Lifeline disability grade dividing method, device and system and storage medium

Abstract

The invention discloses a lifeline disability grade division method, device and system, and a storage medium. The method comprises the following steps: S1, acquiring group psychological state influence factors of an emergency; s2, acquiring influence factors of the sudden electric power disaster event; and S3, associating the group psychological state influence factors with the sudden electric power disaster event influence factors, and performing sudden electric power disaster event severity evaluation through a comprehensive fuzzy evaluation method. By adopting the technical scheme of the invention, the severity levels of different sudden electric power disaster events can be given according to the actual conditions of the events.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power disasters, and in particular relates to a method and device, a system, and a storage medium for classifying lifeline disability levels. Background Art

[0002] A long-term, large-scale power outage caused by a natural disaster that is difficult to repair in a short period of time and affects a large area, and the chain reaction to other social systems, is a highly destructive event that poses a serious threat to people's production and survival. At present, there is no assessment of the severity of sudden power disasters based on the psychological and behavioral orientation of the group in the context of power disasters. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device, a system and a storage medium for classifying lifeline failure levels taking into account sudden power disasters.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A method for classifying lifeline failure levels considering sudden power disasters, comprising:

[0006] Step S1, obtaining the influencing factors of the group psychological state of the emergency;

[0007] Step S2, obtaining influencing factors of sudden power disaster events;

[0008] Step S3: Correlate the influencing factors of the group psychological state with the influencing factors of sudden power disaster events, and evaluate the severity of sudden power disaster events through a comprehensive fuzzy evaluation method.

[0009] Preferably, step S1 comprises:

[0010] Obtaining the characteristic factors of residential users;

[0011] Conduct hierarchical grey correlation analysis of individual psychological stress system on the characteristic factors of resident users, and obtain the relative correlation between the characteristic factors of resident users and emergencies;

[0012] Relative correlation is used to obtain the influencing factors of the group’s psychological state.

[0013] Preferably, step S2 comprises:

[0014] Obtain the fault time and fault location data of electricity, water and gas;

[0015] Based on the fault time and fault plot data of electricity, water and gas, an urban disaster chain formed by various disaster chains occurring within the city is established.

[0016] According to the urban disaster chain, the influencing factors of sudden power disaster events are obtained by measuring the coupling relationship between lifeline systems.

[0017] The present invention also provides a lifeline disability level classification device, comprising:

[0018] The first acquisition module is used to obtain the influencing factors of the group psychological state of the emergency;

[0019] The second acquisition module is used to obtain the influencing factors of sudden power disaster events;

[0020] The evaluation module is used to associate the influencing factors of the group psychological state with the influencing factors of sudden power disaster events, and evaluate the severity of sudden power disaster events through a comprehensive fuzzy evaluation method.

[0021] Preferably, the first acquisition module includes:

[0022] A first acquisition unit is used to acquire characteristic factors of resident users;

[0023] The analysis unit is used to perform hierarchical grey correlation analysis on the individual psychological stress system of the resident user characteristic factors, and obtain the relative correlation between the resident user characteristic factors and the emergency events;

[0024] The first processing unit is used to obtain the influencing factors of the group psychological state according to the relative correlation degree.

[0025] Preferably, the second acquisition module includes:

[0026] The second acquisition unit is used to acquire the fault time and fault plot data of electricity, water and gas;

[0027] The construction unit is used to establish an urban disaster chain formed by various disaster chains occurring within the city based on the fault time and fault plot data of electricity, water and gas.

[0028] The second processing unit is used to obtain the influencing factors of sudden power disaster events based on the urban disaster chain by measuring the coupling relationship between lifeline systems.

[0029] The present invention also provides a lifeline disability level classification system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a lifeline disability level classification method when executed by the processor.

[0030] The present invention also provides a storage medium, on which a computer program is stored, and the computer program executes the lifeline disability level classification method when running.

