CN113034833B - Artificial intelligence information management system based on multisensor fuses - Google Patents
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Abstract
The invention discloses an artificial intelligence information management system based on multi-sensor fusion, which comprises a fire-fighting acquisition and positioning terminal, a mobile terminal acquisition module, a data fusion processing module, a fire behavior simulation analysis module, a spreading trend prediction module and a fire behavior tracking and calibrating module. According to the artificial intelligence information management system based on multi-sensor fusion, the collected fire parameter information is subjected to data analysis and fusion processing to analyze the fire burning danger level in the detection area where each fire-fighting detection unit is located, the emergency danger coefficient of escape of people in the detection area is judged by combining the number of mobile terminals in each detection area, the data collected by the multi-sensors are fused and displayed, the artificial intelligence information management system has the characteristic of intelligent information management, a reliable escape route is provided for guiding people to escape in the later period, and the safety of people is improved.
Description
Technical Field
The invention belongs to the field of multi-sensor fusion, and relates to an artificial intelligence information management system based on multi-sensor fusion.
Background
Along with the emergence of fire incident causes a large amount of casualties and loss of property, fire control safety is the problem that people relatively paid attention to always, in particular to places such as schools, markets, forests that cause fires easily, in case the conflagration takes place will cause casualties and loss of property, current fire detection generally only gathers the on-spot smog of fire, temperature isoparametric, if detect smog or temperature when unusual, send out fire alarm and remind, can cause all people in place to flee simultaneously, increased the crowd at the mouth of escaping, very easily caused the personnel to trample the incident, increased casualties quantity.
And the problems in the existing fire detection technology are as follows: the accurate position of the fire disaster can not be positioned and acquired, so that all people can escape at the same time, and the possibility of casualty is increased; the fire data of a fire scene cannot be analyzed to judge the fire burning danger degree, and the escape emergency danger degree of personnel at the position cannot be analyzed according to the personnel distribution condition and the fire burning danger degree at each position; the prediction time of the fire reaching the position where the fire does not occur cannot be predicted according to the fire spreading degree, intelligent management in the fire monitoring process is lacked, and further, the optimal guide route, escape sequence and the like cannot be provided for escape of each person.
Disclosure of Invention
The invention aims to provide an artificial intelligence information management system based on multi-sensor fusion, which solves the problems in the prior art.
The purpose of the invention can be realized by the following technical scheme:
an artificial intelligence information management system based on multi-sensor fusion comprises a fire-fighting acquisition and positioning terminal, a mobile terminal acquisition module, a data fusion processing module, a fire behavior simulation analysis module, a spread trend prediction module and a fire behavior tracking and calibrating module.
The fire-fighting acquisition and positioning terminal comprises a plurality of fire-fighting detection units, the fire-fighting detection units are respectively distributed in each detection area, and the fire-fighting detection units are used for detecting fire parameter information in the detection areas and analyzing the detected fire parameter information to obtain the position information of the fire;
the mobile terminal acquisition module is used for acquiring the number of mobile terminals in each detection area and the corresponding positions of the mobile terminals, and sending the acquired number of the mobile terminals and the corresponding positions of the mobile terminals to the data fusion processing module;
the data fusion processing module is used for extracting fire parameter information sent by the fire-fighting acquisition and positioning terminal and analyzing the obtained fire position information, extracting temperature, smoke concentration and fire image information in the fire parameter information and carrying out fusion analysis to obtain the fire burning danger level corresponding to the fire-fighting detection unit, and receives the number of mobile terminals and the corresponding positions of the mobile terminals sent by the mobile terminal acquisition module, analyzing the position corresponding to each mobile terminal and the position of each fire-fighting detection unit to obtain the number of the mobile terminals distributed in the detection area of each fire-fighting detection unit, and analyzes the number of the mobile terminals in the fire-fighting detection unit and the fire burning danger coefficient and the fire burning danger level corresponding to the fire-fighting detection unit, acquiring escape emergency danger coefficients corresponding to all mobile terminals in a detection area where the fire detection unit is located;
The fire behavior simulation analysis module is used for extracting fire behavior occupation ratios corresponding to fire parameter information in detection areas where the fire detection units are located, analyzing and comparing the fire behavior occupation ratios in adjacent interval time periods, simulating fire behavior increasing speed in the areas where the same fire detection unit is located, sequentially acquiring initial time points of fire parameter information appearing in the detection areas where the fire detection units are located, extracting position distribution corresponding to the fire detection units, and comprehensively separating fire behavior spreading promotion influence coefficients of fires in the fire detection units where the fire detection units are located and fires in the detection areas where other adjacent fire detection units are located;
the fire behavior simulation analysis module is used for analyzing the fire behavior of the fire detection units in the detection area, predicting the predicted time length of the fire in the detection area by adopting a transmission propagation time length calculation formula, and sending the predicted time length of the fire in the detection area to the fire tracking calibration module;
The fire tracking and calibrating module extracts the predicted time length of the fire in the detection area, judges whether fire parameter information of the detection area is detected by the fire extinguishing acquisition and positioning terminal in the predicted time length, extracts the actual time length of the fire if the fire parameter information is not detected, and dynamically adjusts the secondary predicted time length of the fire in the detection area where other adjacent fire fighting detection units are located to the fire in the detection area according to the error between the actual time length and the predicted time length.
