CN117540887A - Dredging route determining method and device for intelligent fire fighting - Google Patents
Dredging route determining method and device for intelligent fire fighting Download PDFInfo
- Publication number
- CN117540887A CN117540887A CN202311382350.4A CN202311382350A CN117540887A CN 117540887 A CN117540887 A CN 117540887A CN 202311382350 A CN202311382350 A CN 202311382350A CN 117540887 A CN117540887 A CN 117540887A
- Authority
- CN
- China
- Prior art keywords
- target detection
- route
- detection area
- disaster
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000001514 detection method Methods 0.000 claims abstract description 186
- 238000013528 artificial neural network Methods 0.000 claims description 15
- 238000012216 screening Methods 0.000 claims description 10
- 238000004891 communication Methods 0.000 claims description 5
- 238000007654 immersion Methods 0.000 claims description 5
- 239000000779 smoke Substances 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 206010000369 Accident Diseases 0.000 description 6
- 238000012549 training Methods 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 238000001931 thermography Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Computer Security & Cryptography (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a dredging route determining method and device for intelligent fire fighting, wherein the method comprises the following steps: acquiring a plurality of sensing data of a plurality of modes and corresponding sensing positions in a target detection area; determining at least one disaster point and disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions; acquiring human body movement sensing information in a target detection area in real time; predicting an employee predicted escape route in the target detection area according to the human body movement sensing information and the passing rule of the target detection area; and determining the optimal escape route and a corresponding route guidance instruction of the target detection area according to the predicted escape route of staff in the target detection area and at least one disaster point and disaster type in the target detection area. Therefore, the invention can realize more accurate and efficient crowd evacuation and reduce the damage caused by fire events.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a dredging route determining method and device for intelligent fire fighting.
Background
With the increasing popularity and popularity of smart city concepts, more and more city buildings are beginning to pay attention to the technical development of intelligent fire protection, wherein how to efficiently dredge people when a fire event occurs is one of the important issues. However, in the prior art, a fixed escape route indicator lamp is generally only arranged in a building to guide a user to escape, and a better and more time-efficient escape route is not determined by combining sensing data and an intelligent algorithm. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a dredging route determining method and device for intelligent fire fighting, which can realize more accurate and efficient crowd evacuation and reduce damage caused by fire fighting events.
To solve the above technical problems, a first aspect of the present invention discloses a method for determining a dredging route for intelligent fire protection, the method comprising:
acquiring a plurality of sensing data of a plurality of modes and corresponding sensing positions in a target detection area;
determining at least one disaster point and disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions;
Acquiring human body movement sensing information in the target detection area in real time;
predicting an employee predicted escape route in the target detection area according to the human body movement sensing information and the passing rule of the target detection area;
determining an optimal escape route and a corresponding route guiding instruction of the target detection area according to an escape route predicted by staff in the target detection area and at least one disaster point and disaster type in the target detection area; the route guiding instruction is used for being sent to a plurality of guiding devices of the target detection area so as to display the optimal escape route.
As an optional implementation manner, in the first aspect of the present invention, the determining at least one disaster point and a disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions includes:
judging whether the data value of the sensing data is in the normal value interval of the sensing data of the corresponding mode for any sensing data, if so, deleting the sensing data, otherwise, reserving the sensing data;
after screening all the sensing data, obtaining a plurality of residual sensing data;
Determining corresponding sensing positions of the rest sensing data as disaster points;
and inputting each sensing data and the corresponding sensing position in the remaining sensing data into a pre-trained neural network prediction model of a corresponding mode to obtain a disaster type corresponding to the disaster point corresponding to each sensing data so as to obtain at least one disaster point and disaster type in the target detection area.
In an optional implementation manner, in a first aspect of the present invention, the predicting an escape route predicted by an employee in the target detection area according to the human body movement sensing information and a traffic rule of the target detection area includes:
acquiring a three-dimensional model corresponding to the target detection area, and determining all passable routes of the target detection area according to model parameters in the three-dimensional model;
determining a preliminary movement track of a user in the target detection area according to the human body movement sensing information;
and determining the predicted escape route of staff in the target detection area according to the similarity between the preliminary movement track of the user and all the passable routes.
In an optional implementation manner, in the first aspect of the present invention, the determining the employee predicted escape route in the target detection area according to the similarity between the user preliminary movement track and the all trafficable routes includes:
Calculating the route similarity between the user preliminary movement track and each passable route;
determining the route with highest similarity in the passable routes as a target communication route;
determining the latest position of the user according to the human body movement sensing information;
and judging whether the latest position of the user is on the target passing route, if so, generating a route from the latest position of the user to the end point of the target passing route so as to obtain an escape route predicted by staff in the target detection area.
As an optional implementation manner, in the first aspect of the present invention, the disaster type includes at least one of open fire disaster, electric leakage disaster, water immersion disaster and thick smoke disaster; the determining an optimal escape route and a corresponding route guiding instruction of the target detection area according to the predicted escape route of staff in the target detection area and at least one disaster point and disaster type in the target detection area comprises:
determining a disaster influence range corresponding to the disaster point according to the corresponding disaster type;
determining disaster influence areas corresponding to the target detection areas according to the disaster influence ranges of all the disaster points in the target detection areas;
And determining the optimal escape route and a corresponding route guidance instruction of the target detection area according to the intersection degree between the predicted escape route of the staff and the disaster area.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to an intersection degree between the predicted escape route of the employee and the disaster situation affecting area, the optimal escape route of the target detection area and a corresponding route guidance instruction includes:
calculating the proportion information of the part of the employee predicted escape route in the disaster area to the total length;
judging whether the proportion information is larger than a preset proportion threshold value or not;
if the judgment result is negative, determining the predicted escape route of the staff as the optimal escape route of the target detection area, and generating a corresponding route guidance instruction;
if the judgment result is yes, calculating all candidate escape routes corresponding to the latest position of the user according to all the passable routes of the latest position of the user in the target detection area;
calculating the proportion information of the part of each candidate escape route in the disaster affected area to the total length, screening out the candidate escape route with the lowest proportion information, determining the optimal escape route of the target detection area, and generating a corresponding route guiding instruction.
