CN112613749A - 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 the cross-border hidden high-risk factor risk, 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, qualitative and quantitative evaluation is conducted on the cross-border hidden factor risk based on the cleaned basic data, so that the potential cross-border hidden high-risk factors are focused, the entering risk and the colonization risk of the cross-border hidden high-risk factors are predicted and comprehensively judged respectively, and the prediction result 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 integrating data collection, cleaning, processing, evaluation and visualization systems.
Background
Cross-border organisms may cause great harm to Chinese public health, food safety, ecosystems and the like. In addition, the cross-border biological population can change the ecosystem structure of the invaded land, and greatly threatens the diversity of local organisms. Therefore, it is necessary to analyze the risk of the cross-border organisms ("cross-border biological factors" or "cross-border factors", hereinafter referred to as "cross-border factors"), specifically, to analyze the pressure of the propagation material entering the invasion area by the cross-border factors, so as to determine the entering risk; and (3) analyzing the adaptability of the invaded region, determining the possibility, the adaptability range and degree of the invaded region for colonization, identifying the cross-border hidden high-risk factors with higher risk, and making corresponding prevention and control measures based on the cross-border hidden high-risk factors.
At present, a great deal of research and application on cross-border hidden high-risk factor risk analysis exist, but an intelligent analysis system for the cross-border hidden high-risk factor risk does not exist, the 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 risk of the cross-border hidden factors is qualitatively and quantitatively evaluated based on the cleaned basic data, the cross-border hidden high-risk factors are identified, the entrance risk and the colonization risk of the cross-border hidden high-risk factors are predicted and comprehensively judged, and the prediction result can be visually output.
Disclosure of Invention
According to the application, a cross-border hidden high-risk factor risk intelligent analysis system is provided and used for carrying out risk analysis on cross-border 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 evaluation 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 pre-processing submodule and a data processing post-processing submodule; the data processing pretreatment submodule processes the basic data cleaned by the data cleaning module through an intelligent algorithm so as to determine the respective entrance risk and colonization risk of a plurality of cross-border factors; the data processing post-processing sub-module determines cross-border high-risk factors according to respective entry risks and colonization risks of the multiple cross-border factors, establishes a propagule pressure model and a species distribution model of the cross-border high-risk factors by adopting an intelligent algorithm and based on basic data corresponding to the determined cross-border high-risk factors, predicts the risks of the cross-border high-risk factors based on the established propagule pressure model and the established species distribution model, and determines the entry risks and the colonization risks of the cross-border high-risk factors; the risk evaluation module carries out risk qualitative or quantitative evaluation through an intelligent algorithm according to a risk evaluation index system based on the basic data cleaned by the data cleaning module, the entrance risk and the colonization risk of each of the multiple cross-border factors determined by the data processing pretreatment sub-module and/or the entrance risk and the colonization risk of the cross-border high-risk factors determined by the data processing post-processing sub-module, so as to determine a comprehensive risk evaluation value; the visual output module carries out visual output on the basic data cleaned by the data cleaning module, the respective entry risk and colonization risk of the multiple cross-border factors determined by the data processing pretreatment sub-module, the entry risk and colonization risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module and/or the comprehensive risk 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 a cross-border hidden factor according to the basic data acquired by the data acquisition 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 further 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 entry risk and colonization risk of a plurality of cross-border factors determined by the data processing pre-processing sub-module, the entry risk and colonization risk of the cross-border high-risk factors determined by the data processing post-processing 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 geographical distribution data, biological data, environmental climate data, host data, trade data and/or geographical information data of cross-border factors; and/or the basic data at least comprises the distribution condition of the cross-border factors, the hazard information of the cross-border factors, the movement information of the cross-border factors, the hazard management information of the cross-border factors and host information.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, a cross-border factor list is determined at least based on a target area; determining respective entry risks and colonization risks of the plurality of cross-border factors based on at least the cross-border factor entry probability, the colonization probability, and the degree of potential loss; determining the entry risk of the cross-border high risk factor based on at least the frequency of entry and the population size; a risk of colonization by a cross-border high risk factor determined based at least on the spatial extent and extent of the fitness analysis; the risk evaluation module also determines quantitative indexes for the risk evaluation index system based on the basic data of the external database and the internal database, and calculates a comprehensive risk evaluation value according to the determined quantitative indexes; and determining a risk evaluation index system at least based on the distribution condition of the cross-border factors, the harmfulness of the cross-border factors, the moving possibility of the cross-border factors, the harm management difficulty of the cross-border factors 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 pretreatment submodule is used for processing the basic data cleaned by the data cleaning module through a clustering algorithm so as to determine the respective entry risk and colonization risk 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 factor through a machine learning algorithm, or the data processing post-processing sub-module is used for establishing the propagule pressure model and the data processing post-processing sub-module through @ Risk Risk analysis software and establishing the species distribution model of the cross-border high-Risk factor through Maxent software.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, the data cleaning module further removes cross-border factors which do not correspond to the target area from the target cross-border factor list according to the geographical distribution data of the cross-border factors; 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 internal database has a comprehensive risk evaluation value of the cross-border hidden high-risk factor 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 carries out visual output through map software, or the visual output module can send a visual output result to a predetermined decision maker.