Furthermore, the fire-fighting detection unit comprises a temperature sensor, a smoke concentration sensor, a fire image acquisition module, an image feature processing module and a fire calibration positioning module;
the temperature sensor detects the temperature in the detection area;
the smoke concentration sensor detects the smoke concentration in the detection area;
the fire disaster image acquisition module is used for acquiring fire disaster image information in a detection area and sending the acquired fire disaster image information to the image characteristic processing module, the image characteristic processing module is used for extracting characteristics of the acquired image information and comparing a plurality of characteristics in the extracted image with fire disaster characteristics in the existing acquired image so as to judge whether a fire source exists in the acquired image, and if the fire source exists, a fire disaster calibration instruction is sent to the fire disaster calibration positioning module;
The fire calibration and positioning module extracts the serial number of the fire detection unit with the fire source by receiving the fire calibration instruction, and screens out the position corresponding to the fire detection unit through the serial number of the fire detection unit.
Further, the fusion analysis of the data fusion processing module specifically includes the following steps:
step one, extracting fire image information in a detection area where a fire detection unit is located, and screening out a fire coverage ratio and a fire coverage ratio in the fire image information
S0For the total area of the acquired image, S' is the area covered by the fire source in the acquired image, S0＞S′≥0；
Substituting the temperature, the smoke concentration and the fire covering ratio beta in the detection area of the fire prevention detection unit into a fire combustion fusion model to perform primary fusion analysis on fire data to obtain a fire combustion danger coefficient Q, wherein Q is more than or equal to lambda 1 and less than or equal to lambda 6;
step three, judging the fire burning risk coefficient Q after the fusion analysis in the step two, and determining the fire burning risk grade K, wherein K belongs to W1, W2, W3, W4 and W5;
and step four, extracting the quantity of the mobile terminals in the fire detection unit and the fire combustion danger coefficient and the fire combustion danger grade corresponding to the fire detection unit, substituting the quantity of the mobile terminals into an escape emergency danger fusion model so as to perform secondary fusion analysis on the escape emergency danger degree and obtain the escape emergency danger coefficient.
Further, the escape emergency danger fusion model is
Ei represents an emergency escape danger coefficient corresponding to personnel in a detection area where the ith fire detection unit is located, the larger the emergency escape danger coefficient is, the larger the emergency escape degree of the personnel in the detection area is, and valiThe number of mobile terminals in the detection area of the ith fire-fighting detection unit, valmaxAnd valminRespectively being the largest in the detection area of each fire-fighting detection unitNumber of mobile terminals and minimum number of mobile terminals, QiIs the fire combustion danger coefficient in the detection area of the ith fire-fighting detection unit,
the average fire combustion risk in the fire combustion risk class K corresponding to the fire combustion risk coefficient,
QKmaxand QKminRespectively expressed as the maximum fire combustion risk coefficient and the minimum fire combustion risk coefficient in the fire combustion risk level K corresponding to the fire combustion risk coefficient.
Further, when a fire disaster occurs in the area where the at least one fire protection detection unit is located, the fire behavior simulation analysis module analyzes the influence relation of the fire disaster occurring in other fire protection detection units on the impending fire disaster in the detection area where the fire protection detection unit which does not generate the fire disaster is located, and the specific analysis comprises the following steps:
Step one, extracting the position of each fire-fighting detection unit, and screening other fire-fighting detection units adjacent to the fire-fighting detection unit to be detected;
sequentially extracting the fire increasing speed, the fire covering and occupying ratio and the initial time point of fire parameter information in the detection area of each fire detection unit adjacent to the detection area of the fire detection unit to be detected;
step three, calculating the correlation ratio value between the detection area of the fire-fighting detection unit to be detected and the detection area of each adjacent fire-fighting detection unit
m is the number of the fire-fighting detection units adjacent to the fire-fighting detection units to be detected;
and step four, establishing a fire spread training model by training that the fire in the detection area of the fire detection unit is influenced by the fire spread in the detection area of the adjacent fire detection unit, and obtaining a fire spread promotion influence coefficient of the fire in the detection area of the fire detection unit to be detected influenced by the fire in the detection areas of other adjacent fire detection units.
Further, the established fire spread training model is as follows:
for detecting the fire in the detection area of the fire detection unit to be detected, the fire spreading promotion influence coefficient beta of the fire in the detection area of other adjacent fire detection units jExpressed as the fire covering proportion in the jth adjacent fire detection unit, and e is expressed as a natural number, 2.71828 is taken,
indicated as the j-th adjacent fire detection unit at the initial time point of the fire occurrence from the fire detection unit
The corresponding average fire increasing speed in the T time point,
expressed as the initial time point, T, of the jth adjacent fire detection unit in the event of a firePreset ofExpressed as the preset duration of adjacent interval periods.
Further, the propagation trend prediction module adopts a calculation formula of transmission propagation duration as
T phi is the time length for predicting the fire in the detection area where other fire detection units adjacent to the fire detection unit to be detected are located to be transmitted to the detection area where the fire detection unit to be detected is located,
in order that the fire in the detection area where the fire detection unit to be detected is influenced by the fire spread promotion influence coefficient of the fire in the detection area where other adjacent fire detection units are located,
is the average distance V from the position of other adjacent fire-fighting detection units adjacent to the fire-fighting detection unit to be detected to the position of the fire-fighting detection unit to be detectedm maxThe maximum fire increasing speed in the detection environment of the m adjacent fire detection units is obtained.
