CN113537727A - Intelligent risk level identification system based on Bayesian judgment technology - Google Patents
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Abstract
The application provides a cross-border hidden factor intelligent risk grade recognition system based on a Bayesian judgment technology, which is used for recognizing risks of cross-border hidden factors in different cross-border flows according to cross-border overall process decomposition of the cross-border hidden factors and by adopting the Bayesian judgment technology. According to the cross-border hidden factor intelligent risk level identification system based on the Bayesian judgment technology, the average value measurement standard type sampling inspection method or the unqualified product rate measurement standard type sampling inspection method is adopted for sampling inspection, so that the cross-border hidden factor intelligent risk level identification system can be used for identifying risks of the cross-border hidden factor in different cross-border flows according to cross-border overall process analysis of the cross-border hidden factor and the Bayesian judgment technology, and therefore the cross-border hidden high-risk factor can be effectively managed.
Description
Technical Field
The application relates to the field of biological safety risk analysis, in particular to a risk level identification system based on a Bayesian judgment technology, and particularly relates to a cross-border hidden factor intelligent risk level identification system based on the Bayesian judgment technology.
Background
Cross-border organisms, particularly cross-border pests, can be harmful to the invaded area, and thus an in-depth analysis of the risk of cross-border organisms ("cross-border biological factors" or "cross-border factors", hereinafter "cross-border factors") is required.
A cross-border factor or pest must follow a particular path (Pathway) to the area it invades, where the path may be some manner, process or flow, and the process of a cross-border factor following its path to the area it invades is referred to herein as the cross-border process of the cross-border factor. For example, weeds or insects may reach areas invaded by them by natural diffusion; pathogenic microorganisms usually require a certain medium to reach. With the economic globalization, human activities have become the most dominant means of pest dispersal over natural transmission, and goods, passenger carriers, mailings, transportation, wood packaging, containers, etc. in the path associated with human activities can become the vehicle for pest dispersal. Since various factors in the cross-border process of the cross-border factor inevitably affect the risk of the cross-border factor, it is necessary to analyze and identify the risk of the cross-border factor according to the cross-border process of the cross-border factor.
In addition, the risk judgment technology based on the Bayesian theory can effectively synthesize the prior knowledge and the continuously obtained posterior knowledge for prediction, and corrects the prediction model based on the posterior knowledge, so that the risk judgment technology has good self-upgrading capability.
An intelligent risk level identification system based on a Bayesian judgment technology is needed, which can be used for identifying risks of cross-border hidden factors in different cross-border flows according to cross-border overall process decomposition of the cross-border hidden factors and by adopting the Bayesian judgment technology, so that cross-border hidden high-risk factors can be more effectively managed.
Disclosure of Invention
According to the application, an intelligent risk level identification system based on a Bayesian judgment technology is provided, which is used for identifying the risk of a cross-border hidden factor according to the cross-border overall process decomposition of the cross-border hidden factor and by adopting the Bayesian judgment technology, and the intelligent risk level identification system based on the Bayesian judgment technology can comprise:
the data acquisition module is used for acquiring data and providing the data;
the cross-border overall process risk analysis module analyzes the cross-border overall process of the cross-border hidden factors through a PRACCP method, and determines a plurality of primary risk indexes corresponding to a plurality of links and a plurality of secondary risk indexes corresponding to each primary risk index, wherein the plurality of links form the cross-border overall process;
the risk grade building module is used for building a risk grade system to determine the risk grade corresponding to the risk index;
the data preparation module is used for converting the data provided by the data acquisition module into data represented by risk levels according to the links, the primary risk indexes and the secondary risk indexes determined by the cross-border overall-process risk analysis module and a risk level system constructed by the risk level construction module, so as to provide prior data; and
and the Bayesian risk judgment module is used for determining the risk of the cross-border hidden factors by utilizing the prior data provided by the data preparation module and based on a Bayesian method.
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the Bayesian risk judgment module can comprise:
the Bayesian risk calculation sub-module is used for determining a risk value corresponding to the primary risk index and a risk value corresponding to the secondary risk index based on a Bayesian method;
the risk weight calculation submodule determines the weight of the risk value corresponding to the first-level risk index and the weight of the risk value corresponding to the second-level risk index through an analytic hierarchy process; and
and the risk determination module is used for determining the risk of the cross-border hidden factors based on the risk value corresponding to the primary risk index and the risk value corresponding to the secondary risk index determined by the Bayesian risk calculation sub-module and the weight determined by the risk weight calculation sub-module.
