CN115018283A - Intelligent fire-fighting management method for industrial park - Google Patents
Intelligent fire-fighting management method for industrial park Download PDFInfo
- Publication number
- CN115018283A CN115018283A CN202210572094.4A CN202210572094A CN115018283A CN 115018283 A CN115018283 A CN 115018283A CN 202210572094 A CN202210572094 A CN 202210572094A CN 115018283 A CN115018283 A CN 115018283A
- Authority
- CN
- China
- Prior art keywords
- fire
- path
- evacuation
- representing
- personnel
- 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.)
- Withdrawn
Links
- 238000007726 management method Methods 0.000 title claims abstract description 35
- 238000012544 monitoring process Methods 0.000 claims abstract description 24
- 238000011156 evaluation Methods 0.000 claims abstract description 10
- 238000005457 optimization Methods 0.000 claims abstract description 9
- 230000014509 gene expression Effects 0.000 claims description 25
- 230000005540 biological transmission Effects 0.000 claims description 16
- 238000005259 measurement Methods 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 4
- 230000000977 initiatory effect Effects 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 7
- 230000006870 function Effects 0.000 description 40
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 11
- 239000007789 gas Substances 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 7
- 239000000779 smoke Substances 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000009826 distribution Methods 0.000 description 4
- 230000005611 electricity Effects 0.000 description 3
- 230000006855 networking Effects 0.000 description 3
- 206010000369 Accident Diseases 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000015654 memory Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 239000003016 pheromone Substances 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Emergency Management (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses an intelligent fire-fighting management method for an industrial park, which comprises the steps of establishing the position of a miniature fire-fighting station of the industrial park by utilizing a multi-objective decision model and a constraint equation; establishing a fitness evaluation function to evaluate the established miniature fire station; monitoring data of the whole industrial park by using a fire sensor, and establishing a fire-fighting trust function for the data; accurately positioning the position of a person by using a UWB positioning base station; establishing a personnel moving model by utilizing the number of personnel and the positions of the personnel in the industrial park; establishing an optimal path optimization model; establishing an evacuation three-element model to update an evacuation path in real time; establishing a path real-time updating model and constraint conditions; sending the evacuation path to the personnel in the industrial park in real time; the method can help the personnel in the industrial park to effectively evacuate in the shortest time, and can effectively ensure the life safety of the personnel in the industrial park when the fire-fighting danger occurs.
Description
Technical Field
The invention relates to the field of fire safety management, in particular to an intelligent fire management method for an industrial park.
Background
Fire safety management plays an important role in fire accident prevention, and at present, fire fighting equipment is mainly arranged in key fire-fighting units, particularly places with intensive personnel activities. Traditional fire-fighting equipment generally needs manual work to manage, maintain, has more drawback. In order to eliminate the defects caused by manual management, at present, automatic fire fighting facilities are generally installed in high-rise buildings, factory and mining enterprises and places with dense personnel, but the problems of lack of effective management generally exist in the automatic fire fighting facilities, and due to the fact that the automatic fire fighting facilities are difficult to fully and effectively manage and inspect, the automatic fire fighting facilities cannot guarantee reliable operation, and cannot play due roles when a fire disaster occurs. The city fire-fighting remote monitoring system is a fire-fighting informatization application system which is used for networking independent fire automatic alarm systems in all buildings through a modern communication network, comprehensively utilizing information technologies such as a geographic information system and digital video monitoring, monitoring fire alarm conditions of all the networked buildings in real time in a monitoring center and carrying out centralized management on fire-fighting facilities.
The patent application with the publication patent number of CN113256929A discloses a fire-fighting management method and a fire-fighting management system for a production unit, wherein the invention comprises the fire-fighting management system for the production unit; the production unit fire-fighting management system comprises a sensor group, an alarm host and a cloud server; the sensor group and the alarm host are both arranged in a production unit; the sensor group is arranged at the inner top of a monitoring area in a production unit; the number of the sensor groups is multiple; the sensor components are distributed in an array manner; the sensor group comprises a smoke sensor, a sound sensor and a temperature sensor which correspond to each other; the sensor group is in communication connection with the alarm host; the alarm host is in communication connection with the cloud server; the fire-fighting management method for the production units, provided by the invention, can not only monitor whether the fire happens in time, but also generate the fire grade or severity information so as to facilitate the fire-fighting troops to quickly assign a reasonable fire-fighting scheme, thereby improving the efficiency of fire-fighting and extinguishing work.
