TWI737074B - Firefighting data analysis method, device, computer device and storage medium - Google Patents

Abstract

The present disclosure provides a firefighting data analysis method, a firefighting data analysis device, a computer device and a computer storage medium. The method includes: obtaining firefighting status information of an evaluating place, the items in the firefighting status information include environmental status information, object status information and the number of people; inputting the firefighting status information into a preset analysis model, and outputting the firefighting input data and / or firefighting loss data with minimum total loss, required by the evaluating place in a preset fire scene.

Description

Fire protection data analysis method, device, computer device and storage medium

The invention relates to the field of fire protection data analysis, in particular to a fire protection data analysis method, a fire protection data analysis device, a computer device and a computer storage medium.

The implementation of any fire protection technology must calculate the fire protection input data. In the analysis of fire prevention and control technology, how to minimize the fire input data, fire loss data, and total loss cost before meeting the fire safety performance is a technical problem that needs to be solved urgently. The existing fire-fighting data analysis methods mostly use mathematical statistical table analysis as the basis to study the relationship between fire-fighting input data and fire losses. The existing fire-fighting data analysis methods have slow calculation speed and low accuracy.

In view of the above content, it is necessary to propose a fire-fighting data analysis method and device, a computer device and a computer storage medium, and the fire-fighting data analysis can be carried out in a faster and more accurate manner.

The first aspect of this application provides a fire protection data analysis method. The method includes: obtaining fire protection status information of a place to be assessed, and items in the fire protection status information include environmental status information, item status information, and number of personnel; The fire-fighting status information is input to a preset analysis model, and the fire-fighting input data and/or fire loss data required for the site to be assessed in the preset fire scene with the least total loss are output.

Preferably, the method further includes: comparing the outputted fire fighting input data with actual fire fighting input data; If the outputted firefighting input data is less than the actual firefighting input data, output the prompt information indicating that the actual firefighting input data of the place to be assessed is too high; if the outputted firefighting input data is greater than the actual firefighting input data, Then output the prompt information that the fire protection input data of the place to be assessed is too low.

Preferably, the method further includes: if the outputted firefighting input data is greater than the actual firefighting input data, outputting improvement opinions based on the fire protection status information of the place to be assessed, wherein the method for obtaining the improvement opinions includes: The preset query table searches for the fire protection status design requirements corresponding to different items in the fire protection status information of the place to be assessed. Fire protection status information; determine whether the items in the fire protection status information of the place to be assessed meet the design requirements of the fire protection status; if not, the design requirements of the fire protection status are output as the improvement opinions.

Preferably, the method for generating the preset analysis model includes: obtaining fire-fighting status information, fire-fighting input data, and fire-loss data of different places in different fire scenarios, and comparing the fire-fighting status information and fire-fighting input of each of the places Data and fire loss data are stored correspondingly; the fire protection status information, fire input data, and fire loss data of the multiple places are divided into training sets and verification sets; an analysis model based on neural network is established, and the training set is used to pair The parameters of the analysis model based on the neural network are trained, wherein the fire-fighting status information in the training set is used as the input data of the model, and the fire-fighting input data and fire loss data are used as the output data of the model; The validation set validates the neural network-based analysis model after training, and obtains the prediction accuracy rate of the model based on the verification results; judges whether the prediction accuracy rate of the model is less than a preset threshold; If the prediction accuracy of the model is not less than the preset threshold, the trained analysis model based on the neural network is used as the preset analysis model.

Preferably, the method further includes: if the prediction accuracy rate of the analysis model based on the neural network is less than the preset threshold, adjusting the structure of the neural network algorithm, and using the training set to re-adjust the adjusted The neural network-based analysis model is trained, wherein the structure of the neural network-based analysis model includes at least one of the number of convolution kernels, the number of elements in the pooling layer, and the number of elements in the fully connected layer ; Use the verification set to verify the adjusted neural network-based analysis model, and re-statistics the adjusted neural network-based analysis model prediction accuracy based on the verification results, and determine the adjusted neural network-based analysis model Whether the prediction accuracy of the analysis model is less than the preset threshold; if the prediction accuracy of the model obtained by re-statistics is not less than the preset threshold, the adjusted analysis model based on the neural network is used as the preset Analytical model; and if the model prediction accuracy rate obtained by the re-statistics is less than the preset threshold, repeat the above adjustment and training steps until the model prediction accuracy rate obtained by the verification set verification is not less than the prediction Set the threshold.