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the data preparation module can also combine the risk determined by the Bayesian risk judgment module with the existing prior data to form new prior data.
According to an embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the data preparation module can
Performing spot check on the data provided by the data acquisition module and calculating the failure rate of the spot check by the following steps:
wherein, XijRepresenting a secondary index of the cross-border overall process X, with kijA secondary index of
I is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to k, and n represents a primary index X corresponding to a link in the whole cross-border processiK represents the number of links corresponding to the primary index,
kijindicates a secondary index XijThe number of the (c) component (a),
mlthe result of the spot check is shown, acceptance is 1, rejection is 0, and ml∈{0，1}，l＝0,1,…kij(ii) a And
a risk level of a secondary indicator may be determined based on the calculated spot check failure rate and based on the risk rating system.
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the data preparation module can determine the risk level of the secondary index based on the sampling failure rate and the following:
| failure rate of spot check | Risk description | Risk rating |
| [0.0,0.0001] | Is very low | 0 |
| (0.0001,0.001] | Is low in | 1 |
| (0.001,0.01] | In | 2 |
| (0.01 0.1] | Height of | 3 |
| (0.1,1.0] | Is very high | 4 |
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the Bayesian risk calculation sub-module can calculate the risk by the following steps:
wherein R (X) represents the risk of the whole cross-border process X,
Riindicates the risk class, Ri∈{R1,R2,…,RsS denotes the number of risk levels,
xkrepresents the risk value of the kth risk index of the whole process X, k is more than or equal to 1 and less than or equal to t, t represents the number of the risk indexes of the whole process X,
P(Ri) Represents a risk class RiThe probability of (a) of (b) being,
P(xk|Ri) Denotes xkThe conditional probability of (a) of (b),
diindicates a risk rating of RiD represents the total number of data samples, dikIndicates a risk rating of RiIndex X in the data samplekValue of xkThe number of (2).
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the cross-border overall process risk analysis module can analyze the cross-border overall process of the cross-border implicit factor through a PRACCP method to determine a plurality of links forming the cross-border overall process, wherein the links comprise production, processing, shipping, outbound inspection, shipping and inbound inspection.
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the risk determination module can determine the risk of the cross-border hiding factor by the following steps:
wherein R (X) represents the risk of the overall process X,
i-th primary index X representing XiThe risk of (a) of (b),
represents XiThe weight of (a) is determined,
wherein the content of the first and second substances,
represents XiJ second level index X ofijThe risk of (a) of (b),
to represent
The weight of (c).
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the data preparation module can adopt an average value measurement standard type sampling inspection method for sampling inspection.
According to the embodiment of the application, in the intelligent risk level identification system based on the Bayesian judgment technology, the data preparation module can adopt a defective rate measurement standard type sampling inspection method for sampling inspection.
The intelligent risk grade recognition system based on the Bayesian judgment technology can be used for recognizing the risks of the cross-border hidden factors in different cross-border flows according to the cross-border overall process of the cross-border hidden factors and by adopting the Bayesian judgment technology, so that the cross-border hidden high-risk factors can be more effectively managed
Drawings
FIG. 1 illustrates a block diagram of an intelligent risk level identification system based on Bayesian decision techniques in accordance with embodiments of the present application; and
fig. 2 shows a block diagram of a bayesian risk decision module of an intelligent risk level identification system based on bayesian decision techniques according to an embodiment of the application.
Detailed Description
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 risk level identification system based on a Bayesian judgment technology is provided, and is used for identifying the risk of the cross-border hidden factors according to the cross-border overall process of the cross-border hidden factors and by adopting the Bayesian judgment technology. Fig. 1 illustrates an intelligent risk level recognition system based on a bayesian decision making technique according to an embodiment of the present application, and as shown in fig. 1, the intelligent risk level recognition system 10 based on the bayesian decision making technique includes: a data acquisition module 100 that can acquire data and provide the data; a cross-border overall process Risk Analysis module 200, which can analyze the cross-border overall process of the cross-border hidden factors by a PRACCP (Pest Risk Analysis and clinical Control Point) method, and determine a plurality of primary Risk indicators corresponding to a plurality of links and a plurality of secondary Risk indicators corresponding to each primary Risk indicator, wherein the plurality of links and the plurality of links form the cross-border overall process; a risk level construction module 300, which can construct a risk level system to determine a risk level corresponding to the risk index; the data preparation module 400 can convert the data provided by the data acquisition module into data represented by risk levels according to the links, the primary risk indexes and the secondary risk indexes determined by the cross-border overall process risk analysis module and a risk level system constructed by the risk level construction module, so as to provide prior data; and a bayesian risk determination module 500 that determines the risk of the cross-border hiding factor based on a bayesian approach using the prior data provided by the data preparation module.