In an optional implementation manner, in a first aspect of the present invention, the route guidance instruction includes a display intensity instruction corresponding to the guidance device; the generating the corresponding route guidance instruction includes:
determining all guiding devices needing to emit light according to the optimal escape route;
generating first weight information according to a proportion difference value between the proportion information corresponding to the optimal escape route and the proportion threshold value;
generating second weight information according to the distance value of the latest position of the user from the outlet corresponding to the optimal escape route;
calculating the product of a preset display intensity reference value and the first weight information and the second weight information to obtain the display intensity instruction;
generating a route guidance instruction comprising the display intensity instruction sent to all the guidance devices needing to emit light.
The second aspect of the invention discloses a dredging route determining device for intelligent fire protection, the device comprises:
the first acquisition module is used for acquiring a plurality of sensing data of a plurality of modes and corresponding sensing positions in the target detection area;
the first determining module is used for determining at least one disaster point and disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions;
The second acquisition module is used for acquiring the human body movement sensing information in the target detection area in real time;
the prediction module is used for predicting an employee predicted escape route in the target detection area according to the human body movement sensing information and the passing rule of the target detection area;
the second determining module is used for determining an optimal escape route and a corresponding route guiding instruction of the target detection area according to the predicted escape route of staff in the target detection area and at least one disaster point and disaster type in the target detection area; the route guiding instruction is used for being sent to a plurality of guiding devices of the target detection area so as to display the optimal escape route.
As an optional implementation manner, in the second aspect of the present invention, the first determining module determines, according to the plurality of sensing data and the corresponding sensing positions, a specific manner of at least one disaster point and a disaster type in the target detection area, where the specific manner includes:
judging whether the data value of the sensing data is in the normal value interval of the sensing data of the corresponding mode for any sensing data, if so, deleting the sensing data, otherwise, reserving the sensing data;
After screening all the sensing data, obtaining a plurality of residual sensing data;
determining corresponding sensing positions of the rest sensing data as disaster points;
and inputting each sensing data and the corresponding sensing position in the remaining sensing data into a pre-trained neural network prediction model of a corresponding mode to obtain a disaster type corresponding to the disaster point corresponding to each sensing data so as to obtain at least one disaster point and disaster type in the target detection area.
In a second aspect of the present invention, the predicting module predicts a specific manner in which the employee in the target detection area predicts the escape route according to the human movement sensing information and the traffic rule of the target detection area, including:
acquiring a three-dimensional model corresponding to the target detection area, and determining all passable routes of the target detection area according to model parameters in the three-dimensional model;
determining a preliminary movement track of a user in the target detection area according to the human body movement sensing information;
and determining the predicted escape route of staff in the target detection area according to the similarity between the preliminary movement track of the user and all the passable routes.
In a second aspect of the present invention, the predicting module determines a specific manner in which the employee in the target detection area predicts the escape route according to the similarity between the user preliminary movement track and the all trafficable routes, including:
calculating the route similarity between the user preliminary movement track and each passable route;
determining the route with highest similarity in the passable routes as a target communication route;
determining the latest position of the user according to the human body movement sensing information;
and judging whether the latest position of the user is on the target passing route, if so, generating a route from the latest position of the user to the end point of the target passing route so as to obtain an escape route predicted by staff in the target detection area.
As an alternative embodiment, in the second aspect of the present invention, the disaster type includes at least one of open fire disaster, electric leakage disaster, water immersion disaster, and thick smoke disaster; the second determining module determines an optimal escape route of the target detection area and a specific mode of a corresponding route guiding instruction according to an escape route predicted by staff in the target detection area and at least one disaster point and disaster type in the target detection area, and the method comprises the following steps:
Determining a disaster influence range corresponding to the disaster point according to the corresponding disaster type;
determining disaster influence areas corresponding to the target detection areas according to the disaster influence ranges of all the disaster points in the target detection areas;
and determining the optimal escape route and a corresponding route guidance instruction of the target detection area according to the intersection degree between the predicted escape route of the staff and the disaster area.
In a second aspect of the present invention, the second determining module determines, according to the intersection degree between the predicted escape route of the employee and the disaster area, a specific manner of the optimal escape route and the corresponding route guidance instruction of the target detection area, including:
calculating the proportion information of the part of the employee predicted escape route in the disaster area to the total length;
judging whether the proportion information is larger than a preset proportion threshold value or not;
if the judgment result is negative, determining the predicted escape route of the staff as the optimal escape route of the target detection area, and generating a corresponding route guidance instruction;
if the judgment result is yes, calculating all candidate escape routes corresponding to the latest position of the user according to all the passable routes of the latest position of the user in the target detection area;
Calculating the proportion information of the part of each candidate escape route in the disaster affected area to the total length, screening out the candidate escape route with the lowest proportion information, determining the optimal escape route of the target detection area, and generating a corresponding route guiding instruction.
