CN116596355A - Intelligent evaluation method and system for fire emergency drilling scheme - Google Patents
Intelligent evaluation method and system for fire emergency drilling scheme Download PDFInfo
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Abstract
The application discloses an intelligent evaluation method and system for a fire emergency drilling scheme, and relates to the technical field of data processing, wherein the method comprises the following steps: analyzing N pieces of drilling information in the fire emergency drilling scheme to obtain M pieces of drilling effective information, then respectively inputting the N pieces of drilling information into a security evaluation model to obtain M drilling security levels, preprocessing the M drilling security levels to obtain the security level of the fire emergency drilling scheme, finally judging whether the security level of the fire emergency drilling scheme is smaller than a preset security level, and if so, carrying out scheme optimization on the fire emergency drilling scheme to obtain a fire emergency drilling optimization scheme. The application solves the technical problem of low safety of the fire emergency drilling due to low safety level of the fire emergency drilling scheme in the prior art, and achieves the technical effects of improving the safety level of the fire emergency drilling scheme and enhancing the safety of the fire emergency drilling.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent evaluation method and system for a fire emergency drilling scheme.
Background
The fire emergency exercise is used for enhancing the fire safety awareness of people, cultivating the proficiency of people in using various fire-fighting equipment and carrying out an activity of organizing capacity and processing capacity of fire disaster extinguishing work, aiming at realizing that people can rapidly and orderly develop rescue work under emergency conditions, minimizing the harm of accidents and guaranteeing the life and property safety of people. Therefore, a safe and effective fire emergency exercise scheme needs to be formulated to guide the fire emergency exercise.
However, from the conventional fire emergency event processing result, the current fire emergency exercise scheme has the technical problem of low safety of the fire emergency exercise due to low safety level of the fire emergency exercise scheme.
Disclosure of Invention
The application provides an intelligent evaluation method and system for a fire emergency drilling scheme, which are used for solving the technical problem of low safety of the fire emergency drilling caused by low safety level of the fire emergency drilling scheme in the prior art.
In a first aspect of the present application, there is provided an intelligent assessment method for a fire emergency exercise program, the method comprising: acquiring N pieces of drilling information in a fire emergency drilling scheme, wherein N is a positive integer greater than 1; carrying out validity analysis on the N pieces of exercise information to obtain M pieces of exercise valid information, wherein M is a positive integer which is more than 1 and less than N; respectively inputting the N pieces of exercise information into a security evaluation model to obtain M pieces of exercise security levels, wherein the security evaluation model comprises M pieces of exercise security evaluation units corresponding to the M pieces of exercise effective information; preprocessing the M drilling safety levels to obtain the safety level of a fire emergency drilling scheme; judging whether the safety level of the fire emergency drilling scheme is smaller than a preset safety level or not; if the safety level of the fire emergency drilling scheme is smaller than the preset safety level, scheme optimization is carried out on the fire emergency drilling scheme, and a fire emergency drilling optimization scheme is obtained.
In a second aspect of the present application, there is provided an intelligent assessment system for a fire emergency exercise program, the system comprising: the system comprises a drilling information acquisition module, a drilling information acquisition module and a control module, wherein the drilling information acquisition module is used for acquiring N pieces of drilling information in a fire emergency drilling scheme, wherein N is a positive integer greater than 1; the effectiveness analysis module is used for performing effectiveness analysis on the N pieces of exercise information to obtain M pieces of exercise effective information, wherein M is a positive integer greater than 1 and smaller than N; the safety evaluation module is used for respectively inputting the N pieces of exercise information into a safety evaluation model to obtain M pieces of exercise safety levels, wherein the safety evaluation model comprises M pieces of exercise safety evaluation units corresponding to the M pieces of exercise effective information; the security level preprocessing module is used for preprocessing the M drilling security levels to obtain the security level of a fire emergency drilling scheme; the safety level judging module is used for judging whether the safety level of the fire emergency drilling scheme is smaller than a preset safety level or not; the scheme optimization module is used for carrying out scheme optimization on the fire-fighting emergency drilling scheme if the safety level of the fire-fighting emergency drilling scheme is smaller than the preset safety level, and obtaining the fire-fighting emergency drilling optimization scheme.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the application provides an intelligent evaluation method of a fire emergency exercise scheme, which relates to the technical field of data processing. The technical problem that the fire emergency drilling is low in safety due to the fact that the fire emergency drilling scheme is low in safety level in the prior art is solved, the technical effect of improving the safety level of the fire emergency drilling scheme and enhancing the safety of the fire emergency drilling is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an intelligent evaluation method of a fire emergency drilling scheme provided by an embodiment of the application;
fig. 2 is a schematic flow chart of obtaining a security level of a fire emergency drilling scheme in an intelligent evaluation method of the fire emergency drilling scheme according to an embodiment of the present application;
fig. 3 is a schematic flow chart of acquiring a fire emergency drilling optimization scheme in the intelligent evaluation method of the fire emergency drilling scheme provided by the embodiment of the application;
fig. 4 is a schematic structural diagram of an intelligent evaluation system of a fire emergency exercise scheme according to an embodiment of the present application.
