CN113592371B - Comprehensive risk analysis system, method and equipment based on multi-dimensional risk matrix - Google Patents

Comprehensive risk analysis system, method and equipment based on multi-dimensional risk matrix Download PDF

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CN113592371B
CN113592371B CN202111168435.3A CN202111168435A CN113592371B CN 113592371 B CN113592371 B CN 113592371B CN 202111168435 A CN202111168435 A CN 202111168435A CN 113592371 B CN113592371 B CN 113592371B
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李季梅
姚晓晖
钱重阳
李海鹏
宁利君
倪慧荟
王彤彤
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Institute of Urban Safety and Environmental Science of Beijing Academy of Science and Technology
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Abstract

The invention belongs to the field of data analysis, and particularly relates to a comprehensive risk analysis system, method and device based on a multi-dimensional risk matrix, aiming at solving the problems that the existing risk gap method is difficult to realize multi-target fusion, and the results are consistent and difficult to meet the requirements on disaster management. The invention comprises the following steps: acquiring multivariate risk data; constructing a four-dimensional risk matrix based on the multivariate risk data; based on the four-dimensional risk matrix, respectively obtaining a risk evaluation result of the target area through a total ordinal number method, a weighted average method, an Euclidean distance method and a two-norm method; obtaining a potential risk condition based on the risk assessment result. According to the method, comprehensive risk conditions can be comprehensively represented through the multi-dimensional risk matrix, decision basis is provided for regional natural disaster risk prevention, the robustness, consistency and operability of the risk assessment method are improved, and the characteristics of high randomness and strong volatility of natural disaster risks are effectively aimed at by comparing evaluation results of the four methods.

Description

Comprehensive risk analysis system, method and equipment based on multi-dimensional risk matrix
Technical Field
The invention belongs to the field of data analysis, and particularly relates to a comprehensive risk analysis system, method and device based on a multi-dimensional risk matrix.
Background
In the world, natural disasters, production accidents, public health, social security and other various disasters frequently occur, and the phenomena of cluster and mass-sending of sudden events cause the disaster condition to be aggravated. For natural disasters, a report released by the international disaster reduction strategy shows that about 135 million people die of natural disasters in 20 years from 1996 to 2015, and the reduction of natural disaster risks is also highly regarded by various countries. Experts and scholars in 3 rd world disaster reduction meeting in 2015 emphasize importance of regional comprehensive disaster prevention and reduction capacity construction, and improvement of regional risk management and emergency management level gradually becomes an important basis for better realizing regional sustainable development. China is one of the most serious countries in the world with sudden disasters, and the disasters are various, high in occurrence frequency, wide in distribution region and cause weight loss. Along with global climate change, rapid development of economic society and continuous acceleration of urbanization process, the resource, environment and ecological pressure is increased, various regional risks are further increased, and the tasks of disaster prevention and reduction are very heavy.
The disaster risk assessment is a basic basis for comprehensively reflecting disaster grades, determining a disaster reduction target, optimizing disaster prevention, disaster resistance and relief measures and evaluating disaster reduction benefits. From the viewpoint of the system theory, the disaster causing factors, the pregnant disaster environment, the disaster-bearing body and the disaster are mutually influenced and associated, so that a complex system with certain structure, function and characteristic is formed. Multiple disaster risks coexist in a complex social environment, and one disaster is evaluated independently, so that the requirement of disaster risk management cannot be met, and the comprehensive evaluation of the multiple disaster risks becomes a key link of disaster management and is also a basic basis of disaster prevention and reduction planning. The comprehensive evaluation is to make total evaluation on objective objects according to index data extracted from different sides, and a multi-target problem needs to be synthesized into a single index form. Due to different principles and different analysis angles of various evaluation methods, when different comprehensive evaluation methods evaluate the same research object, evaluation results are often different. Therefore, multi-target fusion, robust method, consistent result and the like are important problems to be solved in the research of the comprehensive evaluation method.
Disclosure of Invention
In order to solve the above problems in the prior art, that is, the problems that the existing risk analysis method is difficult to realize multi-target fusion and the result is consistent and difficult to satisfy the demand for disaster management, the invention provides a comprehensive risk analysis system based on a multi-dimensional risk matrix, comprising: the system comprises a data acquisition module, a multi-dimensional risk matrix construction module, a comprehensive risk evaluation module and a result analysis module;
the data acquisition module is configured to acquire multivariate risk data;
the multi-dimensional risk matrix construction module is configured to construct a four-dimensional risk matrix based on the multivariate risk data;
the comprehensive risk evaluation module is configured to obtain a total ordinal method risk evaluation result, a weighted average method risk evaluation result, an Euclidean distance method risk evaluation result and a two-norm method risk evaluation result of the target area through a total ordinal method, a weighted average method, an Euclidean distance method and a two-norm method respectively based on the four-dimensional risk matrix;
and the result analysis module is configured to perform comprehensive analysis on the total ordinal number method risk evaluation result, the weighted average method risk evaluation result, the Euclidean distance method risk evaluation result and the two-norm method risk evaluation result to obtain a potential risk condition.
In some preferred embodiments, the multi-dimensional risk matrix building module includes: the multi-dimensional risk matrix building module comprises: the system comprises a data classification unit and a four-dimensional risk matrix construction unit;
the data classification unit is configured to classify the multivariate risk data into necessity analysis data and feasibility analysis data;
wherein the indicators of the necessity analysis data include: destructive power of risk causes, saturation of risk causes, fluctuation of risk causes, exposure of risk receptors; the indicators of the feasibility analysis include: the degree of risk receptor concentration and the ability of risk managers to slow down;
the four-dimensional risk matrix building unit is configured to increase the region range dimension and the time dimension based on the necessity analysis data, the feasibility analysis data and the risk types to which the feasibility analysis data belong, and build a four-dimensional risk matrix:
Figure DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 781620DEST_PATH_IMAGE002
a four-dimensional risk matrix representing a four-dimensional risk r comprising m regional c risk types n risk indicators in t years.
In some preferred embodiments, the total ordinal method specifically comprises:
step A100, based on the four-dimensional risk matrix, summing four-dimensional risk information of n risk indexes of c risk types of t years in m regions by using a matrix addition principle to obtain a summed risk matrix:
Figure 964340DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 540815DEST_PATH_IMAGE004
sum represents the sum, m represents m regions, i represents the risk type number
Figure 467182DEST_PATH_IMAGE005
And j represents a risk indicator number
Figure 547134DEST_PATH_IMAGE006
And k represents a region number
Figure 667799DEST_PATH_IMAGE007
；
Step A200, based on the total risk matrix, averaging by using a matrix number multiplication principle to obtain t-year average risk matrices of m regions:
Figure 200411DEST_PATH_IMAGE008
wherein avg represents averaging;
step A300, projecting the elements in the average risk matrix of the m regions for T years to an xyz axis to form an x-axis tableC risk types are shown, n risk indexes are shown by the y axis, m regions are shown by the z axis, and the normalization processing and weighting are carried out to obtain a weighted normalized three-dimensional risk matrix; the method specifically comprises the following steps: carrying out the target risk index and the elements in the list
Figure 664891DEST_PATH_IMAGE009
Performing secondary standardization processing to obtain a standardized three-dimensional risk matrix, and performing weighting on elements of the standardized three-dimensional risk matrix according to value difference conditions by using an entropy weight method for the same index and the same risk category;
the standardization processing of the target risk index and the elements in the target risk index column for 1 time specifically comprises the following steps: subtracting the minimum value of the element in the column from each element, and dividing the difference by the maximum value and the minimum value of the element in the column;
step A400, based on the weighted normalized three-dimensional risk matrix, for parallel to
Figure 599349DEST_PATH_IMAGE010
Each of each column on the shaft
Figure 389450DEST_PATH_IMAGE011
Elements on the same position in the order two-dimensional matrix are sorted from large to small, the positions of the elements are not exchanged in the sorting, and each element is numbered to obtain an ordinal number;
step A500, the ordinal number is substituted for the element of the weighted normalized three-dimensional risk matrix to obtain
Figure 143780DEST_PATH_IMAGE012
An order sorting matrix;
step A600, subjecting the
Figure 677529DEST_PATH_IMAGE012
Of order-sorting matrices
Figure 466494DEST_PATH_IMAGE009
Summing the number of each digit on the order two-dimensional sequencing matrix to obtain
Figure 896338DEST_PATH_IMAGE013
And the order two-dimensional vector compares ordinals on each row position in the vector to obtain average risk level sequence of each region in T years, namely a total ordinal method risk evaluation result.
