ways in that a comprehensive investigation of alien invasive species requires a lot of time, labor and financial resources, however, many areas are limited in resources and cannot cover all potential invasive species and investigation areas, and some alien invasive species may be difficult to detect and identify, especially in early invasive phases, which species may be similar to or hidden from local species, resulting in investigation difficulties and misjudgment, and incomplete alien species information data, while investigation data may be incomplete or outdated due to the distribution and diffusion of alien invasive species being generally dynamic, which may lead to difficulties in accurate assessment of species distribution and impact.
Disclosure of Invention
The main objective of the present invention is to provide an intelligent analysis method and system for alien invasive risk, which solves the problems that alien invasive species mentioned in the background art may be difficult to detect and identify, especially in early invasive stage.
In order to achieve the above purpose, the invention provides an intelligent analysis method for the invasion risk of foreign species, which comprises the following steps:
s1: target establishment
Selecting a specified foreign invasive species for model prediction;
s2: data retrieval
Creating a data set of geographic distribution points specifying the foreign invasive species;
s3: feature creation
Making biological climate factor data specifying the foreign invasive species;
s4: model importation
Importing the manufactured geographic distribution point location data set and the biological climate factor data into a MaxEnt predictive analysis model;
s5: model prediction
Predicting by using a MaxEnt prediction analysis model to generate a prediction result;
s6: predictive assessment
Performing accuracy assessment on the model prediction result, if the accuracy is high, performing the next step, and if the accuracy is insufficient, returning to the step S2;
s7: chart generation
And (5) preparing a global adaptive region grade distribution prediction result graph of the specified foreign invasive species.
Preferably, the step S2 is mainly used for searching and collecting related data of the foreign invasive species, and specifically includes the following steps:
s201: data collection
Acquiring point location data samples of species distribution through a species diversity data platform, a digital plant specimen library and foreign invasive species distribution acquired by manual investigation;
s202: data verification
And performing thinning treatment on the collected geographic point location distribution samples, deleting species distribution data points with incomplete repeated and data, and obtaining a species geographic distribution point location data set.
Preferably, in the step S3, analysis and retrieval are performed mainly on biological climate factor data of an external invading organism, and the method specifically includes the following steps:
s301: data download
Downloading 19 biological climate factors under the current and future climate conditions worldwide from a world dClim global climate database;
s302: data processing
Preprocessing the downloaded biological climate factors by using ArcGIS software, and generating asc-format climate factor files which can be used by a MaxEnt model.
Preferably, in the step S5, the data is generated by mainly performing tuning with respect to the built MaxEnt predictive analysis model, and the method specifically includes the following steps:
s501: prediction method setting
Opening a cutting method prediction method in a MaxEnt prediction analysis model;
s502: drawing response curves
Opening a response curve drawing function in a MaxEnt predictive analysis model;
s503: making predictive pictures
Opening a predictive graph making function in a MaxEnt predictive analysis model;
s504: parameter setting
Setting the operation parameters of the MaxEnt model, setting a certain proportion of data as a training factor, setting the other part of data as a regression test factor, and repeating the operation for 5000 times and n times.
Preferably, in the step S6, the accuracy of the model prediction result is evaluated mainly by the result and the AUC value generated by the ROC curve method, if the calculated AUC average value is lower, the step S5 is repeated, the training factor proportion and the repetition number are adjusted in the step S5 and the step S4, and iteration is continued until the optimal value of the AUC average value is simulated, at this time, the accuracy of the model prediction result is higher, and the species distribution result can be predicted accurately according to the biological climate factor.
Preferably, in the step S7, the generation of the graph is performed mainly for the prediction structure generated by the MaxEnt prediction analysis model, and specifically includes the following steps:
s701: predictive graph generation
Processing the prediction result by using ArcGis software, converting the result into a grid file, and generating a prediction map of an adaptation area of the specified foreign invasive species;
s702: prediction graph partitioning
Carrying out the classification of the adaptive region on the adaptive region prediction graph by adopting a natural intermittent processing method;
s703: expansion generation
And generating a global adaptation zone level distribution prediction graph.