Reference numerals illustrate: the system comprises an exercise information acquisition module 11, a validity analysis module 12, a security evaluation module 13, a security level preprocessing module 14, a security level judgment module 15 and a scheme optimization module 16.
Detailed Description
The application provides an intelligent evaluation method of a fire emergency drilling scheme, which is used for solving the technical problem of low safety of the fire emergency drilling caused by low safety level of the fire emergency drilling scheme in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides an intelligent evaluation method for a fire emergency exercise scheme, the method comprising:
s100: acquiring N pieces of drilling information in a fire emergency drilling scheme, wherein N is a positive integer greater than 1;
specifically, fire emergency exercise is used for enhancing the safety and fire awareness of people, knowing the process flow of mastering fire and improving the activities of handling emergency events, the fire emergency exercise scheme is an implementation scheme compiled according to actual fire emergency requirements and previous fire emergency experiences, and the fire emergency exercise scheme comprises N pieces of exercise information, including exercise purposes, exercise sites, exercise material equipment, exercise content, exercise processes, emergency events, exercise effects and the like of the fire emergency exercise, wherein N is a positive integer greater than 1, and the number of the exercise information is more than 2.
S200: carrying out validity analysis on the N pieces of exercise information to obtain M pieces of exercise valid information, wherein M is a positive integer which is more than 1 and less than N;
specifically, whether the drilling information is effective or not is judged by comparing the actual drilling effect in the N drilling information with the preset effect of the fire-fighting emergency drilling scheme, and the fire-extinguishing equipment in the drilling information is used for performing on-site fire-extinguishing drilling in an exemplary manner, the dry powder fire extinguisher needs to be inverted and shaken for a few days, then the safety pin is pulled out, the spray head is aligned to the root of the fire, and the switch is pressed down to extinguish the fire. If the fire can be successfully extinguished, the steps are regarded as effective information, and if the fire cannot be successfully extinguished, the steps are regarded as ineffective information. And extracting the exercise information with M actual exercise effects reaching the preset effect of the emergency exercise scheme, wherein the exercise information is used as M exercise effective information and can be used as basic data for subsequent security level evaluation.
Further, as shown in fig. 2, step S200 of the embodiment of the present application further includes:
s210: acquiring a preset exercise emergency effect;
s220: judging whether the N pieces of exercise information accord with the preset exercise emergency effect or not;
s230: and extracting M pieces of drilling information of which the N pieces of drilling information accord with a preset drilling emergency effect, and recording the M pieces of drilling information as M pieces of drilling effective information.
Specifically, the preset drilling emergency effect refers to an effect which is preset in the fire-fighting drilling emergency scheme and is intended to be achieved by a method in the scheme, and for example, the preset drilling effect of evacuation drilling is that all people in an accident area are evacuated safely within a specified time. The preset drilling emergency effect can be obtained by consulting the fire-fighting drilling emergency scheme, the N drilling information is compared with the preset drilling emergency effect one by one, whether actual drilling achieves the preset effect is judged, M drilling information which achieves the preset drilling emergency effect in the N drilling information is extracted, the M drilling information is used as M drilling effective information, and the M drilling effective information can be used as basic data for subsequent security level assessment.
