CN113011789A - Overwater dangerous chemical accident emergency aid decision-making method, terminal equipment and storage medium - Google Patents
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Abstract
The invention relates to an emergency assistant decision-making method for water-borne hazardous chemical substance accidents, terminal equipment and a storage medium, wherein the method comprises the following steps: s1: constructing a case database; s2: extracting attribute values of each characteristic parameter corresponding to the case according to the set characteristic parameters of the case; s3: calculating the weight of each characteristic parameter based on a rough set dependency enhancement method; s4: classifying the cases in the case database, and calculating the class to which the target case belongs through a Bayesian model; s5: extracting all cases in the type in the case database according to the type to which the target case belongs, respectively calculating the global similarity between the target case and each extracted case according to the attribute value and the weight of each characteristic parameter corresponding to the case, taking the case corresponding to the maximum value of the global similarity as the similar case of the target case, and determining an emergency assistant decision scheme of the target case according to the similar case. The invention solves the problems of high difficulty, unscientific scheme and the like of the decision-making scheme after the occurrence of the accident of the existing overwater dangerous chemicals.
Description
Technical Field
The invention relates to the field of emergency aid decision-making of accidents, in particular to an emergency aid decision-making method for water hazardous chemical accidents, terminal equipment and a storage medium.
Background
Because the characteristics of the economic nature, the freight volume of water route transportation are big and the convenience for the dangerization article goods of water route transportation are increasing day by day. However, under the influence of the specificity of dangerous chemical cargo and the complexity and sensitivity of the transportation environment, the transportation of dangerous chemicals on water has a great risk, and the transportation safety is concerned. According to statistics, from the middle of the last 60 th century to the 10 th century, over 3200 dangerous goods accidents happen in nearly 50 years, wherein the proportion of dangerous chemical maritime transportation accidents is as high as 41%, and the frequent occurrence of accidents makes the transportation safety condition of maritime dangerous chemicals unattractive. The research on the emergency aid decision-making of the marine accident of the dangerous chemical substances on the water meets the requirement of safe transportation. The marine dangerous chemical substance ship accidents are influenced by the characteristics of the dangerous chemical substances, and have the characteristics of high risk, great harm, great rescue difficulty, lasting environmental pollution and the like, so that the aquatic dangerous chemical substance accident assistant decision-making research has very important significance on the navigation safety of the aquatic ships.
The traditional emergency assistant decision-making method is mainly based on expert experience or individual decision-making, and decision-making is carried out according to accident information acquired in the existing place and by combining experience knowledge and the successful disposal process of similar accidents in the past. The traditional method has the advantages that the quality of the decision effect depends on the experience and level of experts or decision makers, and the traditional method has the characteristics of long decision time period and high uncertainty of the decision effect.
The decision-making influence factors of the emergency disposal of the water hazardous chemical substance accident are numerous, the decision-making influence factors relate to various factors such as accident information, environmental information, ship information and technical information and are continuously and dynamically changed, the relation between the decision-making effect and the factors is complex, the parameters are in certain correlation with one another, the existing researched decision-making model is difficult to comprehensively consider various information, and the problems of uncertainty of the decision-making effect and the like cannot be reasonably measured.
And the quick, scientific and efficient overwater dangerous chemical accident emergency decision-making needs abundant accident handling experience as a support. At present, aiming at water hazardous chemical substance accidents, an emergency decision scheme is too wide, a specific solution cannot be provided clearly, and the decision content is difficult to achieve completeness. In the prior art, a corresponding case base is usually constructed based on historical accident cases, and accident information is mined to summarize accident handling methods, preventive measures and the like. At present, mining based on related information of water hazardous chemical accidents is less. The existing case reasoning-based accident decision research has the problems of incomplete case base data, unreasonable index selection, low retrieval efficiency and the like. In addition, most case retrieval methods do not have too much fuzzy information considering index attributes, and the determination of the weight depends on subjective judgment of people. If the formed emergency treatment cases of the water-borne dangerous chemical accidents can be used for emergency decision of the water-borne dangerous chemical accidents with high quality, the existing accident cases are fully excavated, the case reasoning method is used for scientifically and efficiently carrying out decision optimization on the emergency treatment methods of similar accident scenes, and it is very important to rapidly provide relatively reasonable decision suggestions.