In the cross-border hidden high-risk factor risk intelligent analysis system according to the embodiment of the application, the data cleaning module further removes cross-border factors which do not correspond to the target area from the target cross-border factor list according to the geographical distribution data of the cross-border factors; 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 internal database has a comprehensive risk evaluation value of the cross-border hidden high-risk factor by searching the internal database, and if the internal database has the comprehensive risk evaluation value of the cross-border hidden high-risk factor and is still scientific and reasonable after 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 carries out visual output through map software, or the visual output module can send a visual output result to a predetermined decision maker.
The data processing pretreatment submodule processes the basic data through an artificial neural network algorithm to determine the respective entrance risk and colonization risk of the multiple cross-border factors; or the data processing pretreatment 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 entrance risk and colonization risk of the multiple 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 breeding body 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 also takes a first part of basic data in the basic data corresponding to the cross-border high-risk factor as a training data set for establishing a species distribution model, and takes a second part of basic data in the basic data corresponding to the cross-border high-risk factor 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 factor is 90%, and the proportion of the second part of basic data in the basic data corresponding to the cross-border high-risk factor is 10%; the data processing post-processing sub-module also evaluates the established species distribution model based on AUC (Area Under the ROC Curve).
According to the intelligent analysis system for the cross-border hidden high-risk factor risk, based on an intelligent technology, the basic data related to a plurality of cross-border factors are collected according to the cross-border factor list, the collected basic data are cleaned, the cross-border hidden factor risk 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 colonization risk of the cross-border hidden high-risk factors are predicted and comprehensively judged, and the prediction result can be visually output.
Drawings
FIG. 1 shows a block diagram of a cross-border hidden high risk factor risk intelligent analysis system according to 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 is a block diagram illustrating another embodiment 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. 5 is a block diagram illustrating a data processing module in a cross-border hidden high risk factor risk intelligent analysis system according to 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 according to an embodiment of the present application;
FIG. 7 is a block diagram illustrating another embodiment of a data collection 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 visualization output module in the cross-border hidden high risk factor risk intelligent analysis system according to an embodiment of the present application.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The present application will be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present application. For the sake of brevity, the same or similar reference numerals are used for the same or similar apparatus/method steps in the description of the various embodiments of the present application.
In addition, the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
According to the application, an intelligent cross-border hidden high risk factor risk analysis system is provided. Fig. 1 shows a block diagram of a cross-border hidden high risk factor risk intelligent analysis system according to 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 risks through an intelligent algorithm to perform 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 visualization 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 pre-processing submodule and a data processing post-processing submodule, wherein the data processing pre-processing submodule processes the basic data cleaned by the data cleaning module through an intelligent algorithm so as to determine the respective entrance risk and colonization risk of a plurality of cross-border factors; the data processing post-processing sub-module determines cross-border high-risk factors according to respective entry risks and colonization risks of the multiple cross-border factors, establishes a propagule pressure model and a species distribution model of the cross-border high-risk factors by adopting an intelligent algorithm and based on basic data corresponding to the determined cross-border high-risk factors, predicts the entry risks and the colonization risks of the cross-border high-risk factors respectively based on the established propagule pressure model and the established species distribution model, and determines the entry risks and the colonization risks of the cross-border high-risk factors; the risk evaluation module carries out qualitative or quantitative risk evaluation through an intelligent algorithm according to a risk evaluation index system based on the basic data cleaned by the data cleaning module, the colonization risk of each of the multiple cross-border factors determined by the data processing pretreatment submodule and/or the colonization risk of the cross-border high-risk factors determined by the data processing post-processing submodule, so as to determine a comprehensive risk evaluation value; and the visual output module carries out visual output on the basic data cleaned by the data cleaning module, the respective entry risk and colonization risk of the multiple cross-border factors determined by the data processing pretreatment sub-module, the entry risk and colonization risk of the cross-border high-risk factors determined by the data processing post-treatment sub-module and/or the comprehensive risk evaluation value determined by the risk evaluation module through an intelligent algorithm.