Preferably, the method for acquiring fire protection information, fire protection input data, and fire loss data of different places in different fire scenarios includes: acquiring fire protection information of any place, and judging the location of the place according to the preset query table Whether the fire protection situation meets the fire safety standards; if it does, calculate the fire input data of the place according to the fire protection situation information, and use the fire numerical simulation system to simulate the place under different fire scenarios, and calculate the Fire loss data of the places mentioned in different fire scenarios.

Preferably, the method of using a fire numerical simulation system to simulate the site under different fire scenarios and calculating the fire loss data of the site under the different fire scenarios includes: Set the combustible time and combustible ratio of each combustible in the place, and divide the combustible according to the preset ratio, and each small piece after division represents the minimum loss of the combustible per unit time in the fire Amount; according to the combustible time of the combustible, combustible ratio, and the minimum amount of loss per unit time in the fire, calculate the fire loss data of the place under different fire protection information.

The second aspect of this application provides a fire protection data analysis device, the device includes: an acquisition module for acquiring fire protection status information of a place to be assessed, and items in the fire protection status information include environmental status information, item status information, The number of personnel; the analysis module is used to input the fire protection status information into the preset analysis model, and output the fire protection input data and/ Or fire damage information.

A third aspect of the present application provides a computer device. The computer device includes a processor for executing the computer program stored in the memory to implement the fire protection data analysis method as described above.

The fourth aspect of the present application provides a computer storage medium on which a computer program is stored, and the computer program is executed by a processor to realize the fire protection data analysis method as described above.

The fire-fighting data analysis method, the fire-fighting data analysis device, the computer device and the computer storage medium of the present invention, by inputting the fire-fighting status information of the place to be evaluated into a preset analysis model for analysis, the analysis shows that the place to be evaluated has the highest investment benefit The cost of fire protection investment. The method can quickly and accurately calculate the fire protection input data and/or fire loss data required in the case of a fire with the smallest total loss.

1: User terminal

2: computer device

10: Fire protection data analysis device

20: memory

30: processor

40: computer program

101: Get modules

101: Analysis Module

FIG. 1 is a schematic diagram of the application environment architecture of the fire protection data analysis method provided by Embodiment 1 of the present invention.

Fig. 2 is a flow chart of the fire protection data analysis method provided by the second embodiment of the present invention.

Fig. 3 is a schematic diagram of the structure of the fire protection data analysis device provided by the third embodiment of the present invention.

FIG. 4 is a schematic diagram of a computer device provided by the fourth embodiment of the present invention.

In order to be able to understand the above objectives, features and advantages of the present invention more clearly, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other if there is no conflict.

In the following description, many specific details are explained in order to fully understand the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

Example one

Refer to FIG. 1, which is a schematic diagram of the application environment architecture of the fire protection data analysis method provided by Embodiment 1 of the present invention.

The fire protection data analysis method of the present invention is applied to the user terminal 1, and the user terminal 1 and a computer device 2 establish a communication connection through the network. The network can be a wired network or a wireless network, such as radio, wireless fidelity (WIFI), cellular, satellite, broadcast, etc. The user terminal 1 is used to obtain the fire protection status information of the place to be assessed, and use the fire protection information to analyze the fire protection input data and fire loss data of the place, and the computer device 2 is used to store different fire scenes in different places. Information on fire fighting conditions, fire fighting input data, and fire loss data below.

The user terminal 1 may be an electronic device installed with fire protection data analysis software, such as a personal computer, a tablet computer, and the like.

The computer device 2 can be an electronic device that stores information on fire protection conditions, fire protection input data, and fire loss data in different places under different fire scenarios, such as a personal computer, a server, etc., wherein the server can be a single Server, server cluster or cloud server, etc.

In another embodiment of the present invention, the fire protection status information, fire protection input data, and fire loss data of the different places in different fire scenarios may also be stored in the user terminal 1.

Example two

Please refer to FIG. 2, which is a flowchart of the fire protection data analysis method provided by the second embodiment of the present invention. According to different needs, the order of the steps in the flowchart can be changed, and some steps can be omitted.

Step S1: Obtain the fire protection status information of the place to be assessed.