Disclosure of Invention
In order to solve the problems, the invention provides an emergency aid decision-making method for water hazardous chemical substance accidents, terminal equipment and a storage medium.
The specific scheme is as follows:
an emergency aid decision-making method for water hazardous chemical accidents comprises the following steps:
s1: acquiring overwater dangerous chemical accident cases corresponding to a research water area to construct a case database;
s2: extracting attribute values of characteristic parameters corresponding to the target case and each case in a case database according to the set characteristic parameters of the water hazardous chemical accident case;
s3: calculating the weight of each characteristic parameter based on a rough set dependency enhancement method;
s4: classifying the cases in the case database, and calculating the class to which the target case belongs through a Bayesian model;
s5: extracting all cases in the type in the case database according to the type to which the target case belongs, respectively calculating the global similarity between the target case and each extracted case according to the attribute value and the weight of each characteristic parameter corresponding to the case, taking the case corresponding to the maximum value of the global similarity as the similar case of the target case, and determining an emergency assistant decision scheme of the target case according to the similar case.
Further, the characteristic parameters of the water hazardous chemical substance accident include: accident occurrence time, accident grade, accident type, accident time environment information, leakage amount, cargo type, accident casualties and economic loss.
Further, the method for extracting the value of the characteristic parameter includes: converting the character information representing the characteristic parameters into numerical attributes, dividing the numerical attributes into 4 attribute types of determined symbol attributes, determined number attributes, interval number attributes and fuzzy number attributes, and respectively adopting different similarity calculation methods for different attribute types.
Furthermore, a rough set dependency enhancement method is adopted for the calculation method of the weight of each characteristic parameter.
Further, step S4 specifically includes the following steps:
s401: all cases in the case database U are divided into k classes, which are: u shape1，U2，…，UkWherein
a is not equal to b and a and b are each an integer between 1 and k, P (U)a) Represents a class UaThe probability occupied by the case database U is as follows:
s402: calculating the proportion P (X) of each characteristic parameter of the target case in each class according to the following formulaj|Ua)：
Wherein, XjDenotes the jth characteristic parameter, P (X)j) Represents the probability, P (U), corresponding to the jth characteristic parametera|Xj) Indicating occurrence of the jth characteristic parameter, UaProbability of occupation in case database U;
S403: calculating the proportion P (X | U) of all characteristic parameters of the target case in each class according to the following formulaa)：
S404: according to the calculation result in step S403, the class SM to which the target case belongs is determined according to the following formula: SM ═ P (X | U)a)P(Ua)。
Further, the global similarity is calculated by using a Euclid (euclidd) closeness method.
Further, step S5 includes: and constructing an expert rule base, correcting the similar cases based on the expert rule base, and determining an emergency aid decision scheme of the target case according to the correction result.
An emergency aid decision-making terminal device for water hazardous chemical accidents comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above for an embodiment of the invention.
By adopting the technical scheme, the problems of high difficulty, unscientific scheme and the like of the conventional emergency aid decision-making scheme after the occurrence of the accident of the overwater dangerous chemicals are solved, the effectiveness and the applicability of emergency aid decision-making information can be ensured to a certain extent, and the emergency disposal efficiency is improved.
Drawings
FIG. 1 is a flow chart illustrating steps of a method according to an embodiment of the present invention.
Fig. 2 is a schematic overall flow chart of the method in this embodiment.
Fig. 3 is a diagram showing membership functions of 4 level fuzzy attributes in this embodiment.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
the embodiment of the invention provides an emergency aid decision-making method for water-borne hazardous chemical substance accidents. And then determining the weight of the characteristic parameters based on a proposed rough set dependency enhancement method to determine the weight value of each characteristic parameter, carrying out case indexing based on a Bayesian model, calculating the class of the target case, and then obtaining the most similar case according to the global similarity. And finally, correcting and optimizing the result of the retrieval case by using rule reasoning to serve as a final emergency aid decision result.
Referring to fig. 1 and 2, the method specifically includes the following steps:
s1: and collecting the water hazardous chemical substance accident cases corresponding to the research water area to construct a case database.
The case database is generally composed of a large number of cases, and in the embodiment, the case database is constructed by collecting dangerous chemical ship accidents for nearly 10 years as the retrieved historical cases.
