Fire-fighting spraying point position planning system based on Internet of Things large model

Abstract

The invention discloses a fire-fighting spraying point planning system based on an Internet of Things large model, and relates to the technical field of fire rescue, the fire-fighting spraying point planning system comprises a management center, the management center is in communication connection with a data acquisition module, a fire analysis module, a data analysis module and a spraying planning module; corresponding data acquisition terminals are arranged at all positions in a target building through the Internet of Things technology, environment data of all sub-areas in the target building are obtained in real time through the arranged data acquisition terminals, and therefore whether a fire occurs or not is judged, and after the fire is analyzed, the target building is monitored. The fire assessment coefficient of the fire area is obtained through the environmental data obtained by each data acquisition terminal, and the risk degree of the non-fire area adjacent to the fire area is determined according to the type and number of combustible materials in each sub-area, so that the corresponding spraying point is planned. And the fire rescue efficiency of fire rescue workers is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire rescue, and specifically to a fire sprinkler point planning system based on the Internet of Things large model. Background Art

[0002] With the development of cities, the available land in cities is decreasing day by day. High-rise buildings can effectively save floor area. However, at the same time, the population density in high-rise buildings is relatively large, and the consequences of a fire are very serious. A fire belongs to a major environmental safety accident, which has great lethality to the life and property safety of humans.

[0003] In the existing fire rescue, it is often necessary for fire rescue personnel to arrive at the scene and then determine how to arrange the fire sprinkler points based on the on-site survey and experience of the fire rescue personnel. This will result in missing the golden rescue period of the fire. How to enable fire rescue personnel to quickly understand the on-site fire situation before arriving at the scene and give a planning scheme for the location of the fire sprinkler points in advance to assist fire rescue personnel in the rescue, thereby shortening the on-site investigation time, is the problem we need to solve. For this reason, a fire sprinkler point planning system based on the Internet of Things large model is provided. Summary of the Invention

[0004] The purpose of the present invention is to provide a fire sprinkler point planning system based on the Internet of Things large model.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A fire sprinkler point planning system based on the Internet of Things large model includes a management center, and the management center is communicatively connected to a data acquisition module, a fire analysis module, a data analysis module, and a sprinkler planning module.

[0006] The data acquisition module consists of several data acquisition terminals, which are set at various positions in the internal area of the target building and are used to obtain the environmental data of the location.

[0007] The fire analysis module is used to judge whether a fire has occurred at the corresponding location according to the obtained environmental data.

[0008] The data analysis module is used to analyze the fire situation inside the building and obtain a corresponding fire analysis report.

[0009] The sprinkler planning module is used to determine the corresponding fire sprinkler points according to the fire situation analysis report.

[0010] Further, the internal area of the target building is divided into several sub-areas.

[0011] The data acquisition terminals are set in each sub-area.

[0012] Construct a corresponding virtual building model according to the internal distribution of the target building and the distribution of data collection terminals;

[0013] Obtain the environmental data at the location in real time through the set data collection terminals, where the environmental data includes temperature data and carbon monoxide concentration data;

[0014] Set corresponding relative position relationships between each sub-region, where the relative position relationships include adjacent region relationships and spaced region relationships.

[0015] Further, the process of constructing the virtual building model includes:

[0016] Generate corresponding virtual sub-regions according to the sub-regions divided by the target building, and generate virtual IoT terminals at the corresponding positions in the virtual building model according to the positions of the data collection terminals in the target building;

[0017] Count the types of combustibles in each sub-region, set corresponding combustible coefficients according to the types of combustibles, and obtain the quantities of various combustibles;

[0018] Obtain the fire influence coefficient of the sub-region according to the types and quantities of combustibles in each sub-region;

[0019] Map the obtained fire influence coefficient into the corresponding virtual sub-region to complete the construction of the virtual building model.