Patent application document with publication patent number CN107274003A discloses a wisdom fire control management system and control method thereof, a wisdom fire control management system, including front end collector, sensor module, video monitoring module and backend server, sensor module and video monitoring module all are connected with the front end collector electricity and are used for gathering fire signal, front end collector and backend server communication connection, its characterized in that: still include BIM building information service platform, fire control big data platform and be used for wearing the alarm on one's body the stranded personnel, the alarm includes host system, GPS module, speed sensor and the frequency sound production module that the volume is all adjustable. The intelligent fire-fighting management system can enable trapped people to effectively carry out self-rescue along the shortest escape route and within the optimal escape time before rescuers arrive. In addition, a control method applied to the intelligent fire fighting management system is further provided.
However, the conventional remote fire-fighting monitoring system is still in a stage of providing only fire-fighting information monitoring and consulting management, and when the system monitors a fire incident through receiving alarm information, the system cannot handle an emergency fire incident well only through the current remote fire-fighting monitoring system, and corresponding fire emergency management measures are lacked.
Disclosure of Invention
In order to overcome the defects and shortcomings of the prior art, the invention provides an intelligent fire-fighting management method for an industrial park.
The technical scheme adopted by the invention is that the method comprises the following steps:
step S1: establishing the position of a miniature fire station of the park by using a multi-objective decision model and a constraint equation;
step S2: establishing a fitness evaluation function to evaluate the established miniature fire station;
step S3: monitoring data of the whole park by using a fire sensor;
step S4: establishing a fire-fighting trust degree function for the data, judging the probability of fire-fighting risks, and repeating the step S3 when no fire-fighting risk occurs;
step S5: when fire-fighting danger occurs, determining the distance between a person and a base station by using a UWB positioning base station, and accurately positioning the position of the person;
step S6: establishing a personnel moving model by utilizing the number of personnel in the park and the positions of the personnel;
step S7: establishing an optimal path optimization model;
step S8: respectively establishing three models of an evacuation time element, an evacuation personnel element and an evacuation path smoothness element to update the evacuation path in real time;
step S9: establishing a path real-time updating model and constraint conditions to ensure that the evacuation personnel have the safest evacuation path;
step S10: and sending the evacuation route to the park personnel in real time.
Further, the multi-objective decision model has an expression as follows:
the constraint equation, the expression is:
a fitness evaluation function, wherein the expression is as follows:
wherein, a nm Denotes the distance of position n to m, b nm Indicating that location n is covered by a mini fire station m, c m Denotes a mini fire station, e 'established at m' max Z table representing the maximum distance between the demand point of the miniature fire station and the miniature fire station closest to the demand point when fire-fighting danger occursSet of all positions, e max Representing the maximum coverage distance of the miniature fire station, delta e representing the relaxation distance, A (n, m) representing the built fitness evaluation function of the miniature fire station, f m Represents the cost of building a miniature fire station at m, (e) max -e′ max ) q The representation is a penalty function and q represents a penalty factor.
Further, the expression is:
wherein y (r, s, t) represents probability value of fire hazard occurrence, r represents fire sensor measurement value, e represents natural constant, s represents monotonic function value, and t represents time of fire hazard occurrence;
y T ＝1-y D -y E
wherein, y D ，y E ，y T Respectively showing a basic probability function with fire-fighting risk signs, a basic probability function without fire-fighting risk signs and a fire-fighting risk uncertainty function.
Further, the distance between the person and the base station is expressed as:
wherein v represents an electromagnetic wave transmission speed, G 1 、G 2 、G 3 、G 4 Respectively expressed as the time required for the base station No. 1, the base station No. 2, the base station No. 3 and the base station No. 4 to receive the personnel information.
Further, the expression of the personnel movement model is as follows:
wherein h represents an optimal path function, g represents the number of employees in any park, i represents the number of employees, j represents the total number of the employees in the park, k and l are respectively represented as the abscissa and the ordinate of the position of the employee, and gamma is kl Weight coefficient representing safety distance, o klg Indicating that employee number g has passed a safe distance.
Further, the optimal path optimization model has the expression:
wherein the content of the first and second substances,
representing the probability of the transition of the person g at the location (k, l) at time p, k and l being respectively represented as the abscissa and ordinate of the position of the person,
a heuristic function is represented that is a function of,
representing how easily a person can receive information at a location (k, l), D g Representing the set of all evacuation paths of people, q representing the path selected when people evacuate, sigma and theta representing the relatively important measurement factors of the initiating function factor and the difficulty degree of receiving information in the evacuation rule, eta kl Represents the update of the heuristic function, F (k) represents the length of the path that has been traversed by the employee when he arrives at position k, R lr Denotes the spacing between two positions, r kl Representing the inverse of the separation between the two locations.