In some preferred embodiments, the weighted average method specifically includes:
step B100, determining various factors influencing an evaluation object as a composed factor set U, dividing the factor set U into c subsets according to c risk types in the four-dimensional risk matrix, and using the c subsets as first-level factor index indexes
Figure 669122DEST_PATH_IMAGE014
；
Based on the first-level factor indexes, dividing second-level factor indexes according to n risk indexes in the four-dimensional risk matrix
Figure 475404DEST_PATH_IMAGE015
；
Step B200, based on the first-stage factor index
Figure 620340DEST_PATH_IMAGE014
Setting a first-level comment index according to various results possibly made by an evaluator on an evaluation object by an entropy weight method
Figure 752244DEST_PATH_IMAGE016
(ii) a The comment indexes are used as the weights of the factor indexes;
based on the second level factor index
Figure 481165DEST_PATH_IMAGE015
Setting a second-level comment index according to various results possibly made by the evaluator on the evaluation object by an entropy weight method
Figure 91138DEST_PATH_IMAGE017
；
Step B210, standardizing the various factor indexes to obtain standardized factor indexes:
Figure 120274DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 891921DEST_PATH_IMAGE019
first finger
Figure 373718DEST_PATH_IMAGE020
Class I risk
Figure DEST_PATH_IMAGE021
A normalization factor index;
Figure 52961DEST_PATH_IMAGE022
first finger
Figure 936603DEST_PATH_IMAGE020
Class I risk
Figure 144731DEST_PATH_IMAGE021
An individual factor index;
Figure 349709DEST_PATH_IMAGE023
first finger
Figure 301485DEST_PATH_IMAGE020
A maximum factor indicator in class risk;
Figure 39634DEST_PATH_IMAGE024
first finger
Figure 418662DEST_PATH_IMAGE020
A minimum factor indicator in class risk;
step B220, calculating the information entropy of the standardization factor index
Figure 609472DEST_PATH_IMAGE025
：
Figure 364939DEST_PATH_IMAGE026
Wherein the content of the first and second substances,
Figure 957594DEST_PATH_IMAGE027
representing an intermediate variable of the entropy of the calculated information, if
Figure 507524DEST_PATH_IMAGE028
Then define
Figure 920051DEST_PATH_IMAGE029
；
Step B230, information entropy by standardization factor index
Figure 479208DEST_PATH_IMAGE025
Setting the index or weight of each factor
Figure 926370DEST_PATH_IMAGE030
：
Figure DEST_PATH_IMAGE031
Wherein the content of the first and second substances,
Figure 912781DEST_PATH_IMAGE030
first finger
Figure 845227DEST_PATH_IMAGE020
Weight of class normalization factor index;
Figure 676917DEST_PATH_IMAGE025
is as follows
Figure 978585DEST_PATH_IMAGE020
Information entropy value of the class standardization factor index;
Figure 870318DEST_PATH_IMAGE032
number of categories that are risk types;
step B300, carrying out weighted average on the first-level factor indexes and the second-level factor indexes to obtain risk weighted average values of different areas; the method specifically comprises the following steps:
step B310, multiplying the second-level factor index by the factors in the second-level comment index, and summing to obtain a first-level index value:
Figure 523016DEST_PATH_IMAGE033
Figure 158396DEST_PATH_IMAGE034
Figure 580151DEST_PATH_IMAGE035
wherein the content of the first and second substances,
Figure 642784DEST_PATH_IMAGE036
a first level indicator value representing an ith risk type;
Figure DEST_PATH_IMAGE037
second-level factor indexes are indicated;
Figure 48358DEST_PATH_IMAGE038
indicating that the second-level comment indexes are obtained by an entropy weight method,
Figure 221850DEST_PATH_IMAGE039
the number of categories for the type of risk,
Figure 763690DEST_PATH_IMAGE040
representing a risk indicator number;
step B320, multiplying the first-level index value by the corresponding first-level comment index, and summing to obtain a risk comprehensive weighted value of each area:
Figure 498690DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 860401DEST_PATH_IMAGE042
first finger
Figure 837585DEST_PATH_IMAGE020
The area risk comprehensive weighted value of each area is the risk evaluation result of the weighted average method;
Figure 499510DEST_PATH_IMAGE043
the first-level factor index;
Figure 903947DEST_PATH_IMAGE044
the first-level comment indicators are represented,
Figure 487375DEST_PATH_IMAGE045
is the number of regions.
In some preferred embodiments, the euclidean distance method specifically includes:
step C100, obtaining a weighted normalized three-dimensional risk matrix by the method as step A100-step A300;
step C200, based on the weighted standardized three-dimensional risk matrix, taking the maximum value of each row of elements on the same position of each region, namely selecting the maximum value of a certain index of a certain type of risk, and obtaining
Figure 533828DEST_PATH_IMAGE011
The two-dimensional TOP risk matrix of (a);
step C300, calculating the maximum value
Figure 784681DEST_PATH_IMAGE046
The three-dimensional risk matrix of order being regarded as the R layer
Figure 360019DEST_PATH_IMAGE011
Respectively calculating the two-dimensional risk matrix of each area and the two-dimensional risk matrix
Figure 430743DEST_PATH_IMAGE011
Euclidean distance of the two-dimensional TOP risk matrix of (a):
Figure DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 280887DEST_PATH_IMAGE048
the Euclidean distance between a two-dimensional risk matrix and a TOP risk matrix of each area is referred to;
Figure 386247DEST_PATH_IMAGE049
means the first in the area of evaluation
Figure 911249DEST_PATH_IMAGE020
Class I risk
Figure 734848DEST_PATH_IMAGE021
An index;
Figure 591946DEST_PATH_IMAGE050
finger number in TOP risk matrix
Figure 817391DEST_PATH_IMAGE020
Class I risk
Figure 110DEST_PATH_IMAGE021
An index;
Figure 45427DEST_PATH_IMAGE051
finger-shaped
Figure 706215DEST_PATH_IMAGE052
Class risk;
Figure 786167DEST_PATH_IMAGE053
finger-shaped
Figure 874208DEST_PATH_IMAGE054
An individual risk indicator;
step C400, based on the Euclidean distance, obtainingTo obtain
Figure 672400DEST_PATH_IMAGE055
And ordering the elements in the two-dimensional Euclidean distance vector to obtain the average risk level ordering condition of R regions in T years, namely the risk evaluation result of the Euclidean distance method.
In some preferred embodiments, the two-norm method specifically includes:
step D100, obtaining a weighted normalized three-dimensional risk matrix by the method as the step A100-the step A300;
step D200, considering the weighted normalized three-dimensional risk matrix as R layer
Figure 136879DEST_PATH_IMAGE011
And (3) calculating two norms of each layer by using an order two-dimensional risk matrix:
Figure 71337DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 97324DEST_PATH_IMAGE057
the two-norm value of the regional risk matrix of different regions is referred to;
Figure 117233DEST_PATH_IMAGE058
each risk indicator information referring to each type of risk;
Figure DEST_PATH_IMAGE059
representing the numerical product of the c risk types and the n risk indicators;
step D300, based on the two norms, obtaining
Figure 650983DEST_PATH_IMAGE013
And ordering elements in the two-dimensional two-norm matrix to obtain the average risk level ordering condition of m regions in t years, namely a two-norm method risk evaluation result.
In some preferred embodiments, the potential risk condition is obtained based on the risk evaluation result, and preferably, in an area where the risk evaluation results of the total ordinal number method, the weighted average method, the euclidean distance method, and the two-norm method are all arranged in the preset top n, the comprehensive risk is prompted to be greater than that in other areas; and in the regions with ranking difference larger than m bits in the risk evaluation results of the total ordinal number method, the weighted average method, the Euclidean distance method and the two-norm method, prompting that the potential risk is larger than that of other regions.