The cross-border hidden high-risk factor risk intelligent analysis system adopts an intelligent technology, acquires basic data related to a plurality of cross-border factors according to a cross-border factor list, cleans the acquired basic data, qualitatively and quantitatively evaluates the risk of the cross-border hidden factors based on the cleaned basic data, focuses on potential cross-border hidden high-risk factors, predicts and comprehensively evaluates the entrance risk and colonization risk of the cross-border hidden high-risk factors, and can visually output the prediction result.
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 present 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 the cross-border factor list. In one embodiment according to the application, the data collection module may determine a 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 an 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 resource sharing service platform for animal and plant quarantine information of the chinese inspection and quarantine academy, related papers published at home and abroad, and a species distribution database GBIF and CABI, 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, 1:400 ten thousand of administrative zoning maps of Chinese country and provincial and county borders can be obtained through a national basic geographic information system, or 1:1000 thousand of world vector maps can be obtained from national Earth (http: www.naturalearth-data.com); host information can also be obtained through Chinese plant signs (http:// frps. eflora. cn/sheng); or information about planting area and total production data from FAO (http:// faostat3.FAO. org /).
In another embodiment according to the present application, the data collection module may further collect the basic data from the internet through a web crawler algorithm.
In another embodiment according to the present application, the data acquisition module may further acquire basic data based on an internal database (also referred to as a background database) established by a cross-border multi-carrier hidden high-risk biological factor data processing method. The applicant filed chinese patent application No. CN201910396568.2 (CN110276518A) on 14/05/2019, which relates to the technical field of biological identification and information processing, in particular to a method for processing cross-border multi-carrier hidden high-risk biological factor data, comprising: collecting biological information in a cross-border carrier, wherein the cross-border carrier comprises one or more of the following components: cross-border people and carried objects, cross-border goods and e-commerce, cross-border vehicles, and/or aerosol ballast water, wherein the organisms comprise one or more of the following: pests, weeds, pathogenic microorganisms, molluscs, or other preselected pests; carrying out risk analysis on the cross-border intercepted organisms to determine whether the organisms are quarantine organisms and the risk management measures to be taken; the inspection and/or monitoring of the organisms to be quarantined, and the corresponding quarantine treatment. The patent application realizes the identification and treatment measures of the cross-border pests, can effectively prevent and kill harmful cross-border organisms, reduces the probability of malignant propagation of the cross-border organisms, and is favorable for better protecting the agriculture and forestry production and the natural ecological environment. The patent application describes a processing method for hiding high-risk biological factor data by cross-border multi-carrier, which comprises the following steps of collecting biological information in a cross-border carrier, wherein the cross-border carrier comprises one or more of the following components: cross-border people and carried objects, cross-border goods and e-commerce, cross-border vehicles, and/or aerosol ballast water, wherein the organisms comprise 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 required to be taken; the inspection and/or monitoring of the organisms to be quarantined, and the corresponding quarantine treatment. After the step of collecting the biological information in the cross-border carrier, storing the biological information in the cross-border carrier in a first database; inquiring one or more specified databases according to preselected identification information in the first biological information in the first database to update the identification information of the first biological information, and then storing the updated identification information of the first biological information 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 process to a second database; transmitting the collected biological information to a computer in real time; the computer extracts the pest information, and matches the pest information with the data of the cross-border creatures pre-stored in a pre-designated first database to determine whether cross-border biological factors exist; when the matching is successful, the computer controls to carry out pest inspection and/or monitoring; the computer records the time for monitoring and/or checking the pests and the identification information of the pests, and a second database is constructed by utilizing the recorded information; and searching the data in the second database, wherein database index, memory and cache acceleration or one or more acceleration modes in a search engine are adopted during the searching operation.
In the patent, the second database is a self-built database, in the process of processing cross-border multi-carrier hidden high-risk biological factor data, various information is inquired, compared and acquired through different paths, the information is stored, the second database with more perfect data is synchronously built, the second database is continuously updated and perfected, in the subsequent process of processing the hidden high-risk biological factor data, the hidden high-risk biological factor data can be processed only by using the second database or by using fewer databases, and therefore the processing efficiency and the processing accuracy are improved. The second database in this patent application is the internal database in the embodiments of this application.
The collected basic data can comprise geographical distribution data of the cross-border factors, biological data, environmental climate data, host data, trade data, geographical information data, distribution conditions of the cross-border factors, hazard information of the cross-border factors, movement information of the cross-border factors, hazard management information of the 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 present application. As shown in fig. 3, the data cleansing module 120 may cleanse the basic data collected by the data collection module 110 through an intelligent algorithm. By cleaning the basic data, the data which are obviously wrong in the acquired basic data can be processed, so that the reliability and the rationality of the data are improved.
In one embodiment of the present application, when cleaning, the following operations may be performed: and judging whether the basic data is related to the cross-border factors, namely judging the reliability of the basic data, and removing the basic data which does not belong to the corresponding cross-border factors. For example, by cleaning, the distribution data of the bactrocera dorsalis can be avoided as the basic data of the bactrocera dorsalis.
In one embodiment according to the application, the rationality of the basic data is judged, and cross-border factors which do not correspond to the target area are removed from the target cross-border factor list according to the geographical distribution data of the cross-border factors. For example, by cleaning, it is possible to avoid the case where distribution of terrestrial organisms occurs in the sea and in tropical regions and the case where organisms are distributed in cold regions. Furthermore, cleaning can be performed, for example, by formulating rules that: only one point is reserved in a certain longitude and latitude range, and the data of the other points are cleaned.
Fig. 4 shows a block diagram of another embodiment 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. As shown in fig. 4, as another embodiment of the present application, the data cleansing module 120 may determine a cross-border hiding 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 hiding factor, so that the updated cross-border factor list includes the cross-border hiding factor.
Fig. 5 shows a block diagram of a data processing module in the cross-border hidden high risk factor risk intelligent analysis system according to an embodiment of the present application. As shown in fig. 5, the data processing module 130 includes a data processing pre-processing sub-module 1310 and a data processing post-processing sub-module 1320; the data processing pre-processing sub-module 1310 processes the basic data cleaned by the data cleaning module 120 through an intelligent algorithm to determine the respective entry risk and colonization risk of the plurality of cross-border factors; the data processing preprocessing sub-module 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 entry risk and the colonization risk of each of multiple cross-border factors (i.e., the entry risk and the colonization risk of the cross-border factors are determined); as a specific embodiment, the data processing pre-processing 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 continuing reference to fig. 5, as shown in the figure, the data processing post-processing sub-module 1320 determines the cross-border high-risk factor according to the entry risk and the colonization risk of each 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 using an intelligent algorithm and based on the basic data corresponding to the determined cross-border high-risk factor, and predicts the entry risk and the colonization risk of the cross-border high-risk factor and determines the entry risk and the 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 the propagule pressure model and the species distribution model of the cross-border high risk factor established by a machine learning algorithm, or the data processing post-processing sub-module is used for the propagule pressure model and the data processing post-processing sub-module established by risk model analysis software and/or used for the species distribution model of the cross-border high risk factor established by Maxent software. In other embodiments according to the present application, the data processing post-processing sub-module is further configured to establish a breeding body pressure model and a species distribution model of the cross-border high risk factors through other intelligent algorithms besides 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 sub-module based on the propagule pressure model established by the Risk model analysis software may be @ Risk software, self-editing software, or other commercially available Risk model analysis software.
