CN112232389A - Dynamic adjustment method and system for traffic emergency plan of large-scale activity emergency - Google Patents
Dynamic adjustment method and system for traffic emergency plan of large-scale activity emergency Download PDFInfo
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Abstract
The invention relates to a method and a system for dynamically adjusting a traffic emergency plan of a large-scale activity emergency. The adjusting method comprises the following steps: extracting the current characteristic attribute of the emergency; determining the best matching source plan in a source plan library according to the current characteristic attribute and a naive Bayes classification algorithm; establishing a traffic emergency plan database knowledge base and a traffic emergency plan database rule base by using a rule reasoning algorithm; and adjusting the best matching source plan by using a forward reasoning mode according to the current characteristic attribute based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base. The system comprises a current characteristic attribute extraction module, an optimal matching source plan determination module, a knowledge base and rule base establishment module and an adjustment module. The invention closely combines the characteristic attributes of the emergency at the current stage and dynamically adjusts the source plan, so that the finally adjusted optimal matching source plan is more specific and has pertinence, and the emergency efficiency and the rescue efficiency of the traffic emergency plan are improved.
Description
Technical Field
The invention relates to a method and a system for dynamically adjusting a traffic emergency plan of a large-scale activity emergency, and belongs to the technical field of emergency rescue.
Background
Along with the enhancement of comprehensive national strength of China, the physical and cultural living standard of people is continuously improved, and the holding times of large-scale activities such as sports activities, cultural exhibitions, concerts and the like are gradually increased. The performance of large events can result in a large number of people and vehicles concentrating in a short time in the floor and surrounding areas. Meanwhile, the existence of various uncertain factors is considered, and the emergency happens sometimes. The emergency requirement of the large-scale active emergency is different from that of the general emergency, the requirement on safety and timeliness is higher, and the object of emergency response is hierarchically graded. Therefore, when an emergency happens to a large-scale activity, a traffic emergency plan for pertinence must be generated in a short time, so that after the emergency happens, the traffic emergency rescue can quickly maintain normal traffic operation, the safety of the activity participation crowd is guaranteed, and the large-scale activity is guaranteed to be carried out according to a plan.
At present, the construction of a traffic emergency plan for a large-scale activity emergency is not perfect: most traffic emergency plans are still in text form, and lack of quantitative emergency plan content, which causes certain difficulty in rapid implementation of traffic emergency measures; most traffic emergency plan contents only aim at conventional emergencies, but for emergencies in large-scale activities, targeted traffic emergency measures are lacked, and the emergency efficiency is low; the optimization of the traffic emergency plan of the large-scale activity emergency is limited to static modification and cannot be dynamically adjusted along with the change of the emergency. These factors are not conducive to the rapid generation and optimization of traffic emergency plans after a large-scale event emergency occurs.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method and a system for dynamically adjusting a traffic emergency plan of a large-scale activity emergency, so as to solve the problems that the existing traffic emergency plan of the large-scale activity emergency is low in emergency efficiency, is only limited to static modification and cannot be dynamically adjusted along with the change of the emergency.
The invention specifically adopts the following technical scheme to solve the technical problems:
a dynamic adjustment method for a traffic emergency plan of a large-scale activity emergency event comprises the following steps:
extracting the current characteristic attribute of the emergency;
determining the best matching source plan in a source plan library according to the extracted current characteristic attribute of the emergency and a naive Bayes classification algorithm;
establishing a traffic emergency plan database knowledge base and a traffic emergency plan database rule base by using a rule reasoning algorithm; the traffic emergency plan base knowledge base comprises a response mechanism knowledge base, a rescue configuration knowledge base, an emergency organization measure knowledge base and an emergency treatment knowledge base; the traffic emergency plan library rule base comprises reasoning rules of linkage and site disposal of large-scale activity emergency departments, traffic and patrol police department emergency resource configuration rule reasoning, non-traffic and patrol police department emergency resource configuration rule reasoning, emergency organization and traffic recovery rule reasoning;
and based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base, adjusting the best matching source plan by utilizing a forward reasoning mode according to the extracted current characteristic attribute of the emergency event.
Further, as a preferred technical solution of the present invention, the determining a best matching source plan in a source plan library according to the current feature attribute and a naive bayes classification algorithm specifically includes:
determining the event type of the emergency according to the current characteristic attribute; each event type comprises a plurality of typical cases;
and determining the conditional probability of each typical case according to a naive Bayes classification algorithm, and determining the typical case with the maximum conditional probability as the best matching source plan.
Further, as a preferred technical solution of the present invention, the adjusting the best matching source plan by using a forward reasoning method according to the current characteristic attribute based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base specifically includes:
based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base, performing rule reasoning optimization according to the current characteristic attribute, and determining an optimized rule;
judging whether the optimized rule is the last rule in a rule base of a traffic emergency plan library to obtain a first judgment result;
if the first judgment result shows that the optimized rule is the last rule in a rule base of a traffic emergency plan library, adjusting the best matching source plan and determining the plan of the current stage;
judging whether the emergency is finished or not according to the preset plan processing site of the current stage to obtain a second judgment result;
if the second judgment result indicates that the emergency is over, determining that the plan at the current stage is the best matching source plan of the emergency;
if the second judgment result indicates that the emergency is not finished, the current characteristic attribute of the emergency is obtained again, and the step of 'performing rule reasoning optimization based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base according to the current characteristic attribute and determining the optimized rule' is returned.