The fire protection status information includes the number of evacuated persons, environmental status information, and item status information. The environmental status information may include information on fixed firefighting facilities and mobile firefighting facilities, such as: fire alarms, smoke alarms, ceiling fire sprinklers, fire detectors, indoor fire hydrants, and the number and placement of outdoor fire hydrants Location etc. The item status information includes combustible items in the place to be evaluated, such as the quantity and placement of manufacturing equipment, materials, office computers, and furniture.

In one embodiment, the method for acquiring fire protection status information of the place to be assessed may include: receiving user input, such as the number of evacuated persons in the place to be evaluated, the type and quantity of fire-fighting facilities, and the type and quantity of combustible materials, etc. .

In another embodiment, the method for acquiring fire protection information may also be by receiving multiple images of the place to be evaluated collected by multiple camera devices, and identifying the number of people in the images and the number of firefighters by the image recognition method. Type and quantity of facilities, type and quantity of combustible materials, etc. News.

Step S2: Input the fire protection status information into a preset analysis model, and output the fire protection input data and/or fire loss data required for the site to be assessed in the preset fire scene with the least total loss.

The method for generating the preset analysis model includes the following steps:

(1) Obtain the fire-fighting status information, fire-fighting input data, and fire loss data of different places under different fire scenarios, and store the fire-fighting status information, fire-fighting input data, and fire loss data of each of the said places.

The methods for obtaining the fire protection status information, fire protection input data, and fire loss data of different places under different fire scenarios include:

a) Obtain the fire protection status information of any place, and judge whether the fire protection status of the place meets the fire safety standards according to the preset query table.

The preset query table records the fire protection information required by the unit area under the condition of meeting the fire safety standards, such as the number of fire alarms in the unit area, the number of ceiling fire sprinklers; the number of personnel in the unit area Quantity, the number of fire extinguishers and gas masks that match the number of personnel; the number of flammable products per unit area, the number of hydrants and fire extinguishers that match the number of flammable products, etc.

b) If yes, calculate the fire input data of the place according to the fire protection information, and use the fire numerical simulation system to simulate the place under different fire scenarios, and calculate the said place under different fire scenarios. Fire loss data of the site.

The calculation method of the fire protection input data includes querying the environmental status information in the fire protection status information and the value of each item in the item status information, and calculating the fire protection input data of the place based on the value.

The calculation method of the fire loss data includes the installation of each combustible in the place Combustible time and combustible ratio, and divide the combustibles according to the preset ratio, and each small piece after division represents the minimum amount of combustibles lost per unit time in the fire; according to the combustible time of the combustibles , Combustible ratio, and the minimum amount of loss per unit time in the fire. Calculate the fire loss data of the places described under different fire protection information.

For example, if a machine tool is divided into 64 equal parts, the value of the machine tool is 640,000, the value of each equal part is 10,000, and the burning time of each equal part is 2 minutes. Calculate the loss of the machine tool in a fire according to the information on the fire protection situation where the machine tool is located. For example, the fire loss data of the machine tool in a place with a ceiling fire sprinkler, and the fire loss data of the machine tool in a place with only one fire extinguisher. The sequential method can calculate the fire loss data of any place.

(2) Divide the fire-fighting status information, fire-fighting input data, and fire loss data of the multiple places into a training set and a verification set.

(3) Establish an analysis model based on the neural network, and use the training set to train the parameters of the analysis model based on the neural network, where the fire protection status information in the training set is used as the input data of the model , Fire input data and fire loss data are used as the output data of the model.

(4) Use the verification set to verify the neural network-based analysis model after training, and obtain the prediction accuracy of the model based on the verification results.

(5) Determine whether the prediction accuracy rate of the model is less than a preset threshold.

(6) If the prediction accuracy of the model is not less than the preset threshold, then the trained analysis model based on the neural network is used as the preset analysis model.

In some embodiments, the step further includes:

(7) If the prediction accuracy of the neural network-based analysis model is less than the preset threshold, adjust the structure of the neural network algorithm, and use the training set to re-adjust the adjusted neural network-based The analysis model is trained, wherein the structure of the neural network-based analysis model It includes at least one of the number of convolution kernels, the number of elements in the pooling layer, and the number of elements in the fully connected layer.

(8) Use the verification set to verify the adjusted analysis model based on the neural network, until the model prediction accuracy obtained by the re-statistics is not less than the preset threshold, then the adjusted neural network-based The analysis model is used as the preset analysis model.

