CN112613749B - Cross-border hidden high-risk factor risk intelligent analysis system - Google Patents
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Abstract
The application provides a cross-border hidden high-risk factor risk intelligent analysis system which can be used for carrying out risk analysis on cross-border biological factors through an intelligent algorithm so as to carry out biosafety decision management. According to the intelligent analysis system for cross-border hidden high-risk factors, disclosed by the application, based on an intelligent technology, basic data related to a plurality of cross-border factors are collected according to a cross-border factor list, the collected basic data are cleaned, and the risks of the cross-border hidden factors are qualitatively and quantitatively evaluated based on the cleaned basic data, so that the potential cross-border hidden high-risk factors are focused, the entering risks and the colonization risks of the cross-border hidden high-risk factors are respectively predicted and comprehensively evaluated, and the prediction results can be visually output.
Description
Technical Field
The application relates to the field of pest risk analysis, in particular to a cross-border hidden high-risk factor risk intelligent analysis system integrated by a data collection, cleaning, processing, assessment and visualization system.
Background
Cross-border organisms may cause great harm to public health, grain safety, ecosystems, etc. in China. In addition, cross-border organism populations can also alter the structure of the invaded ground ecosystem, greatly threatening local biodiversity. Therefore, there is a need to analyze the risk of cross-border organisms ("cross-border biological factors" or "cross-border factors", hereinafter "cross-border factors"), in particular, the pressure analysis of propagules of cross-border factors entering an invaded region, in order to determine the size of their risk of entry; and (3) in the adaptability analysis of the invaded region, determining the possibility, the adaptability range, the adaptability degree and the like of the colonisation of the invaded region, and identifying the cross-border hidden high-risk factors with higher risks, so that corresponding prevention and control measures are formulated based on the cross-border hidden high-risk factors.
At present, a great deal of research and application related to cross-border hidden high-risk factor risk analysis exist, but an intelligent analysis system for cross-border hidden high-risk factor risk does not exist yet, the intelligent analysis system is based on an intelligent algorithm, basic data related to a plurality of cross-border factors are collected according to a cross-border factor list, the collected basic data are cleaned, the risks of the cross-border hidden factors are qualitatively and quantitatively evaluated based on the cleaned basic data, so that the cross-border hidden high-risk factors are identified, the entering risks and the colonizing risks of the cross-border hidden high-risk factors are predicted and comprehensively evaluated, and visual output can be carried out on prediction results.
Disclosure of Invention
According to the application, a cross-border hidden high-risk factor risk intelligent analysis system is provided, which is used for carrying out risk analysis on cross-border factors through an intelligent algorithm so as to carry out biological safety decision management, and comprises a data acquisition module, a data cleaning module, a data processing module, a risk assessment module and a visual output module, wherein the data acquisition module acquires basic data related to a plurality of cross-border factors through the intelligent algorithm according to a cross-border factor list; the data cleaning module cleans the basic data acquired by the data acquisition module through an intelligent algorithm; the data processing module comprises a data processing preprocessing sub-module and a data processing post-processing sub-module; the data processing preprocessing sub-module processes the basic data cleaned by the data cleaning module through an intelligent algorithm to determine the respective entering risk and colonizing risk of a plurality of cross-border factors; the data processing post-processing sub-module determines a cross-border high-risk factor according to respective entering risks and colonization risks of a plurality of cross-border factors, establishes a propagule pressure model and a species distribution model of the cross-border high-risk factor by adopting an intelligent algorithm based on basic data corresponding to the determined cross-border high-risk factor, predicts the risks of the cross-border high-risk factor based on the established propagule pressure model and the established species distribution model, and determines the entering risks and colonization risks of the cross-border high-risk factor; the risk assessment module is used for carrying out risk qualitative or quantitative assessment through an intelligent algorithm according to a risk assessment index system based on the basic data cleaned by the data cleaning module, the respective entering risk and colonization risk of the plurality of cross-border factors determined by the data processing pretreatment sub-module and/or the entering risk and colonization risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module, so as to determine a risk comprehensive assessment value; the visual output module is used for visually outputting the basic data cleaned by the data cleaning module, the respective entering risk and colonizing risk of the cross-border factors determined by the data processing pretreatment sub-module, the entering risk and colonizing risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module and/or the risk comprehensive evaluation value determined by the risk evaluation module through an intelligent algorithm.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, the data cleaning module further determines the cross-border hidden factor according to the basic data collected by the data collecting module, and updates the cross-border factor list based on the determined cross-border hidden factor so that the updated cross-border factor list contains the cross-border hidden factor.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, a data acquisition module acquires basic data through an external database and an internal database; and/or the data acquisition module acquires basic data from the Internet through a web crawler algorithm.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, the data acquisition module also establishes an internal storage database based on the basic data acquired by the data acquisition module, the basic data cleaned by the data cleaning module, the respective entering risk and colonizing risk of the plurality of cross-border factors determined by the data processing pretreatment sub-module, the entering risk and colonizing risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module and/or the risk comprehensive evaluation value determined by the risk evaluation module.