The intelligent risk grade recognition system based on the Bayesian judgment technology can be used for recognizing the risk of the cross-border hidden factors according to the cross-border overall process of the cross-border hidden factors and by adopting the Bayesian judgment technology, so that the cross-border hidden high-risk factors can be effectively recognized.
The inventors of the present application noticed the following information in the course of conducting a research on the risk of cross-border hiding factors:
the PM5/2 (EPPO), the Pest isk analysis on detection of a Pest in an imported cargo, provides a decision framework for the pests found in cargo inspections: pest identification (identity pest), analysis area (The PRA area), early analysis (early analysis), geographic criteria (geographic criteria), likelihood of introduction (Potential for introduction), Potential economic importance (Potential environmental importance), and Management options (Management options).
In addition, ISPM Standard No. 5 defines Pathway as "Any way in which pests can enter or spread" (Any means of ways the entry or spread of a pest) "; the ISPM Standard No. 11 also explicitly sets forth "PRA initiated by the identification of a route". Pathway Risk Analysis (Pathway Risk Analysis) is the process of pest Risk assessment of one or more pathways for pest afferent and spread and selection of Risk management programs, according to NAPPO regional Standard RSPM31 guideline for Pathway Risk Analysis (General Guidelines for Pathway Risk Analysis).
The inventors believe that since the afferent spread of pests in space is an iterative process of entry-colonization, pest risk can be analyzed and predicted in a full-flow managed manner. Further, considering that different measures are taken for different management objects in different diffusion stages, and the effects and costs need to be balanced, key control points should be comprehensively evaluated, so that key intervention is performed in these stages to achieve the minimum cost for achieving the goal.
GB/T20879-2007 for quarantine pest risk management measures may suggest that for plants and plant products, plant infection prevention or reduction, ensuring no pest or other measures in the production area or production site, and for defined non-quarantine pest management measures may suggest measures that may be required to be taken in the production area, on the parent material of the plant for planting and on the plant goods themselves for planting.
The theory of Hazard Analysis and Critical Control Point (HACCP) and the method thereof are applied in the field of food safety. In the field of food safety, the occurrence of food-borne diseases is prevented and food safety is ensured by using an analysis-control-detection-correction system.
In the field of food safety, the HACCP method is mainly used to identify and control potential hazards in food production, and the system established using this theory is called "hazard analysis and key control point system". With this hazard analysis and key control point system, it is possible to determine in which process, in which manner, and how to prevent hazards that affect food safety.
In consideration of the natural relation between the plant-derived food safety and the plant quarantine, the inventor creatively proposes to combine the hazard Analysis and key Control Point theory with the Pest Risk Analysis process, and creatively proposes the theory and the method of 'Pest Risk Analysis and key Control Point' (PRACCP) for the first time to better serve the plant quarantine work. In fact, this is also reflected in the ISPM No. 14 standard and its corresponding national standard GB/T27617-2011.
In view of the above, the inventors of the present application innovatively apply the PRACCP method to risk analysis and identification of cross-border hidden factors.
Generally, the cross-border process of the hidden high-risk factors can include 6 links of production, processing, shipping, outbound inspection, shipping and inbound inspection, so that the hazards and key control points of the cross-border process can be analyzed based on the PRACCP theory.
According to one embodiment of the application, the cross-border overall process risk analysis module analyzes the cross-border overall process of the cross-border implicit factor through a PRACCP method to determine a plurality of links forming the cross-border overall process, wherein the links comprise production, processing, shipping, outbound inspection, shipping and inbound inspection.
According to an embodiment of the application, quarantine requirements and key control points in the cross-border process of the hidden high-risk biological factors (pests) along with the carrier (agricultural products) are analyzed through PRACCP, and the result of the analysis is shown in the table.
Watch 1
Note: the cases were derived from customs administration bulletin No. 59 (bulletin about quarantine requirements of American plants exported from fresh-eating Citrus in China) http:// www.customs.gov.cn/customs/302249/2480148/3018503/index
As shown in the table I, each link in the cross-border process of the cross-border hidden factors and whether each link is a key control point are identified and determined according to the HACCP method. In table one, the 6 links of production, processing, shipping, outbound inspection, shipping, inbound inspection all include key control points.