Furthermore, the artificial intelligence information management system also comprises an intelligent management service platform and an evacuation rescue guide module;
the intelligent management service platform is used for extracting the fire combustion danger level in the detection area where each fire detection unit is located and the emergency danger coefficient for escaping corresponding to each mobile terminal in the detection area, which are obtained by analysis of the data fusion processing module, judging whether the fire combustion level is greater than the fire combustion level corresponding to the preset equipment protection, if the fire combustion level is greater than the fire combustion level corresponding to the preset equipment protection, the intelligent management service platform sends a power supply cut-off instruction to the power supply equipment so as to protect the safety of the equipment, and simultaneously extracts the secondary predicted time length of the fire in the detection area where each fire detection unit is located after calibration of the fire tracking calibration module, and the intelligent management service platform analyzes the escape priority sequence of personnel in the detection area where each fire detection unit is located through the emergency danger coefficient for escaping in the detection area where each fire detection unit is located and the secondary predicted time length of the fire in the detection area, the escape priority sequence of the personnel in the detection area where each fire detection unit is located is sent to an evacuation rescue guide module;
The evacuation rescue guide module is used for receiving escape priority orders of people in the detection area where the fire detection units are located and sent by the intelligent management service platform, screening the nearest safety exits of the detection area where the fire detection units are located, and sequentially guiding the people in the detection area with high escape priority to the nearest safety exits according to the escape priority orders.
The invention has the beneficial effects that:
the artificial intelligence information management system based on the multi-sensor fusion can accurately acquire the position of fire occurrence by analyzing the collected fire parameter information.
According to the invention, the data fusion processing module is used for carrying out fusion analysis on the fire parameter information and the fire position information, so that the fire burning danger coefficient in the detection area where each fire detection unit is located can be obtained, the fire burning danger level is determined through the fire burning danger coefficient, the fire danger degree can be quantitatively displayed, meanwhile, the escape emergency danger coefficient of personnel in the detection area where each fire detection unit is located is comprehensively analyzed by combining the number of the mobile terminals in the detection area where each fire detection unit is located, the escape emergency danger degree of the personnel in the detection area is displayed in a standardized manner, the fusion processing of the data collected by the multiple sensors is realized, and the intelligent information management of the data collected by the multiple sensors is greatly improved.
According to the invention, by extracting the position distribution among the fire detection units, combining the fire increasing speed, the fire coverage ratio and the initial time point of occurrence of fire parameter information in the detection areas of other fire detection units adjacent to the detection area of the fire detection unit to be detected, the fire in the detection area of other adjacent fire detection units is transmitted to the detection area of the fire detection unit to be detected, so that the fire spread promotion influence coefficient of the fire in the detection area of the fire detection unit to be detected is triggered, and the time length of the fire in the detection area to be detected is predicted through the fire spread promotion influence coefficient.
According to the invention, the personnel in the detection area of the fire detection unit with high escape priority order are guided to escape preferentially according to the escape priority order of the personnel in the detection area of each fire detection unit, so that the escape route of the personnel can be reasonably guided, the best and safest route can be provided for the personnel in the fire area, the casualties can be avoided, the casualties number can be reduced, the escape route can be predicted and formulated, and the fire safety can be improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the distribution of fire detection units according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be fully described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
an artificial intelligence information management system based on multi-sensor fusion comprises a fire-fighting acquisition and positioning terminal, a mobile terminal acquisition module, a data fusion processing module, a fire behavior simulation analysis module, a spread trend prediction module and a fire behavior tracking and calibrating module.
The fire-fighting acquisition and positioning terminal comprises a plurality of fire-fighting detection units which are respectively distributed in each detection area, the fire-fighting detection units are used for detecting fire parameter information in the detection areas and analyzing the detected fire parameter information to obtain the position information of fire, wherein the fire parameter information comprises environment temperature, smoke concentration, fire image information and the like, the positions where the fire-fighting detection units are installed are distributed according to the shape and the area of the fire-fighting monitoring field in a preset sequence, each fire-fighting detection unit corresponds to different position numbers, the position corresponding to each position number is fixed, the detection areas corresponding to the fire-fighting detection units form the fire-fighting monitoring field, the fire-fighting monitoring field is divided, the fire-fighting monitoring field is conveniently and fully observed and monitored, and the fire monitoring sufficiency of the fire-fighting monitoring field is improved, the fire monitoring site can be a school and other places.
The fire control detecting unit comprises a temperature sensor, a smoke concentration sensor, a fire image acquisition module, an image characteristic processing module and a fire calibration and positioning module, wherein the temperature sensor detects the temperature in the detection area, and the smoke concentration sensor detects the smoke concentration in the detection area.