In the cross-border hidden high risk factor risk intelligent analysis system according to the embodiment of the application, the basic data at least comprises geographic distribution data, biological data, environmental climate data, host data, trade data and/or geographic information data of the cross-border factors; and/or the base data includes at least a distribution of the cross-border factor, hazard information of the cross-border factor, movement information of the cross-border factor, hazard management information of the cross-border factor, and host information.
In a cross-border hidden high risk factor risk intelligent analysis system according to an embodiment of the application, a cross-border factor list is determined at least based on a target area; determining respective entry and colonization risks for the plurality of cross-border factors based at least on the cross-border factor entry likelihood, the colonization likelihood, and the potential loss degree; determining an entry risk of the cross-border high risk factor based at least on the frequency and population size of entry; a risk of colonization of cross-border high risk factors determined based at least on spatial extent and extent of the adaptive analysis; the risk assessment module further determines a quantization index for the risk assessment index system based on basic data of the external database and the internal database, and calculates a risk comprehensive assessment value according to the determined quantization index; the risk assessment index system is determined based at least on the distribution of the cross-border factor, the hazard of the cross-border factor, the movement probability of the cross-border factor, the hazard management difficulty of the cross-border factor, and the economic importance of the host.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, the data processing preprocessing sub-module is used for processing the basic data cleaned by the data cleaning module through a clustering algorithm so as to determine the entering risk and the colonizing risk of each of a plurality of cross-border factors; the data processing post-processing sub-module is used for establishing a propagule pressure model and a species distribution model of the cross-border high-Risk factors through a machine learning algorithm, or the data processing post-processing sub-module is used for establishing the propagule pressure model through @ Risk analysis software, and the data processing post-processing sub-module is used for establishing the species distribution model of the cross-border high-Risk factors through Maxent software.
In the intelligent analysis system for cross-border hidden high-risk factor risk according to the embodiment of the application, the data cleaning module also removes cross-border factors which do not correspond to the target area from the target cross-border factor list according to the geographic distribution data of the cross-border factors; the data cleaning module is used for cleaning the basic data acquired by the data cleaning module so as to remove the basic data which do not belong to the corresponding cross-border factors; the risk evaluation module determines whether the comprehensive risk evaluation value of the cross-border hidden high-risk factor exists in the internal database by searching the internal database, and if so, the comprehensive risk evaluation value of the cross-border hidden high-risk factor in the internal database is output as a result; the visual output module performs visual output through map software, or can send visual output results to a predetermined decision maker.
In the intelligent analysis system for cross-border hidden high-risk factor risk according to the embodiment of the application, the data cleaning module also removes cross-border factors which do not correspond to the target area from the target cross-border factor list according to the geographic distribution data of the cross-border factors; the data cleaning module is used for cleaning the basic data acquired by the data cleaning module so as to remove the basic data which do not belong to the corresponding cross-border factors; the risk evaluation module searches the internal database to determine whether the comprehensive risk evaluation value of the cross-border hidden high-risk factor exists in the internal database, and if the comprehensive risk evaluation value exists and is still scientific and reasonable through evaluation, the comprehensive risk evaluation value of the cross-border hidden high-risk factor in the internal database is output as a result; the visual output module performs visual output through map software, or can send visual output results to a predetermined decision maker.