In some preferred embodiments, the method further comprises an evaluation result testing and combining module configured to perform a fitting degree test based on the risk evaluation result, and using the risk evaluation result with the fitting degree larger than a preset first threshold value as a secondary combined evaluation;
forming a quadratic combination evaluation result matrix based on the quadratic combination evaluation and combining with the region dimension
Figure 439947DEST_PATH_IMAGE060
：
Figure 135371DEST_PATH_IMAGE061
Comparing the secondary combination evaluation results, and setting the number of secondary combination evaluation results with the ranking higher than that of the j area in the secondary combination evaluation result ranking of the i area as P and the number of secondary combination evaluation results with the ranking lower than that of the j area in the secondary combination evaluation result ranking of the i area as Q; if P is greater than Q, note
Figure 642575DEST_PATH_IMAGE062
(ii) a If P = Q, is recorded as
Figure 448857DEST_PATH_IMAGE063
；
The combined evaluation matrix is obtained as:
Figure 357907DEST_PATH_IMAGE064
Figure 224232DEST_PATH_IMAGE065
the evaluation score of the region i is
Figure 953154DEST_PATH_IMAGE066
；
According to
Figure 563127DEST_PATH_IMAGE067
The m regions are ranked by the evaluation score to obtain a final evaluation result ranking.
In another aspect of the present invention, a comprehensive risk analysis method based on a multi-dimensional risk matrix is provided, the method comprising:
s100, acquiring multivariate risk data;
s200, constructing a four-dimensional risk matrix based on the multivariate risk data;
step S300, based on the four-dimensional risk matrix, respectively obtaining a total ordinal method risk evaluation result, a weighted average method risk evaluation result, an Euclidean distance method risk evaluation result and a two-norm method risk evaluation result of the target area through a total ordinal method, a weighted average method, an Euclidean distance method and a two-norm method;
and S400, carrying out comprehensive analysis on the total ordinal number method risk evaluation result, the weighted average method risk evaluation result, the Euclidean distance method risk evaluation result and the two-norm method risk evaluation result to obtain a potential risk condition.
In a third aspect of the present invention, an electronic device is provided, including: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement the above-described method for comprehensive risk analysis based on a multi-dimensional risk matrix.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for being executed by the computer to implement the above-mentioned comprehensive risk analysis method based on a multi-dimensional risk matrix.
The invention has the beneficial effects that:
(1) according to the invention, time, regions, risk types and risk evaluation indexes are uniformly presented and expressed through the multi-dimensional risk matrix, comprehensive risk conditions can be comprehensively represented, decision basis is provided for regional natural disaster risk prevention, and the robustness, consistency and operability of the risk evaluation method are improved.
(2) By comparing the evaluation results of the four methods, the method effectively aims at the characteristics of high randomness and strong volatility of natural disaster risks, and provides decision basis for strengthening the construction guarantee of disaster prevention and reduction infrastructures in key areas and the like.
(3) According to the risk assessment method and the risk assessment system, the carrier concentration of the risk management and control layer and the coping ability factor of the management and control person are added into the risk assessment matrix, so that the risk of multiple disaster types and multiple time points can be simultaneously and uniformly expressed and assessed.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a block diagram of a comprehensive risk analysis system based on a multi-dimensional risk matrix according to an embodiment of the present invention;
fig. 2 is a schematic diagram of variation of deviation coefficients of four types of regional risk evaluation methods in the embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings. In order to more clearly describe the comprehensive risk analysis system based on the multi-dimensional risk matrix of the present invention, details of each functional module in the embodiment of the present invention are described below with reference to fig. 1.
The current risk comprehensive evaluation method and the application research result thereof can be seen as follows: in the aspect of disaster risk expression, more attention is paid to the attributes of the disaster, such as disaster-causing factors, fragility and exposure, the concentration of a bearing body and the handling capacity of a manager, which are considered from the aspect of risk management and control, are lacked, and meanwhile, the research on the aspect of a multi-disaster risk unified expression method at multiple time points is less; in the aspect of comprehensive evaluation methods, the application of a single method is mostly concentrated, and the comprehensive analysis and comparative research on the aspects of robustness, consistency, operability and the like of various evaluation methods is less; in the aspect of global risk management, the comprehensive evaluation and prevention work of natural disaster risks is of great importance, and risk prevention needs to be realized through a set of scientific evaluation system.
Therefore, the research re-analyzes the risk system, constructs a unified characteristic index and a multi-dimensional matrix expression mode suitable for multiple disasters, multiple regions and multiple time points, provides four types of comprehensive risk evaluation methods such as total ordinal number, weighted average, Euclidean distance, two norms and the like, performs comprehensive risk evaluation comparison and inspection on 3 types of natural disasters in 30 regions in China, researches the advantages and disadvantages of different risk evaluation methods, provides a new thought for the comprehensive risk evaluation theory and method research, and provides support and basis for the natural disaster risk management strategies in various regions.
In the existing research, most risk matrixes are two-dimensional risk matrixes, and the two-dimensional risk matrixes can be roughly divided into two types: one type is a result type risk matrix, namely, all the results obtained by evaluation are only put in four quadrants and are regarded as a result matrix, and the risk matrix is just a true matrix like a shape matrix; the other type is an index type risk matrix, namely, each index used for obtaining an evaluation result is expressed in a matrix form, and the risk matrix can be regarded as the true application of the matrix in the risk field. The most central role of the risk matrix widely used at present is to comprehensively evaluate the probability of occurrence of a risk and the severity generated after the occurrence of the risk, and then to obtain the possible influence degree caused by the occurrence of the risk, wherein the two-dimensional risk matrix is a two-dimensional risk matrix consisting of two index dimensions of probability and severity, and is proposed by the adoption engineering group of the american air force Electronic System Center (ESC) in 4 months 1995.
However, the conventional two-dimensional risk matrix only performs risk evaluation from two index dimensions of risk probability and severity, which is relatively wide, cannot comprehensively represent risk system constituent elements, and when performing comprehensive evaluation on multiple objects and multiple time points, two dimensions cannot present these information at the same time, so that expansion needs to be performed on the basis of the conventional risk matrix to comprehensively reflect and express multiple indexes, multiple objects and multiple time points.
The multidimensional risk matrix can simultaneously incorporate four-dimensional information such as region, time, risk type, risk index and the like into an evaluation system, and the construction of each multidimensional risk matrix is like generating a unique cube-ID. Each evaluation object is set with a special 'cube-ID', and the risk condition of any year, any category and any index in the region is contained on the evaluation object; in the time series to be evaluated, there is also one "cube-ID" containing all the evaluation target information every year. In the evaluation system, multi-dimensional risk information can be extracted at any time, and comprehensive risks can be evaluated by taking different dimensions as entry points.
The invention provides a comprehensive risk analysis system based on a multidimensional risk matrix, which comprises: the system comprises a data acquisition module, a multi-dimensional risk matrix construction module, a comprehensive risk evaluation module and a result analysis module;
the data acquisition module is configured to acquire multivariate risk data;
the multi-dimensional risk matrix construction module is configured to construct a four-dimensional risk matrix based on the multivariate risk data;
in this embodiment, the multidimensional risk matrix building module includes: the data classification unit and the four-dimensional risk matrix construction unit:
the data classification unit is configured to classify the multivariate risk data into necessity analysis data and feasibility analysis data;
the core indicators of risk evaluation are elements in a multidimensional risk matrix. Based on the element analysis of the risk system and the research result analysis of relevant scholars, the risk system mainly comprises three types of elements, namely a risk cause, a risk receptor and a risk controller. The risk causes include dangerous substances, energy and carriers thereof, the risk causes objectively cause occurrence of emergency events, the risk recipients include people, property, objects, environment and the like, which are objects generating value loss, and the risk manager is a manager of the risk recipients and is a behavior subject for risk identification, analysis, evaluation and management and control work implementation. The purpose of risk evaluation is to manage and control, and the evaluation result provides decision basis for the necessity of work implementation and the feasibility of management and control implementation. Therefore, the risk assessment indexes can carry out index design and association degree analysis from two dimensions of feasibility and necessity around the three types of components.