S300: respectively inputting the N pieces of exercise information into a security evaluation model to obtain M pieces of exercise security levels, wherein the security evaluation model comprises M pieces of exercise security evaluation units corresponding to the M pieces of exercise effective information;
specifically, the safety evaluation model is a neural network model which can be continuously subjected to self iterative optimization in machine learning, and can be obtained through a training data set and a supervision data set, and the construction process of the safety evaluation model can be as follows: firstly, inputting each group of training data in a training data set into a safety evaluation model, then outputting and supervising adjustment to the safety evaluation model by using supervising data corresponding to the group of training data, if the output result of the safety evaluation model is consistent with the supervising data, finishing the current group of training, and further, training all data in the training data set until the training of all the training data is finished, thereby finishing the training of the climate characteristic prediction model. In order to ensure the accuracy of the security assessment model, the security assessment model can be tested by using a test data set, the test accuracy is set to be 85%, and if the test accuracy of the current test data set meets 85%, the construction of the security assessment model is completed.
Specifically, the M exercise safety evaluation units are units for performing safety evaluation on exercise information in the safety evaluation model. The N pieces of drilling information are input into the security evaluation model, and M pieces of drilling security levels can be output after being evaluated by M pieces of drilling security evaluation units, so that the M pieces of drilling security levels can be used as basic data for calculating the security levels of a fire emergency drilling scheme.
Further, step S300 of the embodiment of the present application further includes:
s310: constructing a first security assessment unit according to first exercise effective information in the M exercise effective information;
s320: constructing other M-1 security assessment units according to other M-1 exercise effective information, wherein the M security assessment units form the security assessment model;
s330: and respectively inputting the N pieces of exercise information into the M pieces of security evaluation units to obtain the M pieces of exercise security levels.
Specifically, first exercise effective information in the M pieces of exercise effective information is used as basic data to perform neural network training, a rule of exercise information safety evaluation is found, a first safety evaluation unit is constructed, similarly, other M-1 pieces of exercise effective information are subjected to neural network training, other M-1 pieces of safety evaluation units are constructed, and on the whole, the M pieces of safety evaluation units form the safety evaluation model. The N pieces of exercise information are respectively input into M pieces of security evaluation units of the security evaluation model, after the security evaluation is carried out on the N pieces of exercise information by the M pieces of security evaluation units, M pieces of exercise security levels are output by an output unit of the security evaluation model, and the M pieces of exercise security levels can be used as basic data for calculating the security levels of a fire emergency exercise scheme.
Further, step S310 of the embodiment of the present application further includes:
s311: according to the data of the M pieces of exercise effective information after exercise in the history time, detecting and analyzing to obtain a first exercise effective information set of a plurality of samples;
s312: taking the first drilling effective information set of the plurality of samples as a construction data set;
s313: based on a BP neural network, a first security assessment unit is constructed, and the constructed data set is adopted to perform iterative supervision training, verification and test on the first security assessment unit, so that the first security assessment unit meeting preset conditions is obtained.
Specifically, the actual exercise data in the past period of time in the M pieces of exercise effective information, including exercise emergency effect, exercise processing effect and the like, are taken as references, and all information samples reaching a preset effect are extracted and taken as a sample first exercise effective information set. The construction dataset refers to a dataset of data of each node of the BP neural network model, which is composed of the plurality of sample first exercise effective information sets, the BP neural network model is a neural network model which can be continuously subjected to self-iterative optimization in machine learning, and can be obtained through a training dataset and a supervision dataset, wherein the supervision dataset is security rule supervision data corresponding to the training dataset, and the first security evaluation unit is constructed by referring to the rule: inputting training data in the constructed data set into the first safety evaluation unit, performing output supervision adjustment of the first safety evaluation unit through supervision data corresponding to the training data set, finishing the current training set if the output result of the first safety evaluation unit is consistent with the supervision data, further training all data in the training data set until training of all the training data is finished, thereby finishing training of the first safety evaluation unit, performing test processing on the first safety evaluation unit through the test data set, and setting the test accuracy to be 85%, wherein if the test accuracy of the current test data set meets 85%, obtaining the first safety evaluation unit meeting preset conditions, and being capable of being used for evaluating the safety level of the training information of the fire emergency training scheme.