The data processing preprocessing submodule processes basic data through an artificial neural network algorithm to determine respective entering risks and colonizing risks of a plurality of cross-border factors; or the data processing preprocessing submodule processes the basic data through an unsupervised artificial neural network algorithm, a k-means algorithm or a hierarchical clustering algorithm to determine the entering risk and the colonizing risk of each of the plurality of cross-border factors.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, a data processing post-processing sub-module establishes a propagule pressure model of the cross-border high-risk factor through a maximum likelihood estimation algorithm and establishes a species distribution model of the cross-border high-risk factor through a maximum entropy algorithm; the data processing post-processing sub-module further uses a first part of basic data in basic data corresponding to the cross-border high-risk factors as a training data set for establishing a species distribution model, and uses a second part of basic data in basic data corresponding to the cross-border high-risk factors as a test data set for testing the established species distribution model, wherein the proportion of the first part of basic data in the basic data corresponding to the cross-border high-risk factors is 90%, and the proportion of the second part of basic data in the basic data corresponding to the cross-border high-risk factors is 10%; the data processing post-processing sub-module also evaluates the established species distribution model based on AUC (Area Under the Curve, i.e., area under ROC curve).
According to the intelligent analysis system for cross-border hidden high-risk factors, disclosed by the application, based on an intelligent technology, basic data related to a plurality of cross-border factors are collected according to a cross-border factor list, the collected basic data are cleaned, and the risks of the cross-border hidden factors are qualitatively and quantitatively evaluated based on the cleaned basic data, so that the potential cross-border hidden high-risk factors are focused, the entering risks and the colonization risks of the cross-border hidden high-risk factors are predicted and comprehensively evaluated, and the prediction results can be visually output.
Drawings
FIG. 1 shows a block diagram of a cross-border, hidden, high risk factor risk intelligent analysis system in accordance with an embodiment of the present application;
FIG. 2 shows a block diagram of a data acquisition module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the present application;
FIG. 3 shows a block diagram of a data cleaning module in a cross-border hidden high risk factor risk intelligent analysis system according to an embodiment of the present application;
FIG. 4 illustrates a block diagram of another embodiment of a data cleaning module in a cross-border, hidden high risk factor risk intelligent analysis system in accordance with an embodiment of the present application;
FIG. 5 shows a block diagram of a data processing module in a cross-border, hidden high risk factor risk intelligent analysis system in accordance with an embodiment of the present application;
FIG. 6 shows a block diagram of a risk assessment module in a cross-border, hidden high risk factor risk intelligent analysis system in accordance with an embodiment of the present application;
FIG. 7 shows a block diagram of another embodiment of a data acquisition module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the present application; and
FIG. 8 shows a block diagram of a visual output module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The present application will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the substances, and not restrictive of the application. For simplicity, the same or similar reference numbers are used in the description of embodiments of the application for the same or similar apparatus/method steps.
In addition, the embodiments of the present application and the features of the embodiments may be combined with each other without collision.
According to the application, a cross-border hidden high-risk factor risk intelligent analysis system is provided. FIG. 1 shows a block diagram of a cross-border, hidden high risk factor risk intelligent analysis system in accordance with an embodiment of the present application. As shown in fig. 1, the cross-border hidden high risk factor risk intelligent analysis system 10 is configured to analyze cross-border factor risk through an intelligent algorithm for biosafety decision management, and the cross-border hidden high risk factor risk intelligent analysis system 10 includes a data acquisition module 110, a data cleaning module 120, a data processing module 130, a risk assessment module 140, and a visual output module 150. The data acquisition module acquires basic data related to a plurality of cross-border factors through an intelligent algorithm according to the cross-border factor list; the data cleaning module cleans the basic data acquired by the data acquisition module through an intelligent algorithm; the data processing module comprises a data processing preprocessing sub-module and a data processing post-processing sub-module, wherein the data processing preprocessing sub-module processes the basic data cleaned by the data cleaning module through an intelligent algorithm so as to determine the respective entering risk and colonizing risk of a plurality of cross-border factors; the data processing post-processing sub-module determines a cross-border high-risk factor according to respective entering risks and colonization risks of a plurality of cross-border factors, establishes a propagule pressure model and a species distribution model of the cross-border high-risk factor by adopting an intelligent algorithm and based on basic data corresponding to the determined cross-border high-risk factor, predicts the entering risks and colonization risks of the cross-border high-risk factor respectively based on the established propagule pressure model and the established species distribution model, and determines the entering risks and colonization risks of the cross-border high-risk factor; the risk assessment module is used for carrying out risk qualitative or quantitative assessment through an intelligent algorithm according to a risk assessment index system based on the basic data cleaned by the data cleaning module, the respective colonization risk of the plurality of cross-border factors determined by the data processing pretreatment sub-module and/or the colonization risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module, so as to determine a risk comprehensive assessment value; and the visual output module performs visual output on the basic data cleaned by the data cleaning module, the respective entering risk and colonizing risk of the cross-border factors determined by the data processing pretreatment sub-module, the entering risk and colonizing risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module and/or the risk comprehensive evaluation value determined by the risk evaluation module through an intelligent algorithm.