Further, the evacuation time element is expressed as:
wherein, W l max represents the upper limit of the maximum time expected to be allowed in the path for evacuation of persons, W l Indicating the specific time, U, required for evacuation of persons a (l) Represents an evacuation time element, the larger it is, the longer the evacuation actually takes.
Further, the evacuation people component has an expression:
wherein, U b (l) Representing the evacuated person element, is the ratio between the persons in evacuation and the most expected persons, the larger the value, the more evacuated persons are represented, and U a (l) Representing an evacuation time element, U b (l) Indicating an element of evacuation, V l Representing the number of persons in the evacuation; v l max represents the most expected people in evacuation, u l Representing the real-time number of employees in the campus, v, as the evacuation occurs on that day l Representing the number of people outside the campus in real time, w, as the evacuation of the day l Representing the number of real-time personnel errors in the campus as the evacuation of the day takes place.
Further, the evacuation path smoothness element has an expression as follows:
U(l)＝ζU a (l)+aU b (l)+υU c (l)
wherein, U c (l) The factor representing the smoothness of the path is the ratio of the smoothness of the path to the lowest tolerance of the smoothness of the path, and the larger the value isThe more unobstructed the selected path is when the representative person evacuates; x l Indicates the degree of smoothness of the path, X l max represents the lowest tolerance of the path smoothness, U (l) represents evacuation elements, and zeta, alpha and upsilon represent the actual proportion of time, personnel and the path smoothness occupied by fire-fighting evacuation in the garden respectively.
Further, the path real-time updating model has the expression:
wherein, T b Represents the best path traversed, | T b I denotes the optimal path length, T w Represents the worst path traversed, | T w L represents the worst path length, μ represents the path update weight, ρ kl (z) represents the current optimal path, ρ kl (z +1) represents the optimal path predicted value at the next moment, and t represents a path updating coefficient;
constraint conditions, the expression is:
where ρ is kl Denotes path selection, p min Representing the minimum number of path choices, p max Representing the maximum number of path selections.
Has the advantages that:
the invention provides an intelligent fire-fighting management method for an industrial park, which is different from the traditional fire-fighting management method, and the method ensures that a miniature fire station can play the greatest role when sending fire-fighting hazards by carrying out site selection layout on the miniature fire station of the whole industrial park through an algorithm, simultaneously judges the possibility of fire-fighting hazards by utilizing a plurality of algorithms, forms an optimal evacuation path when the fire-fighting hazards occur and helps the employees of the industrial park to evacuate effectively in the shortest time.
Drawings
FIG. 1 is a flow chart of the overall steps of the present invention;
FIG. 2 is a diagram of three element models according to the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments can be combined with each other without conflict, and the present application will be further described in detail with reference to the drawings and specific embodiments.
As shown in fig. 1, an intelligent fire-fighting management method for an industrial park includes the steps:
step S1: establishing the position of a miniature fire station of the industrial park by utilizing a multi-objective decision model and a constraint equation; the fire-fighting dangerous events responded by the miniature fire station have low occurrence frequency and large influence, and once the fire-fighting dangerous events occur, large-scale emergency rescue needs to be obtained at the first time. The establishment of an emergency facility siting model for such events requires consideration of more factors for more comprehensive analysis. Under comprehensive consideration, the invention establishes a multi-target decision model for the facility site selection of the fire station. First, the fire station facility points should cover all fire hazard demand points. Secondly, when considering a specific address location, three aspects are taken into consideration: 1) in the aspect of quick response or fairness, the maximum distance between the miniature fire station and other fire demand points is required to be minimum; 2) the total weighted distance of service demand points of the fire station is required to be minimum in the aspects of accessibility and use efficiency of the miniature fire station; 3) in the aspect of urban planning economy, the number of the miniature fire stations is required to be as small as possible on the premise that the construction of the miniature fire stations meets the requirements of the first two points.
Step S2: establishing a fitness evaluation function to evaluate the established miniature fire station; the fitness value is an important index for describing individual performance of the position of the miniature fire station established by using the multi-objective decision model and the constraint equation in the step S1, is a basis for distinguishing the quality of individuals in a group determined according to an objective function, and can realize 'winning or losing' of the miniature fire station according to the fitness.
Step S3: monitoring data of the whole industrial park by using a fire sensor; the fire-fighting sensor comprises a temperature sensor, a smoke detector, a gas detector, a water level and water pressure detector, intelligent video monitoring and current and voltage monitoring.
A temperature sensor: to the temperature monitoring in the environmental scope, temperature sensor can use with the smoke is felt, uses temperature sensor as starting switch in spray set.
A smoke detector: smoke is felt for short, and the wireless smoke of intelligence is felt in intelligent fire extinguishing system commonly used, need not the wiring, and the installation is simple, combines to remove APP through intelligent fire extinguishing system and realizes the sharing and the many ways linkage of alert feelings information.
A gas detector: the gas sensor is short for gas sensing, and aiming at gas concentration monitoring in a special environment, the gas sensor is not only limited to fire prevention and control work, but also is commonly used in places such as chemical enterprises, factories and the like, and corresponding gas detectors are selected according to different gases.
Water level water pressure detector: the intelligent fire fighting system is used for monitoring the water level water pressure in the tail end of a fire fighting water source, the water level of a high-level water tank and the like, and simultaneously, the water source distribution situation of a networking terminal is seen by combining with a GIS electronic map. Through information sharing, water source information is provided for fire fighting and rescue.
Intelligent video monitoring: the remote monitoring of safety channels such as a monitoring fire-fighting channel, a stair, a corridor and the like, the off-duty condition of a person on duty in a fire-fighting control room and the linkage of a traffic video monitoring system.
Monitoring current and voltage: in the overall data of fire-fighting hazard, fire accidents caused by electricity consumption account for three factors, which is why the electrical hazard is one of the objects of major concern in the fire-fighting equipment facilities, and the intelligent sensing equipment can be used for acquiring relevant parameters for monitoring the electricity consumption safety.
Step S4: establishing a fire-fighting trust degree function for the data, judging the probability of fire-fighting risks, and repeating the step S3 when no fire-fighting risk occurs; fuzzy mathematics can represent the likelihood that an event belongs to a certain set by fuzzy membership. Thus, the membership result can be all values in 0 to 1. Fuzzy membership is used to indicate that the probability of the likelihood of a fire hazard measured by different sensors exactly matches the signature of the single signal at risk filled with uncertainty. And finding that the membership function most suitable for describing the fire risk probability assignment obeys the sigmf distribution according to the fire risk development rule characteristics. The invention describes the measurement trust degree distribution of the fire sensor by using the membership function sigmf distribution.
Step S5: when fire-fighting danger occurs, determining the distance between a person and a base station by using a UWB positioning base station, and accurately positioning the position of the person; all persons entering the industrial park wear a UWB watch model WT01B3/B4, the distance of positioning of this watch: the positioning distance is long, and the network can automatically identify, track, position and collect without manual intervention; the intelligent electronic device has the advantages that the encrypted ID is used, the safety is high, the UWB working frequency range is adopted, the data transmission speed is high, the anti-interference capability is high, the power consumption is low, the vibration prompt of a built-in motor is realized, a 1.54-inch touch color screen is arranged in the intelligent electronic device, a 600mAh rechargeable lithium battery is arranged in the intelligent electronic device, the LED lamp is used for indicating, and the working state is clear at a glance. The UWB positioning base station utilizes an S3 optical fiber type waterproof base station, is suitable for high-precision positioning requirements of personnel, articles, vehicles and the like in fixed areas such as mines, outdoors and the like without explosion-proof requirements such as indoor areas, industrial parks, construction sites, courtyards, non-coal mines and the like, and provides data acquisition and transmission. The functions are rich: the device integrates the functions of positioning data acquisition, uploading, downlink control, alarming and the like. Networking capability is strong: a chain type network. The network capacity is large: one synchronous controller can provide up to 12 channels, support 120 base station IPs, and the synchronous controllers can be cascaded for capacity expansion. The anti-interference capability is strong: the data transmission link adopts optical fiber transmission.
Step S6: establishing a personnel moving model by utilizing the number of personnel and the positions of the personnel in the industrial park; the model is established to ensure that the whole industrial park is effectively controlled when fire risks occur, and because the positions of the personnel in the industrial park are different and the evacuation paths are different when the fire risks occur, detailed path planning needs to be performed on each personnel.
Step S7: establishing an optimal path optimization model; when fire-fighting danger occurs, the situation changes in real time, and the latest and safe evacuation path is provided for the personnel in the industrial park by continuously optimizing the path, so that the personnel can be evacuated safely in time. The optimal path optimization problem of the evacuation path is to achieve established targets, such as shortest evacuation time, shortest path, unobstructed evacuation path and meeting constraint conditions to a certain extent, and the most scientific and reasonable evacuation path is searched for people at different evacuation points.
Step S8: respectively establishing three models of an evacuation time element, an evacuation personnel element and an evacuation path smoothness element to update the evacuation path in real time; aiming at the personnel evacuation process, the optimal path selection influences factors specifically include transportation time, personnel smoothness and road smoothness. In order to quickly obtain the optimal solution, a mathematical model based on multiple constraint conditions is constructed based on evacuation time elements, personnel elements and road smoothness element oriented to path selection, and is combined with an ant colony algorithm, so that path updating and dynamic selection with the multiple constraint conditions as carriers are realized, and personnel are guided to select a path which tends to the optimal path during evacuation so as to accurately obtain the optimal solution.
Step S9: establishing a path real-time updating model and constraint conditions to ensure that the evacuation personnel have the safest evacuation path; in order to accelerate the convergence speed of the algorithm, moderately weaken the worst path, add penalty factors to reduce the selected probability, strengthen the pheromone concentration of the path with better quality, prompt the evacuation personnel to select the path with the best quality, and update and improve the pheromone of the evacuation path in real time.
Step S10: the evacuation path is sent to industrial park personnel in real time. The UWB watch which sends the evacuation path to park personnel in real time utilizes W310 wireless signal transmission equipment, the equipment is outdoor trunk high-bandwidth wireless communication transmission equipment which accords with the 802.11 AC standard, a high-pass 9563CPU (750Mhz) and high-pass 9882 wireless radio frequency module, 64MBytes memories and 16MBytes flash memories are adopted, and strong hardware configuration enables the equipment to have the advantages of long transmission distance, high transmission rate, strong long-time large data transmission stability and the like. The W310 can be equipped with various high-gain directional antennas according to different field requirements, and the transmission distance can reach more than 20 kilometers under the line-of-sight condition. The transmission rate can keep 350Mbps in 10 kilometers, the actually measured transmission rate can reach 867Mbps in a close-range signal saturation state, the big data transmission of various main lines is completely met, and the problems that an ordinary wireless network bridge is unsmooth in image card, lost in data, halted in long-time working and the like due to insufficient bandwidth in transmission are solved.
The multi-objective decision model has the expression as follows:
the constraint equation is expressed as:
a fitness evaluation function, wherein the expression is as follows:
punishment is carried out on the part of the distance between the demand point and the fire station exceeding the maximum coverage distance when the fire hazard occurs, and the method is used for converting the fire station site selection problem with the maximum distance limit constraint into the unconstrained optimization problem. Wherein, a nm Denotes the distance of position n to m, b nm Indicating that location n is covered by a mini fire station m, c m Denotes a mini fire station, e 'established at m' max Representing the maximum distance between the demand point of the micro fire station and the nearest micro fire station when fire danger occurs, Z representing the set of all positions, e max Representing the maximum coverage distance of the miniature fire station, delta e representing the relaxation distance, A (n, m) representing the built fitness evaluation function of the miniature fire station, f m Represents the cost of building a mini-station at m, (e) max -e′ max ) q The representation is a penalty function and q represents a penalty factor.
The fire protection trust function has the expression:
wherein y (r, s, t) represents probability value of fire hazard occurrence, r represents fire sensor measurement value, e represents natural constant, s represents monotonic function value, and t represents time of fire hazard occurrence; when s is greater than 0, the function y is a monotonically increasing function of the independent variable r, and the y has a limit value of 1 and conforms to the basic probability function of the fire hazard; when s <0, the function y is a monotonically decreasing function of the argument r, and y has a limit value of 0, coinciding with the fire hazard fundamental probability function.
y T ＝1-y D -y E
Wherein, y D ，y E ，y T Respectively showing a basic probability function with fire-fighting risk signs, a basic probability function without fire-fighting risk signs and a fire-fighting risk uncertainty function.
The distance between the personnel and the base station is expressed as follows:
wherein v represents an electromagnetic wave transmission speed, G 1 、G 2 、G 3 、G 4 The time required for receiving the personnel information is respectively represented as base station No. 1, base station No. 2, base station No. 3 and base station No. 4. the base station No. 1, base station No. 2, base station No. 3 and base station No. 4 utilize the layout mode of the square, when the personnel is at the central point of the square, the distance between the personnel and the base stations No. 4 is equal, and when the distance between the personnel and the base stations No. 4 is unequal, the position of the personnel can be rapidly positioned by using the distance difference between the personnel and different base stations.
The human movement model has the expression:
wherein h represents an optimal path function, g represents the employee number of any park, i represents the employee number, j represents the total number of the employees in the industrial park, k and l respectively represent the abscissa and the ordinate of the position of the employee, and gamma is kl Weight coefficient representing safety distance, o klg Indicating that employee number g has passed a safe distance.
The optimal path optimization model has the expression as follows:
wherein the content of the first and second substances,
representing the probability of the transition of the person g at the location (k, l) at time p, k and l being respectively represented as the abscissa and ordinate of the position of the person,
a heuristic function is represented that is a function of,
representing how easily a person can receive information at a location (k, l), D g Representing the set of all evacuation paths of people, q representing the path selected when people evacuate, sigma and theta representing the relatively important measurement factors of the initiating function factor and the difficulty degree of receiving information in the evacuation rule, eta kl Represents the update of the heuristic function, F (k) represents the length of the path that has been traversed by the employee when he arrives at position k, R lr Denotes the spacing between two positions, r kl Representing the inverse of the separation between the two locations.
As shown in fig. 2, the evacuation time element is expressed as:
wherein, W l max represents the upper limit of the maximum time expected to be allowed in the path for evacuation of persons, W l Indicating the specific time, U, required for evacuation of persons a (l) Represents an evacuation time element, the larger it is, the longer the evacuation actually takes.
The evacuation personnel element is expressed as follows:
wherein, U b (l) The component representing the evacuated persons is the ratio between the persons at the time of evacuation and the most expected persons, and the larger the value is, the more evacuated persons are represented, and V l Representing the number of persons in the evacuation; v l max denotes the most expected person in the evacuation, u l Representing the real-time number of employees in the industrial park, v, as they evacuate the day l Representing the number of real-time foreign persons in the industrial park, w, as the day evacuates l Representing the number of real-time personnel errors in the industrial park as they evacuate the day.
An escape path smoothness element, expressed as:
U(l)＝ζU a (l)+αU b (l)+υU c (l)
wherein, U c (l) The element of the path patency degree is represented as the ratio of the path patency degree to the lowest tolerance of the path patency degree, and the larger the value of the element of the path patency degree is, the more unobstructed the path is selected when people evacuate; u shape a (l) Representing an evacuation time element, U b (l) Elements representing evacuated persons, X l Indicates the degree of smoothness of the path, X l max represents a clear pathAnd U (l) represents an evacuation element, and zeta, alpha and upsilon represent actual proportions occupied by time, personnel and path smoothness when fire-fighting evacuation is carried out on the industrial park respectively.
The path real-time updating model has the expression:
wherein, T b Represents the best path traversed, | T b I denotes the optimal path length, T w Represents the worst path traversed, | T w L represents the worst path length, μ represents the path update weight, ρ kl (z) represents the current optimal path, ρ kl (z +1) represents the optimal path predicted value at the next moment, and t represents a path updating coefficient;
constraint conditions, the expression is:
where ρ is kl Denotes path selection, p min Representing the minimum number of path choices, p max Representing the maximum number of path selections.
The invention provides an intelligent fire-fighting management method for an industrial park, which is different from the traditional fire-fighting management method, and is characterized in that the miniature fire-fighting stations of the whole industrial park are distributed by an algorithm in a site selection manner, so that the miniature fire-fighting stations can play the greatest role when fire-fighting hazards occur, meanwhile, the possibility of the fire-fighting hazards is judged by utilizing various algorithms, an optimal evacuation path is formed when the fire-fighting hazards occur, and the industrial park workers are helped to evacuate effectively in the shortest time.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made herein without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims (10)
1. An intelligent fire-fighting management method for an industrial park is characterized by comprising the following steps:
step S1: establishing the position of a miniature fire station of the industrial park by utilizing a multi-objective decision model and a constraint equation;
step S2: establishing a fitness evaluation function to evaluate the established miniature fire station;
step S3: monitoring data of the whole industrial park by using a fire sensor;
step S4: establishing a fire-fighting trust degree function for the data, judging the probability of fire-fighting risks, and repeating the step S3 when no fire-fighting risk occurs;
step S5: when fire-fighting danger occurs, determining the distance between a person and a base station by using a UWB positioning base station, and accurately positioning the position of the person;
step S6: establishing a personnel moving model by utilizing the number of personnel and the positions of the personnel in the industrial park;
step S7: establishing an optimal path optimization model;
step S8: respectively establishing three models of an evacuation time element, an evacuation personnel element and an evacuation path smoothness element to update an evacuation path in real time;
step S9: establishing a path real-time updating model and constraint conditions to ensure that the evacuation personnel have the safest evacuation path;
step S10: the evacuation path is sent to industrial park personnel in real time.
2. The intelligent fire management method for industrial parks as claimed in claim 1, wherein the multi-objective decision model has the expression:
the constraint equation is expressed as:
a fitness evaluation function, the expression of which is:
wherein, a nm Denotes the distance of position n to m, b nm Indicating that location n is covered by a mini fire station m, c m Denotes a miniature fire station, e 'established at m' max Representing the maximum distance between the demand point of the micro fire station and the nearest micro fire station when fire danger occurs, Z representing the set of all positions, e max Represents the maximum coverage distance of the miniature fire station, delta e represents the relaxation distance, A (n, m) represents the built fitness evaluation function of the miniature fire station, f m Represents the cost of building a miniature fire station at m, (e) max -e′ max ) q The representation is a penalty function and q represents a penalty factor.
3. The intelligent fire management method for industrial parks, as recited in claim 1, wherein the fire confidence function has the expression:
wherein y (r, s, t) represents probability value of fire hazard occurrence, r represents fire sensor measurement value, e represents natural constant, s represents monotonic function value, and t represents time of fire hazard occurrence;
y T ＝1-y D -y E
wherein, y D ，y E ，y T Respectively indicate fire controlA hazard sign basis probability function, a no fire hazard sign basis probability function, a fire hazard uncertainty function.
4. The intelligent fire management method for industrial parks, as set forth in claim 1, wherein the distance between the person and the base station is expressed as:
wherein v represents an electromagnetic wave transmission speed, G 1 、G 2 、G 3 、G 4 Respectively expressed as the time required for the base station No. 1, the base station No. 2, the base station No. 3 and the base station No. 4 to receive the personnel information.
5. The intelligent fire management method for industrial parks as claimed in claim 1, wherein the human movement model has an expression:
wherein h represents an optimal path function, g represents the employee number of any industrial park, i represents the number of employees, j represents the total number of the employees in the industrial park, k and l are respectively represented as the abscissa and the ordinate of the position of the employee, and gamma is kl Weight coefficient representing safety distance, o klg Indicating that employee number g has passed a safe distance.
6. The intelligent fire management method for industrial parks according to claim 1, wherein the optimal path optimization model has the expression:
wherein the content of the first and second substances,
representing the probability of the transition of the person g at the location (k, l) at time p, k and l being respectively represented as the abscissa and ordinate of the position of the person,
a heuristic function is represented that is a function of,
representing how easily a person can receive information at a location (k, l), D g Representing the set of all evacuation paths of people, q representing the path selected when people evacuate, sigma and theta representing the relatively important measurement factors of the initiating function factor and the difficulty degree of receiving information in the evacuation rule, eta kl Represents the update of the heuristic function, F (k) represents the length of the path that has been traversed by the employee when he arrives at position k, R lr Denotes the spacing between two positions, r kl Representing the inverse of the separation between the two locations.
7. The intelligent fire management method for industrial parks as claimed in claim 1, wherein the evacuation time element is expressed as:
wherein, W lm ax denotes the upper limit of the maximum time expected to be allowed in the path for evacuation of persons, W l Indicating the specific time, U, required for evacuation of persons a (l) Represents an evacuation time element, the larger it is, the longer the evacuation actually takes.
8. The intelligent fire management method for industrial parks as claimed in claim 1, wherein the evacuation people elements are expressed by the following expressions:
wherein, U b (l) Representing the evacuated person element, is the ratio between the persons in evacuation and the most expected persons, the larger the value, the more evacuated persons are represented, and U a (l) Representing an evacuation time element, U b (l) Indicating an element of evacuation, V l Representing the number of persons in the evacuation; v l max denotes the most expected person in the evacuation, u l Representing the real-time number of employees in the industrial park, v, as they evacuate the day l Representing the number of real-time foreign persons in the industrial park, w, as the day evacuates l Representing the number of real-time personnel errors in the industrial park as they evacuate the day.
9. The intelligent fire management method for industrial parks as claimed in claim 1, wherein the evacuation route patency element is expressed as:
wherein, U c (l) The element representing the smoothness of the path is the smoothness of the path andthe larger the value of the ratio between the lowest tolerance degrees of the path patency degree is, the more unobstructed the selected path is when the personnel evacuate; x l Indicates the degree of patency of the path, X l max represents the lowest tolerance of the path smoothness, U (l) represents evacuation elements, and zeta, alpha and upsilon represent actual proportions of time, personnel and the path smoothness occupied by fire-fighting evacuation in an industrial park respectively.