In order to achieve the above object, the present invention provides an intelligent analysis system for alien species invasion risk, the intelligent analysis system for alien species invasion risk comprises the following modules:
the database module is used for collecting data related to foreign species invasion, and the data collection module comprises a data collection module, a data inspection module, a self-updating module and a data classification module;
the model building module is used for building a MaxEnt predictive analysis model and comprises a data processing module, a feature extraction module and an algorithm updating module;
the model verification module is used for verifying and optimizing the built MaxEnt predictive analysis model and comprises a parameter estimation module, a model evaluation module and a data feedback module;
the data cleaning module is used for cleaning and optimizing the data of the predictive analysis data generated by the MaxEnt predictive analysis model, and comprises a data translation module, a data impurity removal module and a data transmission module;
and the chart generation module is used for generating an adaptive region prediction chart aiming at the collected prediction result by using ArcGis software, and comprises a chart conversion module, a chart division module and a data storage module.
Preferably, the data collection module is configured to perform format conversion on collected data through a system internal history information and an external information network on a point location distribution sample of an alien invasive species, a suitable living climate, and a relevant survey record obtained by field survey collection, so that the data can be fed back to the model building module in the same format, and meanwhile, perform mean interpolation, median interpolation and rejection replacement on missing and repeated data, the self-updating module is configured to keep real-time interconnection with the external data, perform real-time updating on the alien species information generating change, and synchronize the real-time updating with the alien species information database for inductive collection, and the data classification module is configured to perform multi-stage classification on alien species of different types, including living environment division, hazard division and hazard type division, and support rapid positioning of the alien invasive species to be analyzed in the database in a multi-stage classification manner.
Preferably, the parameter estimation module is used for customizing parameters or importing historical parameters, inputting the set parameters into the model building module for operation calculation, providing historical data and setting output data for the model estimation module to carry out comparison and judgment, the model estimation module is used for comparing the input customized parameters or imported historical data output results with the set output values and the historical results, judging the performance of the model according to the similarity degree of the characteristic points of the two groups of data, and the data feedback module is used for generating a corresponding MaxEnt predictive analysis model data report according to the results fed back by the model estimation module, and giving a reference modification scheme according to an abnormal curve area for researchers to carry out reference modification.
Preferably, the data translation module is configured to translate the generated electrical signal or digital signal report into a text report, and feed back the generated text report to a data terminal for research by a developer, and the data impurity removal module is configured to perform signal filtration on transmission of the electrical signal in the system through a band reject filter, where a general frequency domain response formula is as follows:
H_bandstop(u,v)＝H_high(u,v)×(1-H_low(u,v))；
wherein:
h_bandstop (u, v) represents the frequency domain response function of the band reject filter, which is the filter response of the input signal in the frequency domain.
H_high (u, v) is the frequency domain response function of the high pass filter.
H_low (u, v) is the frequency domain response function of the low pass filter.
(1-h_low (u, v)) represents the complement of the low-pass filter, i.e., the inverse of the low-pass filter response function;
the data transmission module is used for transmitting the predictive analysis data generated by the MaxEnt predictive analysis model to the chart analysis module, and reducing the interference possibly suffered by the signal in the transmission process in a mode of adding an external shielding component in the signal transmission process, so that the efficiency of the chart generation module in executing the predictive graph generation is improved, and the data quantity of abnormal information in the chart is reduced.
Compared with the prior art, the invention has the following beneficial effects:
(1) When the intelligent analysis method for the invasion risk of the foreign species is used, species distribution information and environmental climate data are combined, and relevant information data are input into a MaxEnt adaptive region analysis model, so that the MaxEnt adaptive region analysis model can predict an adaptive region of the invasive species, and the drawn prediction result diagram of the adaptive region of the foreign invasive species is beneficial to people to master the distribution situation and distribution density of the invasive species, and guide local foreign species invasion managers to take corresponding protection measures so as to maintain stability and health of biodiversity.