According to the intelligent analysis system for the risk of the cross-border hidden high-risk factors, an intelligent technology is adopted, basic data related to a plurality of cross-border factors are collected according to a cross-border factor list, the collected basic data are cleaned, the risk of the cross-border hidden factors is qualitatively and quantitatively evaluated based on the cleaned basic data, so that the potential cross-border hidden high-risk factors are focused, the entering risk and the colonizing risk of the cross-border hidden high-risk factors are predicted and comprehensively evaluated, and a prediction result can be visually output.
The cross-border hidden high risk factor risk intelligent analysis system according to the present application is described in detail below with reference to fig. 2 to 8.
FIG. 2 shows a block diagram of a data acquisition module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application. As shown in fig. 2, the data collection module 110 collects basic data related to a plurality of cross-border factors through an intelligent algorithm according to a cross-border factor list. In one embodiment according to the application, the data acquisition module may determine the cross-border factor list based at least on the target area.
The data acquisition module may acquire the underlying data in a variety of suitable ways. In one embodiment according to the present application, the data collection module may collect the basic data through an external database, and the external database may include a customs database, a national institute of inspection and quarantine information resource sharing service platform, related papers and species distribution databases GBIF and CABI published at home and abroad, and the like. For example, for ambient climate data, it may be obtained from a database provided by WorldClim (http:// www.worldclim.org /); for map data, a 1:400 ten thousand world vector map can be obtained through a national basic geographic information system, namely China national borders and provincial boundaries and county border administrative demarcation drawings, or a 1:1000 ten thousand world vector map can be obtained from Natural Earth (http: www.naturalearth-data.com); host information can also be obtained by Chinese plant lineage (http:// frps. Eflora. Cn/sheng); or obtaining data information about the planting area and the total yield from FAO (http:// faostat3.FAO. Org /).
In another embodiment according to the application, the data acquisition module may also acquire the underlying data from the internet via a web crawler algorithm.
In yet another embodiment according to the present application, the data collection module may also collect the base data based on an internal database (also referred to as a background library) established by the cross-border multi-carrier hidden high risk biological factor data processing method. The applicant submits a Chinese patent application (CN 110276518A) No. 201910396568.2 on 14 th 05 month 2019, which relates to the technical field of biological identification and information processing, in particular to a processing method for cross-border multi-carrier hidden high-risk biological factor data, comprising the following steps: the method comprises the steps of collecting biological information in a cross-border carrier, wherein the cross-border carrier comprises one or more of the following steps: cross-border population and carrying, cross-border cargo and e-commerce, cross-border vehicles, and/or aerosol ballast water, the living being comprising one or more of the following: pests, weeds, pathogenic microorganisms, molluscs or other preselected pests; performing risk analysis on the cross-border intercepted organisms to determine whether the cross-border intercepted organisms are quarantine organisms and risk management measures to be adopted; and (3) checking and/or monitoring organisms needing quarantine treatment and performing corresponding quarantine treatment. The patent application realizes the recognition and treatment measures of the cross-border pests, can effectively prevent and kill the harmful cross-border pests, reduces the probability of malignant transmission of the cross-border pests, and is beneficial to better protecting agriculture and forestry production and natural ecological environment. In the patent application, a processing method for cross-border multi-carrier hidden high-risk biological factor data is described, the method comprises the steps of collecting biological information in a cross-border carrier, and the cross-border carrier comprises one or more of the following: cross-border population and carrying, cross-border cargo and e-commerce, cross-border vehicles, and/or aerosol ballast water, the living being comprising one or more of the following: pests, weeds, pathogenic microorganisms, molluscs and other preselected pests; performing risk analysis on the biological information to determine whether the biological information is a quarantine organism and a risk management measure to be adopted; and (3) checking and/or monitoring organisms needing quarantine treatment and corresponding quarantine treatment. After the step of collecting biological information in the cross-border carrier, the biological information in the cross-border carrier is stored in a first database; according to the preselected identification information in the first biological information in the first database, one or more appointed databases are queried to update the identification information of the first biological information, and then the updated identification information of the first biological information is stored in the second database; saving the results of the risk analysis, the results of the inspection and/or monitoring, and the results of the quarantine treatment to a second database; transmitting the collected biological information to a computer in real time; extracting pest information by the computer, and matching the pest information with cross-border organism data pre-stored in a pre-designated first database to determine whether cross-border organism factors exist; when the matching is successful, the computer controls to carry out pest inspection and/or monitoring; the computer records the time of monitoring and/or checking the pests and the identification information of the pests, and constructs a second database by using the recorded information; and (3) carrying out search operation on the data in the second database, wherein one or more acceleration modes of database index, memory and cache acceleration or search engine are adopted in the search operation.