In a second aspect of the present invention, the route guidance instruction includes a display intensity instruction corresponding to the guidance device; the specific mode of the second determining module generating the corresponding route guiding instruction comprises the following steps:
determining all guiding devices needing to emit light according to the optimal escape route;
generating first weight information according to a proportion difference value between the proportion information corresponding to the optimal escape route and the proportion threshold value;
generating second weight information according to the distance value of the latest position of the user from the outlet corresponding to the optimal escape route;
calculating the product of a preset display intensity reference value and the first weight information and the second weight information to obtain the display intensity instruction;
generating a route guidance instruction comprising the display intensity instruction sent to all the guidance devices needing to emit light.
In a third aspect the invention discloses another dredged route determination device for intelligent fire protection, the device comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform some or all of the steps in the method for determining a pull through route for intelligent fire protection disclosed in the first aspect of the present invention.
A fourth aspect of the present invention discloses a computer storage medium storing computer instructions which, when invoked, are adapted to perform part or all of the steps of the method for determining a pull through route for intelligent fire protection disclosed in the first aspect of the present invention.
Compared with the prior art, the invention has the following beneficial effects:
the invention can combine the sensing data to determine the disaster position and type in the area, then forecast the predicted escape route of staff, and combine the optimal escape route and the corresponding guiding instruction, thereby realizing more accurate and efficient crowd evacuation and reducing the damage caused by fire-fighting events.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for determining a dredging route for intelligent fire protection according to an embodiment of the invention;
FIG. 2 is a schematic structural view of a dredging route determining device for intelligent fire protection according to an embodiment of the present invention;
fig. 3 is a schematic structural view of another dredging route determining device for intelligent fire protection according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or article.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a dredging route determining method and device for intelligent fire protection, which can be used for determining disaster positions and types in an area by combining sensing data, predicting an estimated escape route of staff, and determining an optimal escape route and corresponding guiding instructions by combining, so that more accurate and efficient crowd evacuation can be realized, and damage caused by fire events is reduced. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a dredging route determining method for intelligent fire protection according to an embodiment of the invention. The method described in fig. 1 may be applied to a corresponding prediction device, a prediction terminal, and a prediction server, where the server may be a local server or a cloud server, and the embodiment of the present invention is not limited to the method shown in fig. 1, and the method for determining a dredging route for intelligent fire protection may include the following operations:
101. Multiple sensing data of multiple modes and corresponding sensing positions in the target detection area are acquired.
Optionally, the sensing data of the plurality of modalities may include a plurality of image data, infrared ranging data, temperature data, humidity data, thermal imaging data, light reflection three-dimensional data, text input data, numerical input data.
Alternatively, the sensed data may be obtained by different types of sensors, a plurality of sensors may be arranged in the target detection area to form a sensor network, and the sensed location may be determined directly as the location of the sensor, or in some embodiments, the temperature of a particular location may be detected when an infrared temperature sensor is employed, for example, the determination of the detected particular location may be determined as the sensed location.
102. And determining at least one disaster point and disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions.
103. And acquiring human body movement sensing information in the target detection area in real time.
Alternatively, the human body movement sensing information may be a human body movement image in the target detection area acquired by the above sensor, and the movement track of the human body in the target detection area may be determined later according to the positions of different sensors and the human body distances analyzed by the images.
104. And predicting an escape route estimated by staff in the target detection area according to the human body movement sensing information and the passing rule of the target detection area.
105. And determining the optimal escape route and a corresponding route guidance instruction of the target detection area according to the predicted escape route of staff in the target detection area and at least one disaster point and disaster type in the target detection area.
Optionally, the route guidance instruction is used for sending to a plurality of guiding devices of the target detection area to display the optimal escape route.
Therefore, the method described by the embodiment of the invention can be combined with the sensing data to determine the disaster position and type in the area, then predict the predicted escape route of staff, and combine with the determination of the optimal escape route and the corresponding guiding instruction, thereby realizing more accurate and efficient crowd evacuation and reducing the damage caused by fire events.
As an alternative embodiment, in the step, determining at least one disaster point and a disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions includes:
judging whether the data value of any sensing data is in the normal value interval of the sensing data of the corresponding mode, if so, deleting the sensing data, otherwise, reserving the sensing data;
After screening all the sensing data, obtaining a plurality of residual sensing data;
corresponding sensing positions of the rest multiple sensing data are determined to be disaster points;
and inputting each sensing data and the corresponding sensing position in the residual sensing data into a pre-trained neural network prediction model of a corresponding mode to obtain a disaster type corresponding to the disaster point corresponding to each sensing data so as to obtain at least one disaster point and disaster type in the target detection area.
Optionally, the neural network prediction model is obtained through training a training set comprising training sensing data of a plurality of corresponding modes, sensing positions and corresponding disaster type labels. Alternatively, the neural network prediction model may be a CNN neural network structure, an RNN neural network structure, a random forest model structure, or a prediction model of an LTSM structure, and a person skilled in the art may select a corresponding model structure according to data characteristics of sensing data of a specific modality, which is not limited by the present invention.
Therefore, by implementing the alternative embodiment, abnormal sensing data can be screened out to determine disaster points, then the disaster types are predicted through the neural network prediction model, and the optimal escape route and corresponding guiding instructions can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by fire events is reduced.