Further, as a preferred technical solution of the present invention, after the adjusting the best matching source plan by using a forward reasoning method based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base according to the current characteristic attribute, the method further includes the steps of:
and establishing a plan emergency capacity level and emergency severity evaluation index by using a fuzzy analytic hierarchy process, and carrying out fuzzy evaluation on the adjusted optimal matching source plan.
Further, as a preferred technical solution of the present invention, the establishing of the plan emergency ability level and the emergency severity evaluation index by using the fuzzy analytic hierarchy process, and the performing of the fuzzy evaluation on the adjusted best matching source plan, specifically includes:
performing single factor evaluation, performing single factor evaluation on each single factor in the emergency through survey statistics, and determining a plurality of single factor evaluation sets;
determining a fuzzy matrix according to a plurality of single factor evaluation sets by using a fuzzy analytic hierarchy process;
according to the importance of the plan emergency capacity index, taking the investigation scoring result of each plan emergency capacity index as an initial value, and determining the plan emergency capacity level and the emergency severity evaluation index weight according to the scoring result by using the fuzzy analytic hierarchy process;
determining a fuzzy comprehensive evaluation index of the predetermined plan according to the emergency capacity level of the predetermined plan, the severity evaluation index weight of the emergency and the fuzzy matrix; the plan fuzzy comprehensive evaluation index is a plan emergency capacity level and an emergency severity evaluation index;
and carrying out fuzzy evaluation on the adjusted optimal matching source plan by using the plan fuzzy comprehensive evaluation index.
The invention also provides a system for dynamically adjusting the traffic emergency plan of the large-scale activity emergency, which comprises the following steps:
the current characteristic attribute extraction module is used for extracting the current characteristic attribute of the emergency;
the optimal matching source plan determining module is used for determining an optimal matching source plan in a source plan library according to the extracted current characteristic attribute of the emergency and a naive Bayes classification algorithm;
the knowledge base and rule base establishing module is used for establishing a traffic emergency plan base knowledge base and a traffic emergency plan base rule base by using a rule reasoning algorithm; the traffic emergency plan base knowledge base comprises a response mechanism knowledge base, a rescue configuration knowledge base, an emergency organization measure knowledge base and an emergency treatment knowledge base; the traffic emergency plan library rule base comprises inference rules of linkage and site disposal of large-scale activity emergency departments, inference of emergency resource configuration rules of traffic and patrol police departments, inference of emergency resource configuration rules of non-traffic and patrol police departments, and inference of emergency organization and traffic recovery rules;
and the adjusting module is used for adjusting the optimal matching source plan by utilizing a forward reasoning mode according to the extracted current characteristic attribute of the emergency on the basis of a traffic emergency plan library knowledge base and the traffic emergency plan library rule base.
Further, as a preferred technical solution of the present invention, the best matching source plan determining module specifically includes:
the event type determining unit is used for determining the event type of the emergency according to the extracted current characteristic attribute of the emergency; each event type comprises a plurality of typical cases;
and the optimal matching source plan determining unit is used for determining the conditional probability of each typical case according to a naive Bayes classification algorithm, and determining the typical case with the maximum conditional probability as the optimal matching source plan.
Further, as a preferred technical solution of the present invention, the adjusting module specifically includes:
the optimization unit is used for carrying out rule reasoning optimization according to the extracted current characteristic attribute of the emergency on the basis of a traffic emergency plan database knowledge base and the traffic emergency plan database rule base, and determining an optimized rule;
the first judgment unit is used for judging whether the optimized rule is the last rule in a traffic emergency plan library rule base or not to obtain a first judgment result;
the plan determining unit at the current stage is used for adjusting the best matching source plan and determining the plan at the current stage if the first judgment result shows that the optimized rule is the last rule in the traffic emergency plan library rule base;
the second judgment unit is used for judging whether the emergency is finished or not according to the site of the plan processing of the current stage to obtain a second judgment result;
an emergency ending unit, configured to determine that the plan at the current stage is the best matching source plan of the emergency if the second determination result indicates that the emergency is ended;
and a current feature attribute re-obtaining unit, configured to re-obtain the current feature attribute of the emergency if the second determination result indicates that the emergency is not ended, and return to the step "based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base, perform rule inference optimization according to the extracted current feature attribute of the emergency, and determine an optimized rule".
Further, as a preferred technical solution of the present invention, the method further includes:
and the fuzzy evaluation module is used for establishing a plan emergency capacity level and an emergency severity evaluation index by using a fuzzy analytic hierarchy process and carrying out fuzzy evaluation on the adjusted optimal matching source plan.