The second database is a self-built database, and in the process of processing cross-border multi-carrier hidden high-risk biological factor data, various information is queried, compared and acquired in different paths, the information is stored, the second database with more perfect data is synchronously built, the second database is continuously updated and perfected, and in the subsequent hidden high-risk biological factor data processing process, the hidden high-risk biological factor data processing can be completed by using only the second database or fewer databases, so that the processing efficiency and the processing precision are improved. The second database in this patent application is the internal database in the embodiment of the present application.
The collected base data may include geographic distribution data, biological data, environmental climate data, host data, trade data, geographic information data, distribution status of cross-border factors, hazard information of cross-border factors, movement information of cross-border factors, hazard management information of cross-border factors, host information, and the like.
FIG. 3 shows a block diagram of a data cleaning module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application. As shown in fig. 3, the data cleaning module 120 may clean the basic data collected by the data collection module 110 through an intelligent algorithm. The data with obvious errors in the collected basic data can be processed by cleaning the basic data, so that the reliability and the rationality of the data are improved.
In one embodiment of the application, the following operations may be performed while cleaning is performed: judging whether the basic data is related to the cross-border factor or not, namely judging the reliability of the basic data, and removing the basic data which does not belong to the corresponding cross-border factor. For example, by cleaning, the distribution data of the bactrocera dorsalis can be prevented from being used as the basic data of the bactrocera dorsalis.
In one embodiment according to the application, the cross-border factor which does not correspond to the target area is removed from the target cross-border factor list according to the geographic distribution data of the cross-border factor by judging that the basic data are reasonable. For example, by cleaning, it is possible to avoid the situation that the distribution of terrestrial organisms occurs in the sea and the organisms in tropical regions are distributed in the cold zone. Furthermore, cleaning may be performed by, for example, formulating such a rule: only one point is reserved in a certain longitude and latitude range, and the data of the rest points are cleaned.
FIG. 4 illustrates a block diagram of another embodiment of a data cleansing module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application. As shown in fig. 4, as another embodiment of the present application, the data cleaning module 120 may determine a cross-border hidden factor according to the basic data collected by the data collecting module 110, and update the cross-border factor list based on the determined cross-border hidden factor, so that the cross-border hidden factor is included in the updated cross-border factor list.
FIG. 5 shows a block diagram of data processing modules in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application. As shown in fig. 5, the data processing module 130 includes a pre-processing data processing sub-module 1310 and a post-processing data processing sub-module 1320; the data processing preprocessing submodule 1310 processes the basic data cleaned by the data cleaning module 120 through an intelligent algorithm to determine respective entering risks and colonization risks of a plurality of cross-border factors; the data processing preprocessing submodule 1310 uses cluster analysis as a basic algorithm, performs data mining corresponding to multiple groups of basic data, and determines the entry risk and the colonization risk of each hidden factor, so that high-risk factors can be selected according to the respective entry risk and colonization risk of multiple cross-border factors (namely, the entry risk and the colonization risk of the cross-border factors are determined); as a specific embodiment, the data processing preprocessing sub-module 1310 processes the basic data through an artificial neural network algorithm, where the artificial neural network algorithm is an unsupervised artificial neural network algorithm, a k-means algorithm, or a hierarchical clustering algorithm.