(2) When the intelligent analysis method for the invasion risk of the foreign species is used, the accuracy of the model is estimated through a constructed ROC curve in the prediction estimation process, and the accuracy of the model is specifically scored through an AUC value below the ROC curve, wherein the AUC value is closer to 1, the better the performance of the model is represented, the positive case and the negative case can be better distinguished, the performance of the model is equal to random guess when the AUC value is 0.5, the performance of the model is not equal to random guess when the AUC value is smaller than 0.5, so that the accuracy of the model is continuously optimized and calibrated through the prediction estimation process, and the accuracy and the integrity of investigation data are guaranteed.
(3) When the intelligent foreign species invasion risk analysis system is used, the electric signals transmitted in the system are subjected to filtering treatment through the band-stop filter in the data cleaning module, so that only signals in a specified frequency range can be normally transmitted in the transmission process, abnormal interference signals can be filtered in the transmission process, the accuracy in the signal transmission process is ensured, and meanwhile, the chart conversion module can be used for converting stable signals into charts more quickly after receiving the stable signals.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a flow chart of the method S2 of the present invention;
FIG. 3 is a flow chart of the method S3 of the present invention;
FIG. 4 is a flow chart of the method S5 of the present invention;
FIG. 5 is a flow chart of the method S7 of the present invention;
FIG. 6 is a flow diagram of an intelligent analysis system for risk of foreign species invasion;
FIG. 7 is a block diagram of a database module according to the present invention;
FIG. 8 is a flow chart of a model building module of the present invention;
FIG. 9 is a flow chart of a model verification module of the present invention;
FIG. 10 is a flow chart of a data cleaning module according to the present invention;
FIG. 11 is a flowchart of a chart generation module of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Referring to fig. 1-5, the present invention provides a technical solution: an intelligent analysis method for alien species invasion risk comprises the following steps:
s1: target establishment
Selecting a specified foreign invasive species for model prediction;
s2: data retrieval
Creating a data set of geographic distribution points specifying the foreign invasive species;
s3: feature creation
Making biological climate factor data specifying the foreign invasive species;
s4: model importation
Importing the manufactured geographic distribution point location data set and the biological climate factor data into a MaxEnt predictive analysis model;
s5: model prediction
Predicting by using a MaxEnt prediction analysis model to generate a prediction result;
s6: predictive assessment
Performing accuracy assessment on the model prediction result, if the accuracy is high, performing the next step, and if the accuracy is insufficient, returning to the step S2;
s7: chart generation
And (5) preparing a global adaptive region grade distribution prediction result graph of the specified foreign invasive species.
Specifically, the step S2 is mainly used for searching and collecting relevant data of the foreign invasive species, and specifically includes the following steps:
s201: data collection
Acquiring point location data samples of species distribution through a species diversity data platform, a digital plant specimen library and foreign invasive species distribution acquired by manual investigation;
s202: data verification
And performing thinning treatment on the collected geographic point location distribution samples, deleting species distribution data points with incomplete repeated and data, and obtaining a species geographic distribution point location data set.
S3, analyzing and searching biological climate factor data mainly aiming at external invasive organisms, wherein the method specifically comprises the following steps of:
s301: data download
Downloading 19 biological climate factors under the current and future climate conditions worldwide from a world dClim global climate database;
s302: data processing
Preprocessing the downloaded biological climate factors by using ArcGIS software, and generating asc-format climate factor files which can be used by a MaxEnt model.
S5, mainly aiming at the built MaxEnt predictive analysis model, performing adjustment to generate data, and specifically comprising the following steps of:
s501: prediction method setting
Opening a cutting method prediction method in a MaxEnt prediction analysis model;
s502: drawing response curves
Opening a response curve drawing function in a MaxEnt predictive analysis model;
s503: making predictive pictures
Opening a predictive graph making function in a MaxEnt predictive analysis model;
s504: parameter setting
Setting the operation parameters of the MaxEnt model, setting a certain proportion of data as a training factor, setting the other part of data as a regression test factor, and repeating the operation for 5000 times and n times.
And S6, evaluating accuracy of the model prediction result mainly through a result and an AUC value generated by an ROC curve method, repeating S5 if the calculated AUC average value is lower, and adjusting the proportion and the repetition times of the training factors in the steps S5 and S4, and continuously iterating until an optimal value of the AUC average value is simulated, wherein the accuracy of the model prediction result is higher, and the species distribution result can be predicted accurately according to the biological climate factors.
