CN115392619A - Early warning method based on fire safety level, storage medium and electronic equipment - Google Patents
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Abstract
The invention provides an early warning method based on fire safety level, a storage medium and electronic equipment, wherein the method comprises the following steps: s1: determining a plurality of fire safety evaluation indexes and initial weight values corresponding to the evaluation indexes; s2: carrying out fuzzy operation on the plurality of fire safety evaluation indexes and the initial weight values corresponding to the evaluation indexes to obtain adjusted fire safety evaluation indexes and adjustment weight values corresponding to the adjusted evaluation indexes; s3: and determining the current fire safety level according to the adjusted fire safety evaluation indexes and the adjustment weight values corresponding to the adjusted fire safety evaluation indexes, and performing early warning according to a processing strategy corresponding to the current fire safety level. According to the scheme, the initial weight values corresponding to the evaluation indexes are subjected to fuzzy operation to adjust the weight values, so that the fire safety level is determined more accurately, and the accuracy of fire early warning is effectively improved.
Description
Technical Field
The invention relates to the field of fire safety, in particular to an early warning method based on fire safety level, a storage medium and electronic equipment.
Background
The fire safety is influenced by various factors, the requirements on the fire safety are met, all enterprises and public institutions set relevant fire safety standards according to respective actual conditions, and the fire safety evaluation methods are different. The fire safety is not a single problem, but is formed by overlapping different factors influencing indexes, and the results caused by the interaction of different index combinations are different. The data on fire safety cannot be simply judged to be absolutely qualified or absolutely unqualified, but has certain ambiguity.
Therefore, the fuzzy analysis method is adopted by many evaluators for evaluating fire safety, the fuzzy mathematics theory can well solve the ambiguity and uncertainty of indexes, and in addition, the result obtained by fuzzy comprehensive evaluation is a vector, and provides more information compared with the single-point value result obtained by other methods. However, when the fuzzy comprehensive evaluation method is applied, the membership function of each index is determined to have a large subjective tendency and have a large relation with the level and subjective consciousness of an evaluator; the principle of maximum membership masks the difference between two membership degrees.
Therefore, based on the disadvantages of the fuzzy comprehensive evaluation, a method which gives consideration to both subjective and objective measures to determine the weight of each index is needed to correct the result of the fuzzy evaluation.
Disclosure of Invention
Therefore, a technical scheme of early warning based on fire safety level needs to be provided, the fuzzy evaluation result is corrected by determining the weight of each index, and the problems that the ambiguity and the uncertainty of the index cannot be solved by the existing fuzzy analysis method are solved.
In a first aspect, the invention provides a fire safety level-based early warning method, which comprises the following steps:
s1: determining a plurality of fire safety evaluation indexes and initial weight values corresponding to the evaluation indexes;
s2: carrying out fuzzy operation on the plurality of fire safety evaluation indexes and the initial weight values corresponding to the evaluation indexes to obtain adjusted fire safety evaluation indexes and adjusted weight values corresponding to the adjusted fire safety evaluation indexes;
s3: and determining the current fire safety level according to the adjusted fire safety evaluation indexes and the adjustment weight values corresponding to the adjusted evaluation indexes, and performing early warning according to the processing strategy corresponding to the current fire safety level.
Further, the evaluation indexes comprise subjective evaluation indexes and objective evaluation indexes;
the model operation comprises:
the subjective evaluation index determines a subjective weight value according to a fuzzy hierarchy analysis method, and the objective evaluation index determines an objective weight value by adopting an entropy weight method.
Further, the subjective evaluation index determining the subjective weight value according to the fuzzy hierarchy analysis method includes:
constructing a hierarchical model and establishing an index evaluation system; the hierarchical model comprises a target layer, a criterion layer and an index layer;
constructing fuzzy judgment matrixes of all single layers, and checking the consistency of the fuzzy judgment matrixes;
and calculating the relative importance weight of each evaluation index of the index layer relative to the target layer, wherein the relative importance weight of each evaluation index of the index layer relative to the target layer is the product of the weight value of each evaluation index relative to the affiliated criterion layer and the weight value of the affiliated criterion layer relative to the target layer.