As another specific embodiment of the application, the data processing post-processing sub-module establishes a breeding body 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 can also take a first part of basic data in the basic data corresponding to the cross-border high-risk factor as a training data set for establishing a species distribution model, and take a second part of basic data in the basic data corresponding to the cross-border high-risk factor 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 factor is 90%, and the proportion of the second part of basic data in the basic data corresponding to the cross-border high-risk factor is 10%; the data processing post-processing sub-module can also evaluate the established species distribution model based on AUC. In an embodiment of the present application, the ratio of the basic data corresponding to the first part of basic data and the second part of basic data at the cross-border high-risk factor may also be other ranges that meet the requirement, for example, as an embodiment of the present application, the ratio of the basic data corresponding to the first part of basic data at the cross-border high-risk factor is 80%, and the ratio of the basic data corresponding to the second part of basic data at 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 screening out the hidden factors with potential high entry risk and colonization risk from a large number of cross-border hidden factors to serve as hidden high-risk factors; and the data processing post-processing submodule (data processing post-processing) carries out risk entering 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 present application. As shown in fig. 6, the risk assessment module 140 determines a comprehensive risk assessment value by performing a 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 120, the entry risk and the colonization risk of each of the multiple cross-border factors determined by the pre-data processing sub-module 1310, and the entry risk and the colonization risk of the cross-border high-risk factors determined by the post-data processing sub-module 1320, and using one of the above entry risk/colonization risk and comprehensive risk assessment value, or one of the three values for risk assessment. As a specific embodiment, determining the respective entry risk and colonization risk of a plurality of cross-border factors based on at least the cross-border factor entry probability, colonization probability and potential loss degree; determining the entrance risk of cross-border high risk factors at least based on the entrance frequency and the population size; a risk of colonization by a cross-border high risk factor determined based at least on the spatial extent and extent of the fitness analysis; and determining a risk evaluation index system at least based on the distribution condition of the cross-border factors, the harmfulness of the cross-border factors, the moving possibility of the cross-border factors, the harm management difficulty of the cross-border factors and the economic importance of the host. The risk evaluation module 140 further determines a quantitative index for the risk evaluation index system based on the basic data of the external database and the internal database, and calculates a comprehensive risk evaluation value according to the determined quantitative index, the risk evaluation module 140 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 the comprehensive risk evaluation value of the cross-border hidden high-risk factor exists in the internal database, the comprehensive risk evaluation value of the cross-border hidden high-risk factor in the internal database is output as a result, for example, a new cross-border factor has data similar to that of a cross-border factor that has been previously evaluated, and then the risk of the new cross-border factor can be automatically judged to be similar to that of the cross-border factor that has been evaluated.
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. As shown in fig. 7, according to another embodiment of the present application, the data acquisition module 110 further establishes an internal database based on one or more sets of data in the basic data acquired by the data acquisition module 110, the basic data cleaned by the data cleaning module 120, the respective entry risk and colonization risk of the multiple cross-border factors determined by the pre-processing data processing sub-module 1310, the entry risk and colonization risk of the cross-border high risk factors determined by the post-processing data processing sub-module 1320, and the comprehensive risk assessment value determined by the risk assessment module 140.
Fig. 8 shows a block diagram of a visualization output module in the cross-border hidden high risk factor risk intelligent analysis system according to an embodiment of the present application. As shown in fig. 8, the visual output module 150 visually outputs at least one value of the basic data cleaned by the data cleaning module 120, the entry risk and the colonization risk of each of the multiple cross-border factors determined by the pre-processing data processing sub-module 1310, the entry risk and the colonization risk of the cross-border high-risk factor determined by the post-processing data processing sub-module 1320, or the comprehensive risk 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 the visual output result to a predetermined decision maker.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode 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/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It should be understood by those skilled in the art that the above embodiments are only for clarity of explanation and are not intended to limit the scope of the present application. Other variations or modifications will occur to those skilled in the art based on the foregoing disclosure and are still within the scope of the present application.
Claims (10)
1. The cross-border hidden high-risk factor risk intelligent analysis system is used for carrying out risk analysis on 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 evaluation module and a visual output module, wherein,
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 pre-processing submodule and a data processing post-processing submodule, wherein,
the data processing pretreatment submodule processes the basic data cleaned by the data cleaning module through an intelligent algorithm so as to determine the respective entrance risk and colonization risk of the plurality of cross-border factors; and
the data processing post-processing sub-module determines cross-border high-risk factors according to respective entry risks and colonization risks of the multiple cross-border factors, establishes a propagule pressure model and a species distribution model of the cross-border high-risk factors by adopting an intelligent algorithm and based on basic data corresponding to the determined cross-border high-risk factors, predicts the entry risks and the colonization risks of the cross-border high-risk factors respectively based on the established propagule pressure model and the established species distribution model, and determines the entry risks and the colonization risks of the cross-border high-risk factors;
the risk assessment module determines a comprehensive risk assessment value based on the basic data cleaned by the data cleaning module, the entrance risk and the colonization risk of each of the plurality of cross-border factors determined by the data processing pre-processing sub-module and/or the entrance risk and the colonization risk of the cross-border high-risk factor determined by the data processing post-processing sub-module, and performs risk qualitative or quantitative assessment through an intelligent algorithm according to a risk assessment index system; and
the visual output module carries out visual output on the basic data cleaned by the data cleaning module, the respective entry risk and colonization risk of the multiple cross-border factors determined by the data processing pre-processing sub-module, the entry risk and colonization risk of the cross-border high-risk factors determined by the data processing post-processing sub-module, and/or the comprehensive risk 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,
the data cleaning module also determines a cross-border hidden factor according to the basic data acquired by the data acquisition 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.