S7, generating a chart mainly aiming at a prediction structure generated by a MaxEnt prediction analysis model, wherein the method specifically comprises the following steps of:
s701: predictive graph generation
Processing the prediction result by using ArcGis software, converting the result into a grid file, and generating a prediction map of an adaptation area of the specified foreign invasive species;
s702: prediction graph partitioning
Carrying out the classification of the adaptive region on the adaptive region prediction graph by adopting a natural intermittent processing method;
s703: expansion generation
And generating a global adaptation zone level distribution prediction graph.
Referring to fig. 6-11, the present invention provides a technical solution: a method for intelligent analysis of alien-species invasion risk, the system comprising the following modules:
the database module is used for collecting data related to foreign species invasion, and the data collection module comprises a data collection module, a data inspection module, a self-updating module and a data classification module;
the model building module is used for building a MaxEnt predictive analysis model and comprises a data processing module, a feature extraction module and an algorithm updating module;
the data processing module is used for carrying out processing including format conversion, processing of missing data and processing of abnormal values on the collected data before the MaxEnt model is built, the feature extraction module is used for extracting features used for modeling from the collected data, the features can include extracting relevant features representing the habitat requirements of species from environment variables, including temperature, precipitation and soil type information, the algorithm updating module is used for receiving feedback signals in the model checking module, and can optimize the abnormal data generated in the algorithm operation process when the built MaxEnt predictive analysis model is in doubt, and the accuracy of the model predictive result is evaluated through a result generated by an ROC curve method and an AUC value.
The model verification module is used for verifying and optimizing the built MaxEnt predictive analysis model and comprises a parameter estimation module, a model evaluation module and a data feedback module;
the data cleaning module is used for cleaning and optimizing the data of the predictive analysis data generated by the MaxEnt predictive analysis model, and comprises a data translation module, a data impurity removal module and a data transmission module;
and the chart generation module is used for generating an adaptive region prediction chart aiming at the collected prediction result by using ArcGis software, and comprises a chart conversion module, a chart division module and a data storage module.
The chart conversion module is used for filling the received digital signal information into a table and presenting the generated prediction analysis data through standardized table text, the chart division module is used for dividing a suitable area by adopting a natural intermittent processing method suitable area prediction chart, and the data storage module is used for storing the generated chart information and supporting a self-defined tracing period, so that people can store a prediction result according to a specific storage space.
The data collection module is used for carrying out format conversion on collected data through system internal historical information and an external information network to obtain relevant investigation records of point location distribution samples, suitable living climates and field investigation collection of foreign invasive species, the data inspection module is used for carrying out format conversion on the collected data, so that the data can be fed back to the model building module in the same format data, mean interpolation, median interpolation and rejection replacement of abnormal data are carried out on missing and repeated data, the self-updating module is used for keeping real-time interconnection with the external data, carrying out real-time updating on the information of the foreign species which generates changes and synchronously carrying out induction collection on the information of the foreign species which generates changes in a foreign species information database, and the data classification module is used for carrying out multistage classification on the foreign species of different types, including living environment division, hazard division and hazard type division, and supporting rapid positioning of the foreign invasive species to be analyzed in the database in a multistage classification mode.
The parameter estimation module is used for customizing parameters or importing historical parameters, inputting the set parameters into the model establishment module for operation calculation, providing historical data and setting output data for the model estimation module to carry out comparison and judgment, the model estimation module is used for comparing input customized parameters or imported historical data output results with set output values and historical results, judging the performance of the model according to the similarity degree of characteristic points of two groups of data, and the data feedback module is used for generating a corresponding MaxEnt predictive analysis model data report according to the results fed back by the model estimation module, and giving a reference modification scheme according to an abnormal curve area for reference modification by researchers.