S2: and extracting attribute values of the characteristic parameters corresponding to the target case and each case in the case database according to the set characteristic parameters of the water hazardous chemical accident case.
The characteristic parameters of the accident case of the aquatic hazardous chemical substances need to be preset and are obtained by analyzing and mining the information of the accident case. The characteristic parameters of the water hazardous chemical substance accident set in the embodiment comprise: accidentTime of occurrence Ci1Accident class Ci2Type of accident Ci3Environmental information at the time of the event Ci4Leakage rate Ci5Cargo type Ci6Casualty people in accident Ci7Economic loss Ci8Etc., as shown in table 1.
TABLE 1
| Numbering | Properties | Data type | Value range |
| 1 | Time of occurrence of an accident Ci1 | Determining symbolic attributes | [ day, night ]] |
| 2 | Accident class Ci2 | Determining symbolic attributes | [ class I, II, III, IV] |
| 3 | Accident type Ci3 | Determining symbolic attributes | [ collision accident, grounding accident, fire explosion, etc. ]] |
| 4 | Environmental information at the time of the event Ci4 | Fuzzy number attribute | [ good, general, and poor Environment] |
| 5 | Leakage rate Ci5 | Determining a number attribute | [0，500]: ton (cube) |
| 6 | Cargo type Ci6 | Determining symbolic attributes | [ flammable liquids, flammable and explosive gases, others] |
| 7 | Casualty Ci7 | Determining a number attribute | [0，50]: human being |
| 8 | Economic loss Ci8 | Determining a number attribute | [0，1]: hundred million yuan |
Since the attributes of some feature parameters are text information, which cannot be used for calculating the similarity, the attribute values of the feature parameters in this embodiment are extracted based on the parameter numerical attribute extraction rule, that is, the attribute values are converted into different types of numerical attributes according to the characteristics of the attribute values, which are respectively: 4 attribute types of a determined symbol attribute, a determined number attribute, an interval number attribute and a fuzzy number attribute. Aiming at different attribute types, different similarity calculation methods are respectively adopted, and the specific calculation method is as follows:
(1) determining the symbol attribute: the selected ranges of the characteristic values of the type can be listed one by one, and meanwhile, the value ranges have no magnitude comparison relation and only a single description on the characteristic listing exists. Time of occurrence Ci1Accident class Ci2Type of accident Ci3Cargo type Ci6Etc. belong to determining the symbol attributes. Therefore, the similarity is 1 when the attribute values are the same, and is not 0 at the same time. The formula is expressed as follows:
(2) determining a number attribute: for two differences in the substantive features, the determination of the distance is calculated using a slightly modified Manhattan distance model. Leakage rate Ci5Casualty people in accident Ci7Economic loss Ci8And so on belong to the deterministic number attribute. Similarity calculation based on Hamming distance is adopted, and the calculation formula is as follows:
wherein, β and α respectively represent the maximum value and the minimum value of the value range of the deterministic number attribute f.
(3) The number of intervals attribute: because the data acquisition of the water hazardous chemical substance accident site has great uncertainty or is obtained through manual observation, the data can be an interval range, and a calculation method for similarity between a determined number and an interval and between intervals is necessarily defined, wherein the calculation formula is as follows:
wherein a is a definite number attribute value, [ a ]1,a2]，[b1,b2]Is the value of the interval number attribute, and a, a1，a2，b1，b2∈[α,β]. The integral depends on the relation between the intervals.
(4) Fuzzy number attribute: aiming at the fuzzy case characteristics, the membership function is adopted to calculate the similarity between cases, and the environmental information C is used for the incidenti4The equal attribute belongs to the section number attribute. The calculation formula is as follows:
FIG. 3 is a graph of membership functions for attributes, where u represents the degree of membership of the fuzzy attribute and x represents the value of the expert's scores for 4 levels.
S3: and calculating the weight of each characteristic parameter based on a rough set dependency enhancement method.
The relative importance degree of the characteristic parameters in case similarity evaluation is reflected by the weight of each characteristic parameter, the larger the weight is, the higher the influence degree on the case retrieval result is, and the distribution of the weight determines the retrieval precision of the case, so that the quality of the weight value directly influences the quality of the prediction result.