Based on the links of the cross-border flow of the hidden high-risk biological factors shown in the table I, key control points and related factors influencing the risks of the hidden high-risk biological factors in all the links can be continuously analyzed according to the HACCP method.
According to one embodiment of the application, potential risks and key control points which affect risks in all links of the cross-border process of the hidden high-risk biological factors are analyzed continuously according to the HACCP method, and the second table shows analysis results.
Watch two
As shown in table two, in the production process, the potential danger or risk influencing factors include pest occurrence, whether soil pollution is caused, and whether monitoring is standardized; in the processing link, factors of potential danger or influence risk comprise pest and soil pollution, whether quarantine treatment is in place or not and whether sampling is standard or not; in the shipping link, factors which potentially cause danger or influence risks include whether the packaging material is suitable, whether the packaging process is reasonable, whether the storage mode is appropriate, whether the storage environment is qualified, and whether the transportation mode meets the requirements; and in the inbound inspection and the outbound inspection, the factors of potential danger or influencing risks comprise whether sampling is standard or not.
Fig. 2 shows a block diagram of a bayesian risk decision module in an intelligent risk level identification system based on bayesian decision techniques according to an embodiment of the application. As shown in fig. 2, the bayesian risk determination module 500 may comprise: a bayesian risk calculation sub-module 510, which may determine a risk value corresponding to the primary risk indicator and a risk value corresponding to the secondary risk indicator based on a bayesian method; a risk weight calculation submodule 520, configured to determine, by using an analytic hierarchy process, a weight of a risk value corresponding to the first-level risk indicator and a weight of a risk value corresponding to the second-level risk indicator; and a risk determination module 530, which may determine the risk of the cross-border hiding factor based on the risk value corresponding to the primary risk indicator and the risk value corresponding to the secondary risk indicator determined by the bayesian risk calculation sub-module and the weight determined by the risk weight calculation sub-module.
In the intelligent risk level identification system based on the Bayesian judgment technology according to the embodiment of the application, the data preparation module further combines the risk determined by the Bayesian risk judgment module with the existing prior data to form new prior data.
Under the Bayes theory framework, the unknown quantity is used as a random variable, the unknown situation of the unknown quantity is described through the probability distribution of the random variable, and the risk identification and prediction can be realized. In this sense, under the condition of mastering enough historical data, the Bayesian theory and the method are suitable for risk management of the cross-border factor cross-border overall process. In the technical scheme of the application, the data collected and/or cleaned by the data collection module is subjected to sampling inspection (sampling inspection), so that the data can be represented by risks according to the result of the sampling inspection.
In addition, by analyzing the potential risks and key control points and the like which affect the risks in each link of the cross-border process of the hidden high-risk biological factors according to the PRACCP method, the key control indexes (the potential risks marked as the key control points, such as pest occurrence and pest and soil pollution) and corresponding standards during the spot check can be determined according to the analysis results, and therefore the spot check can be implemented.
In the technical solution according to the present application, any suitable method may be adopted to perform the spot check on the data.
According to one embodiment of the present application, a sample check may be performed using a mean value metric standard type.
According to another embodiment of the present application, a sampling inspection may be performed using a defective rate measurement standard.
In the intelligent risk level identification system based on the Bayesian judgment technology according to the embodiment of the application, the data preparation module performs the spot check on the data provided by the data acquisition module and calculates the spot check failure rate by:
wherein, XijRepresenting a secondary index of the cross-border overall process X, with kijSecond level index, memoryIs composed of
I is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to k, and n represents a primary index X corresponding to a link in the whole cross-border processiK represents the number of links corresponding to the primary index,
kijindicates a secondary index XijThe number of the (c) component (a),
mlthe result of the spot check is shown, acceptance is 1, rejection is 0, and ml∈{0，1}，l＝0,1,…kij(ii) a And
the data preparation module may also determine a risk level of a secondary indicator based on the calculated spot check failure rate and based on the risk rating system.
In the intelligent risk level identification system based on the bayesian decision technology according to the embodiment of the application, the data preparation module may further determine the risk level of the secondary index based on the sampling failure rate and the following table three:
watch III
| Failure rate of spot check | Risk description | Risk rating |
| [0.0,0.0001] | Is very low | 0 |
| (0.0001,0.001] | Is low in | 1 |
| (0.001,0.01] | In | 2 |
| (0.01 0.1] | Height of | 3 |
| (0.1,1.0] | Is very high | 4 |
As shown in table three, the risk levels are divided into 5 levels, wherein a "very high" risk corresponds to a risk level with a value of 4, a "high" risk corresponds to a risk level with a value of 3, a "medium" risk corresponds to a risk level with a value of 2, a "low" risk corresponds to a risk level with a value of 1, and a "very low" risk corresponds to a risk level with a value of 0.