The data translation module is used for translating the generated electric signals or digital signal reports into text reports and feeding the generated text reports back to the data terminal for research by research personnel, and the data impurity removal module is used for carrying out signal filtration on the transmission of the electric signals in the system through the band elimination filter, and the general frequency domain response formula is as follows:
H_bandstop(u,v)＝H_high(u,v)×(1-H_low(u,v))；
wherein:
h_bandstop (u, v) represents the frequency domain response function of the band reject filter, which is the filter response of the input signal in the frequency domain.
H_high (u, v) is the frequency domain response function of the high pass filter.
H_low (u, v) is the frequency domain response function of the low pass filter.
(1-h_low (u, v)) represents the complement of the low-pass filter, i.e., the inverse of the low-pass filter response function;
the data transmission module is used for transmitting the predictive analysis data generated by the MaxEnt predictive analysis model to the chart analysis module, and reducing the interference possibly suffered by the signal in the transmission process in a mode of adding an external shielding component in the signal transmission process, so that the efficiency of the chart generation module in executing the predictive graph generation is improved, and the data quantity of abnormal information in the chart is reduced.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An intelligent analysis method for alien species invasion risk is characterized by comprising the following steps: the method comprises the following steps:
s1: target establishment
Selecting a specified foreign invasive species for model prediction;
s2: data retrieval
Creating a data set of geographic distribution points specifying the foreign invasive species;
s3: feature creation
Making biological climate factor data specifying the foreign invasive species;
s4: model importation
Importing the manufactured geographic distribution point location data set and the biological climate factor data into a MaxEnt predictive analysis model;
s5: model prediction
Predicting by using a MaxEnt prediction analysis model to generate a prediction result;
s6: predictive assessment
Performing accuracy assessment on the model prediction result, if the accuracy is high, performing the next step, and if the accuracy is insufficient, returning to the step S2;
s7: chart generation
And (5) preparing a global adaptive region grade distribution prediction result graph of the specified foreign invasive species.
2. The intelligent analysis method for the risk of foreign species invasion according to claim 1, wherein: the step S2 is mainly used for searching and collecting related data of the foreign invasive species, and specifically comprises the following steps:
s201: data collection
Acquiring point location data samples of species distribution through a species diversity data platform, a digital plant specimen library and foreign invasive species distribution acquired by manual investigation;
s202: data verification
And performing thinning treatment on the collected geographic point location distribution samples, deleting species distribution data points with incomplete repeated and data, and obtaining a species geographic distribution point location data set.
3. The intelligent analysis method for the risk of foreign species invasion according to claim 1, wherein: in the step S3, analysis and retrieval are mainly carried out on biological climate factor data of external invading organisms, and the method specifically comprises the following steps:
s301: data download
Downloading 19 biological climate factors under the current and future climate conditions worldwide from a world dClim global climate database;
s302: data processing
Preprocessing the downloaded biological climate factors by using ArcGIS software, and generating asc-format climate factor files which can be used by a MaxEnt model.
4. The intelligent analysis method for the risk of foreign species invasion according to claim 1, wherein: in the step S5, the data is generated mainly by adjusting the built MaxEnt predictive analysis model, and the method specifically comprises the following steps:
s501: prediction method setting
Opening a cutting method prediction method in a MaxEnt prediction analysis model;
s502: drawing response curves
Opening a response curve drawing function in a MaxEnt predictive analysis model;
s503: making predictive pictures
Opening a predictive graph making function in a MaxEnt predictive analysis model;
s504: parameter setting
Setting the operation parameters of the MaxEnt model, setting a certain proportion of data as a training factor, setting the other part of data as a regression test factor, and repeating the operation for 5000 times and n times.
5. The intelligent analysis method for the risk of foreign species invasion according to claim 1, wherein: and in the step S6, the accuracy of the model prediction result is evaluated mainly through the result and the AUC value generated by the ROC curve method, if the calculated AUC average value is lower, the step S5 is repeated, the training factor proportion and the repetition times are adjusted in the step S5 and the step S4, iteration is continued until the optimal value of the AUC average value is simulated, the accuracy of the model prediction result is higher, and the species distribution result can be predicted accurately according to the biological climate factors.