[0020] Further, the process by which the fire analysis module determines whether a fire has occurred at the corresponding position according to the obtained environmental data includes:

[0021] Construct a coordinate system of time with respect to temperature and carbon monoxide concentration;

[0022] Generate corresponding temperature change curves and carbon monoxide concentration change curves respectively according to the temperature data and carbon monoxide concentration data obtained by the data collection terminals;

[0023] Set temperature threshold lines and concentration threshold lines in the coordinate system;

[0024] Obtain the positional relationship between the generated temperature change curve and the temperature threshold line, and the temperature relationship between the carbon monoxide concentration change curve and the concentration threshold line respectively;

[0025] When the temperature value corresponding to the temperature change curve exceeds the temperature threshold line, mark the corresponding time point as a temperature over-standard point, and record the moment corresponding to the temperature over-standard point as t1;

[0026] When the carbon monoxide concentration corresponding to the carbon monoxide concentration change curve exceeds the concentration threshold line, record the corresponding time point as a concentration over-standard point, and record the moment corresponding to the concentration over-standard point as t2;

[0027] According to the generated temperature over - standard points, obtain the temperature over - standard time interval, and record the start time and end time of the temperature over - standard time interval as t1 and t3 respectively;

[0028] When t3 ≤ t2, it indicates that there is no fire situation;

[0029] When t1 ≤ t2 < t3, mark the temperature change curve and carbon monoxide concentration change curve between t1 and t3, and record t1 as the determination time;

[0030] According to the marked temperature change curve and carbon monoxide concentration change curve, obtain the corresponding fire evaluation coefficient;

[0031] When t2 < t1, mark the temperature change curve and carbon monoxide concentration change curve between t2 and t3, record t2 as the determination time, and obtain the corresponding fire evaluation coefficient;

[0032] Compare the obtained fire evaluation coefficient with the set evaluation threshold;

[0033] When the fire evaluation coefficient is greater than the evaluation threshold, it indicates that a fire has occurred at the location where the corresponding data acquisition terminal is located, otherwise no fire has occurred.

[0034] Furthermore, the process of the data analysis module analyzing the fire situation inside the building includes:

[0035] Construct a corresponding virtual building model according to the target building, and generate a virtual IoT terminal at the corresponding position in the virtual building model according to the position of the data acquisition terminal in the target building;

[0036] Mark the virtual IoT terminal corresponding to the data acquisition terminal where the fire occurs as a fire anomaly point;

[0037] Obtain the fire evaluation coefficients of all fire anomaly points, and mark the fire anomaly point with the highest fire evaluation coefficient as the fire peak point;

[0038] Starting from the fire peak point, generate fire trend vectors connecting to other fire anomaly points;

[0039] Record the direction corresponding to the fire trend vector with the largest modulus as the main fire spreading direction;

[0040] Obtain the determination time of each fire anomaly point, and mark the fire anomaly point with the earliest judgment time as the suspected fire source point;

[0041] Obtain a sub-region that has a relative position relationship with the suspected fire source point, and obtain whether there is a fire anomaly point in the corresponding sub-region. If there is a fire anomaly point, mark this sub-region as a sub-region to be merged;

[0042] Then obtain the sub-regions that have a relative position relationship with the sub-regions to be merged, and obtain whether there is a fire anomaly point in the corresponding sub-regions. If there is a fire anomaly point, mark this sub-region as a sub-region to be merged, and so on;

[0043] Merge all the sub-regions to be merged, and mark the merged sub-region as the fire occurrence range;

[0044] Map the obtained main fire spread direction, suspected fire source point, fire peak point, and fire occurrence range into the constructed virtual building model, and generate a fire analysis report.