With continued reference to fig. 5, as shown, the data processing post-processing sub-module 1320 determines a cross-border high risk factor according to respective entry risk and colonization risk of the plurality of cross-border factors, establishes a propagule pressure model and a species distribution model of the cross-border high risk factor by adopting an intelligent algorithm and based on basic data corresponding to the determined cross-border high risk factor, and predicts and determines the entry risk and colonization risk of the cross-border high risk factor based on the established propagule pressure model and the species distribution model, respectively; as a specific embodiment, the data processing post-processing sub-module 1320 is based on establishing a propagule pressure model and a species distribution model of the cross-border high risk factor by a machine learning algorithm, or the data processing post-processing sub-module is used for establishing a propagule pressure model and a data processing post-processing sub-module established by risk model analysis software and/or is used for establishing a species distribution model of the cross-border high risk factor by Maxent software. In other embodiments according to the application, the data processing post-processing sub-module is further configured to build a cross-border high risk factor propaganda pressure model and species distribution model by other intelligent algorithms in addition to the machine learning intelligent algorithm. As a specific embodiment of the present application, the Risk model analysis software used by the data processing post-processing submodule in the pressure model of the propagule established by the Risk model analysis software may be @ Risk software, self-organizing software or other commercial Risk model analysis software.
As another specific implementation mode of the application, the data processing post-processing sub-module establishes a propagule pressure model of the cross-border high-risk factors through a maximum likelihood estimation algorithm and establishes a species distribution model of the cross-border high-risk factors through a maximum entropy algorithm; the data processing post-processing sub-module can also take a first part of basic data in basic data corresponding to the cross-border high-risk factors as a training data set for establishing a species distribution model, and take a second part of basic data in basic data corresponding to the cross-border high-risk factors as a test data set for testing the established species distribution model, wherein the proportion of the first part of basic data in the basic data corresponding to the cross-border high-risk factors is 90%, and the proportion of the second part of basic data in the basic data corresponding to the cross-border high-risk factors is 10%; the data processing post-processing sub-module may also evaluate the established species distribution model based on AUC. In an embodiment of the present application, the proportion of the first portion of base data and the second portion of base data corresponding to the cross-border high risk factor may be in other ranges meeting the requirements, for example, as a specific embodiment of the present application, the proportion of the first portion of base data corresponding to the cross-border high risk factor is 80%, and the proportion of the second portion of base data corresponding to the cross-border high risk factor is 20%.
The two basic processes (data processing pretreatment and data processing post-treatment) are sequentially arranged in the data processing module, the data processing pretreatment sub-module (data processing pretreatment) plays a role in screening, and hidden factors with potential high entering risk and colonization risk are screened out from a large number of cross-border hidden factors to serve as hidden high-risk factors; the data processing post-processing sub-module (data processing post-processing) performs access risk and colonization risk assessment aiming at the hidden high-risk factors.
FIG. 6 shows a block diagram of a risk assessment module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application. As shown in fig. 6, the risk assessment module 140 determines a risk comprehensive assessment value based on the basic data cleaned by the data cleaning module 120, the respective entry risk and colonization risk of the plurality of cross-border factors determined by the data processing preprocessing submodule 1310, the entry risk and colonization risk of the cross-border high-risk factors determined by the data processing post-processing submodule 1320, and performs risk qualitative or quantitative assessment by an intelligent algorithm according to a risk assessment index system, and adopts the entry risk/colonization risk and risk comprehensive assessment value or selects one of the three for risk assessment. As one embodiment, determining an entry risk and a colonization risk for each of the plurality of cross-border factors based at least on the cross-border factor entry likelihood, the colonization likelihood, and the potential loss level; determining an entry risk for the cross-border high risk factor based at least on the frequency of entry and the population size; a risk of colonization of cross-border high risk factors determined based at least on spatial extent and extent of the adaptive analysis; the risk assessment index system is determined based at least on the distribution of the cross-border factor, the hazard of the cross-border factor, the movement probability of the cross-border factor, the hazard management difficulty of the cross-border factor, and the economic importance of the host. The risk assessment module 140 further determines a quantization index for the risk assessment index system based on the basic data of the external database and the internal database, and calculates a risk comprehensive assessment value according to the determined quantization index, where the risk assessment module 140 searches the internal database to determine whether there is a comprehensive risk assessment value of the cross-border hidden high risk factor in the internal database, and if so, outputs the comprehensive risk assessment value of the cross-border hidden high risk factor in the internal database as a result, for example, if the new cross-border factor has data similar to the previously evaluated cross-border factor, then it may automatically determine that the risk of the new cross-border factor is similar to the risk of the evaluated cross-border factor.