The fire disaster image acquisition module is used for acquiring fire disaster image information in a detection area, and transmitting the acquired fire disaster image information to the image feature processing module, the image feature processing module is used for extracting features of the acquired image information, and comparing a plurality of features in the extracted image with fire disaster features in the existing acquired image so as to judge whether a fire source exists in the acquired image, if so, a fire disaster calibration instruction is transmitted to the fire disaster calibration and positioning module, when the plurality of features in the extracted image are matched with the fire disaster features in the existing acquired image, the fire disaster exists in the acquired image, and when the extracted image does not have the fire disaster features, the fire disaster does not exist in the acquired image; the fire calibration and positioning module extracts the serial number of the fire detection unit with the fire source by receiving the fire calibration instruction, and screens out the position corresponding to the fire detection unit by the serial number of the fire detection unit to realize the positioning of the position where the fire occurs;
Through the operations of fire image acquisition, image characteristic processing and the like, whether a fire disaster occurs in the acquired image information can be accurately judged, and the screened image information with the fire disaster is positioned, so that the accurate judgment of the position of the fire disaster is achieved.
The mobile terminal acquisition module is used for acquiring the number of the mobile terminals in each detection area and the positions corresponding to the mobile terminals, and sending the acquired number of the mobile terminals and the positions corresponding to the mobile terminals to the data fusion processing module.
The data fusion processing module is used for extracting fire parameter information sent by the fire-fighting acquisition and positioning terminal and analyzing the obtained fire position information, extracting temperature, smoke concentration and fire image information in the fire parameter information, carrying out fusion analysis on the temperature, smoke concentration and fire image information to obtain the fire burning danger level corresponding to the fire-fighting detection unit, receiving the number of mobile terminals sent by the mobile terminal acquisition module and the position corresponding to each mobile terminal, analyzing the position corresponding to each mobile terminal and the position of each fire-fighting detection unit, analyzing the number of mobile terminals distributed in the detection area of each fire-fighting detection unit, and carrying out fusion analysis on the number of mobile terminals in the fire-fighting detection unit, the fire burning danger coefficient and the fire burning danger level corresponding to the fire-fighting detection unit, so as to obtain the emergency escape danger coefficients corresponding to the mobile terminals in the detection area where the fire-fighting detection unit is located.
The fire combustion danger levels are respectively W1, W2, W3, W4 and W5 according to the high and low sequence of the danger levels, the fire combustion danger coefficient ranges corresponding to the fire combustion danger levels are respectively lambda 1-lambda 2, lambda 2-lambda 3, lambda 3-lambda 4, lambda 4-lambda 5, lambda 5-lambda 6, lambda 1 is larger than lambda 2 and smaller than lambda 3 and smaller than lambda 4 and smaller than lambda 5 and lambda 6, the fire combustion danger levels and the fire combustion danger coefficients are respectively correlated with emergency escape danger coefficients, namely the higher the fire combustion danger level is, the higher the emergency escape danger coefficient of the fire detection unit is, and the emergency escape danger coefficient is correlated with the number of mobile terminals in the detection area.
The fusion analysis of the data fusion processing module specifically comprises the following steps:
step one, extracting fire image information in a detection area where a fire detection unit is located, and screening out a fire coverage ratio and a fire coverage ratio in the fire image information
S0For the total area of the acquired image, S' is the area covered by the fire source in the acquired image, S0＞S′≥0；
Substituting the temperature, the smoke concentration and the fire covering ratio beta in the detection area of the fire prevention detection unit into a fire combustion fusion model to perform primary fusion analysis on fire data to obtain a fire combustion danger coefficient Q, wherein Q is more than or equal to lambda 1 and less than or equal to lambda 6, and the fire combustion fusion model has the formula 
T is the temperature detected, TDisaster thresholdAn upper threshold temperature for the fire set for the fire environment, N being the detected smoke concentration, NmaxThe maximum smoke concentration value in the fire environment;
step three, judging the fire burning risk coefficient Q after the fusion analysis in the step two, and determining the fire burning risk grade K, wherein K belongs to W1, W2, W3, W4 and W5;
step four, extracting the quantity of the mobile terminals in the fire detection unit and the fire combustion danger coefficient and the fire combustion danger grade corresponding to the fire detection unit, substituting the quantity of the mobile terminals in the fire detection unit and the fire combustion danger grade into an escape emergency danger fusion model to perform secondary fusion analysis on the escape emergency danger degree to obtain an escape emergency danger coefficient, wherein the escape emergency danger fusion model is
Ei represents an emergency escape danger coefficient corresponding to personnel in a detection area where the ith fire detection unit is located, the larger the emergency escape danger coefficient is, the larger the emergency escape degree of the personnel in the detection area is, and valiThe number of mobile terminals in the detection area of the ith fire-fighting detection unit, valmaxAnd valminThe maximum mobile terminal quantity and the minimum mobile terminal quantity, Q, in the detection area of each fire-fighting detection unitiIs the fire combustion danger coefficient in the detection area of the ith fire-fighting detection unit, 
The average fire combustion risk in the fire combustion risk class K corresponding to the fire combustion risk coefficient,
QKmaxand QKminRespectively expressed as a maximum fire combustion risk coefficient and a minimum fire combustion risk coefficient in the fire combustion risk level K corresponding to the fire combustion risk coefficient.