As an alternative embodiment, in the step, predicting the estimated escape route of the staff in the target detection area according to the human body movement sensing information and the traffic rule of the target detection area includes:
acquiring a three-dimensional model corresponding to the target detection area, and determining all passable routes of the target detection area according to model parameters in the three-dimensional model;
determining a user preliminary movement track in a target detection area according to the human body movement sensing information;
and determining the predicted escape route of staff in the target detection area according to the similarity between the preliminary movement track of the user and all the passable routes.
Optionally, all passable routes in the three-dimensional model can be determined according to model parameters related to the non-passable walls, concrete entities and the like in the three-dimensional model, and optionally, a dynamic programming algorithm can be adopted, the model parameters in the three-dimensional model are used as limiting conditions, the passable routes are formed as target functions, and all passable routes of the target detection area are calculated.
Optionally, the track formed by the movement of the human body in the target detection area before the current moment can be determined according to the human body movement sensing information and the position acquired by the sensor, so as to obtain the initial movement track of the user.
Therefore, by implementing the alternative embodiment, the predicted escape route of staff in the target detection area can be determined according to the similarity between the initial movement track of the user and all the passable routes, and the optimal escape route and the corresponding guiding instruction can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by a fire-fighting event is reduced.
As an alternative embodiment, in the step, determining the predicted escape route of the employee in the target detection area according to the similarity between the preliminary movement track of the user and all the passable routes includes:
calculating the route similarity between the preliminary movement track of the user and each passable route;
determining the route with highest similarity in the passable routes as a target communication route;
determining the latest position of the user according to the human body movement sensing information;
and judging whether the latest position of the user is on the target passing route, if so, generating a route from the latest position of the user to the end point of the target passing route so as to obtain an escape route predicted by staff in the target detection area.
Alternatively, the route similarity may be defined as a line segment overlap ratio between two routes, or an overlap ratio between region blocks of the target detection region traversed by the two routes.
Therefore, by implementing the alternative embodiment, the route from the latest position of the user to the end point of the target passing route can be generated so as to obtain the predicted escape route according to staff in the target detection area, the escape tendency of the user in the disaster area can be predicted more accurately, and the optimal escape route and the corresponding guiding instruction can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by a fire event is reduced.
As an alternative embodiment, the disaster type includes at least one of open fire, electric leakage, water immersion and thick smoke, wherein different disaster types correspond to different ranges of influence and need to be considered later.
In the above steps, according to the predicted escape route of the staff in the target detection area and at least one disaster point and disaster type in the target detection area, determining the optimal escape route and corresponding route guiding instruction of the target detection area includes:
determining a disaster influence range corresponding to disaster points according to the corresponding disaster types;
determining disaster influence areas corresponding to the target detection areas according to disaster influence ranges of all disaster points in the target detection areas;
And determining the optimal escape route of the target detection area and a corresponding route guiding instruction according to the intersection degree between the predicted escape route of the staff and the disaster influence area.
Optionally, determining the disaster influence range of the disaster point may include:
determining the range weight corresponding to the disaster point according to the disaster type and the preset type-weight corresponding relation;
calculating the product of a preset reference influence radius and range weight to obtain an influence radius corresponding to the disaster point;
establishing a circle with the influence radius as a radius by taking the disaster point as a circle center so as to obtain the influence circle;
and determining all the influence circles in the target detection area as disaster influence areas.
Therefore, by implementing the optional embodiment, the disaster influence area corresponding to the target detection area can be accurately determined, and the optimal escape route and the corresponding guide instruction can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by a fire accident is reduced.
As an alternative embodiment, in the step, according to the intersection degree between the predicted escape route of the staff and the disaster area, the determining the optimal escape route of the target detection area and the corresponding route guidance instruction includes:
Calculating the proportion information of the part of the escape route, which is predicted by staff to occupy the total length, in the disaster influence area;
judging whether the proportion information is larger than a preset proportion threshold value or not;
if the judgment result is negative, determining the predicted escape route of the staff as the optimal escape route of the target detection area, and generating a corresponding route guidance instruction;
if the judgment result is yes, calculating all candidate escape routes corresponding to the latest position of the user according to all passable routes of the latest position of the user in the target detection area;
calculating the proportion information of the part of each candidate escape route in the disaster affected area to the total length, screening out the candidate escape route with the lowest proportion information, determining the optimal escape route for the target detection area, and generating a corresponding route guiding instruction.
Therefore, by implementing the alternative embodiment, the optimal escape route of the target detection area can be accurately determined according to the disaster influence degree of the escape route predicted by staff, so that more accurate and efficient crowd evacuation can be realized, and the damage caused by a fire accident is reduced.
As an alternative embodiment, the route guidance instruction includes a display intensity instruction corresponding to the guidance device, for example, the route guidance instruction may be generated and sent by a server connected to all the guidance devices, where the display intensity instruction is included in the instruction. Accordingly, the directing means may be arranged at a plurality of different locations of the target detection area, optimally in a grid-like configuration, for directing a plurality of different routes. Optionally, the guiding device is provided with a light emitting device, and the light emitting device is used for emitting light with corresponding intensity according to the display intensity instruction so as to illuminate the display panel arranged in front, so as to provide guiding for a user.
In the above steps, generating a corresponding route guidance instruction includes:
determining all guiding devices needing to emit light according to the optimal escape route;
generating first weight information according to the proportion difference value between the proportion information corresponding to the optimal escape route and the proportion threshold value;
generating second weight information according to the distance value of the latest position of the user from the exit corresponding to the optimal escape route;
calculating the product of a preset display intensity reference value and the first weight information and the second weight information to obtain a display intensity instruction;
generating a route guidance instruction including a display intensity instruction transmitted to all guidance devices requiring light emission.