[0031] The present invention obtains the influencing factors of the group psychological state of the emergency and the influencing factors of the sudden power disaster event; associates the influencing factors of the group psychological state with the influencing factors of the sudden power disaster event, and evaluates the severity of the sudden power disaster event through a comprehensive fuzzy evaluation method. The technical scheme of the present invention will establish a sudden power disaster event severity grading model based on the user characteristics and behavior orientation under the city lifeline, so that the severity level of different sudden power disaster events can be given according to the actual situation of the event. The correct selection of emergency plan is very important for handling emergencies and reducing losses. Reasonable classification of emergency levels is the primary prerequisite for formulating plans, and is the basis for emergency management personnel to quickly and scientifically dispatch resources and personnel. It is conducive to quickly and accurately formulating emergency plans for emergencies and dispatching emergency resources, reducing the losses caused by power disasters to society, and effectively protecting the lives and property of the people. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 This is a flow chart of a method for classifying lifeline disability levels according to an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the coupling of typical urban lifeline systems. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Embodiment 1:

[0038] like Figure 1 As shown, an embodiment of the present invention provides a method for classifying lifeline failure levels considering sudden power disasters, including:

[0039] Step S1, obtaining the influencing factors of the group psychological state of the emergency;

[0040] Step S2, obtaining influencing factors of sudden power disaster events;

[0041] Step S3: Correlate the influencing factors of the group psychological state with the influencing factors of sudden power disaster events, and evaluate the severity of sudden power disaster events through a comprehensive fuzzy evaluation method.

[0042] As an implementation of the embodiment of the present invention, step S1 includes:

[0043] Obtaining the characteristic factors of residential users;

[0044] Conduct hierarchical grey correlation analysis of individual psychological stress system on the characteristic factors of resident users, and obtain the relative correlation between the characteristic factors of resident users and emergencies;

[0045] According to the relative correlation, the influencing factors of the group psychological state are obtained.

[0046] Furthermore, "traumatic events" and "personal qualities" related to post-traumatic stress disorder are taken as primary indicators, and secondary indicators are selected for them. Combining the characteristics of the gray system, unifying the caliber of indicators, maintaining the consistency and comparability of indicators, and gray quantifying the system indicators, that is, the specific indicator data is divided into several gray range categories according to the qualitative characteristics of the indicators, generally five or three levels of quantification, as follows:

[0047] The event level is divided into 5 gray classes according to the quantitative requirements. The center point, that is, the most likely point, is selected in each gray class. The center point divides the value range of the event level into S small intervals.

[0048] [λ1,λ2],...,[λ k-1 ,λ k ],...,[λ s ,λ s+1 ](1)

[0049] For the observed value X of the event level, its membership degree to the gray class k (k = 1, 2, S) can be obtained:

[0050]

[0051] Calculate the comprehensive clustering coefficient of the i-th sample value with respect to the gray class k (k = 1, 2, s):

[0052]

[0053] in, is the whitening weight function of variable j, η j is the weight coefficient of variable j in comprehensive clustering. Finally, The event level observation value x belongs to If there are multiple values ​​belonging to the same gray class k, you can also judge based on the size of the comprehensive clustering coefficient The merits or ranking of each value in a gray class.

[0054] According to the group's behavioral intentions, the individuals in the group behavior are classified according to certain behavioral characteristics. According to the background, the classification is simplified into four categories: leaders, followers, panic fleeers, and calm searchers.

[0055] In group behavior, leaders play a key role in the direction of group behavior. The main psychological characteristic of followers is herd mentality. Panic fleeers are easily driven by great fear and anxiety in a crisis, which may lead to a decrease in rational thinking ability and impulsive and unpredictable behavior. Calm seekers show a high degree of emotional control and logical thinking ability.

[0056] Combined with the definition of the concept of public psychological influencing factors after an emergency occurs, factors that significantly affect individual psychological conditions are extracted. Based on the extraction and screening method of hierarchical indicators, the following individual psychological stress status influencing indicator system is obtained, as shown in Table 1.

[0057] Table 1

[0058]

[0059]

[0060] The magnitude of the correlation between two groups of elements in the system that change over time or under different conditions is measured. Hierarchical grey correlation analysis is used to obtain both the grey correlation between each factor and the individual psychological state after a sudden power disaster, and the relative correlation between the sub-factors of each factor and the individual psychological state after a sudden power disaster. Cluster analysis is performed on individuals to obtain the clustering characteristics of individuals under different influencing factors.

[0061] The degree of correlation is the measurement of the size of the correlation between two groups of elements in the system that change over time or under different conditions. If the two groups of elements change synchronously and have similar trends during the development and change of the system, it can be called a high degree of correlation; otherwise, it is called a low degree of correlation. Therefore, the grey correlation analysis method measures the degree of correlation between elements based on the different or similar forms of the development and change trends of different elements. Grey correlation analysis includes:

[0062] Step 1: Determine the analysis sequence

[0063] Determine the reference sequence and the comparison sequence of influencing factors according to the research objectives. The reference sequence refers to the data sequence that mainly reflects the behavior in the system or is directly related to the research purpose. The comparison sequence refers to the sequence of elements that affect the main behavior.