The data collected by the multiple sensors can be subjected to fusion analysis through a data fusion processing technology, so that the association degree between the data information collected by each sensor and the fire burning danger degree is comprehensively represented, and the number of the mobile terminals (the number of people) in the detection area and the fire burning danger degree are subjected to secondary fusion simultaneously, so that the intelligent information management of the data collected by the multiple sensors is greatly improved.
The fire simulation analysis module is used for extracting the fire covering and occupying ratio corresponding to the fire parameter information in the detection area where each fire detection unit is located, analyzing and comparing the fire covering and occupying ratios in adjacent interval time periods, and simulating the fire increasing speed in the area where the same fire detection unit is located
Vβi is the fire increasing speed of the ith fire-fighting detection unit in the detection area from the tth time period to the t +1 th time period, beta t+1And betatThe ratio of the fire covering in the T +1 th time period and the T th time period is respectively, and the duration of the time period is TPreset ofAnd sequentially acquiring initial time points of fire parameter information appearing in the detection areas of the fire detection units, and extracting the position distribution corresponding to the fire detection units, analyzing the fire occurrence of each fire detection unit by the position distribution of each fire detection unit, the fire increasing speed, the fire occupancy ratio and the initial time points of the fire parameter information appearing in the detection areas of the fire detection units to obtain the fire spread promotion influence coefficient (meaning of the fire spread promotion influence coefficient, namely the promotion influence degree of the fire in the detection areas of other adjacent fire detection units on the fire occurrence in the detection areas of one fire detection unit if the flammability degree in the detection areas of the fire detection units is the same, and simplifying the simulation of whether the detection areas of the fire detection units are affected by the fire in the detection areas of other adjacent fire detection units Influence coefficient of disaster), and the fire behavior increasing speed of the fire detection unit in the detection area and the fire detection unit The detection area where the unit is located is sent to the spreading trend prediction module by the fire spreading promotion influence coefficient of the fire in the detection area where other adjacent fire detection units are located.
If one of the fire-fighting detection units is in the detection area, the time point of initially detecting that the smoke concentration reaches a preset smoke concentration threshold value or fire information exists in the collected image information is used as the initial time point of fire parameter information in the detection area where the fire-fighting detection unit is located.
As shown in fig. 1, when a fire occurs in an area where at least one fire detection unit is located, the fire simulation analysis module analyzes the influence relationship of the fire occurring in other fire detection units on the impending fire in the detection area where the fire detection unit not having the fire occurs, and the specific analysis includes the following steps:
step one, extracting the position of each fire-fighting detection unit, and screening other fire-fighting detection units adjacent to the fire-fighting detection unit to be detected;
sequentially extracting the fire increasing speed, the fire covering and occupying ratio and the initial time point of fire parameter information in the detection area of each fire detection unit adjacent to the detection area of the fire detection unit to be detected;
Step three, calculating the correlation ratio value between the detection area of the fire-fighting detection unit to be detected and the detection area of each adjacent fire-fighting detection unit
m is the number of the fire detection units adjacent to the fire detection unit to be detected;
step four, obtaining a fire spread training model by training that the fire in the detection area of the previous fire detection unit is influenced by the fire spread in the detection area of the adjacent fire detection unit
For the fire in the detection area of the fire detection unit to be detected to be promoted by the fire spread of the fire in the detection area of other adjacent fire detection unitsCoefficient of influence, βjExpressed as the fire occupancy in the jth adjacent fire detection unit, e is expressed as a natural number, 2.71828 is taken,
indicated as the j-th adjacent fire detection unit at the initial time point of the fire occurrence from the fire detection unit
The corresponding average fire increasing speed in the T time point,
expressed as the initial time point, T, of the jth adjacent fire detection unit in the event of a firePreset ofExpressed as the preset duration of adjacent interval periods.
By extracting the position distribution among the fire-fighting detection units and sequentially screening the fire increasing speed, the fire coverage ratio and the initial time point of fire parameter information in the detection areas of other fire-fighting detection units adjacent to the detection area of the fire-fighting detection unit to be detected, can analyze the fire spread promotion influence coefficient of whether the fire disaster occurs in the detection area of the adjacent other fire detection units to the detection area of the fire detection unit to be detected, and the influence degree of the fire in the detection area of the fire detection unit without the fire to the fire in the detection area of other adjacent fire detection units can be visually displayed through the fire spread promotion influence coefficient, that is, the larger the fire spread promotion influence coefficient is, the greater the ease with which a fire is caused by the influence of a fire in a detection area in which a fire detection unit that does not have a fire is located by a fire in a detection area in which another adjacent fire detection unit is located.
The fire behavior simulation analysis module is used for analyzing the fire behavior of the fire detection units in the detection area, predicting the predicted time length of the fire in the detection area by adopting a transmission spreading time length calculation formula, and sending the predicted time length of the fire in the detection area to the fire tracking calibration module.
The transmission spreading time calculation formula is used for non-accurately and comprehensively evaluating the time from a fire transmission fire source in a detection area where other adjacent fire detection units are located to the detection area where the fire detection unit to be detected is located, and the transmission spreading time calculation formula
T phi is the time length for predicting the fire in the detection area where other fire detection units adjacent to the fire detection unit to be detected are located to be transmitted to the detection area where the fire detection unit to be detected is located,
for the fire in the detection area of waiting to detect the fire detection unit to be spread by the fire of the fire in the detection area of other adjacent fire detection units to promote the influence coefficient, D is the average distance from the position of other adjacent fire detection units adjacent to waiting to detect the fire detection unit to the position of waiting to detect the fire detection unit, V m maxThe maximum fire increasing speed in the detection environment of the m adjacent fire detection units is obtained.