S400: preprocessing the M drilling safety levels to obtain the safety level of a fire emergency drilling scheme;
specifically, according to the types and the importance of the obtained M drilling safety levels, weighted average calculation is sequentially performed on the M drilling safety levels, so that the safety level of the whole scheme, namely the safety level of the fire emergency drilling scheme, is obtained, and the method can be used as a judgment standard for optimizing the subsequent fire emergency drilling scheme.
Further, as shown in fig. 2, step S400 of the embodiment of the present application further includes:
s410: acquiring exercise destination security levels, exercise content security levels, exercise material security levels and exercise place security levels according to the M exercise security levels;
s420: sequentially distributing weights of the exercise destination security level, the exercise content security level, the exercise material security level and the exercise place security level;
s430: and carrying out weight calculation on the drilling destination safety level, the drilling content safety level, the drilling material safety level and the drilling place safety level with weights to obtain the safety level of the fire emergency drilling scheme.
Specifically, out of the M exercise safety levels, an exercise destination safety level, an exercise content safety level, an exercise material safety level, and an exercise location safety level are extracted, and the exercise destination safety level, the exercise content safety level, the exercise material safety level, and the exercise location safety level are respectively assigned with weights according to the importance levels thereof, and the exercise destination safety level is assigned with a weight coefficient of 0.3, the exercise content safety level is assigned with a weight coefficient of 0.3, the exercise material safety level is assigned with a weight coefficient of 0.2, and the exercise location safety level is assigned with a weight coefficient of 0.2, for example. And multiplying the drilling destination security level, the drilling content security level, the drilling material security level and the drilling site security level by corresponding weight coefficients respectively, and summing the obtained results to obtain the security level of the fire-fighting emergency drilling scheme, which can be used as a judgment standard for optimizing the subsequent fire-fighting emergency drilling scheme.
S500: judging whether the safety level of the fire emergency drilling scheme is smaller than a preset safety level or not;
specifically, the preset safety level is a target safety level set according to actual safety requirements in the fire emergency exercise scheme, the safety level of the fire emergency exercise scheme is compared with the preset safety level, and whether the safety level of the fire emergency exercise scheme is smaller than the preset safety level is judged, so that whether the fire emergency exercise scheme needs to be optimized is determined.
S600: if the safety level of the fire emergency drilling scheme is smaller than the preset safety level, scheme optimization is carried out on the fire emergency drilling scheme, and a fire emergency drilling optimization scheme is obtained.
Specifically, if the security level of the fire emergency exercise scheme is smaller than the preset security level, it is indicated that factors affecting the security level exist in the scheme, and for example, there may be problems of equipment mismatch, unreasonable escape route and the like, so that the security level of the scheme is improved by perfecting the equipment, optimizing the escape route, replacing the place and the like, the purpose of optimizing the fire emergency exercise scheme can be achieved, thereby obtaining a fire emergency exercise optimization scheme, and improving the fire emergency exercise effect.
Further, as shown in fig. 3, step S600 of the embodiment of the present application further includes:
s610: sequentially acquiring the exercise destination security level, the exercise content security level, the exercise material security level and the exercise location security level exercise security level lower limit;
s620: respectively comparing the exercise target security level, the exercise content security level, the exercise material security level and the exercise site security level lower limit with the preset security level to obtain A exercise security levels smaller than the preset security level;
s630: acquiring A pieces of exercise information corresponding to the A exercise safety levels;
s640: and adjusting and optimizing the A pieces of drilling information until the A pieces of drilling safety level corresponding to the A pieces of drilling information is greater than the preset safety level, and acquiring the fire emergency drilling optimization scheme.
Specifically, the lower limit of the exercise safety level of the exercise destination safety level, the exercise content safety level, the exercise material safety level and the exercise place safety level in the M exercise safety levels is extracted, and the lower limit of the exercise safety level can meet the minimum safety level of the safety requirements. And comparing the acquired drilling destination security level, drilling content security level, drilling material security level and drilling site security level minimum drilling security level with the preset security level one by one, and screening out drilling security levels smaller than the preset security level, wherein A are assumed to be provided. Extracting A pieces of drilling information corresponding to the A pieces of drilling safety levels, and pertinently solving factors affecting the safety level in each drilling information until the drilling safety level corresponding to the drilling information is greater than the preset safety level, obtaining A pieces of optimized drilling information, and finally replacing A pieces of drilling information in the original scheme by the A pieces of optimized drilling information to obtain a fire emergency drilling optimization scheme, so that the safety level of the fire emergency drilling scheme can be improved, and the fire emergency drilling effect is enhanced.