The steps in the generation method of the above preset analysis model can be changed according to actual needs, and some steps can be omitted. The generating method can be completed online or offline.

According to the fire-fighting situation information of the place to be assessed, the fire-fighting situation information is input into a preset analysis model, and the fire-fighting investment cost and fire loss data cost of the place to be assessed are output.

Before inputting the fire-fighting situation information into the preset analysis model, the method further includes: numerically processing the fire-fighting situation information of the place to be evaluated, and according to the analysis model, the The items in the fire protection situation are mapped to the interval from 0 to 1.

For example, if the number of people that can be analyzed by the analysis model is 2000, and the number of people in the place to be assessed is 500 people, it will be based on the difference between the number of people in the place to be evaluated and the number of people that can be analyzed by the analysis model. The ratio of 0.4 is used as the input data of the first analysis item of the fire protection status information to be registered in the analysis model. For another example, the number of ceiling fire sprinklers in the environmental status information that can be analyzed in the analysis model is 1000, and the number of ceiling fire sprinklers in the place to be evaluated is 100, then it will be based on the place to be evaluated The ratio of 0.1 between the number of ceiling fire sprinklers and the number of ceiling fire sprinklers that can be analyzed by the analysis model is registered in the analysis model as the input data of the second analysis item in the fire protection status information. In this way, multiple items in the fire protection status information are digitized.

Analyze the digitized fire-fighting status information using a preset analysis model, and output the fire-fighting input data required for the site to be assessed in the preset fire scenario with the least total loss And/or fire damage information. The analysis model maps the fire protection input data and the fire loss data to the interval from 0 to 1 according to a preset ratio. For example, the analysis model can analyze the fire protection input data of 30 million and the fire loss data of 50 million. The fire protection information of the place to be assessed is input into the analysis model and the result is The fire protection input data is 0.3 and the fire loss data is 0.5. After calculating by the preset ratio, the fire protection input data of the place to be assessed is 9 million, and the fire loss data is 25 million.

In one embodiment, the step S2 further includes: comparing the outputted fire fighting input data with actual fire fighting input data; if the outputted fire fighting input data is less than the actual fire fighting input data, outputting the to-be-assessed The prompt information that the actual fire input data of the site is too high; if the outputted firefighting input data is greater than the actual firefighting input data, output the prompt information that the firefighting input data of the place to be assessed is too low.

In another embodiment, the step S2 may further include that the method of providing improvement opinions based on the fire protection status information of the place to be assessed includes: if the output fire protection input data is greater than the actual fire protection input data, Output improvement opinions based on the fire-fighting status information of the place to be evaluated, wherein the method for obtaining the improvement opinions includes: searching the fire-fighting status design requirements corresponding to different items in the fire-fighting status information of the place to be evaluated in a preset query table , Wherein the preset query table records the fire-fighting status information required by the unit area under the condition of meeting the fire safety standards; judging whether the items in the fire-fighting status information of the place to be assessed meet the design requirements of the fire-fighting status ; If it does not meet the requirements, the design requirements of the fire protection situation are output as the improvement opinions.

For example, if the fire protection input data of the place to be assessed is greater than the actual fire protection input data analyzed by the preset analysis model, the internal consumption per unit area of the place to be evaluated is searched in the preset query standard. The number of anti-alarms, the number of ceiling fire-fighting sprinklers; the number of personnel in a unit area, the number of fire extinguishers and gas masks that match the number of personnel; the number of flammable products per unit area, and the number of flammable Whether the number of fire hydrants and fire extinguishers that match the product is consistent with the number of fire-fighting items that meet the safety and fire protection standards recorded in the preset query table, if not, the name and quantity of the fire-fighting items required by the site to be assessed will be output.

The above-mentioned Figure 2 describes in detail the fire protection data analysis method of the present invention. In conjunction with Figures 3-4, the functional modules of the software device that implements the fire protection data analysis method and the hardware device architecture that implements the fire protection data analysis method are described below. Make an introduction.

It should be understood that the embodiments are only for illustrative purposes, and are not limited by this structure in the scope of the patent application.

Example three

Figure 3 is a structural diagram of a preferred embodiment of the fire protection data analysis device of the present invention.