Further, constructing the fuzzy judgment matrix of each single layer comprises:
obtaining the importance ranking of n indexes as x according to the scale expansion method 1 ≥x 2 ≥...≥x n To x i And x i+1 Comparing the two values and recording the corresponding scale value as t i Then, calculating other element values in the judgment matrix according to the transmissibility of the importance degree of the index, and finally obtaining the following judgment matrix:
wherein, the comparison score of the index i to be measured relative to the index j to be measured is t ij Then the comparison score of the index j to be measured with respect to the index i to be measured is t ji ＝1/t ij 。
Further, checking consistency of the fuzzy judgment matrix comprises:
if the part of the fuzzy judgment matrix does not meet the consistency test, carrying out consistency adjustment on the part of the fuzzy judgment matrix; the specific method comprises the following steps:
determining an element i with judgment accuracy larger than a preset value and accurate importance value, wherein the obtained importance values are ci1, ci2, and cin respectively;
subtracting the elements corresponding to each row by using the ci1, the ci2, the cin, and if the obtained difference is a constant, adjusting the row; if the difference is not constant, then the row element needs to be adjusted until ci1, ci 2.
Further, before calculating the relative importance weight of each evaluation index of the index layer relative to the target layer, the method further comprises the following steps:
calculating the relative importance weight of each single layer corresponding to the previous layer; the method specifically comprises the following steps:
first, the sum of each row element of the judgment matrix is calculated
Let a satisfy
According to the number n of the influence factors (the content specifically included in the influence factors is shown in fig. 4), the index α and the sum of the elements in each row, the relative importance weight of the previous level corresponding to each single layer is obtained, and the calculation formula is as follows:
further, the objective weight value is an entropy weight, and determining the objective weight value by using an entropy weight method includes:
for m evaluation indexes, each evaluation index contains n data, and the following evaluation matrix is obtained:
normalizing the elements in the matrix can result in: r (R) ij ) mn ；
Wherein the content of the first and second substances,
f is calculated according to the following formula ij ：
For n data of a certain single evaluation index, the entropy value of the single index i is as follows:
the entropy weight of the evaluation index is:
further, the performing early warning according to the processing strategy corresponding to the current fire safety level includes:
determining a fire safety level in historical data corresponding to the current geographic information, determining a processing strategy level corresponding to the current geographic information according to the fire safety level in the historical data corresponding to the geographic information and the determined fire safety level, and early warning according to the determined processing strategy level corresponding to the current geographic information.
In a second aspect, the invention also provides a storage medium storing a computer program which, when executed by a processor, performs the method steps according to the first aspect of the invention.
In a third aspect, the present invention also provides an electronic device comprising a processor and a storage medium, the storage medium being as in the second aspect;
the processor is adapted to execute a computer program stored in the storage medium to perform the method steps as in the first aspect.
Different from the prior art, the invention provides an early warning method based on fire safety level, a storage medium and electronic equipment, wherein the method comprises the following steps: s1: determining a plurality of fire safety evaluation indexes and initial weight values corresponding to the evaluation indexes; s2: carrying out fuzzy operation on the plurality of fire safety evaluation indexes and the initial weight values corresponding to the evaluation indexes to obtain adjusted fire safety evaluation indexes and adjusted weight values corresponding to the adjusted fire safety evaluation indexes; s3: and determining the current fire safety level according to the adjusted fire safety evaluation indexes and the adjustment weight values corresponding to the adjusted evaluation indexes, and performing early warning according to the processing strategy corresponding to the current fire safety level. According to the scheme, the initial weight values corresponding to the evaluation indexes are subjected to fuzzy operation to adjust the weight values, so that the fire safety level is determined more accurately, and the accuracy of fire early warning is effectively improved.
Drawings
Fig. 1 is a flowchart of an early warning method based on fire safety class according to a first embodiment of the present invention;
fig. 2 is a flowchart of an early warning method based on fire safety class according to a second embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a fishbone diagram analysis of the fire safety factor of the present invention;
FIG. 5 is a schematic diagram of fire safety assessment sub-indicators according to the present invention;
FIG. 6 is a diagram illustrating a transition in membership level according to an embodiment of the present invention.
10. An electronic device;
101. a processor;
102. a storage medium.