Specifically, the first weight information is proportional to the proportional difference, the second weight information is proportional to the distance value, and optionally, a technician may obtain a specific mathematical proportional formula between the two according to an experiment or an empirical value, and adjust the mathematical proportional formula according to the effect in implementation.
The setting can determine the corresponding display intensity according to the escape difficulty of the user, and particularly when the escape route of the user is difficult to escape or is farther away from the exit, stronger display information is required to be sent out to guide the user to escape successfully.
Therefore, by implementing the optional embodiment, the proper display intensity instruction can be accurately determined according to the escape difficulty of the user, so that more accurate and efficient crowd evacuation can be realized, and the damage caused by a fire accident is reduced.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a dredging route determining device for intelligent fire protection according to an embodiment of the present invention. The apparatus described in fig. 2 may be applied to a corresponding prediction device, a prediction terminal, and a prediction server, where the server may be a local server or a cloud server, and the embodiment of the present invention is not limited. As shown in fig. 2, the apparatus may include:
a first acquiring module 201, configured to acquire a plurality of sensing data and corresponding sensing positions of a plurality of modes in the target detection area.
Optionally, the sensing data of the plurality of modalities may include a plurality of image data, infrared ranging data, temperature data, humidity data, thermal imaging data, light reflection three-dimensional data, text input data, numerical input data.
Alternatively, the sensed data may be obtained by different types of sensors, a plurality of sensors may be arranged in the target detection area to form a sensor network, and the sensed location may be determined directly as the location of the sensor, or in some embodiments, the temperature of a particular location may be detected when an infrared temperature sensor is employed, for example, the determination of the detected particular location may be determined as the sensed location.
The first determining module 202 is configured to determine at least one disaster point and a disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions.
The second acquiring module 203 is configured to acquire the human movement sensing information in the target detection area in real time.
Alternatively, the human body movement sensing information may be a human body movement image in the target detection area acquired by the above sensor, and the movement track of the human body in the target detection area may be determined later according to the positions of different sensors and the human body distances analyzed by the images.
The prediction module 204 is configured to predict an escape route predicted by staff in the target detection area according to the human movement sensing information and a traffic rule of the target detection area.
The second determining module 205 is configured to determine an optimal escape route and a corresponding route guidance instruction of the target detection area according to the escape route predicted by the staff in the target detection area and at least one disaster point and disaster type in the target detection area.
Optionally, the route guidance instruction is used for sending to a plurality of guiding devices of the target detection area to display the optimal escape route.
Therefore, the device described by the embodiment of the invention can be combined with the sensing data to determine the disaster position and type in the area, then predict the predicted escape route of staff, and combine with the determination of the optimal escape route and the corresponding guiding instruction, thereby realizing more accurate and efficient crowd evacuation and reducing the damage caused by fire events.
As an alternative embodiment, the first determining module 202 determines at least one disaster point and a disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions, including:
judging whether the data value of any sensing data is in the normal value interval of the sensing data of the corresponding mode, if so, deleting the sensing data, otherwise, reserving the sensing data;
after screening all the sensing data, obtaining a plurality of residual sensing data;
corresponding sensing positions of the rest multiple sensing data are determined to be disaster points;
and inputting each sensing data and the corresponding sensing position in the residual sensing data into a pre-trained neural network prediction model of a corresponding mode to obtain a disaster type corresponding to the disaster point corresponding to each sensing data so as to obtain at least one disaster point and disaster type in the target detection area.
Optionally, the neural network prediction model is obtained through training a training set comprising training sensing data of a plurality of corresponding modes, sensing positions and corresponding disaster type labels. Alternatively, the neural network prediction model may be a CNN neural network structure, an RNN neural network structure, a random forest model structure, or a prediction model of an LTSM structure, and a person skilled in the art may select a corresponding model structure according to data characteristics of sensing data of a specific modality, which is not limited by the present invention.
Therefore, by implementing the alternative embodiment, abnormal sensing data can be screened out to determine disaster points, then the disaster types are predicted through the neural network prediction model, and the optimal escape route and corresponding guiding instructions can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by fire events is reduced.
As an alternative embodiment, the predicting module 204 predicts a specific manner of predicting the escape route for the staff in the target detection area according to the human movement sensing information and the traffic rule of the target detection area, including:
acquiring a three-dimensional model corresponding to the target detection area, and determining all passable routes of the target detection area according to model parameters in the three-dimensional model;
Determining a user preliminary movement track in a target detection area according to the human body movement sensing information;
and determining the predicted escape route of staff in the target detection area according to the similarity between the preliminary movement track of the user and all the passable routes.
Optionally, all passable routes in the three-dimensional model can be determined according to model parameters related to the non-passable walls, concrete entities and the like in the three-dimensional model, and optionally, a dynamic programming algorithm can be adopted, the model parameters in the three-dimensional model are used as limiting conditions, the passable routes are formed as target functions, and all passable routes of the target detection area are calculated.
Optionally, the track formed by the movement of the human body in the target detection area before the current moment can be determined according to the human body movement sensing information and the position acquired by the sensor, so as to obtain the initial movement track of the user.
Therefore, by implementing the alternative embodiment, the predicted escape route of staff in the target detection area can be determined according to the similarity between the initial movement track of the user and all the passable routes, and the optimal escape route and the corresponding guiding instruction can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by a fire-fighting event is reduced.