In summary, the embodiment of the application has at least the following technical effects:
according to the method, N pieces of drilling information in a fire emergency drilling scheme are analyzed to obtain M pieces of drilling effective information, then the N pieces of drilling information are respectively input into a security evaluation model to obtain M pieces of drilling security levels, the M pieces of drilling security levels are preprocessed to obtain the security level of the fire emergency drilling scheme, finally whether the security level of the fire emergency drilling scheme is smaller than a preset security level is judged, if so, scheme optimization is conducted on the fire emergency drilling scheme, and a fire emergency drilling optimization scheme is obtained.
The technical effects of improving the safety level of the fire emergency drilling scheme and enhancing the safety of the fire emergency drilling are achieved.
Example two
Based on the same inventive concept as the intelligent evaluation method of a fire emergency exercise scheme in the foregoing embodiments, as shown in fig. 4, the present application provides an intelligent evaluation system of a fire emergency exercise scheme, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the drilling information acquisition module 11 is used for acquiring N pieces of drilling information in a fire emergency drilling scheme, wherein N is a positive integer greater than 1;
the effectiveness analysis module 12 is configured to perform effectiveness analysis on the N pieces of exercise information to obtain M pieces of exercise effective information, where M is a positive integer greater than 1 and less than N;
the safety evaluation module 13 is configured to input the N pieces of exercise information into a safety evaluation model to obtain M exercise safety levels, where the safety evaluation model includes M exercise safety evaluation units corresponding to the M pieces of exercise effective information;
the safety level preprocessing module 14 is used for preprocessing the M drilling safety levels to obtain the safety level of a fire emergency drilling scheme;
the safety level judging module 15 is used for judging whether the safety level of the fire emergency drilling scheme is smaller than a preset safety level or not;
the scheme optimization module 16, the scheme optimization module 16 is configured to perform scheme optimization on the fire emergency drilling scheme if the security level of the fire emergency drilling scheme is smaller than the preset security level, and obtain the fire emergency drilling optimization scheme.
Further, the system further comprises:
the system comprises a preset drilling emergency effect acquisition module, a control module and a control module, wherein the preset drilling emergency effect acquisition module is used for acquiring a preset drilling emergency effect;
the effect judging module is used for judging whether the N pieces of exercise information accord with the preset exercise emergency effect or not;
the drilling machine comprises a drilling effective information acquisition module, a drilling effective information acquisition module and a drilling information processing module, wherein the drilling effective information acquisition module is used for extracting M pieces of drilling information, the N pieces of drilling information correspond to a preset drilling emergency effect, and the M pieces of drilling information are recorded as the M pieces of drilling effective information;
further, the system further comprises:
the first security evaluation unit construction module is used for constructing a first security evaluation unit according to first exercise effective information in the M exercise effective information;
the safety evaluation model building module is used for building other M-1 safety evaluation units according to other M-1 exercise effective information, and the M safety evaluation units form the safety evaluation model;
the exercise safety level acquisition module is used for respectively inputting the N exercise information into the M security evaluation units to obtain the M exercise safety levels;
further, the system further comprises:
the sample first drilling effective information set acquisition module is used for acquiring a plurality of sample first drilling effective information sets through detection and analysis according to data of the M drilling effective information after drilling in the history time;
a construction data set acquisition module, configured to take the first exercise effective information sets of the plurality of samples as a construction data set;
the first security evaluation unit acquisition module is used for constructing the first security evaluation unit based on the BP neural network, and performing iterative supervision training, verification and test on the first security evaluation unit by adopting the constructed data set to obtain the first security evaluation unit meeting preset conditions;
further, the system further comprises:
the sample security level acquisition module is used for acquiring security levels of exercise purposes, exercise content security levels, exercise material security levels and exercise place security levels according to the M exercise security levels;
the weight distribution module is used for sequentially distributing weights of the exercise destination security level, the exercise content security level, the exercise material security level and the exercise place security level;
the scheme security level acquisition module is used for carrying out weight calculation on the drilling destination security level, the drilling content security level, the drilling material security level and the drilling place security level with weights to obtain the security level of the fire emergency drilling scheme;