Detailed Description
In order to explain in detail possible application scenarios, technical principles, practical embodiments, and the like of the present application, the following detailed description is given with reference to the accompanying drawings in conjunction with the listed embodiments. The embodiments described herein are merely for more clearly illustrating the technical solutions of the present application, and therefore, the embodiments are only used as examples, and the scope of the present application is not limited thereby.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or related to other embodiments specifically defined. In principle, in the present application, the technical features mentioned in the embodiments can be combined in any manner to form a corresponding implementable technical solution as long as there is no technical contradiction or conflict.
Unless otherwise defined, technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the use of relational terms herein is intended only to describe particular embodiments and is not intended to limit the present application.
In the description of the present application, the term "and/or" is a expression for describing a logical relationship between objects, meaning that three relationships may exist, for example a and/or B, meaning: there are three cases of A, B, and both A and B. In addition, the character "/" herein generally indicates that the former and latter associated objects are in a logical relationship of "or".
In this application, terms such as "first" and "second" are used merely to distinguish one entity or operation from another entity or operation without necessarily requiring or implying any actual such relationship or order between such entities or operations.
In the present application, without further limitation, the words "comprise," "include," "have" or other similar expressions used in the language of the claims are intended to cover non-exclusive inclusions, which do not exclude the presence of additional elements in a process, method or article comprising elements, such that a process, method or article comprising a list of elements may include not only those elements but also other elements not expressly listed or inherent to such process, method or article.
As is understood in the examination of the guidelines, the terms "greater than", "less than", "more than" and the like in this application are to be understood as excluding the number; the expressions "above", "below", "within" and the like are understood to include the present numbers. In addition, in the description of the embodiments of the present application, "a plurality" means two or more (including two), and expressions related to "a plurality" similar thereto are also understood, for example, "a plurality of groups", "a plurality of times", and the like, unless specifically defined otherwise.
As shown in fig. 1, in a first aspect, the present invention provides a fire safety level-based early warning method, including the following steps:
s1: determining a plurality of fire safety evaluation indexes and initial weight values corresponding to the evaluation indexes;
s2: carrying out fuzzy operation on the plurality of fire safety evaluation indexes and the initial weight values corresponding to the evaluation indexes to obtain adjusted fire safety evaluation indexes and adjusted weight values corresponding to the adjusted fire safety evaluation indexes;
s3: and determining the current fire safety level according to the adjusted fire safety evaluation indexes and the adjustment weight values corresponding to the adjusted evaluation indexes, and performing early warning according to the processing strategy corresponding to the current fire safety level.
According to the scheme, the initial weight values corresponding to the evaluation indexes are subjected to fuzzy operation to adjust the weight values, so that the fire safety level is determined more accurately, and the accuracy of fire early warning is effectively improved.
Preferably, the evaluation index comprises a subjective evaluation index and an objective evaluation index; the model operation comprises the step that the subjective evaluation index determines a subjective weight value according to a fuzzy hierarchy analysis method, and the objective evaluation index determines an objective weight value by adopting an entropy weight method. The determination of the evaluation index weight value is a crucial step for the comprehensive assessment. The method for determining the evaluation index weight can be divided into a subjective weighting method and an objective weighting method. The subjective weighting method is a method of weighting according to the subjective importance degree of an evaluator on each index, such as a Delphi method, a binomial coefficient method, an analytic hierarchy process, and the like. The objective weighting method is to obtain the information weight of the index by calculating the index data, such as the pull-up rank method, the mean square error method, the range method and the entropy weight method.
As shown in fig. 2, the determining of the subjective weight value by the subjective evaluation indicator according to the fuzzy hierarchy analysis method includes:
firstly, step S201 is entered to construct a hierarchical model, and an index evaluation system is established; the hierarchical model comprises a target layer, a criterion layer and an index layer; the target layer refers to a predetermined target or ideal result of an analysis problem; the criterion layer refers to related intermediate links, and the criterion layer to be considered refers to influence factors and related indexes. The target layer, the criterion layer and the index layer can be presented in a numerical mode after being preprocessed so as to facilitate subsequent calculation processing.