[0045] Further, the process by which the sprinkler planning module determines the corresponding fire sprinkler points according to the fire situation analysis report includes:

[0046] Mark the fire anomaly points corresponding to the fire trend vector in the virtual building model according to the main fire spread direction, and obtain the sub-regions that have a relative position relationship with the sub-regions corresponding to the marked fire anomaly points, which are marked as sprinkler planning sub-regions;

[0047] Obtain the fire influence coefficients of each sprinkler planning sub-region, and compare the fire influence coefficients with the set fire influence threshold;

[0048] When the fire influence coefficient exceeds the fire influence threshold, mark the corresponding sub-region as the corresponding main sprinkler point;

[0049] Mark the sub-region corresponding to the fire peak point as the secondary sprinkler point;

[0050] Obtain whether the fire influence coefficient of the sub-regions that have a relative position relationship with the edge sub-regions of the determined fire occurrence range and do not belong to the fire occurrence range exceeds the fire influence threshold. If so, mark the corresponding sub-regions as preventive sprinkler points.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] Deploy corresponding data collection terminals at various locations within the target building through Internet of Things technology, and obtain the environmental data of each sub-region within the target building in real time through the deployed data collection terminals, so as to determine whether a fire has occurred. After analyzing the occurrence of a fire, obtain the fire assessment coefficient of the fire area through the environmental data obtained by each data collection terminal, and determine the risk level of the non-fire areas adjacent to the fire occurrence area according to the types and quantities of combustibles in each sub-region, so as to plan corresponding sprinkler points and assist firefighters in improving the fire fighting and rescue efficiency. Brief Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is the schematic diagram of the present invention. Detailed Embodiments

[0055] As Figure 1 shown, a fire sprinkler point planning system based on an Internet of Things large model includes a management center, and the management center is communicatively connected to a data collection module, a fire analysis module, a data analysis module, and a sprinkler planning module;

[0056] The data collection module is composed of several data collection terminals, which are set at various positions in the internal area of the target building and are used to obtain the environmental data of the location where they are located;

[0057] The fire analysis module is used to determine whether a fire has occurred at the corresponding location according to the obtained environmental data;

[0058] The data analysis module is used to analyze the fire situation inside the building and obtain a corresponding fire analysis report;

[0059] The sprinkler planning module is used to determine the corresponding fire sprinkler points according to the fire situation analysis report.

[0060] It should be further noted that, in the specific implementation process, the interior of the target building is divided into several sub-regions;

[0061] The data collection terminals are set in each sub-region, and at least one data collection terminal is set in one sub-region;

[0062] Construct a corresponding virtual building model according to the internal distribution of the target building and the distribution of the data collection terminals;

[0063] The environmental data at the location is obtained in real time through the set data acquisition terminal, and the environmental data includes temperature data and carbon monoxide concentration data;

[0064] Corresponding relative position relationships are set between each sub-region, and the relative position relationships include adjacent region relationships and spaced region relationships;

[0065] The data acquisition terminals within the same sub-region are numbered respectively, denoted as i, where i = 1, 2,..., n;

[0066] Then the temperature data and carbon monoxide concentration data obtained by the data acquisition terminal numbered i are denoted as W i and CO i ;

[0067] The environmental data obtained by the data acquisition terminals within each sub-region is uploaded to the fire analysis module.

[0068] It should be further noted that in the specific implementation process, the specific process of constructing the corresponding virtual building model includes:

[0069] Generating corresponding virtual sub-regions according to the sub-regions divided by the target building, and generating virtual IoT terminals at the corresponding positions in the virtual building model according to the positions of the data acquisition terminals in the target building;

[0070] Counting the types of combustibles in each sub-region, setting corresponding combustible coefficients according to the types of combustibles, and obtaining the quantities of various combustibles;

[0071] Numbering the types of combustibles in the sub-region, denoted as j, where j = 0, 1,..., m;

[0072] Setting a corresponding danger coefficient for the combustible numbered j, denoted as ρ j ;

[0073] Then the quantity of the combustible numbered j is denoted as Ks j ;

[0074] Obtaining the fire influence coefficient of the sub-region according to the types and quantities of combustibles in each sub-region, denoted as Hx;

[0075] Among them,

[0076] Mapping the obtained fire influence coefficient to the corresponding virtual sub-region to complete the construction of the virtual building model.

[0077] It should be further noted that in the specific implementation process, the process by which the fire analysis module determines whether a fire has occurred at the corresponding position according to the acquired environmental data includes:

[0078] Construct a coordinate system of construction time with respect to temperature and carbon monoxide concentration;

[0079] Generate corresponding temperature change curves and carbon monoxide concentration change curves respectively according to the temperature data and carbon monoxide concentration data obtained by the data acquisition terminal;

[0080] Set a temperature threshold line and a concentration threshold line within the coordinate system;

[0081] Obtain the positional relationship between the generated temperature change curve and the temperature threshold line, and the temperature relationship between the carbon monoxide concentration change curve and the concentration threshold line respectively;

[0082] When the temperature value corresponding to the temperature change curve exceeds the temperature threshold line, mark the corresponding time point as a temperature over - standard point, and record the moment corresponding to the temperature over - standard point as t1;

[0083] When the carbon monoxide concentration corresponding to the carbon monoxide concentration change curve exceeds the concentration threshold line, mark the corresponding time point as a concentration over - standard point, and record the moment corresponding to the concentration over - standard point as t2;

[0084] According to the generated temperature over - standard points, obtain the temperature over - standard time interval, and record the start time and end time of the temperature over - standard time interval as t1 and t3 respectively, where the start time is the temperature over - standard point, and the end time represents the moment when the temperature value first drops to the temperature threshold line after the temperature over - standard point. If the temperature value does not drop to the temperature threshold line, then t3 is the current moment;

[0085] When t3 ≤ t2, that is, the moment of t3 is before and the moment of t2 is after, it means there is no fire situation, and cancel the existing temperature over - standard points and the corresponding temperature over - standard time intervals;

[0086] When t1 ≤ t2 < t3, that is, the moment of t2 is before and the moment of t3 is after, mark the temperature change curve and the carbon monoxide concentration change curve between the moments of t1 and t3, and record the moment of t1 as the determination moment;

[0087] According to the marked temperature change curve and carbon monoxide concentration change curve, obtain the corresponding fire evaluation coefficient Hp;

[0088] Among them,

[0089] Among them, γ represents the temperature threshold, and δ represents the concentration threshold;

[0090] When t2 < t1, that is, the moment t2 is earlier and the moment t1 is later, then mark the temperature change curve and carbon monoxide concentration change curve between the moments t2 and t3, record the moment t2 as the determination moment, and obtain the corresponding fire evaluation coefficient Hp;

[0091] Among them,

[0092] Set an evaluation threshold H0;

[0093] Compare the obtained fire evaluation coefficient with the set evaluation threshold;

[0094] When the fire evaluation coefficient is greater than the evaluation threshold, it indicates that a fire has occurred at the location where the corresponding data acquisition terminal is located, otherwise no fire has occurred.

[0095] It should be further noted that in the specific implementation process, the process of the data analysis module analyzing the fire situation inside the building includes:

[0096] Construct a corresponding virtual building model according to the target building, and generate a virtual IoT terminal at the corresponding position in the virtual building model according to the position of the data acquisition terminal in the target building;

[0097] Mark the virtual IoT terminal corresponding to the data acquisition terminal where the fire occurs as a fire anomaly point;

[0098] Obtain the fire evaluation coefficients of all fire anomaly points, and mark the fire anomaly point with the highest fire evaluation coefficient as the fire peak point;

[0099] Starting from the fire peak point, generate fire trend vectors connecting to other fire anomaly points;

[0100] Record the direction corresponding to the fire trend vector with the largest modulus as the main fire spreading direction;

[0101] Obtain the determination moment of each fire anomaly point, and mark the fire anomaly point with the earliest determination moment as the suspected fire source point;

[0102] Obtain the sub-regions having a relative position relationship with the suspected fire source point, and check whether there are fire anomaly points in the corresponding sub-regions. If there are fire anomaly points, mark the sub-region as a sub-region to be merged;

[0103] Then obtain the sub-regions having a relative position relationship with the sub-region to be merged, and check whether there are fire anomaly points in the corresponding sub-regions. If there are fire anomaly points, mark the sub-region as a sub-region to be merged, and so on;

[0104] Merge all the sub-regions to be merged, and mark the merged sub-region as the fire occurrence range;

[0105] Map the obtained main fire spread direction, suspected fire source point, fire peak point, and fire occurrence range into the constructed virtual building model, and generate a fire analysis report.

[0106] It should be further noted that in the specific implementation process, the process by which the sprinkler planning module determines the corresponding fire sprinkler points according to the fire situation analysis report includes:

[0107] Mark the fire abnormal points corresponding to the fire trend vector according to the main fire spread direction in the virtual building model, and obtain the sub-regions that have a relative position relationship with the sub-regions corresponding to the marked fire abnormal points, which are denoted as sprinkler planning sub-regions;

[0108] Set a fire influence threshold, denoted as Y0;

[0109] Obtain the fire influence coefficients of each sprinkler planning sub-region, and compare the fire influence coefficients with the set fire influence threshold;

[0110] When the fire influence coefficient exceeds the fire influence threshold, mark the corresponding sub-region as the corresponding main sprinkler point;

[0111] Mark the sub-region corresponding to the fire peak point as the secondary sprinkler point;

[0112] Obtain whether the fire influence coefficient of the sub-region that has a relative position relationship with the edge sub-region of the determined fire occurrence range and does not belong to the fire occurrence range exceeds the fire influence threshold. If so, mark the corresponding sub-region as the preventive sprinkler point;

[0113] Generate fire rescue equipment demand information according to the generated various types of sprinkler points;

[0114] Send the fire rescue equipment demand information and the virtual building model to the management center, and the management center dispatches the corresponding fire equipment according to the fire rescue equipment demand information.

[0115] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to form an equivalent embodiment with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any modification or equivalent replacement made to the above embodiment based on the technical essence of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A fire sprinkler point planning system based on the Internet of Things large model, including a management center, characterized in that: The management center is communicatively connected to a data acquisition module, a fire analysis module, a data analysis module and a sprinkler planning module; The data acquisition module is composed of a number of data acquisition terminals, which are arranged at various locations within the target building and are used to obtain environmental data at the location; The fire analysis module is used to determine whether a fire occurs at a corresponding location based on the acquired environmental data; The data analysis module is used to analyze the fire situation inside the building and obtain a corresponding fire analysis report; The sprinkler planning module is used to determine the corresponding fire sprinkler points according to the fire situation analysis report.

2. According to claim 1, a fire sprinkler point planning system based on the Internet of Things large model is characterized in that: The interior of the target building is divided into a plurality of sub-areas; The data collection terminal is arranged in each sub-area; Construct a corresponding virtual building model according to the internal distribution of the target building and the distribution of data collection terminals; Acquire the environmental data of the location in real time through the set data acquisition terminal, wherein the environmental data includes temperature data and carbon monoxide concentration data; A corresponding relative position relationship is set between each sub-area, and the relative position relationship includes an adjacent area relationship and an interval area relationship.

3. According to claim 2, a fire sprinkler point planning system based on the Internet of Things large model is characterized in that: The process of building a virtual building model includes: Generate corresponding virtual sub-areas according to the sub-areas divided by the target building, and generate virtual IoT terminals at corresponding positions in the virtual building model according to the positions of the data acquisition terminals in the target building; Count the types of combustibles in each sub-area, set the corresponding combustibility coefficient according to the types of combustibles, and obtain the quantity of various combustibles; According to the types and quantities of combustibles in each sub-area, the fire impact coefficient of the sub-area is obtained; The obtained fire impact coefficient is mapped to the corresponding virtual sub-area to complete the construction of the virtual building model.

4. According to claim 3, a fire sprinkler point planning system based on the Internet of Things large model is characterized in that: The process of the fire analysis module determining whether a fire occurs at a corresponding location according to the acquired environmental data includes: Construct a coordinate system of time with respect to temperature and carbon monoxide concentration; Generate corresponding temperature change curve and carbon monoxide concentration change curve respectively according to the temperature data and carbon monoxide concentration data obtained by the data acquisition terminal; Setting a temperature threshold line and a concentration threshold line in the coordinate system; Respectively obtaining the positional relationship between the generated temperature change curve and the temperature threshold line, and the temperature relationship between the carbon monoxide concentration change curve and the concentration threshold line; When the temperature value corresponding to the temperature change curve exceeds the temperature threshold line, the corresponding time point is marked as the temperature exceeding point, and the time corresponding to the temperature exceeding point is recorded as t1; When the carbon monoxide concentration corresponding to the carbon monoxide concentration change curve exceeds the concentration threshold line, the corresponding time point is recorded as the concentration exceeding point, and the time corresponding to the concentration exceeding point is recorded as t2; According to the generated temperature exceeding point, the temperature exceeding time interval is obtained, and the starting time and the ending time of the temperature exceeding time interval are recorded as t1 and t3 respectively; When t3≤t2, it means there is no fire situation; When t1≤t2<t3, the temperature change curve and the carbon monoxide concentration change curve between t1 and t3 are marked, and t1 is recorded as the judgment time; According to the marked temperature change curve and carbon monoxide concentration change curve, the corresponding fire assessment coefficient is obtained; When t2<t1, the temperature change curve and the carbon monoxide concentration change curve between t2 and t3 are marked, t2 is recorded as the judgment time, and the corresponding fire assessment coefficient is obtained; The obtained fire assessment coefficient is compared with the set assessment threshold; When the fire assessment coefficient is greater than the assessment threshold, it means that a fire has occurred at the location where the corresponding data acquisition terminal is located, otherwise no fire has occurred.

5. According to claim 4, a fire sprinkler point planning system based on the Internet of Things large model is characterized in that: The process of the data analysis module analyzing the fire situation inside the building includes: According to the virtual building model corresponding to the construction of the target building, a virtual IoT terminal is generated at a corresponding position in the virtual building model according to the position of the data collection terminal in the target building; The virtual IoT terminal corresponding to the data collection terminal where the fire occurred is marked as a fire abnormal point; Obtain the fire assessment coefficients of all fire anomaly points, and mark the fire anomaly point with the highest fire assessment coefficient as the fire peak point; Taking the fire peak point as the starting point, a fire trend vector connected to other fire abnormal points is generated; The direction corresponding to the fire trend vector with the largest modulus length is recorded as the main direction of fire spread; Obtain the judgment time of each fire anomaly point, and mark the fire anomaly point with the earliest judgment time as a suspected fire source point; Obtain a sub-region that has a relative position relationship with the suspected fire source point, and obtain whether there is a fire abnormal point in the corresponding sub-region. If there is a fire abnormal point, the sub-region is recorded as a sub-region to be merged; Then, a sub-region that has a relative position relationship with the sub-region to be merged is obtained, and whether there is a fire abnormality point in the corresponding sub-region is obtained. If there is a fire abnormality point, the sub-region is recorded as the sub-region to be merged, and so on; Merge all the sub-areas to be merged, and record the merged sub-areas as the fire occurrence range; The main fire spread direction, suspected fire source, fire peak point and fire occurrence range are mapped to the constructed virtual building model and a fire analysis report is generated.

6. A fire sprinkler point planning system based on the Internet of Things large model according to claim 5, characterized in that: The process of the sprinkler planning module determining the corresponding fire sprinkler point according to the fire situation analysis report includes: In the virtual building model, the fire abnormal points corresponding to the fire trend vector are marked according to the main spreading direction of the fire, and the sub-areas having a relative position relationship with the sub-areas corresponding to the marked fire abnormal points are obtained and recorded as the sprinkler planning sub-areas; Obtain the fire impact coefficient of each sprinkler planning sub-area, and compare the fire impact coefficient with the set fire impact threshold; When the fire impact coefficient exceeds the fire impact threshold, the corresponding sub-area is marked as the corresponding main sprinkler point; Mark the sub-area corresponding to the peak point of the fire as the secondary sprinkler point; Obtain whether the fire impact coefficient of the sub-area that has a relative position relationship with the edge sub-area of ​​the determined fire occurrence range and does not belong to the fire occurrence range exceeds the fire impact threshold. If so, mark the corresponding sub-area as a preventive sprinkler point.

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