Further, as a preferred technical solution of the present invention, the fuzzy evaluation module specifically includes:
the single factor evaluation set determining unit is used for performing single factor evaluation, performing single factor evaluation on each single factor in the emergency through survey statistics, and determining a plurality of single factor evaluation sets;
the fuzzy matrix determining unit is used for determining a fuzzy matrix according to the plurality of single factor evaluation sets by using a fuzzy analytic hierarchy process;
the device comprises a plan emergency capacity level and emergency severity evaluation index weight determining unit, a fuzzy analytic hierarchy process and a fuzzy analytic hierarchy process, wherein the plan emergency capacity level and emergency severity evaluation index weight determining unit is used for taking the investigation scoring result of each plan emergency capacity index as an initial value according to the importance of the plan emergency capacity index, and determining the plan emergency capacity level and the emergency severity evaluation index weight according to the scoring result by using the fuzzy analytic hierarchy process;
the plan fuzzy comprehensive evaluation index determining unit is used for determining a plan fuzzy comprehensive evaluation index according to the plan emergency capacity level, the emergency severity evaluation index weight and the fuzzy matrix; the plan fuzzy comprehensive evaluation index is a plan emergency capacity level and an emergency severity evaluation index;
and the fuzzy evaluation unit is used for carrying out fuzzy evaluation on the adjusted optimal matching source plan by utilizing the plan fuzzy comprehensive evaluation index.
By adopting the technical scheme, the invention can produce the following technical effects:
the invention provides a dynamic adjustment method and a dynamic adjustment system for a traffic emergency plan of a large-scale event emergency, which are characterized in that an optimal matching source plan is determined based on the characteristic attribute of the emergency, and the optimal matching source plan is adjusted in real time by utilizing a traffic emergency plan base knowledge base and a traffic emergency plan base rule base; the source plan is dynamically adjusted by closely combining the characteristic attributes of the emergency at the current stage, so that the finally adjusted optimal matching source plan is more specific and targeted, and the emergency efficiency and the rescue efficiency of the traffic emergency plan are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flow chart of a method for dynamically adjusting a traffic emergency plan for a large-scale event emergency provided by the invention.
Fig. 2 is a schematic diagram of the current characteristic attributes of an emergency event according to the present invention.
Fig. 3 is a flow chart for generating an initial traffic emergency plan based on case-based reasoning and naive bayes classification in the invention.
Fig. 4 is a schematic diagram of a traffic emergency plan database provided by the present invention.
Fig. 5 is a schematic diagram of the traffic emergency plan library rule base provided by the present invention.
FIG. 6 is a schematic diagram of the best matching source plan adjustment provided by the present invention.
Fig. 7 is a structural diagram of a dynamic adjustment system for a traffic emergency plan of a large-scale event emergency provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention aims to provide a method and a system for dynamically adjusting a traffic emergency plan of a large-scale activity emergency, which can improve the emergency efficiency and the rescue efficiency of the traffic emergency plan.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a method for dynamically adjusting a traffic emergency plan for a large event, as shown in fig. 1, the method for dynamically adjusting a traffic emergency plan for a large event comprises:
step 101: and extracting the current characteristic attribute of the emergency. Fig. 2 is a schematic diagram of current feature attributes, as shown in fig. 2, wherein specific meanings of part of the attributes are as follows:
T1/T2/T3/IF/IPSF is a large activity travel service level, taking a large sports activity as an example, the crowd needing transportation service in the activity is divided into 6 classes (except common audiences), and Table 1 is a specific meaning indication table of part of attributes, as shown in Table 1.
TABLE 1
Step 102: and determining the best matching source plan in the source plan library according to the current characteristic attribute and a naive Bayes classification algorithm.
The step 102 specifically includes: determining the event type of the emergency according to the current characteristic attribute; each type of the event type comprises a plurality of typical cases; and determining the conditional probability of each typical case according to a naive Bayes classification algorithm, and determining the typical case with the maximum conditional probability as the best matching source plan.
Wherein, the initial plan which is rapidly generated based on case-based reasoning and naive Bayes classification in the step 102 is subjected to the best matching source plan under the condition of incomplete attributes according to the posterior probability obtained by the Bayes classification algorithm; fig. 3 is a flow chart of the generation of the initial traffic emergency plan based on the case-based reasoning and naive bayes classification, as shown in fig. 3.
The method for obtaining the best matching item in the source plan library according to the posterior probability obtained by the Bayes classification algorithm (namely, obtaining the typical case with the maximum conditional probability) is as follows: assuming that the large-scale activity incident is X, the part of the attribute that can be collected is X ═ X1,x2,…,xnIs given as an attribute value of { a }1,a2,…,anAnd then matching is divided into the following two steps:
1. and determining the type of the emergency X. First, cases are divided into { C according to the type of emergency1,C2,…,CnSeveral types, each of which includes several typical cases, such as Ci＝{ci1,ci2,…,cimAnd (m > 0), determining the type of the emergency event as C according to the type attribute of the emergency eventi。
2. And calculating the posterior probability. According to the assumed condition of naive Bayes, the characteristic attributes are independent from each other, namely, no dependency exists between the condition attributes, then the conditional probability of each typical case is calculated:
wherein the content of the first and second substances,
wherein c isijIs represented in type CiTypical case of m is type CiNumber of the following typical cases, nijIs a typical case c in case baseijAs the number of best matching cases, N is the total number of cases in the case base, and there are:
wherein x iszIs a characteristic attribute of an incident X, assuming that there is some AzIs of type CiCharacteristic property of (1), then n in the formulaij(xz) Express the characteristic attribute AzHas a value xzC of (A)iThe number of samples of (1). OmegazIs the characteristic attribute AzThe weight of (c). And q is the number of the characteristic attributes in the emergency X. Then, the calculation formula for obtaining the typical case with the maximum conditional probability, i.e. the best matching case, is:
step 103: establishing a traffic emergency plan database knowledge base and a traffic emergency plan database rule base by using a rule reasoning algorithm; the traffic emergency plan base knowledge base comprises a response mechanism knowledge base, a rescue configuration knowledge base, an emergency organization measure knowledge base and an emergency treatment knowledge base; the traffic emergency plan library rule base comprises inference rules of linkage and site disposal of large-scale activity emergency departments, inference of emergency resource allocation rules of traffic and patrol police departments, inference of emergency resource allocation rules of non-traffic and patrol police departments, and inference of emergency organizations and traffic recovery rules.