Then step S202 is carried out to construct fuzzy judgment matrixes of all the single layers, and the consistency of the fuzzy judgment matrixes is checked;
then, step S203 is performed to calculate a relative importance weight of each evaluation index of the index layer with respect to the target layer, where the relative importance weight of each evaluation index of the index layer with respect to the target layer is a product of a weight value of each evaluation index with respect to the associated criterion layer and a weight value of the associated criterion layer with respect to the target layer.
In step S201, the present application establishes a hierarchical structure model of the system by determining the relationship between the factors in the system, then establishes a judgment matrix to calculate a single-rank order of the relative weights of each level, and finally calculates the composite weight of each layer element to the system, where the input of the system may be unstructured text data, and the output is each index weight value. When the fire safety index needs to be evaluated, the fire safety evaluation hierarchical model can be established by subdividing the fire safety index, namely, the hierarchical model is established, and an index evaluation system is established.
In executing step S202, the present invention is different from the 1-9 scaling method of the conventional analytic hierarchy process, and different scaling methods are adopted by modifying the fuzzy analytic hierarchy process, for example, the modified scaling may be: slightly important 1.2, strongly important 1.4, clearly important 1.6, etc. Besides adopting different scales, the improved AHP method related by the application is different from the traditional model in the aspects of judging matrix construction and consistency check, and the method specifically comprises the following steps:
the traditional AHP method compares the importance of each factor of the same level pairwise according to a certain scale, and directly constructs a judgment matrix, wherein the judgment matrix is as follows:
wherein r is ij Indicating the scale value of the ith element compared to the jth element.
Then, consistency check is carried out, and the steps are as follows:
a) Calculating matrix weight coefficients:
wherein a is a numerical value in the judgment matrix.
b) Calculating the product AW of the matrix and the weight coefficient
c) Maximum eigenvalue is found
Calculating a consistency index I CI ：
When compared with the average randomness consistency index (I) CI /I RI ) And when the judgment matrix is less than or equal to 0.10, the judgment matrix is considered to be acceptable, otherwise, pairwise comparison judgment needs to be carried out again. Average randomness consistency index I RI Can be found by looking up a table.
In the invention, constructing the fuzzy judgment matrix of each single layer by adopting an improved fuzzy AHP method comprises the following steps:
the importance ranking of n indexes obtained according to the scale expansion method is x 1 ≥x 2 ≥...≥x n To x i And x i+1 Comparing, and recording the corresponding scale value as t i Then, calculating other element values in the judgment matrix according to the transmissibility of the importance degree of the index, and finally obtaining the following judgment matrix:
wherein, the comparison score of the index i to be measured relative to the index j to be measured is t ij Then, the comparison score of the index j to be measured with respect to the index i to be measured is t ji ＝1/t ij 。
Compared with a judgment matrix obtained by comparing each index pairwise, the judgment matrix has an obvious characteristic: any two rows of elements are proportional. Therefore, the matrix satisfies a positive and inverse matrix of order n × n. The matrix can be judged to meet the consistency by the proportion of any two rows of elements of the n multiplied by n order positive and inverse matrix, and the index weight can be directly calculated according to the following formula:
in some embodiments, checking the consistency of the fuzzy decision matrix comprises:
if the part of the fuzzy judgment matrix does not meet the consistency test, carrying out consistency adjustment on the part of the fuzzy judgment matrix; the specific method comprises the following steps:
determining an element i with judgment accuracy larger than a preset value and accurate importance value, wherein the obtained importance values are ci1, ci2, and cin respectively;
subtracting the elements corresponding to each row from ci1, ci2, ci, and ci, respectively, and if the obtained difference is a constant, not adjusting the row; if the difference is not constant, then the row element needs to be adjusted until ci1, ci 2.
Then, the relative importance weight of each single layer corresponding to the previous level can be found according to the following mode:
wherein, c ij For judging each row element of the matrix, n is the number of factors, and the parameter alpha satisfies
Aiming at the method for calculating the subjective weight value, a specific application scenario is described as follows:
a comparison matrix of the importance degrees of five main factors of the machine influencing fire safety is assumed as shown in the following Table 1:
TABLE 1 evaluation index comparison matrix
The matrix weight coefficients can be calculated according to the AHP principle:
the operation process is shown in the following table 2:
TABLE 2 procedure
The matrix is judged to be an n × n order positive and inverse matrix from table 1, and any two rows of elements are in proportion. Therefore, the consistency is met, and the calculation work of consistency check in the traditional AHP method is reduced.