The fire tracking calibration module extracts the predicted time length of the fire in the detection area, judges whether fire parameter information of the detection area is detected by the fire monitoring acquisition and positioning terminal in the predicted time length, if the fire parameter information is detected in the predicted time length, the predicted time length of the fire spreading model to the fire is accurate, if the fire parameter information is not detected in the predicted time length, extracts the actual time length of the fire in the detection area to realize secondary confirmation of fire spreading, dynamically adjusts the secondary predicted time length of the fire in the detection area by the fire in the detection area of other adjacent fire detection units according to the error between the actual time length and the predicted time length, further realizes dynamic adjustment of the predicted time length, and predicts the time length transmitted by the fire in the detection area of the adjacent fire detection unit in the detection area of the fire detection unit which does not generate the fire in the subsequent prediction, reliable data is provided.
The predicted time length is the theoretical time length from the fact that a fire disaster occurs in any one fire detection unit adjacent to the detection area where the fire detection unit to be detected is located to the fact that the fire disaster occurs in the detection area where the fire detection unit to be detected is located, and the actual time length is the real time length from the fact that the fire disaster occurs in any one fire detection unit adjacent to the detection area where the fire detection unit to be detected is located to the fact that the fire disaster occurs in the detection area where the fire detection unit to be detected is located.
The accuracy of the predicted time length is dynamically adjusted through the error between the actual time length and the predicted time length in the detection area where the fire detection units are located, the interference of wind power, humidity, building materials in the detection area and other uncertain factors on whether a fire occurs in the detection area where each fire detection unit is located can be eliminated, reliable data are provided for the later period of the predicted time length of the fire in the detection area where a certain fire detection unit is located, and meanwhile, reliable accurate time is provided for personnel rescue and evacuation guidance in the detection area where each fire detection unit is located.
The system can accurately analyze the fire burning danger level and the escape emergency danger coefficient in each detection area by adopting the multi-sensor to detect, fuse and analyze the data in the fire environment and the like, can predict the fire spreading degree and carry out time prediction and calibration processing according to the fire spreading degree, has the characteristic of intelligent information management, improves the time calibration in the fire spreading process, and is convenient to provide reliable data support for rescue.
Example 2:
the artificial intelligence information management system further comprises an intelligent management service platform and an evacuation rescue guide module.
The intelligent management service platform is used for extracting the fire combustion danger level in the detection area where each fire detection unit is located and the emergency danger coefficient for escaping corresponding to each mobile terminal in the detection area, which are obtained by analysis of the data fusion processing module, judging whether the fire combustion level is greater than the fire combustion level corresponding to the preset equipment protection, if the fire combustion level is greater than the fire combustion level corresponding to the preset equipment protection, the intelligent management service platform sends a power supply cut-off instruction to the power supply equipment so as to protect the safety of the equipment, and simultaneously extracts the secondary predicted time length of the fire in the detection area where each fire detection unit is located after calibration of the fire tracking calibration module, and the intelligent management service platform analyzes the escape priority sequence of personnel in the detection area where each fire detection unit is located through the emergency danger coefficient for escaping in the detection area where each fire detection unit is located and the secondary predicted time length of the fire in the detection area, and the escape priority order of the personnel in the detection area where each fire detection unit is located is sent to an evacuation rescue guide module, and the escape priority order is judged: and dividing a plurality of prediction time length ranges and a plurality of escape emergency danger coefficient ranges, wherein in the same prediction time length range, the larger the escape emergency danger coefficient is, the more priority the corresponding escape priority is, and in the same escape emergency danger coefficient range, the smaller the prediction time length is, the more priority the corresponding escape priority is.
Evacuation rescue guide module is used for receiving the priority order of fleing of personnel in each fire control detecting element place detection area that intelligent management service platform sent, selects the nearest safety exit of each fire control detecting element place detection area distance to according to the priority order of fleing guide in proper order and flee personnel to nearest safety exit in the high detection area of priority level, the high fire control detecting element of priority level of fleing in the detection area that the personnel of fleing are fleed the deaths in the detection area that the low fire control detecting element of priority level of fleing in priority level place promptly.
This system passes through intelligent information ization management analysis, can guarantee that personnel homoenergetic in the detection area at each fire control detecting element place can flee according to the priority order of fleing in proper order, can be according to the priority order of fleing of personnel in each detection area of fire trend rational planning, and real-time personnel distribution condition guides personnel's route of fleing in real time, be convenient for provide best and safest route for personnel in the fire area, avoid personnel to escape in disorder, casualties quantity has been reduced, realize the prediction and make the route of fleing, the fire control security has been improved.
The foregoing is illustrative and explanatory only of the present invention, and it is intended that the present invention cover modifications, additions, or substitutions by those skilled in the art, without departing from the spirit of the invention or exceeding the scope of the claims.