As an alternative embodiment, the prediction module 204 determines a specific manner in which the employee in the target detection area predicts the escape route according to the similarity between the preliminary movement track of the user and all the passable routes, including:
calculating the route similarity between the preliminary movement track of the user and each passable route;
determining the route with highest similarity in the passable routes as a target communication route;
determining the latest position of the user according to the human body movement sensing information;
and judging whether the latest position of the user is on the target passing route, if so, generating a route from the latest position of the user to the end point of the target passing route so as to obtain an escape route predicted by staff in the target detection area.
Alternatively, the route similarity may be defined as a line segment overlap ratio between two routes, or an overlap ratio between region blocks of the target detection region traversed by the two routes.
Therefore, by implementing the alternative embodiment, the route from the latest position of the user to the end point of the target passing route can be generated so as to obtain the predicted escape route according to staff in the target detection area, the escape tendency of the user in the disaster area can be predicted more accurately, and the optimal escape route and the corresponding guiding instruction can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by a fire event is reduced.
As an alternative embodiment, the disaster type includes at least one of open fire, electric leakage, water immersion and thick smoke, wherein different disaster types correspond to different ranges of influence and need to be considered later.
The second determining module 205 determines, according to the predicted escape route of the staff in the target detection area and at least one disaster point and disaster type in the target detection area, a specific manner of the optimal escape route and the corresponding route guidance instruction in the target detection area, including:
determining a disaster influence range corresponding to disaster points according to the corresponding disaster types;
determining disaster influence areas corresponding to the target detection areas according to disaster influence ranges of all disaster points in the target detection areas;
and determining the optimal escape route of the target detection area and a corresponding route guiding instruction according to the intersection degree between the predicted escape route of the staff and the disaster influence area.
Optionally, the specific manner of determining the disaster influence range of the disaster point by the second determining module 205 may include:
determining the range weight corresponding to the disaster point according to the disaster type and the preset type-weight corresponding relation;
Calculating the product of a preset reference influence radius and range weight to obtain an influence radius corresponding to the disaster point;
establishing a circle with the influence radius as a radius by taking the disaster point as a circle center so as to obtain the influence circle;
and determining all the influence circles in the target detection area as disaster influence areas.
Therefore, by implementing the optional embodiment, the disaster influence area corresponding to the target detection area can be accurately determined, and the optimal escape route and the corresponding guide instruction can be determined in a follow-up auxiliary manner, so that more accurate and efficient crowd evacuation can be realized, and damage caused by a fire accident is reduced.
As an alternative embodiment, the second determining module 205 determines the optimal escape route of the target detection area and the specific manner of the corresponding route guidance instruction according to the intersection degree between the predicted escape route of the employee and the disaster area, including:
calculating the proportion information of the part of the escape route, which is predicted by staff to occupy the total length, in the disaster influence area;
judging whether the proportion information is larger than a preset proportion threshold value or not;
if the judgment result is negative, determining the predicted escape route of the staff as the optimal escape route of the target detection area, and generating a corresponding route guidance instruction;
If the judgment result is yes, calculating all candidate escape routes corresponding to the latest position of the user according to all passable routes of the latest position of the user in the target detection area;
calculating the proportion information of the part of each candidate escape route in the disaster affected area to the total length, screening out the candidate escape route with the lowest proportion information, determining the optimal escape route for the target detection area, and generating a corresponding route guiding instruction.
Therefore, by implementing the alternative embodiment, the optimal escape route of the target detection area can be accurately determined according to the disaster influence degree of the escape route predicted by staff, so that more accurate and efficient crowd evacuation can be realized, and the damage caused by a fire accident is reduced.
As an alternative embodiment, the route guidance instruction includes a display intensity instruction corresponding to the guidance device, for example, the route guidance instruction may be generated and sent by a server connected to all the guidance devices, where the display intensity instruction is included in the instruction. Accordingly, the directing means may be arranged at a plurality of different locations of the target detection area, optimally in a grid-like configuration, for directing a plurality of different routes. Optionally, the guiding device is provided with a light emitting device, and the light emitting device is used for emitting light with corresponding intensity according to the display intensity instruction so as to illuminate the display panel arranged in front, so as to provide guiding for a user.
The specific manner in which the second determining module 205 generates the corresponding route guidance instruction includes:
determining all guiding devices needing to emit light according to the optimal escape route;
generating first weight information according to the proportion difference value between the proportion information corresponding to the optimal escape route and the proportion threshold value;
generating second weight information according to the distance value of the latest position of the user from the exit corresponding to the optimal escape route;
calculating the product of a preset display intensity reference value and the first weight information and the second weight information to obtain a display intensity instruction;
generating a route guidance instruction including a display intensity instruction transmitted to all guidance devices requiring light emission.
Specifically, the first weight information is proportional to the proportional difference, the second weight information is proportional to the distance value, and optionally, a technician may obtain a specific mathematical proportional formula between the two according to an experiment or an empirical value, and adjust the mathematical proportional formula according to the effect in implementation.
The setting can determine the corresponding display intensity according to the escape difficulty of the user, and particularly when the escape route of the user is difficult to escape or is farther away from the exit, stronger display information is required to be sent out to guide the user to escape successfully.
Therefore, by implementing the optional embodiment, the proper display intensity instruction can be accurately determined according to the escape difficulty of the user, so that more accurate and efficient crowd evacuation can be realized, and the damage caused by a fire accident is reduced.