Step 104: and adjusting the best matching source plan by utilizing a forward reasoning mode according to the current characteristic attribute based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base. Fig. 4 is a schematic diagram of a traffic emergency plan database summarized by the present invention, and fig. 5 is a schematic diagram of a traffic emergency plan database rule base summarized by the present invention, as shown in fig. 4-5.
In fig. 5, the emergency resource allocation rule of the traffic and patrol police department is derived from the field disposal measure, the number of influence N of important participators and the number of casualties M of important participators. The table 2 is an example table of the emergency resource allocation rule of the traffic and patrol police department, which is shown in the table 2.
TABLE 2
(Note [ ] indicating rounding)
The emergency resource allocation rule reasoning of the non-traffic patrol police department is jointly determined by field disposal measures, casualties, the number of trapped and damaged vehicles (the sum of the conditions of the important personnel and the non-important personnel in the event). Emergency organization and traffic restoration rules reasoning considerations include: the lane borrowing feasibility, the event influence length and the traffic influence time.
Structuring the information of the emergency, and associating the attributes with the countermeasures according to the general form of the production rule, thereby implementing the optimization of the traffic emergency plan, fig. 6 is a schematic diagram of the adjustment of the best matching source plan provided by the present invention, as shown in fig. 6, the step 104 specifically includes:
based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base, performing rule reasoning optimization according to the current characteristic attribute, and determining an optimized rule; judging whether the optimized rule is the last rule in the traffic emergency plan library rule base, if so, adjusting the optimal matching source plan, determining the plan of the current stage, and if not, performing reasoning optimization again; judging whether the emergency is finished or not according to the plan processing field of the current stage, and if not, determining that the plan of the current stage is the best matching source plan of the emergency; if yes, the current characteristic attribute of the emergency is obtained again, and the step of 'performing rule reasoning optimization according to the current characteristic attribute and determining the optimized rule based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base' is returned.
After the step 104, the method further includes: and establishing a plan emergency capacity level and emergency severity evaluation index by using a fuzzy analytic hierarchy process, and carrying out fuzzy evaluation on the adjusted optimal matching source plan.
The method comprises the following steps of establishing a plan emergency ability level and an emergency severity evaluation index by using a fuzzy analytic hierarchy process, and carrying out fuzzy evaluation on an adjusted best matching source plan, and specifically comprises the following steps:
and performing single-factor evaluation, performing single-factor evaluation on each single factor in the emergency through survey statistics, and determining a plurality of single-factor evaluation sets. Counting single factor u through investigationi(i-1, 2, …, n) to obtain a single factor uiEvaluation of the subset v for bluri(i ═ 1,2,3,4,5) with a degree of membership rijFrom this, a single factor u can be obtainediThe evaluation set is as follows: r isi＝{ri1,ri2,ri3,ri4,ri5}。
And determining a fuzzy matrix according to the plurality of single-factor evaluation sets by using a fuzzy analytic hierarchy process. The fuzzy relation R is obtained by determining the mapping relation between the index set and the comment set in the evaluation object, that is, combining the evaluation sets of the single factors into an evaluation matrix, and the matrix is a fuzzy matrix R as follows:
wherein r isijRepresentative of i-th evaluation index u of evaluation objectiAt the jth level v of the evaluation level setjThe above correspondence is generally normalized to satisfy Σ rijWhen 1, the blur matrix R is dimensionless.
And according to the importance of the plan emergency capacity index, taking the score investigation result of each plan emergency capacity index as an initial value, and determining the plan emergency capacity level and the emergency severity evaluation index weight according to the score result by using the fuzzy analytic hierarchy process.
The importance of the emergency capacity index of the stage plan is judged to conduct form questionnaire survey, the recovered forms are arranged, the grading average value of the relative importance of each index is taken, the judgment matrix of each index is established, and the table 3 is an index weight indication table provided by the invention and is shown in the table 3.
TABLE 3
And all secondary indexes pass through the consistency test of the judgment matrix, namely the weight indexes of all evaluation indexes have rationality.