In the intelligent risk level identification system based on the Bayesian judgment technology according to the embodiment of the application, the Bayesian risk calculation sub-module calculates the risk by:
wherein R (X) represents the risk of the whole cross-border process X,
Riindicates the risk class, Ri∈{R1,R2,…,RsS denotes the number of risk levels,
xka risk value of the kth risk index of the whole process X is represented, k is more than or equal to 1 and less than or equal to t, and the t tableThe number of risk indicators for the overall process X,
P(Ri) Represents a risk class RiThe probability of (a) of (b) being,
P(xk|Ri) Denotes xkThe conditional probability of (a) of (b),
diindicates a risk rating of RiD represents the total number of data samples, dikIndicates a risk rating of RiIndex X in the data samplekValue of xkThe number of (2).
In the intelligent risk level identification system based on the bayesian decision technology according to the embodiment of the application, the risk determination module may determine the risk of the cross-border hiding factor by:
wherein R (X) represents the risk of the overall process X,
i-th primary index X representing XiThe risk of (a) of (b),
represents XiThe weight of (a) is determined,
wherein the content of the first and second substances,
represents XiJ second level index X ofijThe risk of (a) of (b),
to represent
The weight of (c).
The basic principle of the analytic hierarchy process is to convert a complex problem from a high level to a low level by using a hierarchical structure, and to solve the complex target decision problem into a combination of finite hierarchical relations by using the system structuredness. Specifically, in the analytic hierarchy process, a system is divided into a plurality of ordered levels, a hierarchical structure model describing the membership and progressive relationships among different level factors is established, then the relative importance of each level is quantitatively expressed according to the comparison result of the importance of adjacent upper-level elements, so that a comparison judgment matrix is constructed, and the maximum eigenvalue of the comparison judgment matrix and the corresponding eigenvector thereof are determined. And determining the weight of the relative importance order of the elements in each layer on the premise of passing the consistency check. By analyzing each hierarchy, an analysis of the whole problem, so-called total ranking weight, is derived.
The data acquisition module may perform data acquisition in any suitable manner. In one embodiment according to the present application, data may be collected and processed in the following manner as disclosed in chinese patent application No. CN202011557448.5 entitled "cross-border hidden high risk factor risk intelligent analysis system" (publication No. CN112613749A) filed by the applicant on 24/12/2020. 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. 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 submits a chinese patent application No. CN201910396568.2 (publication No. CN110276518A) entitled "a processing method for cross-border multi-carrier hidden high-risk biological factor data" on 14/05/2019, 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, and the method includes: 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.
In the cross-border implicit factor intelligent risk level recognition system based on the Bayesian judgment technology according to the embodiment of the application, the data acquisition module further has a data cleaning function, and basic data acquired by the data acquisition module can be cleaned 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 performing the 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 organisms are distributed in cold regions. Further, the cleaning may be performed, for example, by making a rule that: only one point is reserved in a certain longitude and latitude range, and the data of the other points are cleaned.
As another specific embodiment of the present application, the data collection module may further determine a cross-border hiding factor according to the collected basic data, 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.
In one embodiment according to the application, corresponding processing measures can be taken for defects such as feature missing, text data types, or different numeric value ranges in data.
For example, for missing feature data, the mean of the available features may be used to fill in the missing values, or the mean of similar samples may be used to fill in the missing values, or other machine learning methods may be used to predict the missing values, or samples with missing values may be discarded. In one embodiment according to the application, samples with missing values are discarded directly.
For example, for text data, it can be mapped to a numerical type by a program.
For example, for data with different value ranges, a standardized processing mode can be adopted, that is, the data is scaled and limited in a specific interval and converted into a dimensionless value. In one embodiment according to the present application, the normalization process is performed in a dispersion normalization manner.