Example III
Referring to fig. 3, fig. 3 is a schematic structural diagram of another dredging route determining device for intelligent fire protection according to an embodiment of the present invention. As shown in fig. 3, the apparatus may include:
a memory 301 storing executable program code;
a processor 302 coupled with the memory 301;
the processor 302 invokes the executable program code stored in the memory 301 to perform some or all of the steps in the method for determining a pull through route for intelligent fire protection disclosed in accordance with the embodiment of the present invention.
Example IV
The embodiment of the invention discloses a computer storage medium which stores computer instructions for executing part or all of the steps in the dredging route determining method for intelligent fire fighting disclosed in the embodiment of the invention when the computer instructions are called.
The apparatus embodiments described above are merely illustrative, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a dredging route determining method and device for intelligent fire fighting, which are disclosed by the embodiment of the invention only as the preferred embodiment of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A method for determining a dredging route for intelligent fire protection, the method comprising:
acquiring a plurality of sensing data of a plurality of modes and corresponding sensing positions in a target detection area;
determining at least one disaster point and disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions;
acquiring human body movement sensing information in the target detection area in real time;
predicting an employee predicted escape route in the target detection area according to the human body movement sensing information and the passing rule of the target detection area;
Determining an optimal escape route and a corresponding route guiding instruction of the target detection area according to an escape route predicted by staff in the target detection area and at least one disaster point and disaster type in the target detection area; the route guiding instruction is used for being sent to a plurality of guiding devices of the target detection area so as to display the optimal escape route.
2. The method for intelligent fire fighting dredging route determination according to claim 1, wherein the determining at least one disaster point and disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions includes:
judging whether the data value of the sensing data is in the normal value interval of the sensing data of the corresponding mode for any sensing data, if so, deleting the sensing data, otherwise, reserving the sensing data;
after screening all the sensing data, obtaining a plurality of residual sensing data;
determining corresponding sensing positions of the rest sensing data as disaster points;
and inputting each sensing data and the corresponding sensing position in the remaining sensing data into a pre-trained neural network prediction model of a corresponding mode to obtain a disaster type corresponding to the disaster point corresponding to each sensing data so as to obtain at least one disaster point and disaster type in the target detection area.
3. The method for intelligent fire fighting dredging route determination according to claim 1, wherein predicting an employee predicted escape route in the target detection area based on the human body movement sensing information and a traffic rule of the target detection area, comprises:
acquiring a three-dimensional model corresponding to the target detection area, and determining all passable routes of the target detection area according to model parameters in the three-dimensional model;
determining a preliminary movement track of a user in the target detection area according to the human body movement sensing information;
and determining the predicted escape route of staff in the target detection area according to the similarity between the preliminary movement track of the user and all the passable routes.
4. A method for intelligent fire fighting dredging route determination according to claim 3, wherein the determining the employee predicted escape route in the target detection area based on the similarity between the user preliminary movement trajectory and the all trafficable routes comprises:
calculating the route similarity between the user preliminary movement track and each passable route;
determining the route with highest similarity in the passable routes as a target communication route;
Determining the latest position of the user according to the human body movement sensing information;
and judging whether the latest position of the user is on the target passing route, if so, generating a route from the latest position of the user to the end point of the target passing route so as to obtain an escape route predicted by staff in the target detection area.
5. The method for determining a dredging route for intelligent fire protection according to claim 4, wherein the disaster type includes at least one of open fire disaster, electric leakage disaster, water immersion disaster and thick smoke disaster; the determining an optimal escape route and a corresponding route guiding instruction of the target detection area according to the predicted escape route of staff in the target detection area and at least one disaster point and disaster type in the target detection area comprises:
determining a disaster influence range corresponding to the disaster point according to the corresponding disaster type;
determining disaster influence areas corresponding to the target detection areas according to the disaster influence ranges of all the disaster points in the target detection areas;
and determining the optimal escape route and a corresponding route guidance instruction of the target detection area according to the intersection degree between the predicted escape route of the staff and the disaster area.
6. The method for intelligent fire fighting dredging route determination according to claim 5, wherein the determining the optimal escape route of the target detection area and the corresponding route guidance instruction according to the intersection degree between the employee predicted escape route and the disaster area comprises:
calculating the proportion information of the part of the employee predicted escape route in the disaster area to the total length;
judging whether the proportion information is larger than a preset proportion threshold value or not;
if the judgment result is negative, determining the predicted escape route of the staff as the optimal escape route of the target detection area, and generating a corresponding route guidance instruction;
if the judgment result is yes, calculating all candidate escape routes corresponding to the latest position of the user according to all the passable routes of the latest position of the user in the target detection area;
calculating the proportion information of the part of each candidate escape route in the disaster affected area to the total length, screening out the candidate escape route with the lowest proportion information, determining the optimal escape route of the target detection area, and generating a corresponding route guiding instruction.
7. The method for intelligent firefighting dredging route determination according to claim 6, wherein the route guidance instruction includes a display intensity instruction corresponding to the guidance device; the generating the corresponding route guidance instruction includes:
determining all guiding devices needing to emit light according to the optimal escape route;
generating first weight information according to a proportion difference value between the proportion information corresponding to the optimal escape route and the proportion threshold value;
generating second weight information according to the distance value of the latest position of the user from the outlet corresponding to the optimal escape route;
calculating the product of a preset display intensity reference value and the first weight information and the second weight information to obtain the display intensity instruction;
generating a route guidance instruction comprising the display intensity instruction sent to all the guidance devices needing to emit light.