Determining a fuzzy comprehensive evaluation index of the predetermined plan according to the emergency capacity level of the predetermined plan, the severity evaluation index weight of the emergency and the fuzzy matrix; the plan fuzzy comprehensive evaluation index is a plan emergency capacity level and emergency severity evaluation index. Synthesizing the index weight matrix W and the fuzzy matrix R to obtain a pre-arranged fuzzy comprehensive evaluation index matrix B, namely:
according to the fuzzy comprehensive evaluation index B and the fuzzy evaluation grade set vTA plan comprehensive evaluation value can be obtained, that is:
L＝B·vT＝(b1，b2，b3，b4，b5)[v1，v2，v3，v4，，v5]T (9)
the plan emergency capacity is divided into four grades, and table 4 is a standard table for dividing the grade of the plan emergency capacity provided by the invention, and the division standards are as shown in table 4 below:
TABLE 4
| Division criteria | 21-40 | 41-60 | 61-80 | 81-100 |
| Level of emergency ability | Is lower than | In general | Is higher than | Is very high |
And carrying out fuzzy evaluation on the adjusted optimal matching source plan by using the plan fuzzy comprehensive evaluation index.
Assuming that a traffic accident occurs on a competition route from a certain day of the beijing winter olympic conference to the olympic conference venue in the morning, table 5 is an incomplete attribute suggestion table of an emergency X, and the incomplete attribute of the emergency X collected on site is shown in table 5:
TABLE 5
| Serial number | Attribute name | Attribute value | Serial number | Attribute name | Attribute value |
| 1 | Event rating | 2 | 6 | Event impact length | 1.2km |
| 2 | Location of affairs | Event path | 7 | Traffic congestion situation | 2 |
| 3 | Type of incident location road | Urban road | 8 | Has no influence on the number of lanes | 1 |
| 4 | Weather conditions | In fog weather | 9 | Number of damaged vehicles | 4 |
| 5 | Time of impact of traffic | 1.63h | 10 | Casualty | 2 |
Selecting 3 events with typical characteristics from a traffic accident type traffic emergency plan set, wherein table 6 is an exemplary illustration table of typical cases, as shown in table 6:
TABLE 6
Calculating the prior probability of each typical case of the traffic accident according to the formula (3), i.e. the total number of cases similar to the case in the plan library/the total number of plans in the traffic emergency plan library, and table 4 is a prior probability table of each typical case of the traffic accident, as shown in table 7:
TABLE 7
| p(C11) | p(C12) | p(C13) | |
| Prior probability | 0.355 | 0.214 | 0.132 |
Calculating conditional probability according to formula (1), namely the total number of plans with the same attribute value as the attribute value of the emergency X in the historical case similar to the optimal case/the total number of plans matched with the optimal case in the traffic emergency plan library, as the typical case C11For example, calculate in typical case C11Table 8 is a conditional probability schematic table of each attribute value of the emergency X under the condition, as shown in table 8:
TABLE 8
According to the publicFormulae (4) and (5) give: p (X/C)11)-＝3.0*10-4；p(X/C11)p(C11)＝1.05*10-4The same reasoning can be used to obtain typical case C12And C13The posterior probability of (2):
p(X/C12)p(C12)＝0.88*10-4；p(X/C13)p(C13)＝0.96*10-4
because of the typical case C11Has the highest a posteriori probability, so the case that best matches the incident X is the typical case C11In the typical case C11As an initial traffic emergency protocol for incident X. Typical case C11As an initial traffic emergency protocol for incident X and implemented. After the initial plan is implemented for a period of time, the emergency attribute is updated to obtain a new attribute value, and table 9 is an updated attribute value indication table, as shown in table 9.
TABLE 9
(1) Department linkage and field disposition
And (3) performing rule reasoning according to the linkage of departments and the on-site treatment rule:
IF "event type: traffic accident "and" traffic impact time: 1 "and" incident site: and the type of the event place road of the competition route "and": urban arterial road "and" fire situation: no "and" explosion condition: no "and" rollover condition: no "and" fear of assault: no "and" road yield loss: there is "and" T1/T2/T3 time urgency: 1 "and" athlete and accompanying officer time urgency: 3 "and" IF/IPSF temporal urgency: 0 "and" borrows feasibility: 0 "and" lane feasibility: 0 "and" availability for the transaction: 0 "and" orbital feasibility: 1 "and" T1/T2/T3 casualties: 0 "and" athletes and accompanying officers casualty: 0 "and" IF/IPSF casualties: 0 "and" T1/T2/T3 vehicle damage: 0 "and" athlete and accompanying officer vehicle damage: 0 "and" IF/IPSF vehicle damage: 0 "and" non-essential casualties: 2 "and" inactive vehicle damage: 4, linking the THEN departments: XYJG 7; YJZY 1. And on-site treatment: CZCS 1; CZCS 3; CZCS 11; CZCS 14; CZCS 32; CZCS 34; CZCS 37. ".
(2) Emergency resource allocation for traffic and patrol police department
According to the emergency resource allocation of the traffic and patrol police department, the current field measures are CZCS14, the affected athletes and the accompanying officers are 140, the table 10 is a current field measure indication table according to the emergency resource allocation of the traffic and patrol police department, and the calculation is carried out according to the table 10:
watch 10
IF "field treatment measures: the number of persons affected by CZCS14 "and" athletes and accompanying officers "was 140" THEN "department of transportation to mobilize 5 bus transporters and accompanying officers".
(3) Emergency resource allocation for non-traffic patrol police department
According to the rule setting of the emergency resource allocation of the non-patrol department, when the number of injured people is 2 and the number of damaged vehicles is 4, reasoning is carried out according to the following rules:
IF "field treatment measures: CZCS37 "and" casualties: 2 "and" trapped population: 0 "and" number of damaged vehicles: the 4 'THEN' health department drives 10 people and 2 ambulances to cure the wounded. The road administration moves 4 wreckers to lift and pull the vehicle. ".
