CN103886507B - Emergency preplan digitizes generation method - Google Patents

Abstract

The invention relates to a method for digitally generating an emergency plan in the technical field of railway system emergency plan construction. Firstly, the original emergency plan is decomposed to generate a digital emergency plan library; the information dimension of the emergency plan is generated according to the type and level of the emergency; after matching, the process dimension information of the plan with the highest similarity is selected to generate a preliminary disposal process ; Use the correct route matching strategy from the initial scene to the end scene to verify and revise the correctness to obtain a more reasonable handling process; deal with emergencies until the end of the event handling. If the emergency event expands, update the event information dimension, and execute the above process in a loop until the disposal ends. After the processing is completed, other dimensional information is improved to form a new digital disposal plan and store it in the emergency plan library for future use. The invention can provide fast, accurate and direct reference for emergency handling of emergencies, and minimize the loss of emergencies.

Description

Digital generation method of emergency plan

technical field

The invention relates to a method for digitally generating an emergency plan, in particular to a method for digitizing an emergency plan of a railway system and an emergency disposal process, and belongs to the technical field of construction of an emergency plan for a railway system.

Background technique

At present, my country's railway department has established a three-level emergency management system for the Ministry of Railways, the Railway Bureau, and the station section, and has relatively complete emergency plans for common emergencies. However, the current plans exist in the form of text, and the utilization efficiency is low, which cannot meet the requirements of fast and accurate handling of emergencies. Therefore, the digitization of emergency plans will definitely become its development direction.

In my country, there are few modeling studies on the process analysis of digital emergency plans, which is also a weak link in emergency management. It can be said that the current research on digital emergency plans in my country, especially the digital emergency plans of railway systems, is still in a primitive and enlightening stage. state.

Contents of the invention

The purpose of the present invention is to propose a digital generation method for emergency plans in view of the shortcomings of the current emergency plan flow analysis and digital modeling technology.

A method for digitally generating an emergency plan, the method comprising the following steps:

Step 1: Decompose the original plan to generate a digital emergency plan library;

Step 2: When there is an emergency, generate the information dimension of the emergency plan according to the emergency

Step 3: Match the information dimension generated by the emergency with the content of the relevant target in the information dimension in the plan database, and select the plan with the highest similarity;

Step 4: According to the plan with the highest similarity, extract the process dimension information of the plan with the highest similarity to generate a preliminary treatment process;

Step 5: Verify the correctness of the generated disposal process using the correct route matching strategy from the initial scene to the end scene, and revise the illegal or wrong process to obtain a reasonable disposal process;

Step 6: According to the emergency treatment process obtained in step 5, deal with the emergency, and the treatment is over; when there is a new emergency at the scene of the accident, go to step 2, so as to execute the above process cyclically until the treatment is over.

Step 7: After the disposal is completed, the emergency disposal process described in step 6, that is, the process dimension, is used to improve information in other dimensions to form a new digital disposal plan and store it in the plan library for future use.

The method of decomposing the original emergency plan to generate a digital emergency plan library is as follows:

Decompose the original text-based emergency plan to form a five-dimensional digital plan including information dimension, organization dimension, resource dimension, function dimension, and process dimension;

The information dimension includes two parts: one is the basic information of the emergency plan, including the compilation department, compilation time, and version number; the other is the information of the emergency corresponding to the emergency plan, including the event type, event level, and casualties;

The organizational dimension is the information data of the staff of the unit;

The resource dimension is the information data that describes the emergency process from the perspective of resource allocation;

The process dimension is the key dimension in the contingency plan. It consists of a series of subtasks in the emergency response process. These subtasks are abstract entities that can be directly executed, and they themselves contain attributes: name, function, goal, executor, resource use, and time limit One or more of , start condition, and end condition; process dimension research is to organize these tasks into a process with a certain hierarchical structure according to their constraint relationship;

The functional dimension describes the emergency response process from the perspective of the functional responsibilities of each emergency organization or department, supplements the limitations of the organizational dimension and process dimension that only focus on emergency organization classification and task steps, and increases the relationship between organizational functions, task objectives, and process steps. The connection between them, assisting the formalization of the emergency plan;

Each decomposed plan can be stored in a digital form, and as many text plans as possible can be converted into this form to obtain a digital emergency plan library with a relatively complete coverage.

The method for matching the information dimension generated according to the emergency with the content of the relevant target in the information dimension in the emergency plan digital library is as follows:

Assuming that the target event α is matched with the event β, α and β are respectively described by m attributes α={a ₁ ,a ₂ ,a ₃ ...a _m }, β={b ₁ ,b ₂ ,b ₃ ...b _m } and the weights of each attribute are w _i , i=1, 2, 3, 4...m, then the calculation method of the similarity SIM(α, β) between α and β is expressed as follows:

Among them, ω _{α, β} represents the structural similarity between α and β, which describes the impact of missing attribute data on the similarity calculation between events, 1- _{α, β} represent the substitutable degree of attributes between case α and β, and sim(a _i , b _i ) represents the local similarity of case α and β on attribute i;

The event global similarity SIM(α,β) of event α and event β and the local similarity sim(a _i , _bi ) of the attribute both range from [0,1]. The higher the value, the greater the similarity. high;

1) Calculation of similarity

The calculation process of the structural similarity between target event α and event β is as follows:

Assume that the set composed of all non-empty attributes of the target event is A ₀ , and the set composed of non-empty attributes of the source event is A ₁ ; the intersection of A ₀ and A ₁ is calculated as I, and the union is recorded as U; respectively calculate the sets I and The sum of the weights of all attributes in U is recorded as ω _I and ω _U respectively, then the definition of the structural similarity of α and β is:

2) Calculation of attribute local similarity

The local similarity of attributes refers to the similarity of events in each attribute. The types of emergency event attributes generally include symbolic and numerical types, and the corresponding local similarity measures of attributes are specified according to different data types.

Symbolic attribute similarity algorithm: symbolic data is a simple enumeration value, which lists all possible values of the attribute. Their similarity is calculated as follows:

Among them, a _i and b _i represent the values of target events α and β on attribute i.

Determine the numerical attribute similarity algorithm: Here, the similarity method based on the evolution of the Hamming distance formula is used, and the calculation is as follows:

Where a _i and b _i represent the values of α and β on attribute i, max _i and min _i represent the maximum and minimum values of attribute i;

For each attribute with a certain number type, its value range needs to be determined in advance when calculating the similarity;

3) Degree of difference between target event α and event β attributes Calculation of _{α, β}

Since attributes are often not substitutable for each other, a "variation index" is introduced here to quantify the substitutability between attributes; the variation index reflects the degree of difference between units in the population, and the greater the degree of difference, the more difficult it is to be identified. instead; it is calculated as follows:

in, is the average value of the similarity of each attribute of event α and event β, and σ _α,β is the variance of the similarity value of each attribute of event α and event β.

4) Calculation of event characteristic attribute weight

Here, the attribute hierarchical model (Attribute Hierarchical Model AHM) is used to calculate the weight value of each characteristic attribute of the event;

The Attribute Hierarchical Model (Attribute Hierarchical Model AHM) needs to determine the event feature attribute as an index according to the investigation in advance, and then score and evaluate the relative importance between each attribute according to the method shown in Table 1;

Table 1 Importance evaluation among attributes

After obtaining the relative importance relationship a _ij of attributes a _i and a _j , the transfer importance u _ij between attributes is calculated by formula (7), where u _ij is the transfer importance of attribute a _i to attribute a _j ; where , i=1,2,3,...,m; j=1,2,3...,m.;

Then use the formula (8) to calculate the transfer importance of attributes to get the final weight w _j of each attribute:

Among them, n is the number of attributes contained in event C;

After the matching is completed, a matching set will be obtained. Here, the matching set may be empty, or a unique emergency plan may be stored, or there may be multiple emergency plans; if there are multiple emergency plans, the emergency plan corresponding to the emergency plan shall be listed. The disposal process is displayed in reverse order according to the matching degree, and the relevant emergency response personnel select the optimal solution; if the matching set is empty, adjust the dimension information, delete some attribute characteristics, and re-match until the matching set is not empty

The process dimension information of the matching original plan is extracted to initially generate a disposal process. The information stored in the process dimension in the digital emergency plan is the emergency disposal process information. Therefore, the process dimension in the emergency plan with the highest matching degree is directly extracted, namely The handling process under this event can be obtained.

The correctness verification method using the correct route matching strategy from the initial scene to the end scene is as follows:

Each emergency handling process unit in the digital emergency plan model is regarded as a scenario, so it has isStartScenario (is the initial scenario), isEndScenario (is the end scenario), hasNextScenario (has the next scenario), hasPreScenario (has the previous scenario ) characteristics, and checking the correctness of the emergency handling route can be transformed into checking the sequence or concurrency constraints of each scene;

If the generated emergency handling process unit scenario has any of the following conditions, the process is wrong:

(1) Lack of initial scene or ending scene;

(2) Did not reach the end scene;

(3) The end scene can be reached, but some scenes are missed;

(4) The constraint relationship between each scene is incorrect.

The emergency handling process described in step 6, that is, the process dimension, and improving other dimension information is based on the emergency handling process, that is, the process dimension, and supplements and improves the information dimension, organizational dimension, and function of the disposal plan according to the nature and handling of the emergency. dimension, resource dimension and other dimensional information to form a complete new digital emergency response plan.

Beneficial effects of the present invention:

The invention can provide fast, accurate and direct reference for emergency response to emergencies, facilitate the coordination between departments at all levels and optimize the dispatch of emergency resources, and minimize the loss of emergencies. At the same time, the digital emergency plan Provide basic guarantee for the construction of emergency response system.

Description of drawings

Figure 1 Schematic diagram of the digital generation method for emergency plans.

Figure 2 Schematic diagram of multidimensional decomposition model of emergency plan.

Figure 3 Schematic diagram of emergency plan matching.

Figure 4 Schematic diagram of the correctness verification process of the emergency response process.

detailed description

The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings, and the present invention is not limited to these embodiments.

Figure 1 is a schematic diagram of the digital generation method of the emergency plan. As shown in Figure 1, the steps of the digital generation method for emergency plans are as follows:

Step 1: Decompose the original plan to generate a digital emergency plan library;

Step 2: When there is an emergency, generate the information dimension of the emergency plan according to the emergency;

Step 3: Match the information dimension generated by the emergency with the content of the relevant target in the information dimension in the plan database, and select the plan with the highest similarity;

Step 4: According to the plan with the highest similarity, extract the process dimension information of the plan with the highest similarity to generate a preliminary treatment process;

Step 5: Verify the correctness of the generated disposal process using the correct route matching strategy from the initial scene to the end scene, and revise the illegal or wrong process to obtain a reasonable disposal process;

Step 6: According to the emergency treatment process obtained in step 5, deal with the emergency, and the treatment is completed; when there is a new emergency at the scene of the accident, go to step 2, so as to execute the above process in a loop until the treatment is completed;

Step 7: On the basis of the obtained emergency response process, that is, the process dimension, improve the other dimension information of the disposal plan, and store it in the contingency plan database for future use.

[Example]

A digital generation method for emergency plans, the method

a. Example of digital decomposition of emergency plan

Take the Beijing subway fire plan as an example to show the digital decomposition process of the plan:

The organizational dimension defines the four levels of users at the station site command center level, line traffic dispatching OCC level, operating enterprise general dispatching level, and command center TCC level, and each organization includes subordinate personnel.

Table 1: Schematic of Organizational Dimensions

The process dimension is generally a list of disposal points and the emergency process is divided into an operation process and a reporting process. Some process dimension information is listed in the following table

Table 2: Schematic representation of process dimensions

The information dimension is a description of the nature of the emergency plan, see the table below:

Table 3: Schematic representation of information dimensions

The resource dimension defines the type and quantity of emergency resources needed under the emergency.

Table 4: Resource Dimensions

The functional dimension associates the process dimension with the organizational dimension, and clarifies the tasks of personnel in a specific position. See Table 5 below:

Table 5: Schematic of Functional Dimensions

The above 5 excel worksheets constitute a complete existing digital emergency plan.

b. Examples of digital emergency plan generation under emergencies

Accident background: At 17:35 on the evening of December 12, 2013, the station attendant at Station A of Line 1 found that 1221 times were going down. The controller immediately reported to the traffic dispatcher of Line 1; at 17:37, the train arrived at the downlink platform of Station B, the fire in the fourth carriage was out of control, and 5 passengers were seriously injured. The OCC of Line 1 ordered the station to detain the train. 3 hours, and report to the command center, and report to the command center, start the emergency plan:

(a) Generate the information dimension of the emergency plan under the event

Event time: December 12, 2013 at 17:35 p.m.

Incident location: the fourth compartment of the 1221 down line at Station B of Line 1

Event Type: Fire

Incident Level: Level 2

Casualties: 5 seriously injured

Interruption time: 1-3 hours

(b) Matching of contingency plans

According to the alarm information, the target event C ₀ is obtained, and its attribute characteristics are: {transfer station accident, subway station B, fire, 17:35, level II, 5, 1-3 hours}. Use the search matching algorithm mentioned above to measure the similarity of the events in the event list.

Suppose there are 5 events in the event database, which are described by 7 attributes {accident type, accident location, specific location, occurrence time, accident level, number of casualties, estimated operation interruption time} (see Table 6). Among them, the accident type, accident location, specific location, and accident level are character data, and the accident level and occurrence time are continuous data; the number of casualties and the expected interruption of operation time are fuzzy interval data.

First, calculate the weight of each attribute of the event, as shown in Table 7; then calculate the structural similarity, local attribute similarity and substitutability between different event attributes item by item, as shown in Table 8.

Table 6 Event attribute table

attribute name C ₁ C ₂ C ₃ C ₄ C ₅ Accident Type A ₁ the fire Floods Large passenger flow catenary blackout extraterrestrial damage Accident location A ₂ Station B Station C Station D Station E Station F Specific location A ₄ track range near the railway N/A track range track range Occurrence time A ₅ 16:20 14:20 07:50 18:15 8:25 Accident level A ₆ II II III III IV Casualties A ₇ 2 N/A N/A 1 1 Operation interruption time A ₈ 0.5-1h 3-10h 1-3h 1-3h 0-0.5h

Table 7 Attribute Weight Calculation Table

Attributes A ₁ A ₂ A ₃ A ₄ A ₅ A ₆ A ₇ A ₈ A ₉ A ₁₀ Weights A ₁ 0 0.8 0.17 0.5 0.8 0.2 0.25 0.25 0.5 0.2 0.062431 A ₂ 0.2 0 0.09 0.2 0.5 0.11 0.14 0.14 0.2 0.11 0.032436 A ₄ 0.5 0.8 0.17 0 0.8 0.2 0.25 0.25 0.5 0.2 0.066923 A ₅ 0.2 0.5 0.09 0.2 0 0.11 0.14 0.14 0.2 0.11 0.032435 A ₆ 0.8 0.89 0.71 0.8 0.89 0 0.73 0.73 0.8 0.5 0.118845 A ₇ 0.75 0.86 0.23 0.75 0.86 0.27 0 0.5 0.75 0.27 0.094231 A ₈ 0.75 0.86 0.23 0.75 0.86 0.27 0.5 0 0.75 0.27 0.094231

Table 8 Calculation results of similarity between events and target events

After calculating the set of similarities between each event in the event library and the target event, the event is selected according to the preset threshold θ, θ∈[0.5,1]. When SIM(C _i ,C ₀ )≥θ, select the event and find the corresponding disposal plan or emergency plan.

According to the above calculation results, the global similarity of events is calculated, and the similarity between event C ₁ and the target event is 0.746307. Determine that event C ₁ best matches the target event, and find the corresponding emergency plan.

(c) Extract the process dimension information of the matching original plan to initially generate the disposal process;

(d) Verify the correctness of the generated emergency response plan using the correct route matching strategy from the initial scene to the end scene, and revise the illegal or wrong process to obtain a reasonable process;

(e) According to the emergency handling process obtained in step (d), handle the emergency, and feed back to step 2 in real time according to the event handling situation, so that the above-mentioned process is executed cyclically until the handling is completed.

(f) Finally, other dimensions (information dimension, function dimension, resource dimension, organization dimension) of the emergency plan are improved and stored in the emergency plan database.

The present invention can provide fast, accurate and direct reference for the emergency disposal of emergencies, facilitate the coordination between departments at all levels and the optimal dispatch of emergency resources, and minimize the loss of emergencies. At the same time, the digital emergency plan Provide basic guarantee for the construction of emergency response system.

Claims (6)

1. A kind of emergency plan digitization generation method is characterized in that, the method comprises the following steps: Step 1: Decompose the original emergency plan to generate a digital emergency plan library; Step 2: According to the type, level, and casualties of the emergency, generate the information dimension of the emergency plan under the event; Step 3: Match the information dimension generated according to the emergency with the content of the information dimension-related target in the emergency plan digital library, and select the plan with the highest similarity; Step 4: According to the plan with the highest similarity, extract the process dimension information of the plan with the highest similarity to generate a preliminary treatment process; Step 5: Verify the correctness of the generated preliminary disposal process using the correct route matching strategy from the initial scene to the end scene, and revise the illegal or wrong process to obtain a more reasonable disposal process plan; Step 6: According to the disposal process obtained in step 5, handle the emergency until the end of the event processing; if the emergency event expands, update the dimension of the event information and turn to step 2, so that the above process is cyclically executed until the end of the disposal; The information dimension generated according to the emergency is matched with the content of the relevant target in the information dimension in the emergency plan digital library, the method is as follows: Suppose the target event α is matched with the event β, α and β are described by m attributes respectively α={a ₁ ,a ₂ ,a ₃ ...a _m }, β={b ₁ ,b ₂ ,b ₃ ...b _m } and the weights of each attribute are w _i , i=1, 2, 3, 4...m, then the calculation method of the similarity SIM(α, β) between α and β is expressed as follows: Among them, ω _{α, β} represents the structural similarity between α and β, which describes the impact of missing attribute data on the calculation of similarity between events, Indicates the degree of substitutability of attributes between cases α and β, and sim(a _i , b _i ) represents the local similarity of cases α and β on attribute i; The event global similarity SIM(α,β) of event α and event β and the local similarity sim(a _i , _bi ) of the attribute both range from [0,1]. The higher the value, the greater the similarity. high; 1) Calculation of structural similarity The calculation process of the structural similarity between target event α and event β is as follows: Assume that the set composed of all non-empty attributes of the target event is A ₀ , and the set composed of non-empty attributes of the source event is A ₁ ; the intersection of A ₀ and A ₁ is calculated as I, and the union is recorded as U; respectively calculate the sets I and The sum of the weights of all attributes in U is recorded as ω _I and ω _U respectively, then the definition of the structural similarity of α and β is: ${\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; I</span>}}}{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ 2) Calculation of attribute local similarity The local similarity of attributes refers to the similarity of events on each attribute. The types of emergency event attributes include symbolic and numerical, and the corresponding local similarity measures of attributes are specified according to different data types—— Symbolic attribute similarity algorithm: symbolic data is a simple enumeration value, which lists all possible values of the attribute; their similarity is calculated as follows: $\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&NotEqual;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}\right.<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ Where a _i , b _i represent the values of target events α and β on attribute i; Numerical attribute similarity algorithm: Here, the similarity method based on the evolution of the Hamming distance formula is used, and the calculation is as follows: $\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; max</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; min</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$ Among them, a _i and b _i represent the values of α and β on attribute i, max _i and min _i represent the maximum and minimum values of attribute i; For each numerical attribute, its value range needs to be determined in advance when calculating the similarity; 3) Degree of difference between target event α and event β attributes calculation Since attributes are often not substitutable for each other, a "variation index" is introduced here to quantify the substitutability between attributes; the variation index reflects the degree of difference between units in the population, and the greater the degree of difference, the harder it is to be identified. instead; it is calculated as follows: ${\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}}_{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ ${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}}\sqrt{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}}_{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; \&rsqb;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ in, is the average value of the similarity of each attribute of event α and event β, and σ _α,β is the variance of the similarity value of each attribute of event α and event β; 4) Calculation of event characteristic attribute weight Here, the attribute hierarchical model (Attribute Hierarchical Model AHM) is used to calculate the weight value of each characteristic attribute of the event; The Attribute Hierarchical Model (Attribute Hierarchical Model AHM) needs to determine the event feature attribute as an index according to the investigation in advance, and then score and evaluate the relative importance between each attribute according to the method shown in Table 1; Table 1 Importance evaluation among attributes After obtaining the relative importance relationship a _ij of attributes a _i and a _j , the transfer importance u _ij between attributes is calculated by formula (7), where u _ij is the transfer importance of attribute a _i to attribute a _j ; ${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\\ \mathrm{<span\; class="notranslate">\; 0.5</span>}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\\ \frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>\\ \frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\end{array}\right.<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ Among them, i=1,2,3,...,m; j=1,2,3...,m; Then use the formula (8) to calculate the transfer importance of attributes to get the final weight ω _i of each attribute: ${\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$ Among them, n is the number of attributes contained in event C; After the matching is completed, a matching set will be obtained. Here, the matching set may be empty, or a unique emergency plan may be stored, or there may be multiple emergency plans; if there are multiple emergency plans, the emergency plan corresponding to the emergency plan shall be listed. The disposal process is displayed in reverse order of matching degree, and the relevant personnel in emergency response select the optimal solution; if the matching set is empty, adjust the dimension information, delete some attribute characteristics, and re-match until a non-empty matching set is obtained; In this way, the matching of the target tree or sub-target tree generated according to the emergency with the relevant content in the process dimension of the emergency plan digital library is completed. 2. A kind of emergency plan digital generation method according to claim 1, is characterized in that, this method also comprises: Step 7: After the event processing is completed, complete the disposal process described in step 6, namely the process dimension, to improve other dimension information to form a new digital disposal plan and store it in the emergency plan library for future use. 3. a kind of emergency plan digital generation method according to claim 1, is characterized in that, described original emergency plan is decomposed and generates digitized emergency plan storehouse method as follows: Decompose the original text-based emergency plan into a digital plan including five dimensions: information dimension, organization dimension, resource dimension, process dimension, and function dimension; The information dimension includes two parts: one is the basic information of the emergency plan, including the compilation department, compilation time, and version number; the other is the information of the emergency corresponding to the emergency plan, including the event type, event level, and casualties; The organizational dimension is the information data of the staff of the unit; The resource dimension is the information data that describes the emergency process from the perspective of resource allocation; The process dimension is the key dimension in the contingency plan. It consists of a series of subtasks in the emergency response process. These subtasks are abstract entities that can be directly executed, and they themselves contain attributes: name, function, goal, executor, resource use, and time limit One or more of , start condition, and end condition; process dimension research is to organize these tasks into a process with a certain hierarchical structure according to their constraint relationship; The functional dimension describes the emergency response process from the perspective of the functional responsibilities of each emergency organization or department, supplements the limitations of the organizational dimension and process dimension that only focus on emergency organization classification and task steps, and increases the relationship between organizational functions, task objectives, and process steps. The connection between them, assisting the formalization of the emergency plan; Each decomposed plan is stored in a digital form, so that a digital emergency plan library is obtained. 4. A kind of emergency plan digitization generation method according to claim 1, is characterized in that, the process dimension information of the original plan of described extraction that matches initially generates and handles flow method is: store in the process dimension in the digitized emergency plan The information is the emergency handling process information, so the process dimension in the emergency plan with the highest matching degree can be directly extracted to obtain the handling process under the event. 5. a kind of emergency plan digital generation method according to claim 1, is characterized in that, described adopting initial scene to the correct route matching strategy of end scene line to carry out correctness verification method is: Each emergency handling process unit in the digital emergency plan model is regarded as a scene, so it has the characteristics of "is the initial scene", "is the end scene", "has the next scene", and "has the previous scene". The correctness of the emergency handling route can be transformed into checking the sequence or concurrency constraints of each scenario: If the generated emergency handling process unit scenario has any of the following conditions, the process is wrong: (1) Lack of initial scene or ending scene; (2) Did not reach the end scene; (3) The end scene can be reached, but some scenes are missed; (4) The constraint relationship between each scene is incorrect. 6. A method for digitally generating an emergency plan according to claim 2, characterized in that, the emergency handling process described in step 6, that is, the process dimension, is used to improve other dimension information on the basis of the obtained emergency handling process, that is, the process dimension According to the nature and handling of emergencies, supplement and improve the information dimension, organizational dimension, function dimension, and resource dimension of the disposal plan, so that it can form a complete new digital emergency response plan.