[0064] For example, suppose the reference series of factors affecting individual psychological stress is

[0065]

[0066] The comparison series of factors affecting individual psychological stress is X i ={X i (k)∣k=1,2,...,n},i=1,2,...,m.

[0067] Step 2: Remove the dimension of the variable

[0068] The data of each indicator in the system may have inconsistent dimensions due to different statistical methods, sampling methods and calibers, which is not conducive to comparison and the results will be inaccurate. Therefore, before conducting grey correlation analysis, it is necessary to remove the dimension of the variable.

[0069]

[0070] Among them, X i (l) means that in all X i The largest number in (k).

[0071] Step 3: Calculation of correlation coefficient

[0072] The correlation coefficient indicates the correlation between the reference sequence and the comparison sequence y(k) and x at a certain time or condition. i The calculation formula of the correlation coefficient of (k) is:

[0073]

[0074] Among them, ρ∈(0,∞) is called the resolution coefficient. The smaller the value of ρ, the greater the resolution. The value range of ρ is generally (0,1), which depends on the research purpose or sample situation. When ρ≤0.5463, the resolution is the best, and ρ=0.5 is usually taken.

[0075] Step 4: Calculation of correlation

[0076] The obtained correlation coefficient reflects the degree of correlation between two groups of sequences at a specific time or under specific conditions, so it cannot reflect the overall information and correlation degree of the sequences. Therefore, it is necessary to analyze the values ​​at each moment in a centralized manner, that is, to obtain their average value, which is used to compare the overall correlation between the sequences. The formula is as follows:

[0077]

[0078] Step 5: Sort the relevance

[0079] Sort the correlation degrees from large to small. If r1<r2, the more similar the comparison sequence X is to the reference sequence y, the greater the correlation degree.

[0080] We can first calculate each X i The correlation coefficient between the Y(k) sequence and the Y(k) sequence is calculated, and then the average value of each correlation coefficient is calculated, that is, the correlation coefficient between Y(k) and X i The correlation degree of (k) is sorted, and then the main factors affecting the public psychological stress in emergencies are analyzed.

[0081] The factors that affect the group psychology level under emergencies include event factors, individual factors, group factors, information factors and social factors, as shown in Table 2.

[0082] Table 2

[0083]

[0084] As an implementation of the embodiment of the present invention, step S2 includes:

[0085] Obtain the fault time and fault location data of electricity, water and gas;

[0086] Based on the fault time and fault plot data of electricity, water and gas, an urban disaster chain formed by various disaster chains occurring within the city is established.

[0087] According to the urban disaster chain, the influencing factors of sudden power disaster events are obtained by measuring the coupling relationship between lifeline systems.

[0088] Furthermore, urban disaster chain refers to the general term for various disaster chains occurring within the city. Disaster-causing factors act on the urban system and interact with various disaster-prone bodies. Once the critical value is exceeded, it is amplified through the nonlinear coordination effect within the system, triggering a series of changes and forming a disaster chain.

[0089] According to the definition of disaster chain, the mathematical expression of disaster chain can be given as:

[0090] S(n)={S G (n),R,E} (8)

[0091] Where S(n) represents the disaster chain system, which is composed of n interrelated disaster chain elements, n≥2; S G (n) is the disaster chain element, which refers to each disaster event; R represents the correlation between each disaster event; E is the environment in which the disaster chain exists. The relationship between each disaster event in the disaster chain at time t can be expressed as:

[0092] f{S Gi (n,t),R i,j (t),S Gj (n,t)}=0 (9)

[0093] Among them, S Gi (n, t) represents the i-th disaster in the disaster chain at time t; S Gj (n,t) represents the jth disaster in the disaster chain at time t; R i,j (t) represents the relationship between the i-th disaster and the j-th disaster at time t.

[0094] In summary, we can get a schematic diagram of the disaster chain system. The various disaster elements within the disaster chain system are connected by internal links R i,j (t), which is represented by R(t) outside the system. That is, the environment E acts on the disaster chain system. Under the influence of internal and external forces, the disaster chain leads to disaster consequences H(t).