further, the system further comprises:
the exercise safety level lower limit acquisition module is used for sequentially acquiring the exercise target safety level, the exercise content safety level, the exercise material safety level and the exercise safety level lower limit of the exercise place safety level;
the exercise safety level comparison module is used for respectively comparing the exercise target safety level, the exercise content safety level, the exercise material safety level and the exercise site safety level lower limit with the preset safety level to obtain A exercise safety levels smaller than the preset safety level;
the drilling information acquisition module is used for acquiring A drilling information corresponding to the A drilling safety levels;
the scheme optimizing module is used for adjusting and optimizing the A pieces of drilling information until the A pieces of drilling safety level corresponding to the A pieces of drilling information is greater than the preset safety level, and acquiring the fire emergency drilling optimizing scheme.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (7)
1. An intelligent evaluation method of a fire emergency drilling scheme, which is characterized by comprising the following steps:
acquiring N pieces of drilling information in a fire emergency drilling scheme, wherein N is a positive integer greater than 1;
carrying out validity analysis on the N pieces of exercise information to obtain M pieces of exercise valid information, wherein M is a positive integer which is more than 1 and less than N;
respectively inputting the N pieces of exercise information into a security evaluation model to obtain M pieces of exercise security levels, wherein the security evaluation model comprises M pieces of exercise security evaluation units corresponding to the M pieces of exercise effective information;
preprocessing the M drilling safety levels to obtain the safety level of a fire emergency drilling scheme;
judging whether the safety level of the fire emergency drilling scheme is smaller than a preset safety level or not;
if the safety level of the fire emergency drilling scheme is smaller than the preset safety level, scheme optimization is carried out on the fire emergency drilling scheme, and a fire emergency drilling optimization scheme is obtained.
2. The method of claim 1, wherein the obtaining M pieces of exercise availability information comprises:
acquiring a preset exercise emergency effect;
judging whether the N pieces of exercise information accord with the preset exercise emergency effect or not;
and extracting M pieces of drilling information of which the N pieces of drilling information accord with a preset drilling emergency effect, and recording the M pieces of drilling information as M pieces of drilling effective information.
3. The method of claim 1, wherein inputting the N pieces of exercise information into a security assessment model comprises:
constructing a first security assessment unit according to first exercise effective information in the M exercise effective information;
constructing other M-1 security assessment units according to other M-1 exercise effective information, wherein the M security assessment units form the security assessment model;
and respectively inputting the N pieces of exercise information into the M pieces of security evaluation units to obtain the M pieces of exercise security levels.
4. A method as claimed in claim 3, wherein constructing the first security assessment unit comprises:
according to the data of the M pieces of exercise effective information after exercise in the history time, detecting and analyzing to obtain a first exercise effective information set of a plurality of samples;
taking the first drilling effective information set of the plurality of samples as a construction data set;
and constructing the first security assessment unit based on the BP neural network, and performing iterative supervision training, verification and test on the first security assessment unit by adopting the constructed data set to obtain the first security assessment unit meeting preset conditions.
5. The method of claim 1, wherein obtaining a security level for a fire emergency exercise program comprises:
acquiring exercise destination security levels, exercise content security levels, exercise material security levels and exercise place security levels according to the M exercise security levels;
sequentially distributing weights of the exercise destination security level, the exercise content security level, the exercise material security level and the exercise place security level;
and carrying out weight calculation on the drilling destination safety level, the drilling content safety level, the drilling material safety level and the drilling place safety level with weights to obtain the safety level of the fire emergency drilling scheme.
6. The method of claim 5, wherein the obtaining a fire emergency exercise optimization scheme comprises:
sequentially acquiring the exercise destination security level, the exercise content security level, the exercise material security level and the exercise location security level exercise security level lower limit;
respectively comparing the exercise target security level, the exercise content security level, the exercise material security level and the exercise site security level lower limit with the preset security level to obtain A exercise security levels smaller than the preset security level;
acquiring A pieces of exercise information corresponding to the A exercise safety levels;
and adjusting and optimizing the A pieces of drilling information until the A pieces of drilling safety level corresponding to the A pieces of drilling information is greater than the preset safety level, and acquiring the fire emergency drilling optimization scheme.
7. An intelligent assessment system for a fire emergency exercise program, the system comprising:
the system comprises a drilling information acquisition module, a drilling information acquisition module and a control module, wherein the drilling information acquisition module is used for acquiring N pieces of drilling information in a fire emergency drilling scheme, wherein N is a positive integer greater than 1;
the effectiveness analysis module is used for performing effectiveness analysis on the N pieces of exercise information to obtain M pieces of exercise effective information, wherein M is a positive integer greater than 1 and smaller than N;
the safety evaluation module is used for respectively inputting the N pieces of exercise information into a safety evaluation model to obtain M pieces of exercise safety levels, wherein the safety evaluation model comprises M pieces of exercise safety evaluation units corresponding to the M pieces of exercise effective information;
the security level preprocessing module is used for preprocessing the M drilling security levels to obtain the security level of a fire emergency drilling scheme;
the safety level judging module is used for judging whether the safety level of the fire emergency drilling scheme is smaller than a preset safety level or not;
the scheme optimization module is used for carrying out scheme optimization on the fire-fighting emergency drilling scheme if the safety level of the fire-fighting emergency drilling scheme is smaller than the preset safety level, and obtaining the fire-fighting emergency drilling optimization scheme.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111047174A (en) * | 2019-12-05 | 2020-04-21 | 北京明略软件系统有限公司 | Fire drill evaluation method and system |
| CN112418576A (en) * | 2019-08-22 | 2021-02-26 | 中国石油天然气股份有限公司 | Method and system for petrochemical enterprise emergency situation construction and emergency evaluation |
| CN112530119A (en) * | 2020-11-02 | 2021-03-19 | 深圳市城市公共安全技术研究院有限公司 | Forest fire emergency drilling evaluation and analysis system and method and computer equipment |
| US20210374888A1 (en) * | 2020-05-27 | 2021-12-02 | Talon Tactical Systems Llc | Systems and methods for training and evaluation |
| US20220035818A1 (en) * | 2020-07-28 | 2022-02-03 | Shanghai Rayeye Technology Co., Ltd. | Intelligent remote monitoring method for fire-fighting |
| CN115564620A (en) * | 2022-08-31 | 2023-01-03 | 中国消防救援学院 | Intelligent control method and system for emergency drilling process of emergency event |
| CN115643282A (en) * | 2022-10-10 | 2023-01-24 | 武汉理工光科股份有限公司 | Fire fighting evaluation method based on big data |
| CN115860495A (en) * | 2022-12-16 | 2023-03-28 | 中国建设银行股份有限公司 | Emergency plan processing method, device, equipment, medium and product |
-
2023
- 2023-04-03 CN CN202310343932.5A patent/CN116596355B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112418576A (en) * | 2019-08-22 | 2021-02-26 | 中国石油天然气股份有限公司 | Method and system for petrochemical enterprise emergency situation construction and emergency evaluation |
| CN111047174A (en) * | 2019-12-05 | 2020-04-21 | 北京明略软件系统有限公司 | Fire drill evaluation method and system |
| US20210374888A1 (en) * | 2020-05-27 | 2021-12-02 | Talon Tactical Systems Llc | Systems and methods for training and evaluation |
| US20220035818A1 (en) * | 2020-07-28 | 2022-02-03 | Shanghai Rayeye Technology Co., Ltd. | Intelligent remote monitoring method for fire-fighting |
| CN112530119A (en) * | 2020-11-02 | 2021-03-19 | 深圳市城市公共安全技术研究院有限公司 | Forest fire emergency drilling evaluation and analysis system and method and computer equipment |
| CN115564620A (en) * | 2022-08-31 | 2023-01-03 | 中国消防救援学院 | Intelligent control method and system for emergency drilling process of emergency event |
| CN115643282A (en) * | 2022-10-10 | 2023-01-24 | 武汉理工光科股份有限公司 | Fire fighting evaluation method based on big data |
| CN115860495A (en) * | 2022-12-16 | 2023-03-28 | 中国建设银行股份有限公司 | Emergency plan processing method, device, equipment, medium and product |
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