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 intelligent risk level identification system based on the Bayesian judgment technology is used for identifying the risk of the cross-border hidden factors according to the cross-border overall process decomposition of the cross-border hidden factors and by adopting the Bayesian judgment technology, and comprises the following steps:
the data acquisition module is used for acquiring data and providing the data;
the cross-border overall process risk analysis module analyzes the cross-border overall process of the cross-border hidden factors through a PRACCP method, and determines a plurality of primary risk indexes corresponding to a plurality of links and a plurality of secondary risk indexes corresponding to each primary risk index, wherein the plurality of links form the cross-border overall process;
the risk grade building module is used for building a risk grade system to determine the risk grade corresponding to the risk index;
the data preparation module is used for converting the data provided by the data acquisition module into data represented by risk levels according to the links, the primary risk indexes and the secondary risk indexes determined by the cross-border overall-process risk analysis module and a risk level system constructed by the risk level construction module, so as to provide prior data; and
and the Bayesian risk judgment module is used for determining the risk of the cross-border hidden factors by utilizing the prior data provided by the data preparation module and based on a Bayesian method.
2. The intelligent risk level recognition system based on bayesian decision technique as recited in claim 1, wherein the bayesian risk decision module comprises:
the Bayesian risk calculation sub-module is used for determining a risk value corresponding to the primary risk index and a risk value corresponding to the secondary risk index based on a Bayesian method;
the risk weight calculation submodule determines the weight of the risk value corresponding to the first-level risk index and the weight of the risk value corresponding to the second-level risk index through an analytic hierarchy process; and
and the risk determination module is used for determining the risk of the cross-border hidden factors based on the risk value corresponding to the primary risk index and the risk value corresponding to the secondary risk index determined by the Bayesian risk calculation sub-module and the weight determined by the risk weight calculation sub-module.
3. The intelligent risk level identification system based on bayesian decision technique as claimed in claim 1 or 2, said data preparation module further combining the risk determined by said bayesian risk decision module with existing a priori data to form new a priori data.
4. The cross-border hiding factor intelligent risk level recognition system based on the Bayesian decision technology as recited in claim 2 or 3, wherein the data preparation module performs a spot check on the data provided by the data acquisition module and calculates a spot check failure rate by:
wherein, XijRepresenting a secondary index of the cross-border overall process X, with kijThe number of the second-level indexes,is marked as
I is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to k, and n represents a primary index X corresponding to a link in the whole cross-border processiK represents the number of links corresponding to the primary index,
kijindicates a secondary index XijThe number of the (c) component (a),
mlthe result of the spot check is shown, acceptance is 1, rejection is 0, and ml∈{0，1}，l＝0,1,…kij(ii) a And
and determining the risk grade of the secondary index based on the calculated false positive rate and the risk grade system.
5. The intelligent Bayesian decision-based risk level identification system of claim 4, wherein the data preparation module determines the risk level of the secondary indicator based on a spot check failure rate and:
6. The intelligent Bayesian decision technique-based risk level identification system of claim 4, wherein the Bayesian risk calculation sub-module calculates risk by:
wherein R (X) represents the risk of the whole cross-border process X,
Riindicates the risk class, Ri∈{R1,R2,…,RsS denotes the number of risk levels,
xkrepresents the risk value of the kth risk index of the whole process X, k is more than or equal to 1 and less than or equal to t, t represents the number of the risk indexes of the whole process X,
P(Ri) Represents a risk class RiThe probability of (a) of (b) being,
P(xk|Ri) Denotes xkThe conditional probability of (a) of (b),
diindicates a risk rating of RiD represents the total number of data samples, dikIndicates a risk rating of RiIndex X in the data samplekValue of xkThe number of (2).
7. The intelligent risk level identification system based on bayesian decision technique as claimed in any of claims 1-4, wherein said cross-border overall process risk analysis module analyzes the cross-border overall process of cross-border hiding factors by the PRACCP method to determine the plurality of links comprising the cross-border overall process including production, processing, shipping, outbound check, shipping, and inbound check.
8. An intelligent bayesian decision-based risk level identification system as claimed in any of the preceding claims, wherein said risk determination module determines the risk of a cross-border hiding factor by:
wherein R (X) represents the risk of the overall process X,
i-th primary index X representing XiThe risk of (a) of (b),
represents XiThe weight of (a) is determined,
wherein the content of the first and second substances,
represents XiJ second level index X ofijThe risk of (a) of (b),
to represent
The weight of (c).
9. The intelligent Bayesian decision-based risk level identification system of claim 5, wherein the data preparation module performs a spot check using an average metric standard type spot check method.
10. The intelligent Bayesian decision-based risk level identification system of claim 5, wherein the data preparation module performs a spot check using a reject rate metric standard type spot check method.
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