Claims (6)
1. The utility model provides an artificial intelligence information management system based on multisensor fuses which characterized in that: the fire-fighting monitoring system comprises a fire-fighting acquisition and positioning terminal, a mobile terminal acquisition module, a data fusion processing module, a fire behavior simulation analysis module, a spread trend prediction module and a fire behavior tracking and calibrating module;
the fire-fighting acquisition and positioning terminal comprises a plurality of fire-fighting detection units, the fire-fighting detection units are respectively distributed in each detection area, and the fire-fighting detection units are used for detecting fire parameter information in the detection areas and analyzing the detected fire parameter information to obtain the position information of the fire;
the mobile terminal acquisition module is used for acquiring the number of mobile terminals in each detection area and the positions corresponding to the mobile terminals, and sending the acquired number of the mobile terminals and the positions corresponding to the mobile terminals to the data fusion processing module;
the data fusion processing module is used for extracting fire parameter information sent by the fire-fighting acquisition and positioning terminal and analyzing the obtained fire position information, extracting temperature, smoke concentration and fire image information in the fire parameter information, performing fusion analysis to obtain a fire burning danger level corresponding to the fire-fighting detection unit, and receives the number of mobile terminals and the corresponding positions of the mobile terminals sent by the mobile terminal acquisition module, analyzing the position corresponding to each mobile terminal and the position of each fire-fighting detection unit to obtain the number of the mobile terminals distributed in the detection area of each fire-fighting detection unit, and analyzes the number of the mobile terminals in the fire-fighting detection unit and the fire burning danger coefficient and the fire burning danger level corresponding to the fire-fighting detection unit, acquiring escape emergency danger coefficients corresponding to all mobile terminals in a detection area where the fire detection unit is located;
The fusion analysis of the data fusion processing module specifically comprises the following steps:
step one, extracting fire image information in a detection area where a fire detection unit is located, and screening out a fire coverage ratio and a fire coverage ratio in the fire image information
S0For the total area of the acquired image, S' is the area covered by the fire source in the acquired image, S0＞S′≥0；
Substituting the temperature, the smoke concentration and the fire covering ratio beta in the detection area of the fire prevention detection unit into a fire combustion fusion model to perform primary fusion analysis on fire data to obtain a fire combustion danger coefficient Q, wherein Q is more than or equal to lambda 1 and less than or equal to lambda 6;
step three, judging the fire burning risk coefficient Q after the fusion analysis in the step two, and determining the fire burning risk grade K, wherein K belongs to W1, W2, W3, W4 and W5;
step four, extracting the number of the mobile terminals in the fire detection unit and the fire combustion danger coefficient and the fire combustion danger grade corresponding to the fire detection unit, substituting the extracted number and the fire combustion danger grade into an escape emergency danger fusion model, and performing secondary fusion analysis on the escape emergency danger degree to obtain an escape emergency danger coefficient;
the fire combustion fusion model formula is
T is the temperature detected, TDisaster thresholdAn upper threshold temperature for the fire set for the fire environment, N being the detected smoke concentration, N maxThe maximum smoke concentration value in the fire environment;
the escape emergency danger fusion model is
Ei represents the detection area where the ith fire-fighting detection unit is locatedThe greater the escape emergency danger coefficient corresponding to the personnel in the detection area is, the greater the escape emergency danger coefficient is, the greater the escape emergency degree of the personnel in the detection area is, the valiThe number of mobile terminals in the detection area of the ith fire-fighting detection unit, valmaxAnd valminRespectively the maximum mobile terminal quantity and the minimum mobile terminal quantity in the detection area of each fire-fighting detection unit, QiThe fire combustion risk coefficient of the detection area in which the ith fire detection unit is positioned,
the average fire combustion risk in the fire combustion risk class K corresponding to the fire combustion risk coefficient,
QKmaxand QKminRespectively representing the maximum fire burning danger coefficient and the minimum fire burning danger coefficient in the fire burning danger level K corresponding to the fire burning danger coefficient;
the fire behavior simulation analysis module is used for extracting fire behavior coverage ratios corresponding to fire parameter information in detection areas where the fire detection units are located, analyzing and comparing the fire behavior coverage ratios in adjacent interval time periods, simulating fire behavior increasing speeds in the areas where the same fire detection units are located, sequentially acquiring initial time points of fire parameter information appearing in the detection areas where the fire detection units are located, extracting position distribution corresponding to the fire detection units, and comprehensively separating fire behavior spreading promotion influence coefficients of fires in the detection areas where the fire detection units are located and other adjacent fire detection units are located;
The fire behavior simulation analysis module is used for analyzing the fire behavior of the fire detection units in the detection area, predicting the predicted time length of the fire in the detection area by adopting a transmission spread time length calculation formula, and sending the predicted time length of the fire in the detection area to the fire tracking calibration module;
the fire tracking and calibrating module extracts the predicted time length of the fire in the detection area, judges whether fire parameter information of the detection area is detected by the fire extinguishing acquisition and positioning terminal in the predicted time length, extracts the actual time length of the fire if the fire parameter information is not detected, and dynamically adjusts the secondary predicted time length of the fire in the detection area where other adjacent fire fighting detection units are located to the fire in the detection area according to the error between the actual time length and the predicted time length.