(4) Traffic organization and traffic restoration
According to the rule setting of traffic organization and traffic recovery measures, when the traffic influence time is 1.63h and the event influence length is 1.2km, reasoning is carried out according to the following rules:
IF' borrowing feasibility: 0 "and" accident impact length: medium "and" traffic impact time: 3 "THEN" traffic organization: ZZCS 3. "and" traffic recovery: HFCS 1; HFCS2 ".
Optimizing the initial large-scale activity traffic emergency plan according to rule reasoning to obtain an optimized plan, where table 11 is an optimized plan meaning table, as shown in table 11:
TABLE 11
From table 11, the optimized large-scale traffic emergency plan closely combines the characteristic attribute of the emergency event X at the current stage, is more specific and targeted, and improves the rescue efficiency of the plan.
After the initial traffic emergency plan of the emergency X is implemented for a period of time, the attribute of the emergency is updated, and in order to determine whether the emergency capacity of the initial traffic emergency plan at the present stage matches the severity of the updated emergency, the emergency capacity of the traffic emergency plan and the severity of the emergency need to be evaluated respectively.
According to the emergency capacity division standard, the evaluation grades are divided into 4 grades, namely v ═ 90705030 ═ v.
As can be seen from equation (8):
the evaluation set of the second-level index layer emergency plan is as follows:
therefore, the judgment set of the first-level index layer emergency plan is as follows:
B＝W·R＝[0.3023 0.2668 0.2590 0.1082]
therefore, the emergency capacity of the traffic emergency plan is evaluated as follows: l ═ B · vT＝62.08
According to table 3, the phase traffic emergency plan emergency capacity is higher.
And corresponding to the emergency capacity division standard, evaluating the single severity of the emergency at the present stage, determining the membership degree of the emergency and an evaluation object set, establishing a corresponding single-factor fuzzy evaluation relation matrix, respectively calculating a first-stage index layer severity evaluation set and a second-stage index layer severity evaluation set, and finally obtaining the emergency with the severity of 54.3. Therefore, the plan emergency capacity implemented at this stage is better, and the emergency severity at the present stage is general.
Fig. 7 is a structural diagram of a dynamic adjustment system for a traffic emergency plan of a large event, as shown in fig. 7, the dynamic adjustment system for the traffic emergency plan of the large event comprises:
a current feature attribute extracting module 701, configured to extract a current feature attribute of the emergency.
A best matching source plan determining module 702, configured to determine a best matching source plan in the source plan library according to the extracted current feature attribute of the emergency and according to a naive bayes classification algorithm.
The best matching source plan determining module 702 specifically includes: an event type determining unit, configured to determine an event type of the emergency event according to the current feature attribute; each type of the event type comprises a plurality of typical cases; and the optimal matching source plan determining unit is used for determining the conditional probability of each typical case according to a naive Bayes classification algorithm, and determining the typical case with the maximum conditional probability as the optimal matching source plan.
A knowledge base and rule base establishing module 703, configured to establish a traffic emergency plan base knowledge base and a traffic emergency plan base rule base by using a rule inference algorithm; the traffic emergency plan base knowledge base comprises a response mechanism knowledge base, a rescue configuration knowledge base, an emergency organization measure knowledge base and an emergency treatment knowledge base; the traffic emergency plan library rule base comprises inference rules of linkage and site disposal of large-scale active emergency departments, inference of emergency resource configuration rules of traffic and patrol police departments, inference of emergency resource configuration rules of non-traffic and patrol police departments, and inference of emergency organization and traffic recovery rules.
And an adjusting module 704, configured to adjust the best matching source plan by using a forward reasoning manner according to the extracted current characteristic attribute of the emergency event based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base.
The adjusting module 704 specifically includes:
the optimization unit is used for carrying out rule reasoning optimization according to the extracted current characteristic attribute of the emergency on the basis of the traffic emergency plan database knowledge base and the traffic emergency plan database rule base, and determining an optimized rule;
the first judgment unit is used for judging whether the optimized rule is the last rule in a traffic emergency plan library rule base or not to obtain a first judgment result;
a plan determining unit at the current stage, configured to adjust the best matching source plan and determine a plan at the current stage if the first determination result indicates that the optimized rule is the last rule in the traffic emergency plan library rule base;
the second judgment unit is used for judging whether the emergency is finished or not according to the site of the plan processing of the current stage to obtain a second judgment result;
an emergency ending unit, configured to determine that the plan at the current stage is the best matching source plan of the emergency if the second determination result indicates that the emergency is ended;
and a current feature attribute re-obtaining unit, configured to re-obtain the current feature attribute of the emergency if the second determination result indicates that the emergency is not ended, and return to the step "based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base, perform rule inference optimization according to the current feature attribute, and determine an optimized rule".
The invention also includes: and the fuzzy evaluation module is used for establishing the plan emergency capacity level and the evaluation index of the severity of the emergency event by using a fuzzy analytic hierarchy process and carrying out fuzzy evaluation on the adjusted optimal matching source plan.