FIG. 7 shows a block diagram of another embodiment of a data acquisition module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application. As shown in fig. 7, according to another embodiment of the present application, the data collection module 110 further builds an internal database based on one or more sets of data in the basic data collected by the data collection module 110, the basic data cleaned by the data cleaning module 120, the entry risk and the colonization risk of each of the plurality of cross-border factors determined by the pre-processing sub-processing module 1310, the entry risk and the colonization risk of the cross-border high-risk factors determined by the post-processing sub-processing module 1320, and the risk comprehensive evaluation value determined by the risk evaluation module 140.
FIG. 8 shows a block diagram of a visual output module in a cross-border, hidden high risk factor risk intelligent analysis system according to an embodiment of the application. As shown in fig. 8, the visual output module 150 performs visual output on at least one of the basic data cleaned by the data cleaning module 120, the entry risk and the colonization risk of each of the plurality of cross-border factors determined by the data processing preprocessing submodule 1310, the entry risk and the colonization risk of the cross-border high-risk factor determined by the data processing post-processing submodule 1320, or the risk comprehensive evaluation value determined by the risk evaluation module through an intelligent algorithm. As a specific embodiment, the visual output module 150 performs visual output through map software, or the visual output module 150 can send visual output results to a predetermined decision maker.
In the description of the present specification, reference to the terms "one embodiment/manner," "some embodiments/manner," "example," "a particular example," "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/manner or example is included in at least one embodiment/manner or example of the application. In this specification, the schematic representations of the above terms are not necessarily for the same embodiment/manner or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/modes or examples described in this specification and the features of the various embodiments/modes or examples can be combined and combined by persons skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are 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 the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
It will be appreciated by persons skilled in the art that the above embodiments are provided for clarity of illustration only and are not intended to limit the scope of the application. Other variations or modifications will be apparent to persons skilled in the art from the foregoing disclosure, and such variations or modifications are intended to be within the scope of the present application.
Claims (10)
1. The intelligent analysis system for risk analysis of the cross-border hidden high-risk factors is used for carrying out risk analysis on the cross-border biological factors through an intelligent algorithm so as to carry out biosafety decision management, and comprises a data acquisition module, a data cleaning module, a data processing module, a risk assessment module and a visual output module,
The data acquisition module acquires basic data related to a plurality of cross-border factors through an intelligent algorithm according to the cross-border factor list;
The data cleaning module cleans the basic data acquired by the data acquisition module through an intelligent algorithm;
the data processing module comprises a data processing preprocessing submodule and a data processing post-processing submodule, wherein,
The data processing preprocessing submodule processes the basic data cleaned by the data cleaning module through an intelligent algorithm to determine the respective entering risk and the respective colonizing risk of the plurality of cross-border factors, wherein the respective entering risk and the respective colonizing risk of the plurality of cross-border factors are determined at least based on the entering possibility, the colonizing possibility and the potential loss degree of the cross-border factors; and
The data processing post-processing sub-module determines a cross-border high Risk factor according to respective entering risks and colonization risks of a plurality of cross-border factors, establishes a propagule pressure model and a species distribution model of the cross-border high Risk factor by adopting an intelligent algorithm and based on basic data corresponding to the determined cross-border high Risk factor, wherein the propagule pressure model and the species distribution model of the cross-border high Risk factor are established by a machine learning intelligent algorithm, or the propagule pressure model is established by @ Risk analysis software and the species distribution model of the cross-border high Risk factor is established by Maxent software, and the entering risks and the colonization risks of the cross-border high Risk factor are respectively predicted and determined based on the established propagule pressure model and the species distribution model, wherein the entering risks of the cross-border high Risk factor are determined at least based on the entering frequency and the population size, and the colonization risks of the cross-border high Risk factor are determined at least based on the space range and the degree of fitness analysis;
The risk assessment module is used for qualitatively or quantitatively assessing risks according to a risk assessment index system through an intelligent algorithm based on basic data cleaned by the data cleaning module, the respective entering risks and colonizing risks of the plurality of cross-border factors determined by the data processing pretreatment sub-module and/or the entering risks and colonizing risks of the cross-border high-risk factors determined by the data processing post-treatment sub-module, so as to determine a risk comprehensive assessment value; and
And the visual output module performs visual output on the basic data cleaned by the data cleaning module, the entering risk and the colonizing risk of each of the plurality of cross-border factors determined by the data processing pretreatment sub-module, the entering risk and the colonizing risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module and/or the risk comprehensive evaluation value determined by the risk evaluation module through an intelligent algorithm.