6. The intelligent analysis method for the risk of foreign species invasion according to claim 1, wherein: in the step S7, the generation of the chart is mainly performed aiming at the prediction structure generated by the MaxEnt prediction analysis model, and the method specifically comprises the following steps:
s701: predictive graph generation
Processing the prediction result by using ArcGis software, converting the result into a grid file, and generating a prediction map of an adaptation area of the specified foreign invasive species;
s702: prediction graph partitioning
Carrying out the classification of the adaptive region on the adaptive region prediction graph by adopting a natural intermittent processing method;
s703: expansion generation
And generating a global adaptation zone level distribution prediction graph.
7. The intelligent foreign species intrusion risk analysis system according to claim 1, wherein: the foreign species invasion risk intelligent analysis system comprises the following modules:
the database module is used for collecting data related to foreign species invasion, and the data collection module comprises a data collection module, a data inspection module, a self-updating module and a data classification module;
the model building module is used for building a MaxEnt predictive analysis model and comprises a data processing module, a feature extraction module and an algorithm updating module;
the model verification module is used for verifying and optimizing the built MaxEnt predictive analysis model and comprises a parameter estimation module, a model evaluation module and a data feedback module;
the data cleaning module is used for cleaning and optimizing the data of the predictive analysis data generated by the MaxEnt predictive analysis model, and comprises a data translation module, a data impurity removal module and a data transmission module;
and the chart generation module is used for generating an adaptive region prediction chart aiming at the collected prediction result by using ArcGis software, and comprises a chart conversion module, a chart division module and a data storage module.
8. The intelligent foreign species intrusion risk analysis system according to claim 7, wherein: the data collection module is used for carrying out format conversion on collected data through a system internal historical information and an external information network on point location distribution samples of foreign invasive species, suitable living climate and relevant investigation records obtained through field investigation collection, the data inspection module is used for carrying out format conversion on the collected data, so that the data can be fed back to the model building module in the same format data, mean interpolation, median interpolation and rejection replacement of abnormal data are carried out on missing and repeated data, the self-updating module is used for keeping real-time interconnection with the external data, carrying out real-time updating on the information of the foreign species which generates changes and synchronously carrying out induction collection on the information of the foreign species which generates changes in a foreign species information database, and the data classification module is used for carrying out multistage classification on the foreign species of different types, including living environment division, hazard division and hazard type division, and supporting rapid positioning of the foreign invasive species to be analyzed in the database in a multistage classification mode.
9. The intelligent foreign species intrusion risk analysis system according to claim 7, wherein: the parameter estimation module is used for customizing parameters or importing historical parameters, inputting the set parameters into the model building module for operation calculation, providing historical data and setting output data for the model estimation module to compare and judge, the model estimation module is used for comparing input customized parameters or imported historical data output results with set output values and historical results, judging the performance of the model according to the similarity degree of characteristic points of two groups of data, and the data feedback module is used for generating a corresponding MaxEnt predictive analysis model data report according to the feedback result of the model estimation module, and giving a reference modification scheme according to an abnormal curve area for reference modification by researchers.
10. The intelligent foreign species intrusion risk analysis system according to claim 7, wherein: the data translation module is used for translating the generated electric signals or digital signal reports into text reports and feeding the generated text reports back to the data terminal for research by research personnel, and the data impurity removal module is used for carrying out signal filtration on the transmission of the electric signals in the system through the band elimination filter, and the general frequency domain response formula is as follows:
H_bandstop(u,v)＝H_high(u,v)×(1-H_low(u,v))；
wherein:
h_bandstop (u, v) represents the frequency domain response function of the band reject filter, which is the filter response of the input signal in the frequency domain.
H_high (u, v) is the frequency domain response function of the high pass filter.
H_low (u, v) is the frequency domain response function of the low pass filter.
(1-h_low (u, v)) represents the complement of the low-pass filter, i.e., the inverse of the low-pass filter response function;
the data transmission module is used for transmitting the predictive analysis data generated by the MaxEnt predictive analysis model to the chart analysis module, and reducing the interference possibly suffered by the signal in the transmission process in a mode of adding an external shielding component in the signal transmission process, so that the efficiency of the chart generation module in executing the predictive graph generation is improved, and the data quantity of abnormal information in the chart is reduced.
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