The fuzzy evaluation module specifically comprises: the single factor evaluation set determining unit is used for performing single factor evaluation, performing single factor evaluation on each single factor in the emergency through survey statistics, and determining a plurality of single factor evaluation sets; the fuzzy matrix determining unit is used for determining a fuzzy matrix according to the plurality of single factor evaluation sets by using a fuzzy analytic hierarchy process; the device comprises a plan emergency capacity level and emergency severity evaluation index weight determining unit, a fuzzy analytic hierarchy process and a fuzzy analytic hierarchy process, wherein the plan emergency capacity level and emergency severity evaluation index weight determining unit is used for taking a score investigation result of each plan emergency capacity index as an initial value according to the importance of the plan emergency capacity index, and determining the plan emergency capacity level and the emergency severity evaluation index weight according to the score result by using the fuzzy analytic hierarchy process; the plan fuzzy comprehensive evaluation index determining unit is used for determining a plan fuzzy comprehensive evaluation index according to the plan emergency capacity level, the emergency severity evaluation index weight and the fuzzy matrix; the plan fuzzy comprehensive evaluation index is a plan emergency capacity level and an emergency severity evaluation index; and the fuzzy evaluation unit is used for carrying out fuzzy evaluation on the adjusted optimal matching source plan by utilizing the plan fuzzy comprehensive evaluation index.
The method comprises the steps of extracting the characteristic attributes of the emergency, rapidly generating an initial plan based on case-based reasoning and naive Bayes classification, and selecting the best matching source plan under the condition of incomplete attributes according to the posterior probability obtained by a Bayes classification algorithm; establishing a traffic emergency plan base knowledge base and a rule base based on rule reasoning, and modifying the contents of the plan by adopting forward reasoning; and establishing an index system for evaluating the emergency capacity level of the plan and the severity of the emergency based on a fuzzy hierarchical analysis method, and carrying out fuzzy evaluation. The rapid plan generation and optimization system provided by the invention can provide support for traffic emergency plan decision, and has important realistic meaning.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be obtained by referring to the description of the method part.
The principle and the implementation of the present invention are explained herein by using specific examples, and the above descriptions of the examples are only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A dynamic adjustment method for a traffic emergency plan of a large-scale activity emergency is characterized by comprising the following steps:
extracting the current characteristic attribute of the emergency;
determining the best matching source plan in a source plan library according to the extracted current characteristic attribute of the emergency and a naive Bayes classification algorithm;
establishing a traffic emergency plan database knowledge base and a traffic emergency plan database rule base by using a rule reasoning algorithm; the traffic emergency plan base knowledge base comprises a response mechanism knowledge base, a rescue configuration knowledge base, an emergency organization measure knowledge base and an emergency treatment knowledge base; the traffic emergency plan library rule base comprises inference rules of linkage and site disposal of large-scale activity emergency departments, inference of emergency resource configuration rules of traffic and patrol police departments, inference of emergency resource configuration rules of non-traffic and patrol police departments, and inference of emergency organization and traffic recovery rules;
and based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base, adjusting the best matching source plan by using a forward reasoning mode according to the extracted current characteristic attribute of the emergency.
2. The method according to claim 1, wherein the determining a best matching source plan in a source plan library according to the current feature attributes and a naive bayes classification algorithm specifically comprises:
determining the event type of the emergency according to the current characteristic attribute; each event type comprises a plurality of typical cases;
and determining the conditional probability of each typical case according to a naive Bayes classification algorithm, and determining the typical case with the maximum conditional probability as the best matching source plan.
3. The method according to claim 1, wherein the adjusting the best matching source plan by using a forward reasoning method based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base according to the current feature attributes comprises:
based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base, performing rule reasoning optimization according to the current characteristic attribute, and determining an optimized rule;
judging whether the optimized rule is the last rule in a rule base of a traffic emergency plan library to obtain a first judgment result;
if the first judgment result shows that the optimized rule is the last rule in a rule base of a traffic emergency plan library, adjusting the best matching source plan and determining the plan of the current stage;
judging whether the emergency is finished or not according to the preset plan processing site of the current stage to obtain a second judgment result;
if the second judgment result indicates that the emergency is over, determining that the plan at the current stage is the best matching source plan of the emergency;
if the second judgment result indicates that the emergency is not finished, the current characteristic attribute of the emergency is obtained again, and the step of 'performing rule reasoning optimization based on the traffic emergency plan database knowledge base and the traffic emergency plan database rule base according to the current characteristic attribute and determining the optimized rule' is returned.
4. The method of claim 1, wherein after the optimal matching source plan is adjusted by a forward reasoning method based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base according to the current characteristic attribute, the method further comprises:
and establishing a plan emergency capacity level and emergency severity evaluation index by using a fuzzy analytic hierarchy process, and carrying out fuzzy evaluation on the adjusted optimal matching source plan.