In some embodiments, the fire protection data analysis device 10 runs in a computer device. The computer device is connected to a plurality of user terminals through the network. The fire protection data analysis device 10 may include a plurality of functional modules composed of code segments. The code of each program segment in the fire protection data analysis device 10 can be stored in the memory of the computer device and executed by the at least one processor to realize the fire protection data analysis function.

In this embodiment, the fire protection data analysis device 10 can be divided into multiple functional modules according to the functions it performs. Referring to FIG. 3, the functional modules may include: an acquisition module 101 and an analysis module 102. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

The acquisition module 101 is used to acquire information on the fire protection status of the place to be assessed.

The fire protection status information includes the number of evacuated personnel, environmental status information, and item status News. The environmental status information may include information on fixed firefighting facilities and mobile firefighting facilities, such as: fire alarms, smoke alarms, ceiling fire sprinklers, fire detectors, indoor fire hydrants, and the number and placement of outdoor fire hydrants Location etc. The item status information includes combustible items in the place to be evaluated, such as the quantity and placement of manufacturing equipment, materials, office computers, and furniture.

In one embodiment, the method for acquiring fire protection status information of the place to be assessed may include: receiving user input, such as the number of evacuated persons in the place to be evaluated, the type and quantity of fire-fighting facilities, and the type and quantity of combustible materials, etc. .

In another embodiment, the method for acquiring fire protection information may also be by receiving multiple images of the place to be evaluated collected by multiple camera devices, and identifying the number of people in the images and the number of firefighters by the image recognition method. The type and quantity of facilities, the type and quantity of combustibles, and other fire-fighting information.

The analysis module 102 is used to input the fire protection status information into a preset analysis model, and output the fire protection input data and/ Or fire damage information.

The method for generating the preset analysis model includes the following steps:

(1) Obtain the fire-fighting status information, fire-fighting input data, and fire loss data of different places under different fire scenarios, and store the fire-fighting status information, fire-fighting input data, and fire loss data of each of the said places.

The methods for obtaining the fire protection status information, fire protection input data, and fire loss data of different places under different fire scenarios include:

a) Obtain the fire protection status information of any place, and judge whether the fire protection status of the place meets the fire safety standards according to the preset query table.

The preset query table records that in the case of compliance with fire safety standards, the unit Information on the fire protection conditions required by the product, such as the number of fire alarms in a unit area, the number of ceiling fire sprinklers; the number of people in the unit area, the number of fire extinguishers and gas masks that match the number of people; the unit area The number of flammable products inside, the number of hydrants and fire extinguishers that match the flammable products, etc.

b) If yes, calculate the fire input data of the place according to the fire protection information, and use the fire numerical simulation system to simulate the place under different fire scenarios, and calculate the said place under different fire scenarios. Fire loss data of the site.

The calculation method of the fire protection input data includes querying the environmental status information in the fire protection status information and the value of each item in the item status information, and calculating the fire protection input data of the place based on the value.

The calculation method of the fire loss data includes setting the combustible time and combustible ratio of each combustible in the place, and dividing the combustible according to the preset ratio, and each small piece after the division represents the combustible The minimum amount of loss per unit time in a fire; according to the combustible time of the combustibles, the proportion of combustibility, and the minimum amount of loss per unit time in the fire, calculate the fire loss data of the place under different fire protection information.

For example, if a machine tool is divided into 64 equal parts, the value of the machine tool is 640,000, the value of each equal part is 10,000, and the burning time of each equal part is 2 minutes. Calculate the loss of the machine tool in a fire according to the information on the fire protection situation where the machine tool is located. For example, the fire loss data of the machine tool in a place with a ceiling fire sprinkler, and the fire loss data of the machine tool in a place with only one fire extinguisher. The sequential method can calculate the fire loss data of any place.

(2) Divide the fire-fighting status information, fire-fighting input data, and fire loss data of the multiple places into a training set and a verification set.

(3) Establish an analysis model based on the neural network, and use the training set to train the parameters of the analysis model based on the neural network, where the fire protection status information in the training set As the input data of the model, fire input data and fire loss data are used as the output data of the model.

(4) Use the verification set to verify the neural network-based analysis model after training, and obtain the prediction accuracy of the model based on the verification results.

(5) Determine whether the prediction accuracy rate of the model is less than a preset threshold.

(6) If the prediction accuracy of the model is not less than the preset threshold, then the trained analysis model based on the neural network is used as the preset analysis model.