3. The cross-border hidden high risk factor risk intelligent analysis system of claim 2,
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 according to claim 3, wherein the data collection module further establishes an internal storage database based on the basic data collected by the data collection module, the basic data cleaned by the data cleaning module, the respective entry risk and colonization risk of the plurality of cross-border factors determined by the pre-processing data processing sub-module, the entry risk and colonization risk of the cross-border high-risk factors determined by the post-processing data processing sub-module, and/or the risk comprehensive 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 basic data comprises at least geographical distribution data, biological data, environmental climate data, host data, trade data, and/or geographical information data of cross-border factors; and/or
The basic data at least comprises distribution conditions of the cross-border factors, hazard information of the cross-border factors, possible movement information of the cross-border factors, hazard management information of the cross-border factors and host information.
6. The cross-border hidden high risk factor risk intelligent analysis system of claim 2,
determining the cross-border factor list based at least on a target area;
determining respective entry risks and colonization risks for the plurality of cross-border factors based at least on cross-border factor entry probability, colonization probability, and potential loss degree;
determining the entry risk of the cross-border high risk factor based on at least the frequency of entry and the population size;
a risk of colonization of the cross-border high risk factor determined based at least on a spatial extent and extent of fitness analysis;
the risk evaluation module also determines a quantitative index for the risk evaluation index system based on basic data of an external database and an internal database, and calculates a comprehensive risk evaluation value according to the determined quantitative index; and
and determining the risk evaluation index system at least based on the distribution condition of the cross-border factors, the harmfulness of the cross-border factors, the moving possibility of the cross-border factors, the harm management difficulty of the cross-border factors and the economic importance of the host.
7. The cross-border hidden high risk factor risk intelligent analysis system according to claim 2, wherein the data processing pre-processing sub-module is configured to process the basic data cleaned by the data cleaning module through a clustering algorithm to determine the respective entry risk and colonization risk of the plurality of cross-border factors; and
the data processing post-processing sub-module is used for establishing a breeding body pressure model and a species distribution model of the cross-border high-risk factors through a machine learning intelligent algorithm; or
The data processing post-processing submodule is used for establishing the propagule pressure model through @ Risk Risk analysis software; and/or
And the data processing post-processing sub-module is used for establishing a species distribution model of the cross-border high-risk factor through Maxent software.
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 area from the target cross-border factor list according to geographic distribution data of the cross-border factors;
the basic data collected by the data cleaning module are cleaned, so that the basic data which do not belong to the corresponding cross-border factors are removed;
the risk evaluation module determines whether a 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; and
the visual output module carries out visual output through map software, or the visual output module can send a visual output result to a predetermined decision maker.
9. The cross-border hidden high risk factor risk intelligent analysis system of claim 4, wherein the data processing pre-processing sub-module processes the basic data through an artificial neural network algorithm to determine the respective entry risk and colonization risk of the plurality of cross-border factors; or
The data processing pretreatment submodule processes the basic data through an unsupervised artificial neural network algorithm, a k-means algorithm or a hierarchical clustering algorithm so as to determine the respective entrance risk and colonization risk of the multiple cross-border factors.
10. The cross-border hidden high risk factor risk intelligent analysis system according to claim 9, wherein the data processing post-processing sub-module establishes a breeding body pressure model and a maximum entropy algorithm of the cross-border high risk factor through a maximum likelihood estimation algorithm to establish a species distribution model of the cross-border high risk factor;
the data processing post-processing sub-module further takes a first part of basic data in the basic data corresponding to the cross-border high-risk factor as a training data set for establishing a species distribution model, and takes a second part of basic data in the basic data corresponding to the cross-border high-risk factor 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 factor is 90%, and the proportion of the second part of basic data in the basic data corresponding to the cross-border high-risk factor is 10%; and
and the data processing post-processing submodule also evaluates the established species distribution model based on AUC.
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