5. The method according to claim 4, wherein the method for dynamically adjusting the traffic emergency plan of the large-scale event comprises the steps of establishing a plan emergency capacity level and an emergency severity evaluation index by using a fuzzy analytic hierarchy process, and performing fuzzy evaluation on the adjusted best matching source plan, and specifically comprises the following steps:
performing single factor evaluation, performing single factor evaluation on each single factor in the emergency through survey statistics, and determining a plurality of single factor evaluation sets;
determining a fuzzy matrix according to a plurality of single factor evaluation sets by using a fuzzy analytic hierarchy process;
taking the investigation scoring result of each plan emergency ability index as an initial value according to the importance of the plan emergency ability index, and determining the plan emergency ability level and the severity evaluation index weight of the emergency according to the scoring result by using the fuzzy analytic hierarchy process;
determining a fuzzy comprehensive evaluation index of the predetermined plan according to the emergency capacity level of the predetermined plan, the severity evaluation index weight of the emergency and the fuzzy matrix; the plan fuzzy comprehensive evaluation index is a plan emergency capacity level and an emergency severity evaluation index;
and carrying out fuzzy evaluation on the adjusted optimal matching source plan by using the plan fuzzy comprehensive evaluation index.
6. A dynamic adjustment system for a traffic emergency plan of a large-scale event emergency is characterized by comprising:
the current characteristic attribute extraction module is used for extracting the current characteristic attribute of the emergency;
the optimal matching source plan determining module is used for determining an optimal matching source plan in a source plan library according to the extracted current characteristic attribute of the emergency and a naive Bayes classification algorithm;
the knowledge base and rule base establishing module is used for establishing a traffic emergency plan base knowledge base and a traffic emergency plan base rule base by using a rule reasoning algorithm; the traffic emergency plan base knowledge base comprises a response mechanism knowledge base, a rescue configuration knowledge base, an emergency organization measure knowledge base and an emergency treatment knowledge base; the traffic emergency plan library rule base comprises inference rules of linkage and site disposal of large-scale activity emergency departments, inference of emergency resource configuration rules of traffic and patrol police departments, inference of emergency resource configuration rules of non-traffic and patrol police departments, and inference of emergency organization and traffic recovery rules;
and the adjusting module is used for adjusting the optimal matching source plan by utilizing a forward reasoning mode according to the extracted current characteristic attribute of the emergency on the basis of a traffic emergency plan library knowledge base and the traffic emergency plan library rule base.
7. The system for dynamically adjusting a traffic emergency plan for a large event according to claim 6, wherein the best matching source plan determining module specifically comprises:
the event type determining unit is used for determining the event type of the emergency according to the extracted current characteristic attribute of the emergency; each event type comprises a plurality of typical cases;
and the optimal matching source plan determining unit is used for determining the conditional probability of each typical case according to a naive Bayes classification algorithm, and determining the typical case with the maximum conditional probability as the optimal matching source plan.
8. The system of claim 6, wherein the adjusting module specifically comprises:
the optimization unit is used for carrying out rule reasoning optimization according to the extracted current characteristic attribute of the emergency on the basis of a traffic emergency plan database knowledge base and the traffic emergency plan database rule base, and determining an optimized rule;
the first judgment unit is used for judging whether the optimized rule is the last rule in a traffic emergency plan library rule base or not to obtain a first judgment result;
the plan determining unit at the current stage is used for adjusting the best matching source plan and determining the plan at the current stage if the first judgment result shows that the optimized rule is the last rule in the traffic emergency plan library rule base;
the second judgment unit is used for judging whether the emergency is finished or not according to the site of the plan processing of the current stage to obtain a second judgment result;
an emergency ending unit, configured to determine that the plan at the current stage is the best matching source plan of the emergency if the second determination result indicates that the emergency is ended;
and a current feature attribute re-obtaining unit, configured to re-obtain the current feature attribute of the emergency if the second determination result indicates that the emergency is not ended, and return to the step "based on the traffic emergency plan library knowledge base and the traffic emergency plan library rule base, perform rule inference optimization according to the extracted current feature attribute of the emergency, and determine an optimized rule".
9. The system of claim 6, wherein the traffic emergency plan is dynamically adjusted,
further comprising:
and the fuzzy evaluation module is used for establishing a plan emergency capacity level and an emergency severity evaluation index by using a fuzzy analytic hierarchy process and carrying out fuzzy evaluation on the adjusted optimal matching source plan.
10. The system according to claim 9, wherein the fuzzy evaluation module specifically comprises:
the single factor evaluation set determining unit is used for performing single factor evaluation, performing single factor evaluation on each single factor in the emergency through survey statistics, and determining a plurality of single factor evaluation sets;
the fuzzy matrix determining unit is used for determining a fuzzy matrix according to the plurality of single factor evaluation sets by using a fuzzy analytic hierarchy process;
the device comprises a plan emergency capacity level and emergency severity evaluation index weight determining unit, a fuzzy analytic hierarchy process and a fuzzy analytic hierarchy process, wherein the plan emergency capacity level and emergency severity evaluation index weight determining unit is used for taking the investigation scoring result of each plan emergency capacity index as an initial value according to the importance of the plan emergency capacity index, and determining the plan emergency capacity level and the emergency severity evaluation index weight according to the scoring result by using the fuzzy analytic hierarchy process;
the plan fuzzy comprehensive evaluation index determining unit is used for determining a plan fuzzy comprehensive evaluation index according to the plan emergency capacity level, the emergency severity evaluation index weight and the fuzzy matrix; the plan fuzzy comprehensive evaluation index is a plan emergency capacity level and an emergency severity evaluation index;
and the fuzzy evaluation unit is used for carrying out fuzzy evaluation on the adjusted optimal matching source plan by utilizing the plan fuzzy comprehensive evaluation index.
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