In some embodiments, the step further includes:

(7) If the prediction accuracy of the neural network-based analysis model is less than the preset threshold, adjust the structure of the neural network algorithm, and use the training set to re-adjust the adjusted neural network-based The analysis model is trained, wherein the structure of the neural network-based analysis model includes at least one of the number of convolution kernels, the number of elements in the pooling layer, and the number of elements in the fully connected layer.

(8) Use the verification set to verify the adjusted analysis model based on the neural network, until the model prediction accuracy obtained by the re-statistics is not less than the preset threshold, then the adjusted neural network-based The analysis model is used as the preset analysis model.

The steps in the generation method of the above preset analysis model can be changed according to actual needs, and some steps can be omitted. The generating method can be completed online or offline.

According to the fire-fighting situation information of the place to be assessed, the fire-fighting situation information is input into a preset analysis model, and the fire-fighting investment cost and fire loss data cost of the place to be assessed are output.

Before inputting the fire-fighting status information into the preset analysis model, the method further includes: numerically processing the fire-fighting status information of the place to be evaluated, and press Map the items in the fire protection situation to the interval from 0 to 1 according to the preset ratio.

For example, if the number of people that can be analyzed by the analysis model is 2000, and the number of people in the place to be assessed is 500 people, it will be based on the difference between the number of people in the place to be evaluated and the number of people that can be analyzed by the analysis model. The ratio of 0.4 is used as the input data of the first analysis item of the fire protection status information to be registered in the analysis model. For another example, the number of ceiling fire sprinklers in the environmental status information that can be analyzed in the analysis model is 1000, and the number of ceiling fire sprinklers in the place to be evaluated is 100, then it will be based on the place to be evaluated The ratio of 0.1 between the number of ceiling fire sprinklers and the number of ceiling fire sprinklers that can be analyzed by the analysis model is registered in the analysis model as the input data of the second analysis item in the fire protection status information. In this way, multiple items in the fire protection status information are digitized.

Analyze the digitized fire-fighting status information using a preset analysis model, and output the fire-fighting input data and/or fire loss data required for the site to be assessed in the preset fire scene with the least total loss. The analysis model maps the fire protection input data and the fire loss data to the interval from 0 to 1 according to a preset ratio. For example, the analysis model can analyze the fire protection input data of 30 million and the fire loss data of 50 million. After inputting the fire protection status information of the place to be assessed into the analysis model, the result is The fire protection input data is 0.3 and the fire loss data is 0.5. After calculating by the preset ratio, the fire protection input data of the place to be assessed is 9 million, and the fire loss data is 25 million.

In one embodiment, the analysis module 102 further includes: comparing the outputted firefighting input data with actual firefighting input data; if the outputted firefighting input data is less than the actual firefighting input data, outputting the The prompt information that the actual fire input data of the place to be assessed is too high; if the outputted fire fighting input data is greater than the actual fire fighting input data, output the prompt information that the fire input data of the place to be evaluated is too low.

In another embodiment, the analysis module 102 may further include, according to the The method of providing improvement opinions on the fire-fighting status information of the place to be assessed includes: if the outputted fire-fighting input data is greater than the actual fire-fighting input data, outputting improvement opinions based on the fire-fighting situation information of the place to be evaluated, where the improvement opinions are The obtaining method includes: searching for the design requirements of the fire protection status corresponding to different items in the fire protection status information of the place to be assessed in the preset query table, wherein the preset query table records that the condition of meeting the fire safety standard is recorded in the preset query table. Information on the fire protection status required by the unit area; determine whether the items in the fire protection status information of the place to be assessed meet the design requirements of the fire protection status; if not, the design requirements for the fire protection status will be output as the improvement opinions .

For example, if the fire protection input data of the place to be assessed is greater than the actual fire protection input data analyzed by the preset analysis model, the number of fire alarms per unit area of the place to be evaluated, The number of ceiling fire sprinklers; the number of persons per unit area, the number of fire extinguishers and gas masks matching the number of persons; the number of flammable products per unit area, the fire hydrants matching the number of flammable products, Whether the number of fire extinguishers is consistent with the number of fire-fighting articles that meet the safety and fire-fighting standards recorded in the preset query table, if not, the name and number of fire-fighting articles required by the place to be assessed are output.

Example four

Fig. 4 is a schematic diagram of a preferred embodiment of the computer device of the present invention.