Wherein the indicators of the necessity analysis data include: destructive power of risk causes, saturation of risk causes, fluctuation of risk causes, exposure of risk receptors; the indicators of the feasibility analysis include: the degree of risk receptor concentration and the ability of risk managers to slow down;
in this embodiment, (business index for preservation and control) risk prevention and control Necessity analysis is to analyze that the risk controller should not perform prevention and control, and as a whole, the more serious the loss caused by the risk is, the more the risk controller should perform prevention and control, and the lower the bearing capacity of the risk controller is, the more the risk controller should perform prevention and control. Therefore, the necessity analysis should be measured mainly in terms of the destructive power and saturation of risk factors, the fluctuation of risk factors, and the exposure of risk receptors. (Feasibility index for prevention and control) risk prevention and control Feasibility analysis is to analyze that a risk controller can 'not' perform prevention and control, and the more concentrated the risk receptor and the stronger the slowing capability of the risk controller, the higher the prevention and control Feasibility. Therefore, in this study, the feasibility analysis was measured primarily in terms of both risk receptor concentration and risk controller slowing. Combining the analysis of evaluation purposes of feasibility and necessity, five evaluation indexes of risk cause saturation, risk cause fluctuation, risk receptor exposure, risk receptor concentration and risk control slowing degree are selected.
In the present embodiment, each index is defined as:
saturation degree: the risk is the maximum value of the loss of the risk event in a certain period, and the greater the damage capability and the higher the saturation of the risk cause, the stronger the prevention and control necessity.
The fluctuation degree: a measure of the degree of temporal non-uniformity of risk in a certain area or area of expertise. When the risk comprehensive level is appropriate, the greater the risk fluctuation degree, the higher the risk.
Exposure degree: the value of receptors exposed to the risk profile is measured as a proportion of the value of all receptors of that type in the area, with the higher exposure of the risk receptors to the risk cause the higher the necessity for prevention and control.
Concentration ratio: an indicator reflecting the degree of spatial heterogeneity of risk within a certain area or industry domain. Whether the risk management and control angle analysis is facilitated or not is judged, the lower the risk concentration degree is, the more scattered the risk receptor distribution is, the higher the risk prevention and control difficulty is, and the lower the feasibility is.
Slowing down: refers to the degree to which the risk can be reduced or slowed, primarily measured in terms of emergency resources and readiness, with greater slowing representing greater risk prevention and control feasibility.
In the present embodiment, the saturation, fluctuation, exposure, concentration, and alleviation of agricultural weather disasters, geological disasters, and forest fires are designed as shown in table 1.
TABLE 1 disaster indicators corresponding to respective disaster types
Figure 326683DEST_PATH_IMAGE068
The four-dimensional risk matrix building unit is configured to increase the region range dimension and the time dimension based on the necessity analysis data, the feasibility analysis data and the risk types to which the feasibility analysis data belong, and build a four-dimensional risk matrix:
Figure 865374DEST_PATH_IMAGE069
wherein the content of the first and second substances,
Figure 81592DEST_PATH_IMAGE070
and a four-dimensional risk matrix representing four-dimensional risk information r containing c risk types and n risk indexes of all m regions in T years.
The comprehensive risk evaluation module is configured to obtain a total ordinal method risk evaluation result, a weighted average method risk evaluation result, an Euclidean distance method risk evaluation result and a two-norm method risk evaluation result of the target area through a total ordinal method, a weighted average method, an Euclidean distance method and a two-norm method respectively based on the four-dimensional risk matrix;
in this embodiment, the total ordinal method specifically includes:
step A100, based on the four-dimensional risk matrix, summing four-dimensional risk information of n risk indexes of c risk types of T years in m regions by using a matrix addition principle to obtain a summed risk matrix:
Figure 495256DEST_PATH_IMAGE071
wherein the content of the first and second substances,
Figure 113319DEST_PATH_IMAGE072
sum represents the sum, m represents m regions, i represents a risk type number, j represents a risk index number, and k represents a region number;
step A200, based on the total risk matrix, averaging by using a matrix number multiplication principle to obtain average risk matrices of the m regions in T years:
Figure 321447DEST_PATH_IMAGE008
wherein avg represents averaging;
step A300, projecting elements in the average risk matrix of the m regions for T years onto an xyz axis, representing c risk types by the x axis, representing n risk indexes by the y axis, representing m regions by the z axis, carrying out standardization processing and weighting, and obtaining a weighted standardized three-dimensional risk matrix; the method specifically comprises the following steps: carrying out the target risk index and the elements in the list
Figure 24960DEST_PATH_IMAGE073
Performing secondary standardization processing to obtain a standardized three-dimensional risk matrix, and performing weighting on elements of the standardized three-dimensional risk matrix according to value difference conditions by using an entropy weight method for the same index and the same risk category;
the standardization processing of the target risk index and the elements in the target risk index column for 1 time specifically comprises the following steps: subtracting the minimum value of the element in the column from each element, and dividing the difference by the maximum value and the minimum value of the element in the column;
step A400, based on the weighted normalized three-dimensional risk matrix, for each element of the same area
Figure 242315DEST_PATH_IMAGE073
The two-dimensional matrix is sequenced from large to small to obtain ordinal numbers;
step A600, the ordinal numbers are substituted for the elements on the weighted normalized three-dimensional risk matrix to obtain
Figure 714885DEST_PATH_IMAGE074
An order sorting matrix;
step A700, subjecting the
Figure 93913DEST_PATH_IMAGE074
Of order-sorting matrices
Figure 550303DEST_PATH_IMAGE073
Summing the number of each digit on the order two-dimensional sequencing matrix to obtain
Figure 40190DEST_PATH_IMAGE075
And the order two-dimensional vector compares ordinals on each row position in the vector to obtain average risk level sequence of each region in T years, namely a total ordinal method risk evaluation result.
In this embodiment, the weighted average method specifically includes:
step B100, determining various factors influencing an evaluation object as a composed factor set U, dividing the factor set U into c subsets according to c risk types in the four-dimensional risk matrix, and using the c subsets as first-level factor index indexes
Figure 632845DEST_PATH_IMAGE076
；
Based on the first-level factor indexes, dividing second-level factor indexes according to n risk indexes in the four-dimensional risk matrix
Figure 917196DEST_PATH_IMAGE077
；
Step B200, based on the first-stage factor index
Figure 96767DEST_PATH_IMAGE076
Setting a first-level comment index according to various results possibly made by an evaluator on an evaluation object by an entropy weight method
Figure 655924DEST_PATH_IMAGE078
(ii) a The comment indexes are used as the weights of the factor indexes;
based on the second level factor index
Figure 103086DEST_PATH_IMAGE077
Setting a second-level comment index according to various results possibly made by the evaluator on the evaluation object by an entropy weight method
Figure 823917DEST_PATH_IMAGE079
；
Step B210, standardizing the various factor indexes to obtain standardized factor indexes;
Figure 989319DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 821009DEST_PATH_IMAGE080
first finger
Figure 388257DEST_PATH_IMAGE020
Class I risk
Figure 279989DEST_PATH_IMAGE021
A normalization factor index;
Figure 932688DEST_PATH_IMAGE081
first finger
Figure 568068DEST_PATH_IMAGE020
Class I risk
Figure 989822DEST_PATH_IMAGE021
An individual factor index;
Figure 52456DEST_PATH_IMAGE082
first finger
Figure 926871DEST_PATH_IMAGE020
A maximum factor indicator in class risk;
Figure 867408DEST_PATH_IMAGE083
first finger
Figure 143668DEST_PATH_IMAGE020
A minimum factor indicator in class risk;
step B220, calculating the information entropy of the standardization factor index
Figure 377204DEST_PATH_IMAGE084
：
Figure 4494DEST_PATH_IMAGE026
Wherein the content of the first and second substances,
Figure 981677DEST_PATH_IMAGE085
representing an intermediate variable of the entropy of the calculated information, if
Figure 112444DEST_PATH_IMAGE086
Then define
Figure 516881DEST_PATH_IMAGE087
；
Step B230, information entropy by standardization factor index
Figure 631467DEST_PATH_IMAGE084
Setting the index or weight of each factor
Figure 412342DEST_PATH_IMAGE088
：
Figure 397615DEST_PATH_IMAGE031
Wherein the content of the first and second substances,
Figure 972953DEST_PATH_IMAGE088
first finger
Figure 574836DEST_PATH_IMAGE020
Weight of class normalization factor index;
Figure 395286DEST_PATH_IMAGE084
is as follows
Figure 500646DEST_PATH_IMAGE020
Information entropy value of the class standardization factor index;
Figure 246885DEST_PATH_IMAGE032
number of categories that are risk types;
step B300, carrying out weighted average on the first-level factor indexes and the second-level factor indexes to obtain risk weighted average values of different areas; the method specifically comprises the following steps:
step B310, multiplying the second-level factor index by the factors in the second-level comment index, and summing to obtain a first-level index value:
Figure 336063DEST_PATH_IMAGE033
Figure 193161DEST_PATH_IMAGE089
Figure 418606DEST_PATH_IMAGE035
wherein the content of the first and second substances,
Figure 601326DEST_PATH_IMAGE090
a first level indicator value representing an ith risk type;
Figure 646642DEST_PATH_IMAGE091
second-level factor indexes are indicated;
Figure 307431DEST_PATH_IMAGE092
indicating that the second-level comment indexes are obtained by an entropy weight method,
Figure 387382DEST_PATH_IMAGE039
the number of categories for the type of risk,
Figure 741003DEST_PATH_IMAGE040
representing a risk indicator number;
step B320, multiplying the first-level index value by the corresponding first-level comment index, and summing to obtain a risk comprehensive weighted value of each area:
Figure 273615DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 738095DEST_PATH_IMAGE093
first finger
Figure 174018DEST_PATH_IMAGE020
The area risk comprehensive weighted value of each area is the risk evaluation result of the weighted average method;
Figure 698540DEST_PATH_IMAGE094
the first-level factor index;
Figure 718448DEST_PATH_IMAGE095
the first-level comment indicators are represented,
Figure 721040DEST_PATH_IMAGE096
is the number of regions. In the present embodiment, it is preferred that,
Figure 510004DEST_PATH_IMAGE095
numerically identical to that in step B230
Figure 471007DEST_PATH_IMAGE088
The same is true.
In this embodiment, the euclidean distance method specifically includes:
step C100, obtaining a weighted normalized three-dimensional risk matrix by the method as step A100-step A300;
step C200, based on the weighted standardized three-dimensional risk matrix, taking the maximum value of each row of elements on the same position of each region, namely selecting the maximum value of a certain index of a certain type of risk, and obtaining
Figure 978211DEST_PATH_IMAGE097
The two-dimensional TOP risk matrix of (a);
step C300, calculating the maximum value
Figure 518914DEST_PATH_IMAGE098
The three-dimensional risk matrix of order being regarded as the R layer
Figure 427964DEST_PATH_IMAGE097
Respectively calculating the two-dimensional risk matrix of each area and the two-dimensional risk matrix
Figure 294289DEST_PATH_IMAGE097
Euclidean distance of the two-dimensional TOP risk matrix of (a):
Figure 288790DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 633184DEST_PATH_IMAGE099
the Euclidean distance between a two-dimensional risk matrix and a TOP risk matrix of each area is referred to;
Figure 163784DEST_PATH_IMAGE100
means the first in the area of evaluation
Figure 201011DEST_PATH_IMAGE020
Class I risk
Figure 417228DEST_PATH_IMAGE021
An index;
Figure DEST_PATH_IMAGE101
finger number in TOP risk matrix
Figure 96471DEST_PATH_IMAGE020
Class I risk
Figure 714534DEST_PATH_IMAGE021
An index;
Figure 922662DEST_PATH_IMAGE102
finger-shaped
Figure 891755DEST_PATH_IMAGE052
Class risk;
Figure 577951DEST_PATH_IMAGE053
finger-shaped
Figure 316100DEST_PATH_IMAGE103
An individual risk indicator;
step C400, based on the Euclidean distance, obtaining
Figure 695129DEST_PATH_IMAGE104
And ordering the elements in the two-dimensional Euclidean distance vector to obtain the average risk level ordering condition of m regions in t years, namely the risk evaluation result of the Euclidean distance method.
In this embodiment, the two-norm method specifically includes:
step D100, obtaining a weighted normalized three-dimensional risk matrix by the method as the step A100-the step A300;
step D200, considering the weighted normalized three-dimensional risk matrix as R layer
Figure 885939DEST_PATH_IMAGE097
And (3) calculating two norms of each layer by using an order two-dimensional risk matrix:
Figure 641405DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 458227DEST_PATH_IMAGE105
the two-norm value of the regional risk matrix of different regions is referred to;
Figure 8157DEST_PATH_IMAGE106
each risk indicator information referring to each type of risk;
Figure 951843DEST_PATH_IMAGE107
representing the numerical product of the c risk types and the n risk indicators;
step D300, based on the two norms, obtaining
Figure 245421DEST_PATH_IMAGE075
And ordering elements in the two-dimensional two-norm matrix to obtain the average risk level ordering condition of m regions in t years, namely a two-norm method risk evaluation result.
In this embodiment, the comprehensive risk levels of the three types of risks, namely, the agricultural meteorological disaster, the geological disaster and the forest fire, and five index evaluations, namely, the saturation, the exposure, the concentration, the slowing and the fluctuation, in 30 provinces and cities by the four types of evaluation methods are shown in table 2.
TABLE 2 comprehensive Risk level for five index evaluations in various regions
Province and city Total ordinal number Weighted average Euclidean distance Two norm
Anhui province 15 26 21 26
Beijing City 28 2 9 5
Fujian province 22 20 22 16
Gansu province 5 3 3 3
Guangdong province 26 23 20 22
Guangxi province 27 17 14 11
Guizhou province 12 12 12 14
Hainan province 17 28 28 27
Province of Hebei province 13 1 8 4
Province of Henan province 10 13 13 13
Heilongjiang 3 8 6 8
Province of Hubei province 21 15 15 17
Province of Hunan province 11 6 4 6
Jilin province 19 22 24 28
Jiangsu province 25 25 26 24
Jiangxi province 20 18 19 25
Liaoning province 8 11 11 12
Inner Mongolia 2 5 1 1
Ningxia province 6 27 23 19
Qinghai province 24 24 25 18
Shandong province 16 16 17 15
Shanxi province 14 14 18 21
Province of Shaanxi 23 19 16 20
Sichuan province 4 4 2 2
Tianjin City of Tianjin 18 21 27 23
Tibet medicine 7 10 10 10
Province of Xinjiang 1 7 7 9
Yunnan province 9 9 5 7
Zhejiang province 30 29 29 29
Chongqing city 29 30 30 30
The results of the comprehensive risk ranking of different regions obtained by the four types of evaluation methods have similar parts, but have different places. For example, risk ranking results of Chongqing city, Zhejiang province, Sichuan province, Gansu province, Guizhou province, Shandong province, Guangdong province and the like under four types of evaluation methods are very stable, and ranking differences are not more than +/-2; the rank of Beijing is 28 th under the total ordinal number method, which means that the comprehensive risk is small, but the ranks under the other three evaluation methods are all within 10 th, which means that the comprehensive risk is large, and the ranking difference among the different evaluation methods reaches +/-26; thirdly, under the four methods, the comprehensive risk ranks of Gansu province, Heilongjiang province, inner Mongolia province, Sichuan province, Tibet province, Xinjiang province and Yunnan province are all positioned at the top 10, which shows that the comprehensive risk of the area is large and needs to be mainly prevented; and fourthly, under the four methods, the comprehensive risk ranks of Guangdong province, Jiangsu province, Zhejiang province and Chongqing city are all in the last 10 places, which shows that the comprehensive risk in the region is small.
A result analysis module configured to obtain a potential risk condition based on the risk assessment result.
In this embodiment, the obtaining of the potential risk condition based on the risk evaluation result preferably includes, in a region where the risk evaluation results of the total ordinal number method, the weighted average method, the euclidean distance method, and the two-norm method are all arranged in the preset top n places, prompting that the comprehensive risk is greater than that in other regions; and in the regions with ranking difference larger than m bits in the risk evaluation results of the total ordinal number method, the weighted average method, the Euclidean distance method and the two-norm method, prompting that the potential risk is larger than that of other regions.
The four-dimensional risk matrix can comprehensively and visually represent and compare omnibearing risk conditions of different regions, different periods, different types and different dimensions, is convenient for carrying out empirical test and comparative analysis of different types of risk evaluation methods in the follow-up process, and provides a basis for risk prevention and control work; four types of regional risk evaluation methods such as total ordinal number, weighted average, Euclidean distance, two norms and the like can relatively effectively carry out regional comprehensive risk evaluation, and evaluation results are basically consistent; the four risk evaluation methods have advantages and disadvantages, wherein the accuracy of the risk evaluation result of the Euclidean distance method is obviously higher than that of other evaluation methods, and then a two-norm method and a weighted average method are adopted; in recent years, the natural disaster risk sequence of China has strong volatility, and the natural disaster risk sequence in areas such as Beijing, Shanxi, Tianjin, Hebei, Liaoning, Ningxia, Zhejiang, Shaanxi, Fujian, Gansu and the like in recent 10 years has large volatility.
The risk of area integration itself represents an uncertainty. Even if the risk ranking of inner Mongolia is found to be closer to the front through analysis and the risk of regional natural disaster is larger, the inner Mongolia still can not be said to have natural disaster, and the loss of the inner Mongolia caused by the natural disaster can not be directly determined to be higher than that of other regions. In fact, the accuracy of the risk evaluation method cannot be verified through real disaster data, the essence of comprehensive evaluation is that the current and future risk conditions are predicted through the existing data, and the occurrence of the disaster is influenced by many factors, so that all the factors cannot be quantified and considered. Therefore, the accuracy of the various methods can only be verified indirectly by the consistency of the comparison between the different evaluation methods. The four sorting results can be displayed on the map in a mode that the ranking shows dark color in the first interval, the ranking shows normal color in the second interval and the ranking shows light color in the third interval.
From the perspective of regional individuals, the accuracy of different evaluation methods is judged by calculating the average deviation coefficients of different regional ranks under different evaluation methods, and the calculation formula of the deviation coefficients is as follows:
Figure 692583DEST_PATH_IMAGE108
wherein the content of the first and second substances,
Figure 147835DEST_PATH_IMAGE109
deviation coefficients representing four evaluation methods;
Figure 578816DEST_PATH_IMAGE110
the number of regions;
Figure 410506DEST_PATH_IMAGE111
represents the first
Figure 977753DEST_PATH_IMAGE020
Area under evaluation method
Figure 869486DEST_PATH_IMAGE021
Rank of (2);
Figure 522184DEST_PATH_IMAGE112
representing the ranking average value of each region under four types of evaluation methods,
Figure 157565DEST_PATH_IMAGE020
the number of methods was evaluated.
In the four types of risk evaluation methods, the deviation coefficients of the two-norm method and the euclidean distance method are obviously smaller than those of the weighted average method and the total ordinal number method in terms of data magnitude and variation amplitude, so that the evaluation accuracy of the two-norm method and the euclidean distance method can be preliminarily considered to be superior to that of the weighted average method and the total ordinal number method. The deviation coefficients of the four types of risk assessment methods are shown in table 3.
TABLE 3 deviation coefficients for four types of Risk assessment methods
Evaluation method Total ordinal number Weighted average Euclidean distance Two norm
Mean deviation coefficient 31.72% 19.10% 14.44% 17.53%
The mean deviation coefficient of the euclidean distance method is 14.44%, which is the lowest of the four methods; the average deviation coefficient of the total ordinal number method is 31.72%, which is the highest of the four methods, so that the evaluation accuracy of the two-norm method and the Euclidean distance method can be further considered to be better than that of the weighted average method and the total ordinal number method. The accuracy of the four types of evaluation methods is Euclidean distance method, two norm method, weighted average method and total ordinal method.
Since there is no absolute authoritative evaluation method of "perfect beauty" and there are differences in the intrinsic mechanisms of different evaluation methods, so that the emphasis points of the evaluation may be different, the final result calculation may be performed by combining the evaluation values of several evaluation results.
The risk evaluation system further comprises an evaluation result testing and combining module which is configured to carry out fitting degree testing based on the risk evaluation result, wherein the risk evaluation result with the fitting degree larger than a preset first threshold value is used as secondary combination evaluation;
forming a quadratic combination evaluation result matrix based on the quadratic combination evaluation and combining with the region dimension
Figure 313740DEST_PATH_IMAGE113
：
Figure 877838DEST_PATH_IMAGE114
Comparing the secondary combination evaluation results, and setting the number of secondary combination evaluation results with the ranking higher than that of the j area in the secondary combination evaluation result ranking of the i area as P and the number of secondary combination evaluation results with the ranking lower than that of the j area in the secondary combination evaluation result ranking of the i area as Q; if P is greater than Q, note
Figure 17833DEST_PATH_IMAGE115
(ii) a If P = Q, is recorded as
Figure 456904DEST_PATH_IMAGE116
；
The combined evaluation matrix is obtained as:
Figure 733165DEST_PATH_IMAGE064
Figure 232279DEST_PATH_IMAGE065
wherein the content of the first and second substances,
Figure 593991DEST_PATH_IMAGE117
elements representing a comprehensive evaluation matrix;
the evaluation score of the region i is
Figure 571174DEST_PATH_IMAGE118
；
According to
Figure 701941DEST_PATH_IMAGE119
The m regions are ranked by the evaluation score to obtain a final evaluation result ranking.
In this embodiment, the necessity analysis data or the feasibility analysis data may be selected or other screening condition shielding part indexes may be set as required, and the risk analysis may be performed by a combination of the four evaluation methods of the system of the present invention.
In a second embodiment of the present invention, a comprehensive risk analysis method based on a multi-dimensional risk matrix includes:
constructing a risk type library, and setting agricultural meteorological disasters, address disasters and forest fires according to the risk types;
constructing a risk characteristic library, and dividing characteristic indexes into agricultural meteorological disaster characteristic indexes and geological disaster characteristic indexes;
the agricultural meteorological disaster characteristic indexes comprise: disaster area, seeding area, total area of area, total amount of reservoir, total agricultural seeding area and the like;
the address disaster characteristic indexes comprise mountain area and the like;
constructing an evaluation index library, and dividing the evaluation indexes into saturation, fluctuation, exposure, concentration, slowing and the like;
and constructing an evaluation method library, dividing the evaluation methods into a total ordinal method, a weighted average method, an Euclidean distance method and a two-norm method, and supporting a subsequent increasing method.
S100, acquiring multivariate risk data;
s200, constructing a four-dimensional risk matrix based on the multivariate risk data;
step S300, based on the four-dimensional risk matrix, respectively obtaining a total ordinal method risk evaluation result, a weighted average method risk evaluation result, an Euclidean distance method risk evaluation result and a two-norm method risk evaluation result of the target area through a total ordinal method, a weighted average method, an Euclidean distance method and a two-norm method;
and S400, carrying out comprehensive analysis on the total ordinal number method risk evaluation result, the weighted average method risk evaluation result, the Euclidean distance method risk evaluation result and the two-norm method risk evaluation result to obtain a potential risk condition.
In this embodiment, an evaluation project is created first, that is, an evaluation object is added, and a range (time range, acquisition frequency) of index acquisition is selected; selecting a risk type and a risk characteristic index, selecting an evaluation method and setting an evaluation threshold;
then, executing an evaluation project, collecting characteristic indexes, evaluating and calculating, screening or shielding part of indexes according to needs, evaluating by still adopting the original method, and displaying;
the data can be displayed through a sorting table, a result data fitting graph of various methods, a distribution map and a conformity evaluation graph.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the comprehensive risk analysis method based on the multi-dimensional risk matrix provided in the foregoing embodiment is only illustrated by the division of the functional modules, and in practical applications, the functions may be allocated to different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
An electronic apparatus according to a third embodiment of the present invention includes: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement the above-described method for comprehensive risk analysis based on a multi-dimensional risk matrix.
A computer-readable storage medium of a fourth embodiment of the present invention stores computer instructions for being executed by the computer to implement the comprehensive risk analysis method based on the multi-dimensional risk matrix.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims (9)

1. A comprehensive risk analysis system based on a multi-dimensional risk matrix, the system comprising: the system comprises a data acquisition module, a multi-dimensional risk matrix construction module, a comprehensive risk evaluation module and a result analysis module;
the data acquisition module is configured to acquire multivariate risk data;
the multi-dimensional risk matrix construction module is configured to construct a four-dimensional risk matrix based on the multivariate risk data; the method specifically comprises the following steps: the system comprises a data classification unit and a four-dimensional risk matrix construction unit;
the data classification unit is configured to classify the multivariate risk data into necessity analysis data and feasibility analysis data;
wherein the indicators of the necessity analysis data include: destructive power of risk causes, saturation of risk causes, fluctuation of risk causes, and exposure of risk receptors; the indicators of the feasibility analysis include: the degree of risk receptor concentration and the ability of risk managers to slow down;
the four-dimensional risk matrix building unit is configured to increase a region dimension M and a time dimension T and build a four-dimensional risk matrix based on n risk indexes in the necessity analysis data and the feasibility analysis data and a risk type dimension C to which the n risk indexes belong:
Figure 878350DEST_PATH_IMAGE002
wherein N represents a risk indicator dimension,
Figure DEST_PATH_IMAGE003
a four-dimensional risk matrix representing four-dimensional risk information r containing c risk types and n risk indexes of all m regions in t years;
the comprehensive risk evaluation module is configured to obtain a total ordinal method risk evaluation result, a weighted average method risk evaluation result, an Euclidean distance method risk evaluation result and a two-norm method risk evaluation result of the target area respectively through a total ordinal method, a weighted average method, an Euclidean distance method and a two-norm method based on the four-dimensional risk matrix;
and the result analysis module is configured to perform comprehensive analysis on the total ordinal number method risk evaluation result, the weighted average method risk evaluation result, the Euclidean distance method risk evaluation result and the two-norm method risk evaluation result to obtain a potential risk condition.
2. The comprehensive risk analysis system based on multi-dimensional risk matrix as claimed in claim 1, wherein the total ordinal method specifically comprises:
step A100, based on the four-dimensional risk matrix, summing four-dimensional risk information of n risk indexes of c risk types of t years in m regions by using a matrix addition principle to obtain a summed risk matrix:
Figure DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 988389DEST_PATH_IMAGE006
sum represents the sum, m represents m regions, i represents the risk type number
Figure DEST_PATH_IMAGE007
And j represents a risk indicator number
Figure 64798DEST_PATH_IMAGE008
And k represents a region number
Figure DEST_PATH_IMAGE009
；
Step A200, based on the total risk matrix, averaging by using a matrix number multiplication principle to obtain t-year average risk matrices of m regions:
Figure DEST_PATH_IMAGE011
wherein avg represents averaging;
step A300, projecting elements in the average risk matrix of the m regions for T years onto an xyz axis, representing c risk types by the x axis, representing n risk indexes by the y axis, representing m regions by the z axis, performing standardization processing and weighting to obtain weighted standardized three-dimensional windA risk matrix; the method specifically comprises the following steps: carrying out the target risk index and the elements in the list
Figure 475051DEST_PATH_IMAGE012
Performing secondary standardization processing to obtain a standardized three-dimensional risk matrix, and performing weighting on elements of the standardized three-dimensional risk matrix according to value difference conditions by using an entropy weight method for the same index and the same risk category;
the standardization processing of the target risk index and the elements in the target risk index column for 1 time specifically comprises the following steps: subtracting the minimum value of the element in the column from each element, and dividing the difference by the maximum value and the minimum value of the element in the column;
step A400, based on the weighted normalized three-dimensional risk matrix, for parallel to
Figure DEST_PATH_IMAGE013
Each of each column on the shaft
Figure 41030DEST_PATH_IMAGE014
Elements on the same position in the order two-dimensional matrix are sorted from large to small, the positions of the elements are not exchanged in the sorting, and each element is numbered to obtain an ordinal number;
step A500, the ordinal number is substituted for the element of the weighted normalized three-dimensional risk matrix to obtain
Figure DEST_PATH_IMAGE015
An order sorting matrix;
step A600, subjecting the
Figure 497419DEST_PATH_IMAGE015
Of order-sorting matrices
Figure 128252DEST_PATH_IMAGE012
Summing the number of each digit on the order two-dimensional sequencing matrix to obtain
Figure 924170DEST_PATH_IMAGE016
And the order two-dimensional vector compares ordinals on each row position in the vector to obtain average risk level sequence of each area in t years, namely a total ordinal method risk evaluation result.
3. The comprehensive risk analysis system based on the multi-dimensional risk matrix according to claim 2, wherein the weighted average method specifically comprises:
step B100, determining various factors influencing an evaluation object as a composed factor set U, dividing the factor set U into c subsets according to c risk types in the four-dimensional risk matrix, and using the c subsets as first-level factor index indexes
Figure DEST_PATH_IMAGE017
；
Based on the first-level factor indexes, dividing second-level factor indexes according to n risk indexes in the four-dimensional risk matrix
Figure 681558DEST_PATH_IMAGE018
；
Step B200, based on the first-stage factor index
Figure 828506DEST_PATH_IMAGE017
Setting a first-level comment index according to various results possibly made by an evaluator on an evaluation object by an entropy weight method
Figure DEST_PATH_IMAGE019
(ii) a The comment indexes are used as the weights of the factor indexes;
based on the second level factor index
Figure 387663DEST_PATH_IMAGE018
Setting a second-level comment index according to various results possibly made by the evaluator on the evaluation object by an entropy weight method
Figure 241350DEST_PATH_IMAGE020
；
Step B210, standardizing various factor indexes to obtain standardized factor indexes:
Figure 165443DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
first finger
Figure 517796DEST_PATH_IMAGE024
Class I risk
Figure DEST_PATH_IMAGE025
A normalization factor index;
Figure DEST_PATH_IMAGE027
first finger
Figure 349486DEST_PATH_IMAGE024
Class I risk
Figure 323258DEST_PATH_IMAGE025
An individual factor index;
Figure 418253DEST_PATH_IMAGE028
first finger
Figure 602110DEST_PATH_IMAGE024
A maximum factor indicator in class risk;
Figure DEST_PATH_IMAGE029
first finger
Figure 362124DEST_PATH_IMAGE024
A minimum factor indicator in class risk;
step B220, calculating the information entropy of the standardization factor index
Figure 49457DEST_PATH_IMAGE030
：
Figure 580933DEST_PATH_IMAGE032
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE033
representing an intermediate variable of the entropy of the calculated information, if
Figure 596294DEST_PATH_IMAGE034
Then define
Figure DEST_PATH_IMAGE035
；
Step B230, information entropy by standardization factor index
Figure 300944DEST_PATH_IMAGE030
Setting the weight of each factor index
Figure 232997DEST_PATH_IMAGE036
：
Figure 935374DEST_PATH_IMAGE038
Wherein the content of the first and second substances,
Figure 562664DEST_PATH_IMAGE036
first finger
Figure 946372DEST_PATH_IMAGE024
Weight of class normalization factor index;
Figure 545981DEST_PATH_IMAGE030
is as follows
Figure 481576DEST_PATH_IMAGE024
Information entropy value of the class standardization factor index;
Figure DEST_PATH_IMAGE039
number of categories that are risk types;
step B300, carrying out weighted average on the first-level factor indexes and the second-level factor indexes to obtain risk weighted average values of different areas; the method specifically comprises the following steps:
step B310, multiplying the second-level factor index by the factors in the second-level comment index, and summing to obtain a first-level index value:
Figure DEST_PATH_IMAGE041
Figure DEST_PATH_IMAGE043
Figure DEST_PATH_IMAGE045
wherein the content of the first and second substances,
Figure 192567DEST_PATH_IMAGE046
a first level indicator value representing an ith risk type;
Figure 176704DEST_PATH_IMAGE048
second-level factor indexes are indicated;
Figure DEST_PATH_IMAGE049
indicating that the second-level comment indexes are obtained by an entropy weight method,
Figure 817770DEST_PATH_IMAGE050
is a category of risk typeThe number of the first and second groups is,
Figure DEST_PATH_IMAGE051
representing a risk indicator number;
step B320, multiplying the first-level index value by the corresponding first-level comment index, and summing to obtain a risk comprehensive weighted value of each area:
Figure DEST_PATH_IMAGE053
wherein the content of the first and second substances,
Figure 65211DEST_PATH_IMAGE054
first finger
Figure 932673DEST_PATH_IMAGE024
The area risk comprehensive weighted value of each area is the risk evaluation result of the weighted average method;
Figure DEST_PATH_IMAGE055
the first-level factor index;
Figure 641872DEST_PATH_IMAGE056
the first-level comment indicators are represented,
Figure DEST_PATH_IMAGE057
is the number of regions.
4. The comprehensive risk analysis system based on the multi-dimensional risk matrix according to claim 2, wherein the euclidean distance method specifically includes:
step C100, obtaining a weighted normalized three-dimensional risk matrix by the method as step A100-step A300;
step C200, based on the weighted standardized three-dimensional risk matrix, taking the maximum value of each row of elements on the same position of each region, namely selecting the maximum value of a certain index of a certain type of risk, and obtaining
Figure 12811DEST_PATH_IMAGE014
The two-dimensional TOP risk matrix of (a);
step C300, calculating the maximum value
Figure 227891DEST_PATH_IMAGE058
The three-dimensional risk matrix of order being regarded as the R layer
Figure 192436DEST_PATH_IMAGE014
Respectively calculating the two-dimensional risk matrix of each area and the two-dimensional risk matrix
Figure 580692DEST_PATH_IMAGE014
Euclidean distance of the two-dimensional TOP risk matrix of (a):
Figure 9400DEST_PATH_IMAGE060
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE061
the Euclidean distance between a two-dimensional risk matrix and a TOP risk matrix of each area is referred to;
Figure DEST_PATH_IMAGE063
means the first in the area of evaluation
Figure 113491DEST_PATH_IMAGE024
Class I risk
Figure 893228DEST_PATH_IMAGE025
An index;
Figure 960541DEST_PATH_IMAGE064
finger number in TOP risk matrix
Figure 243755DEST_PATH_IMAGE024
Class I risk
Figure 128534DEST_PATH_IMAGE025
An index;
Figure 129988DEST_PATH_IMAGE057
finger-shaped
Figure DEST_PATH_IMAGE065
Class risk;
Figure 778489DEST_PATH_IMAGE066
finger-shaped
Figure DEST_PATH_IMAGE067
An individual risk indicator;
step C400, based on the Euclidean distance, obtaining
Figure 588313DEST_PATH_IMAGE068
And ordering the elements in the two-dimensional Euclidean distance vector to obtain the average risk level ordering condition of m regions in t years, namely the risk evaluation result of the Euclidean distance method.
5. The comprehensive risk analysis system based on the multi-dimensional risk matrix according to claim 2, wherein the two-norm method specifically includes:
step D100, obtaining a weighted normalized three-dimensional risk matrix by the method as the step A100-the step A300;
step D200, considering the weighted normalized three-dimensional risk matrix as R layer
Figure 581677DEST_PATH_IMAGE014
And (3) calculating two norms of each layer by using an order two-dimensional risk matrix:
Figure 132744DEST_PATH_IMAGE070
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE071
the two-norm value of the regional risk matrix of different regions is referred to;
Figure 525548DEST_PATH_IMAGE072
each risk indicator information referring to each type of risk;
Figure DEST_PATH_IMAGE073
representing the numerical product of the c risk types and the n risk indicators;
step D300, based on the two norms, obtaining
Figure 580091DEST_PATH_IMAGE016
And ordering elements in the two-dimensional two-norm matrix to obtain the average risk level ordering condition of m regions in t years, namely a two-norm method risk evaluation result.
6. The comprehensive risk analysis system based on the multi-dimensional risk matrix according to claim 1, wherein the comprehensive analysis is performed based on the total ordinal number method risk evaluation result, the weighted average method risk evaluation result, the euclidean distance method risk evaluation result, and the two-norm method risk evaluation result to obtain the potential risk condition, and the risk evaluation results of the total ordinal number method, the weighted average method, the euclidean distance method, and the two-norm method are all arranged in the preset first n regions, so that the comprehensive risk is prompted to be greater than that of other regions; and in the regions with ranking difference larger than m bits in the risk evaluation results of the total ordinal number method, the weighted average method, the Euclidean distance method and the two-norm method, prompting that the potential risk is larger than that of other regions.
7. The comprehensive risk analysis system based on the multi-dimensional risk matrix according to claim 1, further comprising an evaluation result checking and combining module configured to perform a fitting degree check based on the risk evaluation result, wherein the risk evaluation result with the fitting degree larger than a preset first threshold value is used as a secondary combined evaluation;
forming a quadratic combination evaluation result matrix based on the quadratic combination evaluation and combining with the region dimension
Figure 416460DEST_PATH_IMAGE074
：
Figure 126927DEST_PATH_IMAGE076
Comparing the secondary combination evaluation results, and setting the number of secondary combination evaluation results with the ranking higher than that of the j area in the secondary combination evaluation result ranking of the i area as P and the number of secondary combination evaluation results with the ranking lower than that of the j area in the secondary combination evaluation result ranking of the i area as Q; if P is greater than Q, note
Figure DEST_PATH_IMAGE077
(ii) a If P = Q, is recorded as
Figure 198789DEST_PATH_IMAGE078
；
The combined evaluation matrix is obtained as:
Figure 763631DEST_PATH_IMAGE080
Figure 833218DEST_PATH_IMAGE082
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE083
elements representing a comprehensive evaluation matrix;
the evaluation score of the region i is
Figure 93298DEST_PATH_IMAGE084
；
According to
Figure DEST_PATH_IMAGE085
The m regions are ranked by the evaluation score to obtain a final evaluation result ranking.
8. A comprehensive risk analysis method based on a multi-dimensional risk matrix is characterized by comprising the following steps:
s100, acquiring multivariate risk data;
s200, constructing a four-dimensional risk matrix based on the multivariate risk data; the method specifically comprises the following steps:
dividing the multivariate risk data into necessity analysis data and feasibility analysis data;
wherein the indicators of the necessity analysis data include: destructive power of risk causes, saturation of risk causes, fluctuation of risk causes, and exposure of risk receptors; the indicators of the feasibility analysis include: the degree of risk receptor concentration and the ability of risk managers to slow down;
increasing a region dimension M and a time dimension T based on n risk indexes in the necessity analysis data and the feasibility analysis data and a risk type dimension C to which the n risk indexes belong, and constructing a four-dimensional risk matrix:
Figure DEST_PATH_IMAGE087
wherein N represents a risk indicator dimension,
Figure 109796DEST_PATH_IMAGE003
four-dimensional risk matrix representing four-dimensional risk information r including n risk indicators of c risk types for all m regions in t years
Step S300, based on the four-dimensional risk matrix, respectively obtaining a total ordinal method risk evaluation result, a weighted average method risk evaluation result, an Euclidean distance method risk evaluation result and a two-norm method risk evaluation result of the target area through a total ordinal method, a weighted average method, an Euclidean distance method and a two-norm method;
and S400, carrying out comprehensive analysis on the total ordinal number method risk evaluation result, the weighted average method risk evaluation result, the Euclidean distance method risk evaluation result and the two-norm method risk evaluation result to obtain a potential risk condition.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement the multi-dimensional risk matrix based synthetic risk analysis method of claim 8.
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