2. The artificial intelligence information management system based on multi-sensor fusion of claim 1, characterized in that: the fire-fighting detection unit comprises a temperature sensor, a smoke concentration sensor, a fire image acquisition module, an image characteristic processing module and a fire calibration positioning module;
The temperature sensor detects the temperature in the detection area;
the smoke concentration sensor detects the smoke concentration in the detection area;
the fire disaster image acquisition module is used for acquiring fire disaster image information in a detection area and sending the acquired fire disaster image information to the image feature processing module, the image feature processing module is used for extracting features of the acquired image information and comparing a plurality of features in the extracted image with fire disaster features in the existing acquired image so as to judge whether a fire source exists in the acquired image, and if the fire source exists, a fire disaster calibration instruction is sent to the fire disaster calibration positioning module;
the fire calibration and positioning module extracts the serial number of the fire detection unit with the fire source by receiving the fire calibration instruction, and screens out the position corresponding to the fire detection unit through the serial number of the fire detection unit.
3. The artificial intelligence information management system based on multi-sensor fusion of claim 2, characterized in that: when a fire disaster occurs in the area where the at least one fire protection detection unit is located, the fire behavior simulation analysis module analyzes the influence relation of the fire disaster occurring in other fire protection detection units on the impending fire disaster in the detection area where the fire protection detection unit which does not generate the fire disaster is located, and the specific analysis comprises the following steps:
Step one, extracting the position of each fire-fighting detection unit, and screening other fire-fighting detection units adjacent to the fire-fighting detection unit to be detected;
step two, sequentially extracting the fire increasing speed, the fire covering duty ratio and the initial time point of fire parameter information in the detection area of each fire detection unit adjacent to the detection area of the fire detection unit to be detected;
thirdly, calculating the correlation ratio value between the detection area of the fire-fighting detection unit to be detected and the detection area of each adjacent fire-fighting detection unit
m is the number of the fire detection units adjacent to the fire detection unit to be detected;
and step four, establishing a fire spread training model by training that the fire in the detection area of the previous fire detection unit is influenced by the fire spread in the detection area of the adjacent fire detection unit, and obtaining a fire spread promotion influence coefficient of the fire in the detection area of the to-be-detected fire detection unit influenced by the fire spread of the fire in the detection areas of other adjacent fire detection units.
4. The artificial intelligence information management system based on multi-sensor fusion of claim 3, characterized in that: the established fire spread training model is as follows: 
For detecting fire in the detection area of the fire detection unit to be detected to be affected by fire in the detection area of other adjacent fire detection unitsFactor of influence, beta, promoting the spread of a firejExpressed as the fire occupancy in the jth adjacent fire detection unit, e is expressed as a natural number, 2.71828 is taken,
indicated as the j-th adjacent fire detection unit at the initial point in time when a fire occurs from the fire detection unit
The corresponding average fire increasing speed in the T time point,
expressed as the initial time point, T, of the jth adjacent fire detection unit in the event of a firePresetExpressed as the preset duration of adjacent interval periods.
5. The artificial intelligence information management system based on multi-sensor fusion as claimed in claim 4, wherein: the propagation trend prediction module adopts a transmission propagation duration calculation formula of
T phi is the time length for predicting the fire in the detection area where other fire detection units adjacent to the fire detection unit to be detected are located to be transmitted to the detection area where the fire detection unit to be detected is located,
in order that the fire in the detection area where the fire detection unit to be detected is influenced by the fire spread promotion influence coefficient of the fire in the detection area where other adjacent fire detection units are located, 
From the position of other adjacent fire-fighting detection units adjacent to the fire-fighting detection unit to the position of the fire-fighting detection unit to be detectedAverage distance of Vm maxThe maximum fire increasing speed in the detection environment of the m adjacent fire detection units is obtained.
6. The artificial intelligence information management system based on multi-sensor fusion as claimed in claim 5, wherein: the artificial intelligence information management system also comprises an intelligent management service platform and an evacuation rescue guide module;
the intelligent management service platform is used for extracting the fire combustion danger level in the detection area where each fire detection unit is located and the emergency danger coefficient for escaping corresponding to each mobile terminal in the detection area, which are obtained by analysis of the data fusion processing module, judging whether the fire combustion level is greater than the fire combustion level corresponding to the preset equipment protection, if the fire combustion level is greater than the fire combustion level corresponding to the preset equipment protection, the intelligent management service platform sends a power supply cut-off instruction to the power supply equipment so as to protect the safety of the equipment, and simultaneously extracts the secondary predicted time length of the fire in the detection area where each fire detection unit is located after calibration of the fire tracking calibration module, and the intelligent management service platform analyzes the escape priority sequence of personnel in the detection area where each fire detection unit is located through the emergency danger coefficient for escaping in the detection area where each fire detection unit is located and the secondary predicted time length of the fire in the detection area, the escape priority sequence of the personnel in the detection area of each fire detection unit is sent to an evacuation rescue guide module;
The evacuation rescue guide module is used for receiving the escape priority sequence of the personnel in the detection area where the fire detection units are located and sent by the intelligent management service platform, screening the nearest safety exits of the detection area where the fire detection units are located, and sequentially guiding the personnel in the detection area with high escape priority to the nearest safety exits according to the escape priority sequence.
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