The computer device 1 includes a memory 20, a processor 30, and a computer program 40 stored in the memory 20 and running on the processor 30, such as a fire protection data analysis program. When the processor 30 executes the computer program 40, the steps in the embodiment of the fire protection data analysis method are implemented, for example, the steps S1 to S2 shown in FIG. 2. Alternatively, when the processor 30 executes the computer program 40, the functions of the modules/units in the above-mentioned embodiment of the fire protection data analysis device are realized, such as the units 101-102 in FIG. 3.

Exemplarily, the computer program 40 may be divided into one or more modules/units, The one or more modules/units are stored in the memory 20 and executed by the processor 30 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of completing a specific function, and the instruction segments are used to describe the execution process of the computer program 40 in the computer device 1. For example, the computer program 40 can be divided into the acquisition module 101 and the analysis module 102 in FIG. 3.

The computer device 1 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. Those skilled in the art can understand that the schematic diagram is only an example of the computer device 1, and does not constitute a limitation on the computer device 1. Components, for example, the computer device 1 may also include input and output devices, network access devices, bus bars, and the like.

The so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), and dedicated integrated circuits (Application Specific Integrated Circuit, ASIC). , Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 30 can also be any conventional processor, etc. The processor 30 is the control center of the computer device 1 and connects the entire computer device 1 with various interfaces and lines. Various parts.

The memory 20 can be used to store the computer programs 40 and/or modules/units, and the processor 30 runs or executes the computer programs and/or modules/units stored in the memory 20, And call the data stored in the memory 20 to realize various functions of the computer device 1. The memory 20 may mainly include a storage program area and a storage data area. The storage program area may store an operating system, an application program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; The area can store data (such as audio data, phone book, etc.) created based on the use of the computer device 1. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, and a Smart Media Card (SMC). Secure Digital (SD) card, flash memory card (Flash Card), at least one magnetic disk memory device, flash memory device, or other volatile solid-state memory device.

If the integrated module/unit of the computer device 1 is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by computer programs instructing related hardware, and the computer programs can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the foregoing method embodiments. Wherein, the computer program includes computer program code, and the computer program code may be in the form of original program code, object code, executable file, or some intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only) Only Memory), Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunications signal, and software distribution media. It should be noted that the content contained in the computer-readable medium can be appropriately added or deleted according to the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, the computer-readable medium Does not include electrical carrier signals and telecommunication signals.

In the several embodiments provided by the present invention, it should be understood that the disclosed computer device and method can be implemented in other ways. For example, the embodiments of the computer device described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation.

In addition, the functional units in the various embodiments of the present invention may be integrated in the same processing unit, or each unit may exist alone physically, or two or more units may be integrated in the same unit. The above-mentioned integrated unit can be realized either in the form of hardware, or in the form of hardware plus software functional modules.

For those skilled in the art, it is obvious that the present invention is not limited to the above exemplary embodiments The details of the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the scope of the appended patent application rather than the above description, so it is intended to fall within the application. The meaning of the equivalent elements of the patent scope and all changes within the scope are included in the present invention. Any reference signs in the scope of the patent application should not be regarded as limiting the scope of the patent application involved. In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or computer devices stated in the scope of the computer device patent application can also be implemented by the same unit or computer device by software or hardware. Words such as first and second are used to denote names, but do not denote any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements are made without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