8. A pull through route determination device for intelligent fire protection, the device comprising:
the first acquisition module is used for acquiring a plurality of sensing data of a plurality of modes and corresponding sensing positions in the target detection area;
the first determining module is used for determining at least one disaster point and disaster type in the target detection area according to the plurality of sensing data and the corresponding sensing positions;
The second acquisition module is used for acquiring the human body movement sensing information in the target detection area in real time;
the prediction module is used for predicting an employee predicted escape route in the target detection area according to the human body movement sensing information and the passing rule of the target detection area;
the second determining module is used for determining an optimal escape route and a corresponding route guiding instruction of the target detection area according to the predicted escape route of staff in the target detection area and at least one disaster point and disaster type in the target detection area; the route guiding instruction is used for being sent to a plurality of guiding devices of the target detection area so as to display the optimal escape route.
9. A pull through route determination device for intelligent fire protection, the device comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the method of determining a pull through route for intelligent fire protection as claimed in any one of claims 1 to 7.
10. A computer storage medium storing computer instructions which, when invoked, are adapted to perform the method for intelligent fire fighting dredging route determination according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311382350.4A CN117540887A (en) | 2023-10-24 | 2023-10-24 | Dredging route determining method and device for intelligent fire fighting |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311382350.4A CN117540887A (en) | 2023-10-24 | 2023-10-24 | Dredging route determining method and device for intelligent fire fighting |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117540887A true CN117540887A (en) | 2024-02-09 |
Family
ID=89785126
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311382350.4A Pending CN117540887A (en) | 2023-10-24 | 2023-10-24 | Dredging route determining method and device for intelligent fire fighting |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117540887A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108880664A (en) * | 2018-07-23 | 2018-11-23 | 北京邮电大学 | A kind of disaster assistance information processing method |
| CN111337028A (en) * | 2020-03-31 | 2020-06-26 | 深圳市泛海三江电子股份有限公司 | Method and system for fire extinguishing guidance and personnel evacuation in complex building body |
| KR20220151490A (en) * | 2021-05-06 | 2022-11-15 | 주식회사 이스트컨트롤 | Emergency evacuation control method |
| CN115564099A (en) * | 2022-09-20 | 2023-01-03 | 广东交通职业技术学院 | Building fire-fighting emergency escape guiding method, system, device and storage medium |
| KR20230013882A (en) * | 2021-07-20 | 2023-01-27 | (주)컴퍼니에스 | Method And System for Monitoring Fire |
-
2023
- 2023-10-24 CN CN202311382350.4A patent/CN117540887A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108880664A (en) * | 2018-07-23 | 2018-11-23 | 北京邮电大学 | A kind of disaster assistance information processing method |
| CN111337028A (en) * | 2020-03-31 | 2020-06-26 | 深圳市泛海三江电子股份有限公司 | Method and system for fire extinguishing guidance and personnel evacuation in complex building body |
| KR20220151490A (en) * | 2021-05-06 | 2022-11-15 | 주식회사 이스트컨트롤 | Emergency evacuation control method |
| KR20230013882A (en) * | 2021-07-20 | 2023-01-27 | (주)컴퍼니에스 | Method And System for Monitoring Fire |
| CN115564099A (en) * | 2022-09-20 | 2023-01-03 | 广东交通职业技术学院 | Building fire-fighting emergency escape guiding method, system, device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9397904B2 (en) | System for identifying, monitoring and ranking incidents from social media | |
| US10997832B1 (en) | Augmented reality based dynamic guidance | |
| CN113859264B (en) | Vehicle control method, device, electronic equipment and storage medium | |
| CN110782095A (en) | Fire fighting route planning method and device, electronic equipment and storage medium | |
| JP2023536025A (en) | Target detection method, device and roadside equipment in road-vehicle cooperation | |
| CN116304250B (en) | Intelligent rule engine management method and system based on visual operation | |
| CN115240264B (en) | Visitor risk value evaluation method and system | |
| CN114997754A (en) | Emergency plan analysis method and device based on cloud model and entropy weight method | |
| CN116758691B (en) | Fire-fighting early warning method and device based on multi-mode data | |
| CN115272656A (en) | Environment detection alarm method and device, computer equipment and storage medium | |
| KR20230083919A (en) | Method and system for safety control for construction site | |
| CN117540887A (en) | Dredging route determining method and device for intelligent fire fighting | |
| CN117077854B (en) | Building energy consumption monitoring method and system based on sensor network | |
| CN111359132B (en) | Intelligent fire-fighting alarm method and system based on artificial intelligence | |
| CN112800841A (en) | Pedestrian counting method, device and system and computer readable storage medium | |
| CN116880514A (en) | Intelligent wheelchair control method, intelligent wheelchair and storage medium | |
| KR102228509B1 (en) | System and method for emergency evacuation route guidance | |
| KR20180036562A (en) | Information processing apparatus, information processing method, and storage medium | |
| KR102349198B1 (en) | Hospital internal navigation method and system | |
| JP2022054294A (en) | Interactive guiding device | |
| CN117830960B (en) | Smart community-based risk identification method, device, equipment and medium | |
| KR102458307B1 (en) | System for intelligently notifying regarding occurrence of forest fire | |
| CN117953642B (en) | BIM-based intelligent building monitoring method and system | |
| JP7219820B2 (en) | SPATIAL SENSING SYSTEM AND SPATIAL SENSING METHOD | |
| CN111508172A (en) | Safe evacuation method and system based on network point type accurate escape route measurement and calculation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |