CN116822964A - Fire-fighting equipment management system and method based on Internet of things - Google Patents

Abstract

The invention relates to a fire-fighting equipment management system and method based on the Internet of things, and belongs to the technical field of fire-fighting data processing. The system comprises: the system comprises various Internet of things devices, a monitoring system and a monitoring system, wherein the various Internet of things devices are used for respectively acquiring various real-time data of a monitoring building and carrying out real-time network transmission; the intelligent judging device is arranged in the fire control management area and is used for intelligently judging the probability of fire disaster of the monitoring building at the next moment of the current moment by adopting a customized feedforward neural network model; and the state switching device is used for switching each fire-fighting device to the standby state at the same time when the probability is larger than or equal to the set probability threshold value. The method corresponds to the system. According to the invention, aiming at the technical problem of response lag caused by lack of future environmental data of the fire-fighting equipment, the prediction state of the building at the future time can be intelligently judged and monitored based on the Internet of things technology, and various fire-fighting equipment in the fire-fighting management area can be configured in advance based on the prediction state, so that the technical problem is solved.

Description

Fire-fighting equipment management system and method based on Internet of things

Technical Field

The invention relates to the technical field of fire control data processing, in particular to a fire control equipment management system and method based on the Internet of things.

Background

In the existing fire control management, a linkage control system is generally adopted to monitor fire and perform fire emergency treatment on buildings, including the buildings, so that control equipment such as an indoor fire hydrant system, an automatic water spraying system, an electric fireproof door, a fireproof roller shutter, a ventilation air conditioner, smoke prevention and exhaust equipment, an electric fireproof valve and the like can be arranged in the buildings to perform on-site fire treatment, and more importantly, the control equipment is linked to a remote fire control management area, such as a fire department place, and fire control emergency treatment is performed by the fire control management area to reach the building on site by fire control equipment and fire fighters of corresponding quantity and types.

For example, chinese patent publication CN102170470a proposes a fire information management platform based on the internet of things, which aims to provide a fire information management platform capable of being installed in different fire equipment manufacturers, intelligently identifying each equipment protocol, converting the fire information management platform into standard data, and transmitting the standard data to the fire information management platform based on the internet of things through GPRS or MOXTCP; the platform adopts the technical concept of the Internet of things, the operation condition of the fire control system of the unit is collected and used through the fire control data collection module, the data is transmitted to the fire control information management platform based on the Internet of things through the information transmission module, the collected information is classified and processed through the computer, the operation condition of the fire control system is fed back to the client in real time, and corresponding reactions and measures are further made.

For example, the high-rise building digital fire-fighting equipment management system based on the Internet of things is provided by Chinese patent publication CN102034154A, and consists of a fire-fighting equipment RFID electronic tag, a front-end card reading device and a property management data exchange center; the front-end card reading device comprises a low-frequency RFID electronic tag reading and writing module, a high-frequency RFID electronic tag reading and writing module, a control module, a USB interface and data storage module, a data communication module, a power module and an LCD display module; the property management data exchange center comprises a data exchange center data communication module, a data storage and processing module, a data exchange center main control module and a display module; the front-end card reading device realizes the information exchange with the RFID electronic tag of the fire-fighting equipment in the high-rise building, the data communication module carries out the intercommunication between the information and the property management data exchange center in real time, and the Internet transmits the information to the fire-fighting management mechanism server; the system can dynamically manage the production information, the working condition information, the maintenance information and the scrapping information of the fire-fighting equipment in the high-rise building in the full life, and provides technical support for the fire-fighting departments to realize the dynamic, digital and real-time management of the fire-fighting equipment of the high-rise building.

However, the above-mentioned prior art is limited to obtaining real-time environment data of a location where a building is located by an internet of things technology, performing a judgment of a real-time state of the building based on the real-time environment data of the location, and performing real-time processing and management of fire fighting equipment based on the real-time state obtained by the judgment, and obviously, the management mode is too late, the obtained real-time state of the building is an already occurring state, and thus, the adopted management measures are not matched with respect to the real-time state, and the key point of the problem is that the state of the building at a future moment which does not occur cannot be predicted based on various data obtained by the internet of things technology, so that the management measures of the fire fighting equipment at the future moment lack reference data.

Disclosure of Invention

In order to solve the technical problems in the prior art, the invention provides a fire-fighting equipment management system and a fire-fighting equipment management method based on the Internet of things, which can acquire various real-time environment data of a monitoring building by adopting various Internet of things equipment, intelligently judge the predicted state of the monitoring building at the future moment based on various real-time environment data by adopting an intelligent model customized for the monitoring building, and perform advanced configuration on various fire-fighting equipment in a fire-fighting management area based on the predicted state, and the current playing channel of a multichannel loudspeaker in the fire-fighting management area is transferred to a fire accident broadcasting channel in advance, so that the emergency response time of a fire-fighting management department is effectively shortened.

According to a first aspect of the present invention, there is provided a fire-fighting equipment management system based on the internet of things, the system comprising:

the network sensing device is arranged inside the monitoring building and comprises a temperature sensing array, a humidity sensing array, a dust sensing array and network transceiver equipment, wherein the temperature sensing array is arranged on each layer of the monitoring building to respectively detect real-time temperature of each layer, the humidity sensing array is arranged on each layer of the monitoring building to respectively detect real-time humidity of each layer, the dust sensing array is arranged on each layer of the monitoring building to respectively detect real-time dust concentration of each layer, and the network transceiver equipment is respectively connected with the temperature sensing array, the humidity sensing array and the dust sensing array;

the visual detection device is arranged right in front of the central position of the front wall surface of the monitoring building and comprises an image sensing assembly and a network transmission assembly, wherein the image sensing assembly is used for facing the monitoring building to acquire a real-time acquisition picture of the environment where the front wall surface is located, and the network transmission assembly is connected with the image sensing assembly and is used for transmitting the real-time acquisition picture through a wireless communication network;

The content identification device is arranged in a fire control management area for storing batch fire control equipment, is connected with the network transmission assembly through a wireless communication network to receive the real-time acquisition picture, and is used for identifying a wall imaging area in the real-time acquisition picture based on imaging characteristics of a front wall of a monitoring building;

the intelligent judging device is arranged in the fire control management area, is connected with the network transceiver through a wireless communication network and is electrically connected with the content identifying device, and is used for intelligently judging the probability of fire occurrence of the monitoring building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values corresponding to the constituent pixel points of the wall imaging area respectively, wherein the time interval between the next moment and the current moment is equal to the set time interval;

the successive training device is electrically connected with the intelligent judging device and is used for sending the feedforward neural network after the preset number of training is finished for each time to the intelligent judging model as a feedforward neural network model for use, and the preset number of values are monotonically and positively associated with the occupied space volume of the monitored building;

The state switching device is electrically connected with the intelligent judging device and simultaneously connected with each fire-fighting equipment wireless network of the fire-fighting management area, and is used for synchronously sending a standby trigger signal to each fire-fighting equipment through the wireless communication network to switch each fire-fighting equipment from a deactivated state to a standby state when the received probability is greater than or equal to a set probability threshold value;

and the channel transfer device is electrically connected with the intelligent judging device and simultaneously connected with the multichannel loudspeaker of the fire control management area, and is used for transferring the current playing channel of the multichannel loudspeaker to the fire accident broadcasting channel in advance at the current moment when the received probability is greater than or equal to a set probability threshold value.

According to a second aspect of the present invention, there is provided a fire-fighting equipment management method based on the internet of things, the method comprising the steps of:

taking the feedforward neural network after the preset number of training is completed as a feedforward neural network model, wherein the value of the preset number is monotonically and positively correlated with the occupied space volume of the monitored building;

acquiring real-time temperatures, real-time humidity and real-time dust concentration of each layer corresponding to each layer in the monitoring building, and transmitting the real-time temperatures, the real-time humidity and the real-time dust concentration of each layer in real time through a wireless communication network;

The method comprises the steps that a real-time acquisition picture of the environment where the front wall surface is located is acquired for a monitoring building right in front of the central position of the front wall surface of the monitoring building, and the real-time acquisition picture is transmitted through a wireless communication network;

receiving the real-time acquisition picture at a fire control management area for storing batch fire control equipment through a wireless communication network, and identifying a wall surface imaging area in the real-time acquisition picture based on imaging characteristics of a front wall surface of a monitoring building;

receiving real-time temperature, real-time humidity and real-time dust concentration of each layer through a wireless communication network, intelligently judging the probability of fire disaster of the monitored building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature, the real-time humidity, the real-time dust concentration of each layer, a set time interval and red component values corresponding to the constituent pixel points of the wall imaging area, wherein the time interval between the next moment and the current moment is equal to the set time interval;

when the received probability is greater than or equal to a set probability threshold, synchronously transmitting a standby trigger signal to each fire-fighting device in the fire-fighting management area through a wireless communication network so as to simultaneously switch each fire-fighting device from a deactivated state to a standby state;

When the received probability is greater than or equal to a set probability threshold, the current playing channel of the multichannel loudspeaker of the fire control management area is transferred to a fire accident broadcasting channel in advance at the current moment;

the method for acquiring the real-time temperature, the real-time humidity and the real-time dust concentration of each layer corresponding to each layer in the monitoring building respectively, and transmitting the real-time temperature, the real-time humidity and the real-time dust concentration of each layer in real time through a wireless communication network comprises the following steps: the method comprises the steps of adopting temperature sensing units respectively arranged on all floors of a monitoring building to respectively acquire real-time temperatures of all floors, adopting humidity sensing units respectively arranged on all floors of the monitoring building to respectively acquire real-time humidity of all floors, adopting gray scale sensing units respectively arranged on all floors of the monitoring building to respectively acquire real-time dust concentrations of all floors, and adopting network transceiver equipment to transmit the real-time temperatures of all floors, the real-time humidity of all floors and the real-time dust concentrations of all floors in real time through a wireless communication network;

wherein, right in front of the central point of the front end wall of the monitoring building to the monitoring building in order to gather the real-time collection picture of the environment that the front end wall is located, and transmit the real-time collection picture through wireless communication network includes: the acquisition operation is performed by using an image sensing component in the visual inspection device, and the transmission operation is performed by using a network transmission component in the visual inspection device.

Accordingly, the present invention has at least the following four important inventive concepts:

inventive concept a: different types of Internet of things equipment are adopted to respectively acquire real-time temperature, real-time humidity and dust concentration of each layer in a monitored building and real-time acquisition pictures of the environment of the front wall of the monitored building, so that reliable and complete multiple basic data are screened for subsequent intelligent judgment of whether fire disaster occurs at the future moment of the monitored building;

inventive conception B: in order to execute intelligent judgment of whether fire disaster occurs at the future moment of the monitoring building, a targeted design feedforward neural network model is constructed, wherein feedforward neural networks after each training of a preset number are used as feedforward neural network models, and the value of the preset number is monotonically and positively associated with the occupied space volume of the monitoring building, so that customization of the feedforward neural network models of different monitoring buildings is realized;

inventive conception C: in each training performed on a feedforward neural network, taking each red component value corresponding to each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration and each constituent pixel point of a wall imaging area, which correspond to a set time interval and a historical time before an initial time of a fire disaster, as parallel input content of the feedforward neural network, taking the probability of being greater than or equal to a set probability threshold value as output content of the feedforward neural network, wherein the time interval duration between the historical time and the initial time is equal to the set time interval, so that the training effect of each training is ensured;

Inventive concept D: when the probability of whether a fire disaster occurs at the future moment of the intelligently judged monitoring building is larger than or equal to a set probability threshold value, a standby trigger signal is synchronously sent to each fire-fighting device in the fire-fighting management area through a wireless communication network so as to switch each fire-fighting device from a deactivated state to a standby state at the same time, and meanwhile, the current playing channel of a multi-channel loudspeaker in the fire-fighting management area is transferred to a fire accident broadcasting channel in advance at the current moment, so that the advanced configuration of emergency measures of the fire-fighting management area based on Internet of things data is realized, the emergency response time of a fire-fighting management department is shortened, and the processing effect of fire-fighting treatment measures is improved.

Drawings

Embodiments of the present invention will be described below with reference to the accompanying drawings, in which:

fig. 1 is a technical flowchart of a fire-fighting equipment management system and method based on the internet of things according to the present invention.

Fig. 2 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to an embodiment of the present invention.

Fig. 3 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to a second embodiment of the present invention.

Fig. 4 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to a third embodiment of the present invention.

Fig. 5 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to a fourth embodiment of the present invention.

Detailed Description

As shown in fig. 1, a technical flowchart of a fire-fighting equipment management system and method based on the internet of things is provided.

As shown in fig. 1, the specific technical process of the present invention is as follows:

the technical process is as follows: various physical network devices with built-in field sensing devices and network transmission devices are adopted to respectively acquire various sensing data of a monitoring building field and perform network transmission;

specifically, the respectively acquiring various sensing data of the monitored building site includes: monitoring real-time temperature, real-time humidity and dust concentration of each layer in a building, and monitoring a real-time acquisition picture of an environment of a front wall surface of the building;

the technical flow is as follows: identifying wall imaging areas in real-time acquisition pictures received by a network in a fire control management area for storing batch fire control equipment, and further acquiring red component values corresponding to each constituent pixel point of the wall imaging areas;

for example, the wall imaging area in the real-time acquisition picture received by the network can be identified based on monitoring the color imaging characteristics or gray imaging characteristics of the front wall of the building;

And the technical flow is as follows: a feedforward neural network model which is designed in a targeted manner in advance is adopted to intelligently judge whether fire disasters occur at the future moment of the monitored building based on various sensing data and derivative information of various sensing data of the monitored building site;

as shown in fig. 1, the feedforward neural network model may output a probability of a fire occurring at a future time of the monitored building, and give a determination that the fire will occur at the future time of the monitored building when the probability is greater than a preset probability threshold;

for example, in order to ensure the pertinence of the feedforward neural network model design and the validity and stability of the intelligent judgment result, the following specific measures are taken:

measure A: the feedforward neural network model is a feedforward neural network after the preset number of training is completed;

measure B: the value of the preset number is monotonically and positively correlated with the occupied space volume of the monitored building;

measure C: in each training performed on the feedforward neural network, taking each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration and each red component value respectively corresponding to each constituent pixel point of a wall imaging area, which correspond to a set time interval and a historical time before an initial time of a fire disaster, as parallel input contents of the feedforward neural network, taking the probability of being greater than or equal to a set probability threshold value as output contents of the feedforward neural network, wherein the time interval duration between the historical time and the initial time is equal to the set time interval;

The technical process is as follows: when a fire disaster occurs in the intelligently judged monitoring building at a future moment, synchronously sending a standby trigger signal to each fire-fighting device in the fire-fighting management area through a wireless communication network so as to simultaneously switch each fire-fighting device from a deactivated state to a standby state, and simultaneously transferring the current playing channel of the multichannel loudspeaker in the fire-fighting management area to a fire accident broadcasting channel in advance at the current moment;

therefore, through the technical processes, the advanced configuration of emergency measures of the fire control management area based on the data of the Internet of things is realized, the emergency response time of the fire control management department is shortened, and the treatment effect of the fire control treatment measures is improved.

The key points of the invention are as follows: the method comprises the steps of acquiring intelligent judgment of whether a fire disaster can occur in a building at a future moment, monitoring various sensing data of a building site and customized screening of derivative information of the various sensing data, executing a targeted construction mechanism of a feed-forward neural network model of the intelligent judgment, switching the state of fire protection equipment based on the probability of the fire disaster occurring at the future moment, and transferring a multichannel loudspeaker broadcasting channel to a fire accident broadcasting channel in advance.

The fire-fighting equipment management system and method based on the Internet of things of the invention are specifically described in the following by way of example.

Example 1

Fig. 2 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to an embodiment of the present invention.

As shown in fig. 2, the fire-fighting equipment management system based on the internet of things comprises the following components:

the network sensing device is arranged inside the monitoring building and comprises a temperature sensing array, a humidity sensing array, a dust sensing array and network transceiver equipment, wherein the temperature sensing array is arranged on each layer of the monitoring building to respectively detect real-time temperature of each layer, the humidity sensing array is arranged on each layer of the monitoring building to respectively detect real-time humidity of each layer, the dust sensing array is arranged on each layer of the monitoring building to respectively detect real-time dust concentration of each layer, and the network transceiver equipment is respectively connected with the temperature sensing array, the humidity sensing array and the dust sensing array;

in this way, a type of sensing mechanism of the Internet of things is constructed through the temperature sensing array, the humidity sensing array, the dust sensing array and the network transceiver, and is used for sending different types of sensing information of each floor of a monitoring building to a monitoring end or a processing end through a network;

For example, each temperature sensing unit in the temperature sensing array may be a non-contact temperature sensing unit or a contact temperature sensing unit;

specifically, the network transceiver is a network transceiver based on a time division duplex communication mechanism or a network transceiver based on a frequency division duplex communication mechanism;

the visual detection device is arranged right in front of the central position of the front wall surface of the monitoring building and comprises an image sensing assembly and a network transmission assembly, wherein the image sensing assembly is used for facing the monitoring building to acquire a real-time acquisition picture of the environment where the front wall surface is located, and the network transmission assembly is connected with the image sensing assembly and is used for transmitting the real-time acquisition picture through a wireless communication network;

in this way, another type of sensing mechanism of the Internet of things is constructed through the image sensing assembly and the network transmission assembly and is used for sending visual information of the monitored building to a monitoring end or a processing end through a network;

the image sensing component may be a CCD sensing component or a CMOS sensing component, for example;

the content identification device is arranged in a fire control management area for storing batch fire control equipment, is connected with the network transmission assembly through a wireless communication network to receive the real-time acquisition picture, and is used for identifying a wall imaging area in the real-time acquisition picture based on imaging characteristics of a front wall of a monitoring building;

For example, a programmable logic device may be selected to enable data processing that connects with the network transmission component over a wireless communications network to receive the real-time acquisition picture and identify a wall imaging area in the real-time acquisition picture based on monitoring imaging characteristics of a front wall of a building;

the intelligent judging device is arranged in the fire control management area, is connected with the network transceiver through a wireless communication network and is electrically connected with the content identifying device, and is used for intelligently judging the probability of fire occurrence of the monitoring building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values corresponding to the constituent pixel points of the wall imaging area respectively, wherein the time interval between the next moment and the current moment is equal to the set time interval;

for example, intelligently judging, by using a feedforward neural network model, a probability of fire occurrence of the monitored building at a next time of a current time based on each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration, a set time interval, and each red component value corresponding to each constituent pixel point of a wall imaging region, wherein the time interval between the next time and the current time is equal to the set time interval, including: the MATLAB tool box can be adopted to realize the data processing of intelligently judging the probability of fire disaster of the monitored building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to each constituent pixel point of the wall imaging area, wherein the time interval between the next moment and the current moment is equal to the set time interval;

The successive training device is electrically connected with the intelligent judging device and is used for sending the feedforward neural network after the preset number of training is finished for each time to the intelligent judging model as a feedforward neural network model for use, and the preset number of values are monotonically and positively associated with the occupied space volume of the monitored building;

for example, the monotonically positive association of the preset number of values with the occupied space volume of the monitored building includes: the preset number is 50 when the occupied space volume of the monitoring building is 10 ten thousand cubic meters, 60 when the occupied space volume of the monitoring building is 20 ten thousand cubic meters, and 70 when the occupied space volume of the monitoring building is 30 ten thousand cubic meters;

the state switching device is electrically connected with the intelligent judging device and simultaneously connected with each fire-fighting equipment wireless network of the fire-fighting management area, and is used for synchronously sending a standby trigger signal to each fire-fighting equipment through the wireless communication network to switch each fire-fighting equipment from a deactivated state to a standby state when the received probability is greater than or equal to a set probability threshold value;

The channel transfer device is electrically connected with the intelligent judging device and simultaneously connected with the multichannel loudspeaker of the fire control management area, and is used for transferring the current playing channel of the multichannel loudspeaker to the fire accident broadcasting channel in advance at the current moment when the received probability is more than or equal to a set probability threshold value;

the feedforward neural network after the preset number of training is used as a feedforward neural network model to be sent to the intelligent judgment model for use, and monotonically and positively associating the preset number of values with the occupied space volume of the monitored building comprises the following steps: in each training performed on the feedforward neural network, taking each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration and each red component value respectively corresponding to each constituent pixel point of a wall imaging area, which correspond to a set time interval and a historical time before an initial time of a fire disaster, as parallel input contents of the feedforward neural network, taking the probability of being greater than or equal to a set probability threshold value as output contents of the feedforward neural network, wherein the time interval duration between the historical time and the initial time is equal to the set time interval;

The method for intelligently judging the probability of fire disaster of the monitored building at the next moment of the current moment based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to all the constituent pixel points of the wall imaging area by adopting a feedforward neural network model, wherein the time interval between the next moment and the current moment is equal to the set time interval comprises the following steps: the red component value is an R component value in an RGB color space;

specifically, in the RGB color space, each pixel includes an R component value, a G component value, and a B component value, which are a green component value and a blue component value, respectively;

and wherein, temperature-sensing array sets up each layer in order to detect each layer real-time temperature respectively of control building, humidity-sensing array sets up each layer in order to detect each layer real-time humidity respectively of control building, dust-sensing array sets up each layer in order to detect each layer real-time dust concentration respectively of control building includes: the temperature sensing array comprises temperature sensing units respectively arranged on all layers of the monitoring building, the humidity sensing array comprises humidity sensing units respectively arranged on all layers of the monitoring building, and the dust sensing array comprises dust sensing units respectively arranged on all layers of the monitoring building.

Example two

Fig. 3 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to a second embodiment of the present invention.

As shown in fig. 3, unlike the embodiment in fig. 2, the fire-fighting equipment management system based on the internet of things further includes the following components:

the telescopic support piece is arranged in front of the monitoring building and is used for fixing the visual detection device in front of the central position of the front wall surface of the monitoring building;

the visual detection device is fixed on the top of the telescopic support, and a telescopic unit and a driving motor are arranged in the telescopic support;

the driving motor may be a brushless dc motor, and the telescopic support member may be a telescopic structure perpendicular to the ground, and is configured to move up and down in a vertical direction under the driving of the brushless dc motor;

the driving motor is connected with the telescopic unit and used for controlling the telescopic length of the telescopic unit.

Example III

Fig. 4 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to a third embodiment of the present invention.

As shown in fig. 4, unlike the embodiment in fig. 3, the fire-fighting equipment management system based on the internet of things further includes the following components:

The timing service device is respectively connected with the network sensing device and the visual detection device and is used for respectively providing the network sensing device and the visual detection device with respective required timing services;

illustratively, the timing service device is built with a quartz oscillation unit for generating a reference clock signal required for timing.

Example IV

Fig. 5 is a schematic structural diagram of a fire-fighting equipment management system based on internet of things according to a fourth embodiment of the present invention.

As shown in fig. 5, unlike the embodiment in fig. 2, the fire-fighting equipment management system based on the internet of things further includes the following components:

the on-site display device is arranged in the fire control management area and is electrically connected with the intelligent judging device, and is used for executing synchronous display of the received probability and the set probability threshold value when the received probability is larger than or equal to the set probability threshold value;

the field display device may be an LED display array, an LCD display array, or a liquid crystal display screen, for performing synchronous display of the received probability and the set probability threshold when the received probability is equal to or greater than the set probability threshold.

Example five

Unlike the embodiment in fig. 5, the fire-fighting equipment management system based on the internet of things according to the fifth embodiment of the present invention further includes the following components:

the optical alarm device is arranged in the fire control management area and is electrically connected with the intelligent judgment device, and is used for executing corresponding optical alarm operation when the received probability is greater than or equal to a set probability threshold value;

for example, when the received probability is greater than or equal to a set probability threshold, the corresponding optical alarm operation executed by the optical alarm device is a buzzing alarm operation with preset frequency.

Next, detailed descriptions of various embodiments of the present invention will be continued.

In the fire-fighting equipment management system based on the Internet of things according to various embodiments of the present invention:

based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to each constituent pixel point of the wall imaging region, intelligently judging the probability of fire disaster of the monitored building at the next moment of the current moment by adopting a feedforward neural network model, wherein the time interval between the next moment and the current moment is equal to the set time interval, and the method comprises the following steps of: taking each red component value corresponding to each constituent pixel point of each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration, a set time interval and a wall imaging area as parallel input content of the feedforward neural network;

The method for intelligently judging the probability of fire disaster of the monitored building at the next moment of the current moment by adopting a feedforward neural network model based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to all the constituent pixel points of the wall imaging area, wherein the time interval between the next moment and the current moment is equal to the set time interval further comprises: executing the feedforward neural network to obtain the probability of fire disaster of the monitoring building at the moment next to the current moment of the feedforward neural network output by the feedforward neural network;

for example, executing the feed forward neural network to obtain a probability of the building fire at a time next to a current time of its output includes: a numerical simulation mode may be selected to implement a simulation process and a test process of monitoring the probability of a building fire at a time next to a current time of execution of the feedforward neural network to obtain an output thereof.

In the fire-fighting equipment management system based on the Internet of things according to various embodiments of the present invention:

the state switching device is further used for synchronously sending standby suspension signals to each fire-fighting device through a wireless communication network to keep each fire-fighting device in a disabled state when the received probability is smaller than the set probability threshold;

And the channel transfer device is further used for maintaining the current playing channel of the multichannel loudspeaker unchanged at the current moment when the received probability is smaller than the set probability threshold value.

And in the fire-fighting equipment management system based on the internet of things according to various embodiments of the present invention:

identifying the wall imaging area in the real-time acquisition picture based on the imaging characteristics of the front wall surface of the monitoring building comprises: identifying a wall surface imaging area in the real-time acquisition picture based on color imaging characteristics or gray imaging characteristics of a front wall surface of the monitoring building;

wherein, based on the color imaging characteristic or the grey scale imaging characteristic of the front end wall of control building discernment wall image area in the real-time collection picture includes: the gray imaging feature is to monitor the gray value range corresponding to the front wall of the building, and when the gray value of a certain pixel point in the real-time acquisition picture falls in the gray value range, the certain pixel point is judged to be a constituent pixel point of the wall imaging area;

specifically, the gray imaging feature is a gray value range corresponding to a front wall surface of a monitoring building, and when a gray value of a certain pixel point in the real-time acquisition picture falls within the gray value range, determining that the certain pixel point is a constituent pixel point of a wall surface imaging area includes: the gray value range corresponding to the front end wall surface of the monitoring building is limited by a preset gray upper limit threshold value and a preset gray lower limit threshold value, and the preset gray upper limit threshold value is larger than the preset gray lower limit threshold value;

Wherein, based on the color imaging characteristic or the grey scale imaging characteristic of the front end wall of control building discernment wall image area in the real-time collection picture still includes: when the gray value of a certain pixel point in the real-time acquisition picture falls outside the gray value range, judging that the certain pixel point is a formed pixel point of other imaging areas outside the wall imaging area in the real-time acquisition picture.

Example six

The fire-fighting equipment management method based on the internet of things, which is shown in the sixth embodiment of the invention, specifically comprises the following steps:

s61: taking the feedforward neural network after the preset number of training is completed as a feedforward neural network model, wherein the value of the preset number is monotonically and positively correlated with the occupied space volume of the monitored building;

for example, the monotonically positive association of the preset number of values with the occupied space volume of the monitored building includes: the preset number is 50 when the occupied space volume of the monitoring building is 10 ten thousand cubic meters, 60 when the occupied space volume of the monitoring building is 20 ten thousand cubic meters, and 70 when the occupied space volume of the monitoring building is 30 ten thousand cubic meters;

S62: acquiring real-time temperatures, real-time humidity and real-time dust concentration of each layer corresponding to each layer in the monitoring building, and transmitting the real-time temperatures, the real-time humidity and the real-time dust concentration of each layer in real time through a wireless communication network;

specifically, step S701 may be performed by constructing a type of internet of things sensing mechanism by using a temperature sensing array, a humidity sensing array, a dust sensing array and a network transceiver, where the constructed type of internet of things sensing mechanism is used to send different types of sensing information of each floor of a monitoring building to a monitoring end or a processing end through a network;

for example, each temperature sensing unit in the temperature sensing array may be a non-contact temperature sensing unit or a contact temperature sensing unit;

specifically, the network transceiver is a network transceiver based on a time division duplex communication mechanism or a network transceiver based on a frequency division duplex communication mechanism;

s63: the method comprises the steps that a real-time acquisition picture of the environment where the front wall surface is located is acquired for a monitoring building right in front of the central position of the front wall surface of the monitoring building, and the real-time acquisition picture is transmitted through a wireless communication network;

Specifically, step S702 may be performed by constructing another type of sensing mechanism of the internet of things by the image sensing assembly and the network transmission assembly, where the constructed sensing mechanism of the internet of things is used to send visual information of the monitored building to the monitoring end or the processing end through the network;

the image sensing component may be a CCD sensing component or a CMOS sensing component, for example;

s64: receiving the real-time acquisition picture at a fire control management area for storing batch fire control equipment through a wireless communication network, and identifying a wall surface imaging area in the real-time acquisition picture based on imaging characteristics of a front wall surface of a monitoring building;

for example, a programmable logic device may be selected to enable data processing that connects with the network transmission component over a wireless communications network to receive the real-time acquisition picture and identify a wall imaging area in the real-time acquisition picture based on monitoring imaging characteristics of a front wall of a building;

s65: receiving real-time temperature, real-time humidity and real-time dust concentration of each layer through a wireless communication network, intelligently judging the probability of fire disaster of the monitored building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature, the real-time humidity, the real-time dust concentration of each layer, a set time interval and red component values corresponding to the constituent pixel points of the wall imaging area, wherein the time interval between the next moment and the current moment is equal to the set time interval;

For example, intelligently judging, by using a feedforward neural network model, a probability of fire occurrence of the monitored building at a next time of a current time based on each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration, a set time interval, and each red component value corresponding to each constituent pixel point of a wall imaging region, wherein the time interval between the next time and the current time is equal to the set time interval, including: the MATLAB tool box can be adopted to realize the data processing of intelligently judging the probability of fire disaster of the monitored building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to each constituent pixel point of the wall imaging area, wherein the time interval between the next moment and the current moment is equal to the set time interval;

s66: when the received probability is greater than or equal to a set probability threshold, synchronously transmitting a standby trigger signal to each fire-fighting device in the fire-fighting management area through a wireless communication network so as to simultaneously switch each fire-fighting device from a deactivated state to a standby state;

S67: when the received probability is greater than or equal to a set probability threshold, the current playing channel of the multichannel loudspeaker of the fire control management area is transferred to a fire accident broadcasting channel in advance at the current moment;

the method for acquiring the real-time temperature, the real-time humidity and the real-time dust concentration of each layer corresponding to each layer in the monitoring building respectively, and transmitting the real-time temperature, the real-time humidity and the real-time dust concentration of each layer in real time through a wireless communication network comprises the following steps: the method comprises the steps of adopting temperature sensing units respectively arranged on all floors of a monitoring building to respectively acquire real-time temperatures of all floors, adopting humidity sensing units respectively arranged on all floors of the monitoring building to respectively acquire real-time humidity of all floors, adopting gray scale sensing units respectively arranged on all floors of the monitoring building to respectively acquire real-time dust concentrations of all floors, and adopting network transceiver equipment to transmit the real-time temperatures of all floors, the real-time humidity of all floors and the real-time dust concentrations of all floors in real time through a wireless communication network;

wherein, right in front of the central point of the front end wall of the monitoring building to the monitoring building in order to gather the real-time collection picture of the environment that the front end wall is located, and transmit the real-time collection picture through wireless communication network includes: the image sensing component in the visual detection device is adopted to execute acquisition operation, and the network transmission component in the visual detection device is adopted to execute transmission operation;

The feedforward neural network after the preset number of training is used as a feedforward neural network model to be sent to the intelligent judgment model for use, and monotonically and positively associating the preset number of values with the occupied space volume of the monitored building comprises the following steps: in each training performed on the feedforward neural network, taking each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration and each red component value respectively corresponding to each constituent pixel point of a wall imaging area, which correspond to a set time interval and a historical time before an initial time of a fire disaster, as parallel input contents of the feedforward neural network, taking the probability of being greater than or equal to a set probability threshold value as output contents of the feedforward neural network, wherein the time interval duration between the historical time and the initial time is equal to the set time interval;

the method for intelligently judging the probability of fire disaster of the monitored building at the next moment of the current moment based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to all the constituent pixel points of the wall imaging area by adopting a feedforward neural network model, wherein the time interval between the next moment and the current moment is equal to the set time interval comprises the following steps: the red component value is an R component value in an RGB color space;

Specifically, in the RGB color space, each pixel includes an R component value, a G component value, and a B component value, which are a green component value and a blue component value, respectively;

and wherein, temperature-sensing array sets up each layer in order to detect each layer real-time temperature respectively of control building, humidity-sensing array sets up each layer in order to detect each layer real-time humidity respectively of control building, dust-sensing array sets up each layer in order to detect each layer real-time dust concentration respectively of control building includes: the temperature sensing array comprises temperature sensing units respectively arranged on all layers of the monitoring building, the humidity sensing array comprises humidity sensing units respectively arranged on all layers of the monitoring building, and the dust sensing array comprises dust sensing units respectively arranged on all layers of the monitoring building.

In addition, the present invention may further incorporate the following technical matters to further demonstrate the prominent essential features of the present invention:

identifying the wall imaging area in the real-time acquisition picture based on the color imaging feature or the gray imaging feature of the front wall surface of the monitored building further comprises: the color imaging feature is to monitor a red component value interval, a green component value interval and a blue component value interval corresponding to the front wall surface of the building;

Wherein, based on the color imaging characteristic or the grey scale imaging characteristic of the front end wall of control building discernment wall image area in the real-time collection picture still includes: when the red component value, the green component value and the blue component value of a certain pixel point in the real-time acquisition picture respectively fall in the red component value interval, the green component value interval and the blue component value interval, judging that the certain pixel point is a constituent pixel point of a wall imaging area, otherwise, judging that the certain pixel point is a constituent pixel point of other imaging areas except the wall imaging area in the real-time acquisition picture.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.

Claims (10)

1. Fire-fighting equipment management system based on thing networking, characterized in that, the system includes:

the network sensing device is arranged inside the monitoring building and comprises a temperature sensing array, a humidity sensing array, a dust sensing array and network transceiver equipment, wherein the temperature sensing array is arranged on each layer of the monitoring building to respectively detect real-time temperature of each layer, the humidity sensing array is arranged on each layer of the monitoring building to respectively detect real-time humidity of each layer, the dust sensing array is arranged on each layer of the monitoring building to respectively detect real-time dust concentration of each layer, and the network transceiver equipment is respectively connected with the temperature sensing array, the humidity sensing array and the dust sensing array;

the visual detection device is arranged right in front of the central position of the front wall surface of the monitoring building and comprises an image sensing assembly and a network transmission assembly, wherein the image sensing assembly is used for facing the monitoring building to acquire a real-time acquisition picture of the environment where the front wall surface is located, and the network transmission assembly is connected with the image sensing assembly and is used for transmitting the real-time acquisition picture through a wireless communication network;

The content identification device is arranged in a fire control management area for storing batch fire control equipment, is connected with the network transmission assembly through a wireless communication network to receive the real-time acquisition picture, and is used for identifying a wall imaging area in the real-time acquisition picture based on imaging characteristics of a front wall of a monitoring building;

the intelligent judging device is arranged in the fire control management area, is connected with the network transceiver through a wireless communication network and is electrically connected with the content identifying device, and is used for intelligently judging the probability of fire occurrence of the monitoring building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values corresponding to the constituent pixel points of the wall imaging area respectively, wherein the time interval between the next moment and the current moment is equal to the set time interval;

the successive training device is electrically connected with the intelligent judging device and is used for sending the feedforward neural network after the preset number of training is finished for each time to the intelligent judging model as a feedforward neural network model for use, and the preset number of values are monotonically and positively associated with the occupied space volume of the monitored building;

The state switching device is electrically connected with the intelligent judging device and simultaneously connected with each fire-fighting equipment wireless network of the fire-fighting management area, and is used for synchronously sending a standby trigger signal to each fire-fighting equipment through the wireless communication network to switch each fire-fighting equipment from a deactivated state to a standby state when the received probability is greater than or equal to a set probability threshold value;

and the channel transfer device is electrically connected with the intelligent judging device and simultaneously connected with the multichannel loudspeaker of the fire control management area, and is used for transferring the current playing channel of the multichannel loudspeaker to the fire accident broadcasting channel in advance at the current moment when the received probability is greater than or equal to a set probability threshold value.

2. The fire protection equipment management system based on the internet of things as set forth in claim 1, wherein:

the feedforward neural network after the preset number of each training is used as a feedforward neural network model to be sent to the intelligent judgment model for use, and monotonically and positively associating the preset number of values with the occupied space volume of the monitored building comprises the following steps: in each training performed on the feedforward neural network, taking each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration and each red component value respectively corresponding to each constituent pixel point of a wall imaging area, which correspond to a set time interval and a historical time before an initial time of a fire disaster, as parallel input contents of the feedforward neural network, taking the probability of being greater than or equal to a set probability threshold value as output contents of the feedforward neural network, wherein the time interval duration between the historical time and the initial time is equal to the set time interval;

The method for intelligently judging the probability of fire disaster of the monitored building at the next moment of the current moment based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to all the constituent pixel points of the wall imaging area by adopting a feedforward neural network model, wherein the time interval between the next moment and the current moment is equal to the set time interval comprises the following steps: the red component value is an R component value in an RGB color space;

the temperature sensing array is arranged on each layer of the monitoring building to detect real-time temperature of each layer respectively, the humidity sensing array is arranged on each layer of the monitoring building to detect real-time humidity of each layer respectively, and the dust sensing array is arranged on each layer of the monitoring building to detect real-time dust concentration of each layer respectively and comprises the following components: the temperature sensing array comprises temperature sensing units respectively arranged on all layers of the monitoring building, the humidity sensing array comprises humidity sensing units respectively arranged on all layers of the monitoring building, and the dust sensing array comprises dust sensing units respectively arranged on all layers of the monitoring building.

3. The internet of things-based fire protection equipment management system of claim 2, wherein the system further comprises:

the telescopic support piece is arranged in front of the monitoring building and is used for fixing the visual detection device in front of the central position of the front wall surface of the monitoring building;

the visual detection device is fixed on the top of the telescopic support, and a telescopic unit and a driving motor are arranged in the telescopic support;

the driving motor is connected with the telescopic unit and used for controlling the telescopic length of the telescopic unit.

4. The internet of things-based fire protection equipment management system of claim 2, wherein the system further comprises:

and the timing service device is respectively connected with the network sensing device and the visual detection device and is used for respectively providing the network sensing device and the visual detection device with respective required timing services.

5. The internet of things-based fire protection equipment management system of claim 2, wherein the system further comprises:

the on-site display device is arranged in the fire control management area and is electrically connected with the intelligent judging device, and is used for executing synchronous display of the received probability and the set probability threshold value when the received probability is larger than or equal to the set probability threshold value.

6. The internet of things-based fire protection equipment management system of claim 2, wherein the system further comprises:

and the optical alarm device is arranged in the fire control management area and is electrically connected with the intelligent judgment device, and is used for executing corresponding optical alarm operation when the received probability is greater than or equal to a set probability threshold value.

7. The fire protection equipment management system based on the internet of things according to any one of claims 2-6, wherein:

based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to each constituent pixel point of the wall imaging region, intelligently judging the probability of fire disaster of the monitored building at the next moment of the current moment by adopting a feedforward neural network model, wherein the time interval between the next moment and the current moment is equal to the set time interval, and the method comprises the following steps of: taking each red component value corresponding to each constituent pixel point of each layer of real-time temperature, each layer of real-time humidity, each layer of real-time dust concentration, a set time interval and a wall imaging area as parallel input content of the feedforward neural network;

The method for intelligently judging the probability of fire disaster of the monitored building at the next moment of the current moment by adopting a feedforward neural network model based on the real-time temperature of each layer, the real-time humidity of each layer, the real-time dust concentration of each layer, the set time interval and the red component values respectively corresponding to all the constituent pixel points of the wall imaging area, wherein the time interval between the next moment and the current moment is equal to the set time interval further comprises: and executing the feedforward neural network to obtain the probability of building fire occurrence at the moment next to the current moment of the feedforward neural network output.

8. The fire protection equipment management system based on the internet of things according to any one of claims 2-6, wherein:

the state switching device is further used for synchronously sending standby suspension signals to each fire-fighting device through a wireless communication network to keep each fire-fighting device in a disabled state when the received probability is smaller than the set probability threshold;

and the channel transfer device is further used for maintaining the current playing channel of the multichannel loudspeaker unchanged at the current moment when the received probability is smaller than the set probability threshold value.

9. The fire protection equipment management system based on the internet of things according to any one of claims 2-6, wherein:

Identifying the wall imaging area in the real-time acquisition picture based on the imaging characteristics of the front wall surface of the monitoring building comprises: identifying a wall surface imaging area in the real-time acquisition picture based on color imaging characteristics or gray imaging characteristics of a front wall surface of the monitoring building;

wherein, based on the color imaging characteristic or the grey scale imaging characteristic of the front end wall of control building discernment wall image area in the real-time collection picture includes: the gray imaging feature is to monitor the gray value range corresponding to the front wall of the building, and when the gray value of a certain pixel point in the real-time acquisition picture falls in the gray value range, the certain pixel point is judged to be a constituent pixel point of the wall imaging area;

wherein, based on the color imaging characteristic or the grey scale imaging characteristic of the front end wall of control building discernment wall image area in the real-time collection picture still includes: when the gray value of a certain pixel point in the real-time acquisition picture falls outside the gray value range, judging that the certain pixel point is a formed pixel point of other imaging areas outside the wall imaging area in the real-time acquisition picture.

10. The fire-fighting equipment management method based on the Internet of things is characterized by comprising the following steps of:

taking the feedforward neural network after the preset number of training is completed as a feedforward neural network model, wherein the value of the preset number is monotonically and positively correlated with the occupied space volume of the monitored building;

acquiring real-time temperatures, real-time humidity and real-time dust concentration of each layer corresponding to each layer in the monitoring building, and transmitting the real-time temperatures, the real-time humidity and the real-time dust concentration of each layer in real time through a wireless communication network;

the method comprises the steps that a real-time acquisition picture of the environment where the front wall surface is located is acquired for a monitoring building right in front of the central position of the front wall surface of the monitoring building, and the real-time acquisition picture is transmitted through a wireless communication network;

receiving the real-time acquisition picture at a fire control management area for storing batch fire control equipment through a wireless communication network, and identifying a wall surface imaging area in the real-time acquisition picture based on imaging characteristics of a front wall surface of a monitoring building;

receiving real-time temperature, real-time humidity and real-time dust concentration of each layer through a wireless communication network, intelligently judging the probability of fire disaster of the monitored building at the next moment at the current moment by adopting a feedforward neural network model based on the real-time temperature, the real-time humidity, the real-time dust concentration of each layer, a set time interval and red component values corresponding to the constituent pixel points of the wall imaging area, wherein the time interval between the next moment and the current moment is equal to the set time interval;

When the received probability is greater than or equal to a set probability threshold, synchronously transmitting a standby trigger signal to each fire-fighting device in the fire-fighting management area through a wireless communication network so as to simultaneously switch each fire-fighting device from a deactivated state to a standby state;

when the received probability is greater than or equal to a set probability threshold, the current playing channel of the multichannel loudspeaker of the fire control management area is transferred to a fire accident broadcasting channel in advance at the current moment;

the method for acquiring the real-time temperature, the real-time humidity and the real-time dust concentration of each layer corresponding to each layer in the monitoring building respectively, and transmitting the real-time temperature, the real-time humidity and the real-time dust concentration of each layer in real time through a wireless communication network comprises the following steps: the method comprises the steps of adopting temperature sensing units respectively arranged on all floors of a monitoring building to respectively acquire real-time temperatures of all floors, adopting humidity sensing units respectively arranged on all floors of the monitoring building to respectively acquire real-time humidity of all floors, adopting gray scale sensing units respectively arranged on all floors of the monitoring building to respectively acquire real-time dust concentrations of all floors, and adopting network transceiver equipment to transmit the real-time temperatures of all floors, the real-time humidity of all floors and the real-time dust concentrations of all floors in real time through a wireless communication network;

Wherein, right in front of the central point of the front end wall of the monitoring building to the monitoring building in order to gather the real-time collection picture of the environment that the front end wall is located, and transmit the real-time collection picture through wireless communication network includes: the acquisition operation is performed by using an image sensing component in the visual inspection device, and the transmission operation is performed by using a network transmission component in the visual inspection device.