A fire-fighting data analysis method, the method includes: obtaining fire-fighting status information of a place to be assessed, the items in the fire-fighting status information include environmental status information, item status information, and the number of personnel; Condition information, fire protection input data, and fire loss data are trained on the neural network-based analysis model to generate a preset analysis model, wherein the calculation method of the fire input data includes: querying the environment in the fire protection status information The value of each item in the status information and the item status information, and calculate the fire protection input data of the place based on the value; input the fire protection situation information into the preset analysis model, and output the place to be evaluated in the forecast In the fire scenario, the fire protection input data and/or fire loss data required when the total loss is the least. The fire protection data analysis method according to claim 1, wherein the method further comprises: comparing the output fire protection input data with the actual fire protection input data; if the output fire protection input data is less than the actual fire protection input data , Then output the prompt information that the actual fire input data of the place to be evaluated is too high; if the output fire fighting input data is greater than the actual fire input data, output the prompt that the fire input data of the place to be evaluated is too low News. The fire protection data analysis method according to claim 2, wherein the method further includes: if the output fire protection input data is greater than the actual fire protection input data, outputting improvement opinions based on the fire protection status information of the place to be assessed, The method for obtaining the improvement opinions includes: searching the fire protection status design requirements corresponding to different items in the fire protection status information of the place to be evaluated in the preset query table, wherein the preset query table records the fire protection status In the case of safety standards, the required fire protection information per unit area; Determine whether the items in the fire protection status information of the place to be assessed meet the design requirements of the fire protection status; if not, output the design requirements of the fire protection status as the improvement opinions. The fire protection data analysis method according to claim 1, wherein the generation method of the preset analysis model includes: obtaining fire protection status information, fire protection input data, and fire loss data of different places in different fire scenarios, and 1. Corresponding storage of fire protection status information, fire protection input data, and fire loss data of the place; divide the fire protection status information, fire input data, and fire loss data of the multiple places into a training set and a verification set; establish a neural network And use the training set to train the parameters of the neural network-based analysis model, where the fire-fighting status information in the training set is used as the input data of the model, the fire-fighting input data, and the fire loss data As the output data of the model; use the verification set to verify the trained neural network-based analysis model, and obtain the prediction accuracy of the model based on the verification results; determine whether the prediction accuracy of the model is Less than a preset threshold; if the model prediction accuracy rate is not less than the preset threshold, the trained analysis model based on the neural network is used as the preset analysis model. The fire protection data analysis method of claim 4, wherein the method further includes: if the prediction accuracy of the neural network-based analysis model is less than the preset threshold, adjusting the structure of the neural network algorithm , And use the training set to retrain the adjusted neural network-based analysis model, where the structure of the neural network-based analysis model includes the number of convolution kernels, the number of elements in the pooling layer, At least one of the number of elements in the fully connected layer; using the verification set to verify the adjusted neural network-based analysis model, and based on the verification Calculate the prediction accuracy of the adjusted neural network-based analysis model after re-statistics, and determine whether the adjusted prediction accuracy of the neural network-based analysis model is less than the preset threshold; if the re-statistical model is obtained If the prediction accuracy rate is not less than the preset threshold value, the adjusted analysis model based on neural network is used as the preset analysis model; and if the prediction accuracy rate of the model obtained by the re-statistics is less than the preset threshold value , Repeat the above adjustment and training steps until the model prediction accuracy rate verified by the verification set is not less than the preset threshold. The method for analyzing fire protection data according to claim 4, wherein the method for obtaining fire protection information, fire protection input data, and fire loss data of different places in different fire scenarios includes: obtaining fire protection information of any place, according to The preset query table judges whether the fire protection status of the place meets the fire safety standards; if it does, the fire input data of the place is calculated based on the fire protection situation information, and the place is evaluated by the fire numerical simulation system Simulation under different fire scenes, and calculation of fire loss data of the place under the different fire scenes. The fire protection data analysis method according to claim 6, wherein the fire numerical simulation system is used to simulate the site under different fire scenarios, and calculate the fire loss data of the site under the different fire scenarios The method includes: setting the combustible time and combustible ratio of each combustible in the place, and dividing the combustible according to the preset ratio, and each small piece after the division indicates that the combustible is within a unit of time in the fire The minimum amount of loss; based on the combustible time of the combustibles, the combustible ratio, and the minimum amount of loss per unit time in the fire, calculate the fire loss data of the place under different fire protection information. A fire protection data analysis device, the device includes: The acquisition module is used to obtain the fire protection status information of the place to be assessed. The items in the fire protection status information include environmental status information, item status information, and the number of personnel; the analysis module is used to perform different fire scenarios according to different places. The fire-fighting status information, fire-fighting input data, and fire-loss data are trained on the neural network-based analysis model to generate a preset analysis model. Among them, by querying the environmental status information and the item status information in the fire protection status information The value of each item is calculated based on the value of the fire input data of the place; the analysis module is also used to input the fire protection information into the preset analysis model, and output the place to be evaluated in the forecast In the fire scenario, the fire protection input data and/or fire loss data required when the total loss is the least. A computer device, the computer device includes a processor, and the processor is used to implement the fire protection data analysis method as described in any one of Claims 1 to 7 when the processor is used to execute a computer program stored in a memory. A computer storage medium has a computer program stored thereon, wherein the computer program is executed by a processor to realize the fire protection data analysis method as described in any one of claim items 1 to 7.

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