The above analysis shows that the weight of each evaluation index determined by the analytic hierarchy process is factual and acceptable. Reflecting the new equipment acceptance, equipment inspection and maintenance conditions, equipment aging degree, risk condition analysis and others of several comprehensive index factors influencing fire safety, wherein the weights are 0.3627, 0.2885, 0.1891, 0.1093 and 0.0505 respectively.
In some embodiments, the objective weight value is an entropy weight, and determining the objective weight value using an entropy weight method includes:
for m evaluation indexes, each evaluation index comprises n data, and the following evaluation matrix is obtained:
wherein r represents a specific numerical value of the influence degree, such as fire safety, and can be the influence degree of temperature on fire in different areas;
normalizing the elements in the matrix can result in: r (R) ij ) mn ；
Wherein the content of the first and second substances,
f is calculated according to the following formula ij ：
For n data of a certain single evaluation index, the entropy value of the single index i is as follows:
the entropy weight of the evaluation index is:
aiming at the objective weight value calculation method related by the invention, a specific application scenario is described as follows:
the factor statistics of the fire safety intermediate computer, the newly-entered equipment acceptance of each unit, the equipment inspection and maintenance condition, the equipment aging degree, the risk condition analysis and other abnormal quantity monthly statistics, and the following table 3 shows the abnormal quantity distribution condition of each unit computer in 10 months.
TABLE 3.10 month statistics of machine anomaly in each unit
| Serial number | Unit of | Acceptance of newly entered equipment | Equipment maintenance status | Degree of equipment aging | Risk status analysis | Others |
| 1 | New pond | 11 | 6 | 9 | 5 | 12 |
| 2 | Suburb | 8 | 4 | 10 | 3 | 4 |
| 3 | Stone beach | 14 | 8 | 7 | 2 | 4 |
| 4 | Zhongxin | 5 | 9 | 6 | 7 | 3 |
| 5 | Tan pool | 3 | 3 | 5 | 9 | 0 |
| 6 | Fruit for health protection | 12 | 7 | 6 | 1 | 1 |
| 7 | Small building | 7 | 8 | 11 | 6 | 0 |
| 8 | Zhu Cun | 9 | 2 | 8 | 3 | 7 |
The data in table 3 above were normalized, and the normalized data are shown in table 4 below.
TABLE 4 raw data normalization
The method comprises the steps of initializing an original matrix formed by statistical data of each index (table 4), and calculating the entropy value and the entropy weight of each evaluation index according to an entropy weight formula after data are sorted.
TABLE 5 entropy and entropy weights of the indices
As described above in 5, the entropy weights of the evaluation indexes calculated by the entropy weight method are: 0.2065, 0.2798, 0.2006, 0.1767, 0.1364.
In the present invention, a subjective weight vector w of each index is obtained 1 ＝(w 1,1 ,w 2,1 ,...,w m,1 ) And an objectivity weight vector w 2 ＝(w 1,2 ,w 2,2 ,...,w m,2 ) Then, a 'multiplication' integration method can be adopted to calculate the final weight value of each evaluation index:
for example, after the new equipment acceptance, equipment inspection and maintenance conditions, equipment aging degree and risk condition analysis and other calculations of several comprehensive index factors influencing fire safety are carried out by applying an evaluation index comprehensive weight calculation formula, the obtained comprehensive weight values are respectively as follows: 0.3408, 0.3673, 0.1726, 0.0878, and 0.0313.
In the application, after the comprehensive weight is determined, the method further comprises the following steps of determining the current fire safety level after fuzzy comprehensive evaluation is carried out on the basis of a fuzzy evaluation model, wherein the specific steps are as follows:
s4.1: determination of evaluation index
Preferably, the fire safety scoring sub-index is shown as the fire safety scoring sub-index in the attached figure 5.
S4.2: determining the degree of membership of each index
The methods for determining the membership functions of the fuzzy sets are various, but the membership functions given by the methods are only approximate, so that the membership functions need to be continuously corrected and perfected in practice. Such as membership functions of the public fire protection facility index (e.g. public fire protection facilities are usually determined by 2 factors: urban and rural fire protection planning and fire protection infrastructure):
in the above formula,. Mu. VDM Is the degree of membership of the index, percent of delta U index deviation, U 1 And U 2 The specific values of the parameters are constant and can be determined according to actual needs.
Because in the process of calculating the membership degree, when-U 2 ≤ΔU≤-U 1 And is very close to-U 1 When the membership degree is not close to 1; when-U 2 ≤ΔU≤-U 1 And is very close to-U 2 When the membership degree is not close to 0; that is, the above formula is modified as follows because a jump occurs when calculating the membership degree (as shown in fig. 6), that is, the membership degree is greatly different when the data is close to the set parameter.
For other indexes, different fuzzy distribution applications are respectively selected.
Then fuzzy comprehensive evaluation can be carried out, and the specific principle and calculation are as follows:
by calculating the membership degree of each index, the following fuzzy comprehensive evaluation matrix can be obtained:
wherein n is the number of indexes; m is the fire safety evaluation grade; r i Is the single factor evaluation of the i index.
The evaluation results were: b = W · R, where W is the integrated weight vector and W = (W) 1 ,w 2 ,...,w m ) (ii) a And B is a fuzzy comprehensive evaluation result vector which shows the membership degree of the overall situation of the fire safety of the evaluation unit to the level fuzzy subset.
Then, fire safety level determination can be carried out, and the specific principle and calculation are as follows:
after the evaluation result vector B is calculated, the fire safety level can be judged according to the maximum membership principle. The m classes are assigned with scores c in turn 1 ,c 2 ,...,c m ，c 1 >c 2 >...>c m And the scores between adjacent grades are equal in distance, and the scores are from high to low to indicate that the fire safety is from good to bad. Then the fire safety evaluation index can be obtained:
wherein f is PQ For fire safety evaluation index, b is a hyperparameter, k is a undetermined coefficient (k =1 or 2), and the purpose is to control the effect exerted by a larger bj.
In some embodiments, the performing early warning according to the processing policy corresponding to the current fire safety level includes: determining the fire safety level in the historical data corresponding to the current geographic information, determining the processing strategy level corresponding to the current geographic information according to the fire safety level in the historical data corresponding to the geographic information and the determined fire safety level, and early warning according to the determined processing strategy level corresponding to the current geographic information.
For example, the processing strategies include sending an adjustment prompt to an enterprise internal mailbox and sending an adjustment alarm to a fire department mailbox, and it is assumed that there are A, B, C, D levels of fire safety levels, the severity of the fire safety levels gradually increases from a to D, and the four fire safety levels respectively correspond to four different processing strategies, wherein the processing strategy corresponding to the fire safety level a is to send the adjustment prompt to the enterprise internal mailbox (hereinafter referred to as "strategy 1"), and the processing strategy corresponding to the fire safety level a is to send the adjustment alarm to the fire department mailbox (hereinafter referred to as "strategy 2"). Assuming that the daily fire safety awareness of an enterprise in a certain area is strong, the fire safety levels calculated in the historical data are all a, and the fire safety level (current fire safety level) obtained by the calculation is D, when a processing strategy corresponding to the fire safety level is determined, the processing strategy is not necessarily executed completely according to the strategy 2, but the processing strategy is comprehensively evaluated and pushed in order to comprehensively consider the fire safety level of the historical data in the area (determined by positioning through geographic information), for example, if the fire safety level in the history of the area appears for 10 times, D appears only this time, because the pushing of the processing strategy is a result of comprehensively considering the fire safety level in the area and the fire safety level in the historical data, the processing strategy for the area is still executed according to the processing strategy 1. On the contrary, if the historical fire protection level of a certain area is D for many times, even if the determined fire protection level is finally B, the fire protection level can still be executed according to the strategy 2, so that the fire protection level can be timely modified.
In some embodiments, when the severity of the fire safety level of a certain area is high, after triggering policy 2 to process, the method further comprises: and recalculating the fire safety level corresponding to the area after a preset time period so that the fire department can timely know the fire correction condition of the area. And recording the subsequent evaluation indexes in the historical data, if the continuous fire safety levels are not qualified (if the continuous fire safety levels are all judged as D), executing the strategy 2 even if the fire safety level of the area or the enterprise is determined as A at a certain time, so that a fire department can monitor the fire safety level, and when the fire safety level is continuously judged as A after the interval time period, executing the processing strategy of the previous grade, so that the area or the enterprise which has good historical performance can be reformed. For example, when a certain fire-fighting class of an enterprise is a and a certain security class is identified as C, the implementation can be performed according to the processing policy of the security class B of the previous file. Otherwise, for example, if all the fire protection levels of a certain enterprise are D and a certain security level is identified as C, the process may be performed according to the processing policy of the next security level D.
By the mode, aiming at historical data of different areas or enterprises, the corresponding processing strategy is dynamically pushed by combining the determined fire safety level, so that fire departments can perform key monitoring on enterprises with low defense consciousness in the historical data, timely follow up the situation of rectification and modification, and realize effective early warning on fire safety.
In a second aspect, the present invention also provides a storage medium storing a computer program which, when executed, performs the method steps of the first aspect of the present invention.
As shown in fig. 3, in a third aspect, the present invention further provides an electronic device 10, comprising a processor 101 and a storage medium 102, wherein the storage medium 102 is the storage medium according to the second aspect; the processor 101 is adapted to execute a computer program stored in the storage medium 102 to implement the method steps as in the first aspect.
In this embodiment, the electronic device is a computer device, including but not limited to: personal computer, server, general-purpose computer, special-purpose computer, network equipment, embedded equipment, programmable equipment, intelligent mobile terminal, intelligent home equipment, wearable intelligent equipment, vehicle-mounted intelligent equipment, etc. Storage media include, but are not limited to: RAM, ROM, magnetic disk, magnetic tape, optical disk, flash memory, U disk, removable hard disk, memory card, memory stick, network server storage, network cloud storage, etc. Processors include, but are not limited to, a CPU (Central processing Unit), a GPU (image processor), an MCU (Microprocessor), and the like.
As will be appreciated by one skilled in the art, the above-described embodiments may be provided as a method, apparatus, or computer program product. These embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. All or part of the steps of the methods related to the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a storage medium readable by a computer device and used for executing all or part of the steps of the methods related to the above embodiments.
The various embodiments described above are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a computer apparatus to produce a machine, such that the instructions, which execute via the processor of the computer apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer apparatus to cause a series of operational steps to be performed on the computer apparatus to produce a computer implemented process such that the instructions which execute on the computer apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the embodiments have been described, once the basic inventive concept is obtained, other variations and modifications of these embodiments can be made by those skilled in the art, so that these embodiments are only examples of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes that can be used in the present specification and drawings, or used directly or indirectly in other related fields are encompassed by the present invention.
Claims (10)
1. An early warning method based on fire safety level is characterized by comprising the following steps:
s1: determining a plurality of fire safety evaluation indexes and initial weight values corresponding to the evaluation indexes;
s2: carrying out fuzzy operation on the plurality of fire safety evaluation indexes and the initial weight values corresponding to the evaluation indexes to obtain adjusted fire safety evaluation indexes and adjusted weight values corresponding to the adjusted fire safety evaluation indexes;
s3: and determining the current fire safety level according to the adjusted fire safety evaluation indexes and the adjustment weight values corresponding to the adjusted evaluation indexes, and performing early warning according to the processing strategy corresponding to the current fire safety level.
2. The fire safety rating-based early warning method according to claim 1, wherein the evaluation index comprises a subjective evaluation index and an objective evaluation index;
the model operation comprises:
the subjective evaluation index determines a subjective weight value according to a fuzzy hierarchy analysis method, and the objective evaluation index determines an objective weight value by adopting an entropy weight method.
3. The fire safety level-based early warning method according to claim 2, wherein the subjective evaluation index determining the subjective weight value according to a fuzzy hierarchy analysis method comprises:
constructing a hierarchical model and establishing an index evaluation system; the hierarchical model comprises a target layer, a criterion layer and an index layer;
constructing fuzzy judgment matrixes of all single layers, and checking the consistency of the fuzzy judgment matrixes;
and calculating the relative importance weight of each evaluation index of the index layer relative to the target layer, wherein the relative importance weight of each evaluation index of the index layer relative to the target layer is the product of the weight value of each evaluation index relative to the affiliated criterion layer and the weight value of the affiliated criterion layer relative to the target layer.
4. The fire safety level-based early warning method according to claim 3, wherein constructing the fuzzy judgment matrix of each single layer comprises:
the importance ranking of n indexes obtained according to the scale expansion method is x 1 ≥x 2 ≥...≥x n To x i And x i+1 Comparing the two values and recording the corresponding scale value as t i Then, calculating other element values in the judgment matrix according to the transmissibility of the importance degree of the index, and finally obtaining the following judgment matrix:
wherein, the comparison score of the index i to be measured relative to the index j to be measured is t ij Then, the comparison score of the index j to be measured with respect to the index i to be measured is t ji ＝1/t ij 。
5. The fire safety level-based early warning method according to claim 3 or 4, wherein the checking consistency of the fuzzy judgment matrix comprises:
if the part of the fuzzy judgment matrix does not meet the consistency test, carrying out consistency adjustment on the part of the fuzzy judgment matrix; the specific method comprises the following steps:
determining an element i with judgment accuracy larger than a preset value and accurate importance score, wherein the obtained importance scores are ci1, ci2, … and cin respectively;
subtracting the elements corresponding to each row by using ci1, ci2, … and cin respectively, and if the obtained difference is a constant, adjusting the row; if the difference is not constant, then the row element needs to be adjusted until the difference subtracted by ci1, ci2, … …, cin is constant.
6. The fire safety level-based early warning method according to claim 3 or 4, wherein before calculating the relative importance weight of each evaluation index of the index layer relative to the target layer, the method further comprises:
calculating the relative importance weight of each single layer corresponding to the previous layer; the method specifically comprises the following steps:
first, the sum of each row element of the judgment matrix is calculated
Let a satisfy
According to the number n of the influencing factors, the index alpha and the sum of the elements of each row, the relative importance weight of each single layer corresponding to the previous layer is calculated, and the calculation formula is as follows:
7. the fire safety level-based early warning method according to claim 2, wherein the objective weight value is an entropy weight, and determining the objective weight value by using the entropy weight method comprises:
for m evaluation indexes, each evaluation index contains n data, and the following evaluation matrix is obtained:
normalizing the elements in the matrix can result in: r (R) ij ) mn ；
Wherein the content of the first and second substances,
f is calculated according to the following formula ij ：
For n data of a certain single evaluation index, the entropy value of the single index i is as follows:
the entropy weight of the evaluation index is:
8. the fire safety level-based early warning method according to claim 1, wherein the early warning according to the processing strategy corresponding to the current fire safety level comprises:
determining a fire safety level in historical data corresponding to the current geographic information, determining a processing strategy level corresponding to the current geographic information according to the fire safety level in the historical data corresponding to the geographic information and the determined fire safety level, and early warning according to the determined processing strategy level corresponding to the current geographic information.
9. A storage medium, characterized in that the storage medium stores a computer program which, when executed, implements the method of any one of claims 1 to 8.
10. An electronic device comprising a processor and a storage medium according to claim 9;
the processor is configured to execute a computer program stored in the storage medium to implement the method of any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116384780A (en) * | 2023-06-07 | 2023-07-04 | 洪恩流体科技有限公司 | Fire-fighting system safety degree judging method |
| CN116402405A (en) * | 2023-06-02 | 2023-07-07 | 北京利达华信电子股份有限公司 | Method, device, system, electronic equipment and medium for determining fire performance |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116402405A (en) * | 2023-06-02 | 2023-07-07 | 北京利达华信电子股份有限公司 | Method, device, system, electronic equipment and medium for determining fire performance |
| CN116402405B (en) * | 2023-06-02 | 2024-02-13 | 北京利达华信电子股份有限公司 | Method, device, system, electronic equipment and medium for determining fire performance |
| CN116384780A (en) * | 2023-06-07 | 2023-07-04 | 洪恩流体科技有限公司 | Fire-fighting system safety degree judging method |
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