2. The cross-border, hidden high risk factor risk intelligent analysis system of claim 1, wherein,
The data cleaning module further determines a cross-border hidden factor according to the basic data collected by the data collection module, and updates the cross-border factor list based on the determined cross-border hidden factor so that the cross-border hidden factor is contained in the updated cross-border factor list.
3. The cross-border, hidden high risk factor risk intelligent analysis system of claim 2, wherein,
The data acquisition module acquires basic data through an external database; and/or
The data acquisition module acquires basic data from the Internet through a web crawler algorithm.
4. The cross-border, hidden, high risk factor risk intelligent analysis system of claim 3, wherein the data collection module further establishes an internal storage database based on the base data collected by the data collection module, the base data cleaned by the data cleaning module, the respective entry and colonization risks of the plurality of cross-border factors determined by the pre-data processing sub-module, the entry and colonization risks of the cross-border, high risk factors determined by the post-data processing sub-module, and/or the risk composite evaluation value determined by the risk evaluation module.
5. The cross-border, hidden, high risk factor risk intelligent analysis system of claim 1, wherein the base data includes at least cross-border factor geographic distribution data, biological data, environmental climate data, host data, trade data, and/or geographic information data; and/or
The base data includes at least a distribution of the cross-border factor, hazard information of the cross-border factor, movement possibility information of the cross-border factor, hazard management information of the cross-border factor, and host information.
6. The cross-border, hidden high risk factor risk intelligent analysis system of claim 2, wherein,
Determining the cross-border factor list based at least on a target region;
the risk assessment module further determines a quantization index for the risk assessment index system based on basic data of an external database and an internal database, and calculates a risk comprehensive assessment value according to the determined quantization index; and
The risk assessment indicator system is determined based at least on the distribution of the cross-border factor, the hazard of the cross-border factor, the likelihood of movement of the cross-border factor, the difficulty of hazard management of the cross-border factor, and the economic importance of the host.
7. The cross-border occult high risk factor risk intelligent analysis system of claim 2, wherein the data processing pre-processing sub-module is configured to process the base data cleaned by the data cleaning module by a clustering algorithm to determine the entry risk and the colonization risk of each of the plurality of cross-border factors.
8. The cross-border hidden high risk factor risk intelligent analysis system of claim 7, wherein the data cleaning module further removes cross-border factors that do not correspond to the target region from the target cross-border factor list according to the geographic distribution data of cross-border factors;
the data cleaning module is used for cleaning the basic data acquired by the data cleaning module so as to remove the basic data which do not belong to the corresponding cross-border factors;
The risk assessment module determines whether the comprehensive risk assessment value of the cross-border hidden high-risk factor exists in the internal database by searching the internal database, and if so, outputs the comprehensive risk assessment value of the cross-border hidden high-risk factor in the internal database as a result; and
The visual output module performs visual output through map software, or can send visual output results to a predetermined decision maker.
9. The intelligent cross-border occult high risk factor risk analysis system of claim 4, wherein the data processing pre-processing sub-module processes the base data by an artificial neural network algorithm to determine the entry risk and the colonization risk of each of the plurality of cross-border factors; or alternatively
The data processing preprocessing sub-module processes the basic data through an unsupervised artificial neural network algorithm, a k-means algorithm or a hierarchical clustering algorithm to determine the respective entering risk and colonizing risk of a plurality of cross-border factors.
10. The intelligent analysis system for cross-border hidden high risk factors risk according to claim 9, wherein the data processing post-processing sub-module establishes a propagule pressure model of the cross-border high risk factors through a maximum likelihood estimation algorithm and establishes a species distribution model of the cross-border high risk factors through a maximum entropy algorithm;
The data processing post-processing sub-module further uses a first part of basic data in basic data corresponding to the cross-border high-risk factors as a training data set to establish a species distribution model, and uses a second part of basic data in basic data corresponding to the cross-border high-risk factors as a test data set to test the established species distribution model, wherein the proportion of the first part of basic data in the basic data corresponding to the cross-border high-risk factors is 90%, and the proportion of the second part of basic data in the basic data corresponding to the cross-border high-risk factors is 10%; and
The data processing post-processing sub-module also evaluates the established species distribution model based on AUC.
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