In order to avoid the problem of excessively depending on subjective factors of decision makers on the setting of the weights, a rough set dependency reinforcement method is adopted in the embodiment to calculate the weights of the characteristic parameters. The method calculates the dependency degree of each attribute by calculating the combination dependency degree among different condition attributes, separating the combination dependency degrees and then combining the combination dependency degrees with each attribute, thereby obtaining the weight occupied by the characteristic parameters in case matching. The method comprises the following implementation steps:
s301: all attribute values are normalized by the range change method.
For attribute values corresponding to different characteristic parameters, because the data have no contrast due to different dimensions, the influence of the dimensions between different attribute values needs to be eliminated, and therefore, the data needs to be standardized so that all attribute data values are between 0 and 1. The method for data normalization used in this example is a range change method, that is:
after transformation Cik∈[0,1]The influence of the dimension can be eliminated.
S302: and constructing a fuzzy similar matrix corresponding to each case according to the attribute values after the standardization processing, and calculating the similarity between any two cases according to the fuzzy similar matrices of any two cases and the Euclidean distance.
Is provided with CiAnd CjOf similar degree C'ij＝R(ci,cj) Then the fuzzy similarity matrix can be obtained as:
in this embodiment, any two cases C are calculated using Euclidean distanceiAnd CjThe similarity d of (a), namely:
Cij＝R(ci,cj)＝1-cd
to ensure that 0 ≦ rijLess than or equal to 1, coefficient should be
S303: and calculating the fuzzy equivalent matrix of each case according to the fuzzy similar matrix of each case.
For the fuzzy similar matrix C ', it is obvious that C ' satisfies reflexibility and symmetry, so C ' is a fuzzy similar matrix, but it is uncertain whether the fuzzy similar matrix at this time has transitivity, so it needs to be converted into a fuzzy equivalent matrix C, and the formula for solving the fuzzy equivalent matrix C is as follows:
C→C2→C4→…→C2n→
when it first appears
When C is obtainedkThe transitivity is provided, and the value is the required transitive closure C.
S304: setting a threshold λ, for different λ ∈ [0,1 ]]Different classifications are obtained from C, C when λ changes from 1 to 0λFrom thin to thick, to form a dynamic cluster map.
S305: f, carrying out statistical test and selecting the optimal threshold value lambda of the case under the decision attribute.
The invention adopts F statistic to determine the optimum value of lambda, and sets lambdaiIs a condition attribute ciUnder the condition of F>Fα(c-1, n-c) classification threshold, i ═ 1,2, …, no，no<n，λjAs decision attribute cjUnder the condition of F>Fα(c-1, n-c) classification threshold, j ═ 1,2, …, n1，n1≤n，γC(D)ijFor λ at different thresholdsiAnd λjThe degree of dependency between the corresponding attributes C and D.
The optimal classification threshold λ of the case under the condition attributeC
λC＝λi
Optimal classification threshold lambda of case under decision attributeD
λD＝λj
Get lack of ciThe dependency of the post-conditional attribute on the decision attribute is c deficiencyiThe average of the dependence of the post-condition attribute on the decision attribute at each satisfied significant class λ:
s306: and calculating the importance of each condition attribute to the decision attribute.
Each condition attribute c can be obtained according to the stepsiThe importance of the attribute decision D is SGF (c)i,C,D)：
If SGF (c)iIf C, D) is 0, then the condition attribute CiThe reduction may be from the conditional attribute set C.
S307: calculating each condition attribute ciWeight ω of (d)iAnd (4) determining.
Finally, the condition attribute c is obtainediWeight ω of (d)iAssigning according to its importance to the decision attribute:
s4: and classifying the cases in the case database, and calculating the class to which the target case belongs through a Bayesian model.
Because the data volume in the case database is often huge, in order to solve the problem of low retrieval efficiency caused by the data volume, in the embodiment, the cases in the case database are preferably classified and stored in a classified manner, and then the class to which the target case belongs is calculated through a Bayesian model. The method comprises the following concrete steps:
s401: all cases in the case database U are divided into k classes, which are: u shape1，U2，…，UkWherein
a is not equal to b and a and b are each an integer between 1 and k, P (U)a) Represents a class UaThe probability occupied by the case database U is as follows:
s402: calculating target cases according toThe ratio P (X) of each characteristic parameter in each classj|Ua)：
Wherein, XjDenotes the jth characteristic parameter, P (X)j) Represents the probability, P (U), corresponding to the jth characteristic parametera|Xj) Indicating occurrence of the jth characteristic parameter, UaThe probability occupied in the case database U.
S403: calculating the proportion P (X | U) of all characteristic parameters of the target case in each class according to the following formulaa)：
S404: according to the calculation result in step S403, the class SM to which the target case belongs is determined according to the following formula:
SM＝P(X|Ua)P(Ua)
s5: extracting all cases in the type in the case database according to the type to which the target case belongs, respectively calculating the total similarity between the target case and each extracted case according to the attribute value and the weight of each characteristic parameter corresponding to the case, taking the case corresponding to the maximum value of the total similarity as a similar case of the target case, and determining an emergency assistant decision scheme of the target case according to the similar case.
In general, a nearest neighbor algorithm (KNN) is used for calculating the global similarity, the target case and each extracted case are compared with each other for different attributes, the similarity of each attribute is calculated, the global similarity between the target case and each case is finally determined according to the attribute weight, and the case with the global similarity exceeding the threshold is returned. The calculation formula of the global similarity between the target case and the historical case is as follows.
In this embodiment, the uncertainty of the feature parameters in case retrieval may affect the calculation of the global similarity. The case search strategy is to judge the historical cases C in the case databaseiThe difference in attribute values from the target case T was evaluated by means of improved Euclid (euclidd) closeness of the fuzzy data set between the two cases.
Setting a threshold value and screening cases, wherein the following conditions are met:
SIM(T,Pi)≥δ
all source cases in the case base that reach the threshold value delta are retrieved and the historical case with the highest similarity is taken for reuse. Wherein, the value of delta is more than 0 and less than or equal to 1, the value of delta is determined by historical experience or data, and the higher the delta is, the higher the matching degree between cases is.
When the threshold condition can not be met, the difference between the historical cases and the target cases in the case base is too large, the source case most similar to the current target case is selected as reference, and then some adjustment and correction are given.
Furthermore, the searched most similar cases are not always completely consistent with the actual situation, and certain deviation exists in the attributes of accident casualties, economic loss, ship types and the like. If the actual situation is more serious than the historical retrieval optimal case or the accident situation is more complex, the needed accident emergency assistant decision-making scheme should be also considered heavily; if the actual situation is better than the case, the required force can be reduced correspondingly. Based on the consideration, the embodiment also comprises the steps of constructing an expert rule base, and based on the expert rule base, utilizing rule reasoning (RBR) to correct and optimize the similar cases, so that the similar cases are more suitable for the actual situation of the water hazardous chemical accident, and are used as an emergency assistant decision scheme of the target case. The concrete implementation steps in the embodiment are as follows:
s501: and determining a correction rule and constructing an expert rule base.
The modification rule in this embodiment includes:
{ cargo type: flammable liquid dangerous cargo, flammable and explosive gas dangerous cargo, others }
{ accident status: accident is happening, accident has happened, others }
{ rescue team: personnel deployment, strength deployment, emergency material type and quantity, others }
{ environmental status: good, general, and poor natural environment }
{ decision target: control of leakage, prevention of explosions, rescue of personnel, other, etc
S502: and correcting the similar cases based on the correction rules of the expert rule base, adjusting to obtain an optimized emergency aid decision scheme, and inviting 2 experts to approve the correction result.
S503: after two evaluation rounds, the corrected and optimized cases are finally determined.
And evaluating the adjusted emergency aid decision-making scheme, wherein IF < satisfies or does not satisfy > THEN < adopts the emergency aid decision-making scheme, and evaluating again >.
The embodiment of the invention has the following beneficial effects:
(1) the attribute feature weight is determined by adopting a rough set dependency enhancement method, so that the problem that the setting of a feature weight vector excessively depends on the subjective weight value set by a decision maker can be effectively solved;
(2) case indexing is carried out based on a Bayesian model, and experts are adopted to correct and optimize the most similar cases, so that the method has high efficiency and accuracy in processing the emergency assistant decision problem of the water hazardous chemical substance accidents;
(3) the embodiment can guarantee the validity and the applicability of the decision information to a certain degree, improves the emergency disposal efficiency of the accidents of the dangerous chemical ships on water, and provides a relatively comprehensive emergency disposal strategy for decision makers.
(4) The embodiment can improve reference and guidance for other types of ship accidents.
Example two:
the invention also provides an emergency assistant decision-making terminal device for the water-borne hazardous chemical substance accident, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the above-water hazardous chemical substance accident emergency assistant decision-making terminal device may be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The overwater dangerous chemical accident emergency aid decision-making terminal equipment can comprise, but is not limited to, a processor and a memory. It is understood by those skilled in the art that the above-mentioned composition structure of the above-mentioned water hazardous chemical substance accident emergency assistant decision-making terminal device is only an example of the water hazardous chemical substance accident emergency assistant decision-making terminal device, and does not constitute a limitation on the water hazardous chemical substance accident emergency assistant decision-making terminal device, and may include more or less components than the above-mentioned one, or combine some components, or different components, for example, the water hazardous chemical substance accident emergency assistant decision-making terminal device may further include an input and output device, a network access device, a bus, etc., which is not limited by the embodiments of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the overwater dangerous chemical substance accident emergency assistant decision-making terminal equipment, and various interfaces and lines are utilized to connect all parts of the whole overwater dangerous chemical substance accident emergency assistant decision-making terminal equipment.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the aquatic hazardous chemical substance accident emergency assistant decision-making terminal equipment by operating or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The integrated module/unit of the overwater dangerous chemical accident emergency assistant decision-making terminal equipment can be stored in a computer readable storage medium if the integrated module/unit is realized in the form of a software functional unit and is sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An emergency aid decision-making method for water hazardous chemical accidents is characterized by comprising the following steps:
s1: acquiring overwater dangerous chemical accident cases corresponding to a research water area to construct a case database;
s2: extracting attribute values of characteristic parameters corresponding to the target case and each case in a case database according to the set characteristic parameters of the water hazardous chemical accident case;
s3: calculating the weight of each characteristic parameter based on a rough set dependency enhancement method;
s4: classifying the cases in the case database, and calculating the class to which the target case belongs through a Bayesian model;
s5: extracting all cases in the type in the case database according to the type to which the target case belongs, respectively calculating the global similarity between the target case and each extracted case according to the attribute value and the weight of each characteristic parameter corresponding to the case, taking the case corresponding to the maximum value of the global similarity as the similar case of the target case, and determining an emergency assistant decision scheme of the target case according to the similar case.
2. The emergency assistant decision-making method for water-borne hazardous chemical accidents according to claim 1, is characterized in that: the characteristic parameters of the water hazardous chemical substance accident comprise: accident occurrence time, accident grade, accident type, accident time environment information, leakage amount, cargo type, accident casualties and economic loss.
3. The emergency assistant decision-making method for water-borne hazardous chemical accidents according to claim 1, is characterized in that: the method for extracting the value of the characteristic parameter comprises the following steps: converting the character information representing the characteristic parameters into numerical attributes, dividing the numerical attributes into 4 attribute types of determined symbol attributes, determined number attributes, interval number attributes and fuzzy number attributes, and respectively adopting different similarity calculation methods for different attribute types.
4. The emergency assistant decision-making method for water-borne hazardous chemical accidents according to claim 1, is characterized in that: the method for calculating the weight of each characteristic parameter adopts a rough set dependency enhancement method.
5. The emergency assistant decision-making method for water-borne hazardous chemical accidents according to claim 1, is characterized in that: step S4 specifically includes the following steps:
s401: all cases in the case database U are divided into k classes, which are: u shape1，U2，…，UkWherein
a is not equal to b and a and b are each an integer between 1 and k, P (U)a) Represents a class UaThe probability occupied by the case database U is as follows:
s402: calculating the proportion P (X) of each characteristic parameter of the target case in each class according to the following formulaj|Ua)：
Wherein, XjDenotes the jth characteristic parameter, P (X)j) Represents the probability, P (U), corresponding to the jth characteristic parametera|Xj) Indicating occurrence of the jth characteristic parameter, UaProbability of occupation in case database U;
s403: calculating the proportion P (X | U) of all characteristic parameters of the target case in each class according to the following formulaa)：
S404: according to the calculation result in step S403, the class SM to which the target case belongs is determined according to the following formula:
SM＝P(X|Ua)P(Ua)。
6. the emergency assistant decision-making method for water-borne hazardous chemical accidents according to claim 1, is characterized in that: and the global similarity is calculated by adopting a Euclidean similarity method.
7. The emergency assistant decision-making method for water-borne hazardous chemical accidents according to claim 1, is characterized in that: step S5 further includes: and constructing an expert rule base, correcting the similar cases based on the expert rule base, and determining an emergency aid decision scheme of the target case according to the correction result.
8. The utility model provides an emergent decision-making terminal equipment of danger article accident on water which characterized in that: comprising a processor, a memory and a computer program stored in said memory and running on said processor, said processor implementing the steps of the method according to any one of claims 1 to 7 when executing said computer program.
9. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implementing the steps of the method as claimed in any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115620893A (en) * | 2022-07-28 | 2023-01-17 | 重庆医科大学附属大学城医院 | Hand-foot-mouth disease condition score fitting distribution-Bayesian correction model and construction method thereof |
| CN116610931A (en) * | 2023-07-17 | 2023-08-18 | 成都飞机工业（集团）有限责任公司 | Method, device, medium and equipment for extracting numerical control countersink influencing factors of airplane |
| CN117350288A (en) * | 2023-12-01 | 2024-01-05 | 浙商银行股份有限公司 | Case matching-based network security operation auxiliary decision-making method, system and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103500423A (en) * | 2013-09-26 | 2014-01-08 | 国家电网公司 | Case adaptation and decision method for power emergency events |
| CN107945082A (en) * | 2017-10-09 | 2018-04-20 | 中国电子科技集团公司第二十八研究所 | A kind of emergency preplan generation method and system |
| CN108898528A (en) * | 2018-06-22 | 2018-11-27 | 公安部天津消防研究所 | A kind of reasoning by cases method towards hazardous chemical accident emergency aid decision |
| WO2021051865A1 (en) * | 2019-09-18 | 2021-03-25 | 平安科技（深圳）有限公司 | Case recommendation method and device, apparatus, and computer readable storage medium |
-
2021
- 2021-04-23 CN CN202110440819.XA patent/CN113011789A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103500423A (en) * | 2013-09-26 | 2014-01-08 | 国家电网公司 | Case adaptation and decision method for power emergency events |
| CN107945082A (en) * | 2017-10-09 | 2018-04-20 | 中国电子科技集团公司第二十八研究所 | A kind of emergency preplan generation method and system |
| CN108898528A (en) * | 2018-06-22 | 2018-11-27 | 公安部天津消防研究所 | A kind of reasoning by cases method towards hazardous chemical accident emergency aid decision |
| WO2021051865A1 (en) * | 2019-09-18 | 2021-03-25 | 平安科技（深圳）有限公司 | Case recommendation method and device, apparatus, and computer readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 徐旭: "《港口物流网络规划与优化研究》", 31 December 2019, 东北大学出版社 * |
| 菅小艳: "《贝叶斯网基础及应用》", 31 May 2019, 武汉大学出版社 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115620893A (en) * | 2022-07-28 | 2023-01-17 | 重庆医科大学附属大学城医院 | Hand-foot-mouth disease condition score fitting distribution-Bayesian correction model and construction method thereof |
| CN115620893B (en) * | 2022-07-28 | 2023-06-27 | 重庆医科大学附属大学城医院 | Hand-foot-mouth disease scoring fitting distribution-Bayesian correction model and construction method thereof |
| CN116610931A (en) * | 2023-07-17 | 2023-08-18 | 成都飞机工业（集团）有限责任公司 | Method, device, medium and equipment for extracting numerical control countersink influencing factors of airplane |
| CN116610931B (en) * | 2023-07-17 | 2023-11-10 | 成都飞机工业（集团）有限责任公司 | Method, device, medium and equipment for extracting numerical control countersink influencing factors of airplane |
| CN117350288A (en) * | 2023-12-01 | 2024-01-05 | 浙商银行股份有限公司 | Case matching-based network security operation auxiliary decision-making method, system and device |
| CN117350288B (en) * | 2023-12-01 | 2024-05-03 | 浙商银行股份有限公司 | Case matching-based network security operation auxiliary decision-making method, system and device |
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