10. The intelligent fire management method for industrial parks as claimed in claim 1, wherein the path real-time update model has the expression:
wherein, T b Represents the best path traversed, | T b I denotes the optimal path length, T w Represents the worst path traversed, | T w L represents the worst path length, μ represents the path update weight, ρ kl (z) represents the current optimal path, ρ kl (z +1) represents the optimal path prediction value at the next moment, and t represents a path updating coefficient;
constraint conditions, the expression is:
where ρ is kl Denotes path selection, p min Representing the minimum number of path choices, p max Representing the maximum number of path selections.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210572094.4A CN115018283A (en) | 2022-05-24 | 2022-05-24 | Intelligent fire-fighting management method for industrial park |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210572094.4A CN115018283A (en) | 2022-05-24 | 2022-05-24 | Intelligent fire-fighting management method for industrial park |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115018283A true CN115018283A (en) | 2022-09-06 |
Family
ID=83069523
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210572094.4A Withdrawn CN115018283A (en) | 2022-05-24 | 2022-05-24 | Intelligent fire-fighting management method for industrial park |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115018283A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116109014A (en) * | 2023-04-11 | 2023-05-12 | 广东广宇科技发展有限公司 | Simulation fire-fighting evacuation method for urban rail transit large transfer station |
| CN116168502A (en) * | 2023-02-28 | 2023-05-26 | 合肥初云信息科技有限公司 | Energy-saving control system of fire sensor of self-optimizing industrial park |
-
2022
- 2022-05-24 CN CN202210572094.4A patent/CN115018283A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116168502A (en) * | 2023-02-28 | 2023-05-26 | 合肥初云信息科技有限公司 | Energy-saving control system of fire sensor of self-optimizing industrial park |
| CN116168502B (en) * | 2023-02-28 | 2024-04-19 | 山西德元致盛建设工程有限公司 | Energy-saving control system of fire sensor of self-optimizing industrial park |
| CN116109014A (en) * | 2023-04-11 | 2023-05-12 | 广东广宇科技发展有限公司 | Simulation fire-fighting evacuation method for urban rail transit large transfer station |
| CN116109014B (en) * | 2023-04-11 | 2023-08-01 | 广东广宇科技发展有限公司 | Simulation fire-fighting evacuation method for urban rail transit large transfer station |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115018283A (en) | Intelligent fire-fighting management method for industrial park | |
| US10605473B2 (en) | Intelligent LED bulb and vent method, apparatus and system | |
| CN102231227B (en) | Solar gridded fireproof monitoring and prewarning system for forestry | |
| CN109724610A (en) | A kind of method and device of full information real scene navigation | |
| CN110567464B (en) | Airport terminal environment autonomous inspection vehicle monitoring system | |
| RU2626028C2 (en) | Street navigation system using infrastructure elements | |
| CN105404231A (en) | Internet of things-based intelligent building monitoring managing system | |
| CN109185743B (en) | Intelligent fire-fighting emergency lamp system | |
| CN107510914B (en) | Intelligent fire-fighting remote monitoring system and method for park | |
| CN107708062A (en) | A kind of fire evacuation system and method based on indoor positioning | |
| CN110971699A (en) | Smart city safety management remote monitoring system and method | |
| CN114723133A (en) | City emergency early warning and evacuation command method and system under emergency | |
| CN117649130A (en) | Intelligent fire safety monitoring system | |
| CN111982092A (en) | Chemical industry park emergency evacuation route planning system based on pedestrian recognition device | |
| CN115752490B (en) | Safe trip path optimization method and system based on big data and positioning technology | |
| CN110779529A (en) | Forest disaster emergency commanding and scheduling system based on big data and cloud computing | |
| CN111399419A (en) | Emergency rescue positioning system based on intelligent emergency evacuation system | |
| CN202084130U (en) | Solar grid fireproof monitoring and pre-warning system for forestry | |
| CN117156386A (en) | Intelligent positioning, calling for help and alarming intercom equipment for fire control | |
| CN107561598A (en) | Minisize distributed type weather information based on communication base station reports and submits system and method in real time | |
| Jiang et al. | Emergency evacuation based on long range communication technology | |
| CN116822764A (en) | Visual intelligent emergency command system of fire control | |
| KR20050068709A (en) | Accident monitoring system | |
| CN209946942U (en) | Disaster emergency response system | |
| CN114067065A (en) | Escape route visualization system and escape method |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220906 |
|
| WW01 | Invention patent application withdrawn after publication |