[0095] The urban disaster system is a dynamic system with multiple inputs, multiple outputs, multiple interferences and multiple time variations. It is composed of a disaster-prone environment, disaster-causing factors and disaster-bearing bodies, and has multiple feedback structures. The urban disaster system has the characteristics of a feedback dynamic system.

[0096] Disaster loss rate n i (t) The corrected expression is:

[0097] n i (t) = q i (t)·exp(V s )·E i (t)-r i (t) (10)

[0098] Among them, r i (t) is the emergency repair factor, which can be expressed as:

[0099] r i (t) = φ i ·x i (t) (11)

[0100] Among them, the largest special rescue and repair capacity i A disaster event i The emergency rescue team and material reserves are determined by factors such as the number of emergency rescue teams and material reserves. For the two different time effect evolution behaviors, the emergency rescue and repair investment ratio x i (t) is also different:

[0101] (1) If the child node a iAffected by the instantaneous evolution behavior, the repair investment ratio x is determined according to the disaster loss rate i (t), when node a i Did not happen, repair investment ratio x i (t) is 0; when node a i The disaster loss rate is greater than the critical value n cr , the largest special rescue and repair capacity will be fully invested, that is, x i (t)=1.

[0102] n i (t)=0,x i (t) = 0

[0103] 0 <n i (t) <n c , 0 <x i (t)<1

[0104] n i (t)≥n cr , x i (t)=1 (12)

[0106] (2) If the child node a i Affected by the continuous evolution behavior, the repair investment ratio x is determined according to the disaster loss degree i (t), when node x i If the disaster loss degree of (t) is 0, then the repair investment ratio x i (t) is 0; when node x i The disaster loss degree of (t) is greater than the critical value N cr , the largest special rescue and repair capacity will be fully invested, that is, x i (t)=1.

[0107] N t (t)=0,x i (t) = 0

[0108] 0 <N i (t) <n cr , 0 <x i (t)<1

[0109] N i (t)≥N cr , x i (t)=1 (13)

[0110] If the value of the rescue and repair factor is greater than the disaster loss degree, the disaster loss rate is zero.

[0111] The lifeline system of a city is composed of multiple key systems, such as electricity, water supply, gas, transportation and other systems, which are interdependent. The disaster chain is a series of disaster reaction chains triggered by nonlinear effects in the urban system when the hazard factors act on the system and exceed the critical point. Each disaster event will affect other events in the system, forming a complex feedback dynamic within the system, which in turn affects the spread of the overall disaster chain. Through the mathematical model of the disaster chain, we can describe the relationship between these disaster events and analyze their mutual relationship at a specific moment and the impact of the external environment on the disaster chain.

[0112] The typical coupling scenario of urban lifeline systems can be represented by G = {A(G), R(G)}, where

[0113] A(G)={A i ∣i=1,2,...n;n≥2}

[0114] It is a collection of typical lifeline systems in cities.

[0115] R(G)={(A i ,A j )∣A i ,A j ∈A(G),i≠j,i,j=1,2,...,n}

[0116] is a set of coupling relationships of typical urban lifeline systems. The reasons for the coupling relationships between systems vary, and the direction and strength of the coupling are also different. In the coupling diagram of typical urban lifeline systems, as shown in Figure 2 As shown in the figure, “●”, “▲” and “■” represent the nodes of the typical urban lifeline systems A1, A2 and A3, corresponding to water stations of the water supply system, substations of the power system or gate stations of the gas system, etc., “-” represents the pipelines controlled by each system node, “→” and “” represent the direct and indirect coupling relationships between different system nodes, respectively. The arrows indicate the coupling direction, and the thickness of the line reflects the strength of the coupling.

[0117] The coupling mechanism of typical urban lifeline systems is analyzed, and a multidimensional measurement method of system coupling based on subjective and objective information is given. First, the description of the system coupling measurement problem is given; then, the principle and steps of the multidimensional measurement method of system coupling are given. For the convenience of analysis, the following symbols are used to describe the sets and quantities involved in the coupling measurement problem of typical urban lifeline systems.

[0118] A={A1,A2,...,A n}: A set of typical urban lifeline systems (n≥2), where A i is the i-th system, i∈{1,2,...,n};

[0119] E={E1,E2,…,Em}: Expert set (m≥2), where E k is the kth expert, k∈{1,2,…,m};

[0120] S={S0,S1,...,S g}: Language phrase evaluation set, where S u is the u-th language phrase, u∈{1,2,...,g};

[0121] Y=[y ij ] n×n : Structural coupling matrix, where y ij A reflection system obtained through GIS technology i and A j The coupling information of the number of adjacent facility spaces, i,j=1,2,...,n;

[0122] Z k =[z kij ] n×n : Functional coupling matrix, where z kij For Expert E k For the normal system A i A j The coupling information given by the functional interaction or dependence degree of z kij ∈S,k=1,2,...,m,i,j=1,2,...,n;

[0123] B k =[b kij ] n×n : Phase coupling matrix, where b kij For Expert E k System A derived from sudden destructive events i A j The coupling information given by the degree of influence, b kij ∈S,k=1,2,...,m,i,j=1,2,...,n;

[0124] W=(w1,w2,w3) T : Coupling dimension weight vector, and satisfies 0≤W l ≤1, l∈{1,2,3}, which is usually given directly by the organizer or obtained through the AHP method.

[0125] The multi-dimensional measurement method for coupling of typical urban lifeline systems based on subjective and objective information of the present invention comprises:

[0126] First, according to the binary semantic conversion function θ, the functional and phase coupling matrix Z k =[zkij ] n×n and B k =[b kij ] n×n Converted into matrices of binary semantic form and The conversion formulas are

[0127]

[0128] Recalculate

[0129]

[0130] Similarly, calculation

[0131]

[0132] Then calculate and

[0133]

[0134] Furthermore, the coupling dimension weight vector W is combined with the coupling normalization matrix of each dimension and Aggregate, get the system direct coupling matrix X = [x ij ] n×n , where x ij The calculation formula is

[0135]

[0136] Then calculate D = [d ij ] n×n ,i,j=1,2,...,n, the calculation formula is:

[0137] D=lim λ→∞ (X 2 +...+X λ )=X 2 (IX) -1 (twenty two)

[0138] On this basis, the system comprehensive coupling matrix T can be obtained according to the following formula: ij ] n×n , where t ij Indicates system A i A j The sum of the direct coupling and indirect coupling degrees of is the comprehensive coupling degree, which is calculated as follows:

[0139] t ij =x ij +d ij ,i,j=1,2,...,n (23)

[0140] Determine the central coupling degree h of each system based on the matrix T i and the relationship coupling degree r i , and the calculation formulas are

[0141]

[0142] Here, the central coupling degree h i Indicates that system A i The role played in the coupling of typical urban lifeline systems, the relationship coupling degree r i Indicates that system A i Coupling type.

[0143] There is a coupling relationship between typical urban lifeline systems, and this coupling can be quantified and analyzed through multidimensional measurement methods. The functional interactions, dependencies and impact levels of different systems in emergencies vary, so it is necessary to measure these relationships through structural, functional and stage coupling matrices. In this step, by calculating the direct and indirect coupling levels of each system, the centrality and relationship of each system in the coupling are obtained. These coupling metrics quantify the mutual influence of each system in disaster response, laying the foundation for further analysis of the impact of disaster events.

[0144] Based on the research on emergencies, a set of influencing factors is established from the following six aspects, and the corresponding relationship between the evaluation objects and levels is given, as shown in Table 3.

[0145] Table 3

[0146]

[0147] For the set of evaluation factors T, divide it into m subsets according to a certain attribute c, so that they satisfy:

[0148]

[0149] In this way, we get the second-level evaluation factor set:

[0150] T| c ={T1,T2,…,T m} (27)

[0151] If the second-level evaluation factors still have different levels, then continue to divide them according to the above method:

[0152]

[0153] And so on.

[0154] According to the characteristics of early warning classification of public emergencies, all factors T that affect severity classification are considered from two aspects: event factors (T1) and emergency management factors (T2). A total of 8 dimensions of influencing factors are summarized, as shown in Table 4.

[0155] Table 4

[0156]

[0157] The idea of ​​MLE quantitatively calculating threat levels is: system decomposition (Decompose) - data collection (Inform) - data analysis (Analyze) - learning improvement (Learn) (DIAL).

[0158] A determinant is a single factor that affects the threat level. Each determinant produces an output based on its input, which includes direct influencing factors and / or potential influencing factors. The sum of all weight factors for a determinant is equal to 1, as shown below.

[0159]

[0160] Where n is the number of impact factors; w i is the weight factor.

[0161] For different threats, the values ​​of direct impact factors are different. The values ​​are mainly determined by combining field investigation with expert opinions, as shown in Table 5.

[0162] Table 5

[0163] Assignment scope Assignment Description 0.0～0.3 There is no direct monitoring facility and security personnel do not conduct inspections 0.3～0.5 Limited surveillance facilities are deployed, and security personnel rarely check 0.5～0.7 Limited monitoring facilities are deployed, and security personnel conduct regular inspections 0.7～0.9 Security personnel patrol 24 hours a day 0.9～1.0 Arrange sufficient monitoring facilities and have security personnel patrol 24 hours a day

[0164] MLE quantitatively calculates each man-made threat and consists of two parts: attack probability A and system effectiveness E. The attack probability refers to the probability of a certain threat occurring, and the system effectiveness refers to the system's ability to resist the threat. The threat level is calculated using the following formula:

[0165] T=A(1-E) (30)

[0166] Based on system coupling analysis, these influencing relationships can be transformed into a set of influencing factors for power disaster events. By analyzing quantitative and qualitative indicators such as system failure duration, failure scope, failure intensity, casualties and economic losses, a standard system for evaluating the severity of disaster events can be constructed. Combined with quantitative calculation methods (such as MLE), the impact of each determinant on the disaster event can be evaluated, and appropriate weights can be assigned to each factor based on expert opinions and actual survey data. The final set of factors will be used to evaluate the overall threat level and response capabilities of sudden power disaster events to urban lifeline systems.

[0167] As an implementation of the embodiment of the present invention, step S3 is specifically:

[0168] Step 31: Determine the severity classification judgment set

[0169] The severity level is divided into 4 levels, and the severity grading judgment set A{red (R), orange (0), yellow (Y), blue (B)} is defined, which represent extremely serious, serious, major and general respectively.

[0170] Step 32: Determine the set of factors affecting severity classification

[0171] The area where a public emergency occurs is usually a large and complex system with many indicators and factors to consider. The specific set of influencing factors of sudden power disaster events is shown in step seven.

[0172] Step 33: Determine the index value of influencing factors

[0173] There are two ways to determine the index values ​​of influencing factors: for influencing factors that are easy to quantify (such as disaster intensity, casualties, economic losses, spread scope, etc.), the index values ​​are directly given as quantitative values ​​through mathematical statistics, numerical calculation and other methods; for influencing factors that are not easy to quantify (such as degree of cognition, degree of social impact, quality of emergency managers, etc.), the index values ​​are determined through fuzzy language and expert scoring methods.

[0174] Step 34: Indicator fuzzification processing

[0175] After determining the index values ​​of all severity grading influencing factors, according to the multi-level fuzzy comprehensive evaluation method, it is necessary to calculate each factor of the low-level influencing factor set of the severity level to obtain the index value of the factor in the higher level and give the membership function. Establish a fuzzy mapping from the low-level influencing factor set and U to P(V):

[0176] U→P(V)

[0177]

[0178] 0≤r ij≤1,0≤i≤n,0≤j≤m (31)

[0180] The fuzzy relationship can be induced by γ, and the fuzzy matrix is ​​obtained:

[0181]

[0182] Among them, ri j The determination of is carried out by using the membership function method in fuzzy mathematics; the severity level is divided into 4 levels, and the severity level (red (R), orange (O), yellow (Y), blue (B)) is recorded as membership function μ R , μ O , μ Y , μ B Has the following form:

[0183]

[0184] Where: i is the number of influencing factors at each level; j = 1, 2, 3, 4; X = R, O, Y, B; a, b, c are the critical values ​​of each influencing factor.

[0185] Step 35: Determination of comprehensive weights of indicators

[0186] Information entropy is a measure of the degree of disorder in a system, which is defined as the probability-weighted average of the amount of information, that is, Where: pi is the probability of the event; H as a function of pi is a representation of average uncertainty. Introducing information entropy into the severity grading evaluation system avoids the subjectivity of the weights of various factors.

[0187] The specific steps are described as follows:

[0188] The values ​​of n evaluation objects for m evaluation indicators can form a decision matrix R. The decision matrix R is normalized, that is, each column of data is normalized according to Normalization can produce a normalized matrix:

[0189] R=(a ij ) n×m , i=1,2,…,n; j=1,2,…,m (34)

[0190] According to the concept of entropy, the entropy of the impact index of the jth severity level can be calculated as:

[0191]

[0192] Calculate the entropy value of the evaluation index. The entropy value of the impact index of the jth severity level is

[0193]

[0194] Similarly, the weights of the influencing indicators of other severity levels are calculated, so as to obtain the weight vector of influencing indicators of severity level based on information entropy W = {w1, w2, ..., w n}.

[0195] The idea of ​​fuzzy decision making is introduced into the analytic hierarchy process (AHP). Triangular fuzzy numbers are used to represent the importance of the decision. The judgment scale is shown in Table 6.

[0196] Table 6

[0197]

[0198] a) Asking each expert a question can obtain the fuzzy triangular number of the two graded influencing factors. in, represents the pessimistic estimate of expert k on the relative importance of two classified influencing factors i and j; It represents the relative importance estimate of expert k for two graded influencing factors i and j; It represents the optimistic estimate of expert k on the relative importance of two graded influencing factors i and j.

[0199] After the fuzzy triangular numbers of each expert are determined, the opinions of each expert can be integrated using the geometric mean generalization model according to the following formula:

[0200]

[0201] b) Calculate the comprehensive fuzzy value D of element i i =(l i ,m i ,u i ) is calculated as follows:

[0202]

[0203] c) Defuzzify and obtain the final weight. i The possibility of being greater than the other n-1 fuzzy numbers can be defined as:

[0204] V(D i ≥D1, D2, …, D n )=min k＝1，2，…，n，k≠i V(D i ≥D k ), i = 1, 2, ..., n (39)

[0205] Where: Let D i =(l i ,m i ,u i ),D k =(lk ,m k ,u k ), then D i ≥D k The triangular fuzzy function of the possibility degree is:

[0206]

[0207] The weights calculated according to the formula are standardized to obtain the subjective weights of each graded impact index Z = (z1, z2, ···, z n ).

[0208] Step 36: Comprehensive fuzzy evaluation

[0209] The last step in calculating the severity level is to perform a comprehensive fuzzy evaluation. The matrix R obtained above induces a fuzzy transformation:

[0210]

[0211] Among them, W is the weight factor of the severity classification influencing factors, R is the fuzzy judgment matrix, is the composition operator of the fuzzy relation. R It is defined as an operation from F(U) fuzzy transformation to F(V). Substitute the weight factor W of severity grading influencing factors into T R () to obtain a comprehensive decision on severity classification

[0212] Step 37: Get the final score of the specified object

[0213] The purpose of fuzzy comprehensive evaluation is to determine the best object from the selected objects, so after calculating the results, they need to be sorted, and the calculated evaluation result B is converted into the final required score M, and then M is sorted in order according to its value, and finally the object that meets the requirements is selected. The object of comprehensive evaluation is sudden power disaster events, and the evaluation result is the sudden power disaster event classification, so as to obtain the sudden power disaster event level that best meets the actual situation.

[0214] The method for classifying lifeline disability levels in the embodiment of the present invention has the following characteristics:

[0215] 1. Combining the characteristics of the grey system, an index system for the impact of individual psychological stress conditions was established. Secondly, the magnitude of the correlation between two groups of elements in the system that change over time or under different conditions was measured, and hierarchical grey correlation analysis was used to improve the generalization ability and reliability of the model.

[0216] 2. The impact of various disaster-causing factors occurring within the city is fully considered, and an urban disaster chain formed by various disaster chains occurring within the city is established to further improve the adaptability of the model.

[0217] 3. Adopt the multidimensional measurement method of the coupling of typical urban lifeline systems, and construct a multidimensional measurement method of the coupling of typical urban lifeline systems based on subjective and objective information, so as to take targeted strategies to optimize the layout and operation of typical urban lifelines and effectively improve the practicality of the model.

[0218] 4. Based on the influencing factors of sudden power disasters, the classification of sudden environmental events can be more comprehensively and detailed. The calculation framework of the classification model is constructed on the basis of the Markov potential impact model. At the same time, feedback adjustment is introduced. In the case of large deviations, the main factors of severity classification can be re-selected and recalculated, which can better determine the severity classification of sudden public events. The model has good robustness and high accuracy.

[0219] Embodiment 2:

[0220] The embodiment of the present invention further provides a lifeline disability level classification device, comprising:

[0221] The first acquisition module is used to obtain the influencing factors of the group psychological state of the emergency;

[0222] The second acquisition module is used to obtain the influencing factors of sudden power disaster events;

[0223] The evaluation module is used to associate the influencing factors of the group psychological state with the influencing factors of sudden power disaster events, and evaluate the severity of sudden power disaster events through a comprehensive fuzzy evaluation method.

[0224] As an implementation manner of the embodiment of the present invention, the first acquisition module includes:

[0225] A first acquisition unit is used to acquire characteristic factors of resident users;

[0226] The analysis unit is used to perform hierarchical grey correlation analysis on the individual psychological stress system of the resident user characteristic factors, and obtain the relative correlation between the resident user characteristic factors and the emergency events;

[0227] The first processing unit is used to obtain the influencing factors of the group psychological state according to the relative correlation degree.

[0228] As an implementation manner of the embodiment of the present invention, the second acquisition module includes:

[0229] The second acquisition unit is used to acquire the fault time and fault plot data of electricity, water and gas;

[0230] The construction unit is used to establish an urban disaster chain formed by various disaster chains occurring within the city based on the fault time and fault plot data of electricity, water and gas.

[0231] The second processing unit is used to obtain the influencing factors of sudden power disaster events based on the urban disaster chain by measuring the coupling relationship between lifeline systems.

[0232] Embodiment 3:

[0233] An embodiment of the present invention further provides a lifeline disability level classification system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a lifeline disability level classification method when executed by the processor.

[0234] Embodiment 4:

[0235] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes the method for classifying lifeline disability levels when running.

[0236] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for classifying lifeline disability levels, characterized in that: include: Step S1, obtaining the influencing factors of the group psychological state of the emergency; Step S2, obtaining influencing factors of sudden power disaster events; Step S3: Correlate the influencing factors of the group psychological state with the influencing factors of sudden power disaster events, and evaluate the severity of sudden power disaster events through a comprehensive fuzzy evaluation method.

2. The method for classifying lifeline disability levels according to claim 1, characterized in that: Step S1 includes: Obtaining the characteristic factors of residential users; Conduct hierarchical grey correlation analysis of individual psychological stress system on the characteristic factors of resident users, and obtain the relative correlation between the characteristic factors of resident users and emergencies; According to the relative correlation, the influencing factors of the group psychological state are obtained.

3. The method for classifying lifeline disability levels as claimed in claim 2, characterized in that: Step S2 includes: Obtain the fault time and fault location data of electricity, water and gas; Based on the fault time and fault plot data of electricity, water and gas, an urban disaster chain formed by various disaster chains occurring within the city is established. According to the urban disaster chain, the influencing factors of sudden power disaster events are obtained by measuring the coupling relationship between lifeline systems.

4. A device for classifying lifeline disability levels, characterized in that: include: The first acquisition module is used to obtain the influencing factors of the group psychological state of the emergency; The second acquisition module is used to obtain the influencing factors of sudden power disaster events; The evaluation module is used to associate the influencing factors of the group psychological state with the influencing factors of sudden power disaster events, and evaluate the severity of sudden power disaster events through a comprehensive fuzzy evaluation method.

5. The device for classifying lifeline disability levels as claimed in claim 4, characterized in that: The first acquisition module includes: A first acquisition unit is used to acquire characteristic factors of resident users; The analysis unit is used to perform hierarchical grey correlation analysis on the individual psychological stress system of the resident user characteristic factors, and obtain the relative correlation between the resident user characteristic factors and the emergency events; The first processing unit is used to obtain the influencing factors of the group psychological state according to the relative correlation degree.

6. The device for classifying lifeline disability levels as claimed in claim 5, characterized in that: The second acquisition module includes: The second acquisition unit is used to acquire the fault time and fault plot data of electricity, water and gas; The construction unit is used to establish an urban disaster chain formed by various disaster chains occurring within the city based on the fault time and fault plot data of electricity, water and gas. The second processing unit is used to obtain the influencing factors of sudden power disaster events based on the urban disaster chain by measuring the coupling relationship between lifeline systems.

7. A lifeline disability classification system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the lifeline disability level classification method as described in any one of claims 1 to 3 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when running, executes the method for classifying lifeline disability levels as claimed in any one of claims 1 to 3.

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