Smoke alarm method and system based on cloud management

Abstract

The invention is suitable for the technical field of smoke alarm, and particularly relates to a smoke alarm method and system based on cloud management, and the method comprises the steps: building a smoke early warning model based on the installation position of a smoke alarm; loading a smoke early warning model of the corresponding area according to the position of the smoke alarm; acquiring smoke concentration data of the smoke area, dividing the smoke area into a plurality of detection grid units, and performing fire behavior simulation to obtain fire behavior simulation data; and acquiring the real-time concentration data of each smoke alarm, calculating the matching relationship between the fire behavior simulation data and the real-time concentration data, determining the position of the fire behavior, and sending out fire behavior early warning through the cloud server. According to the invention, when the smoke concentration is increased preliminarily, the smoke early warning model is used for fire behavior simulation, and the occurrence area of the fire behavior is determined according to the simulation result and the actually detected smoke concentration change, so that the purpose of fire behavior early warning is achieved, the source of the fire behavior is disposed in advance, and the safety is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smoke alarms, and in particular relates to a smoke alarm method and system based on cloud management. Background Art

[0002] Smoke alarm is an important safety device used for early fire warning. It determines whether a fire has occurred by detecting smoke particles in the air, and immediately sounds an alarm when abnormal smoke concentration is detected to remind people to take emergency measures. This device is usually installed in key locations in residential, commercial buildings and industrial sites, such as corridors, outside bedrooms, kitchens, etc., to ensure that it can be discovered in time at the early stage of the fire, thereby reducing the losses and casualties caused by the fire. There are many types of modern smoke alarms, including photoelectric, ionization and multi-sensor combination types. They each have their own characteristics and are suitable for different environments and needs. Regular testing and maintenance of smoke alarms to ensure their normal operation is an important measure to ensure the safety of people in buildings.

[0003] Existing smoke alarms use an independent working mode to sound an alarm. Only when the smoke concentration detected by a certain smoke alarm reaches a certain value will the smoke alarm sound an alarm. When the fire is not serious, it cannot give an early warning, causing the fire to spread. Summary of the invention

[0004] The purpose of the present invention is to provide a smoke alarm method based on cloud management, aiming to solve the problem that the existing smoke alarm will only sound an alarm when the smoke concentration reaches a certain value, and cannot issue an early warning when the fire is not serious, causing the fire to spread.

[0005] The present invention is implemented as follows: a smoke alarm method based on cloud management, the method comprising: Based on the installation location of the smoke alarm, a smoke warning model is constructed, wherein the smoke warning model at least includes a building model and an alarm model, and the smoke alarm establishes a data connection with a cloud server; When the smoke concentration reaches the preset warning value, the system enters the smoke warning mode and loads the smoke warning model of the corresponding area according to the location of the smoke alarm; Obtain smoke concentration data of the smoke area, divide the smoke area into multiple detection grid units, perform fire simulation based on each detection grid unit, and obtain fire simulation data; Obtain the real-time concentration data of each smoke alarm, calculate the matching relationship between the fire simulation data and the real-time concentration data, determine the location of the fire, and issue a fire warning through the cloud server.

[0006] Preferably, when the smoke concentration reaches a preset warning value, the smoke warning mode is entered, and the step of loading a smoke warning model of a corresponding area according to the position of the smoke alarm includes: Polling each smoke alarm at a preset time interval to obtain smoke concentration data of the corresponding area from the smoke alarm; Read the smoke concentration data in each area to determine whether the smoke concentration in the current area exceeds the preset warning value; When the smoke concentration exceeds the preset warning value, the number of the smoke alarm is identified and the corresponding area in the smoke warning model is loaded according to the number.

[0007] Preferably, the step of obtaining smoke concentration data of a smoke area, dividing the smoke area into a plurality of detection grid units, performing fire simulation based on each detection grid unit, and obtaining fire simulation data specifically includes: According to the selected smoke area, obtain the smoke concentration data detected by all smoke alarms in the current area; Count the area of ​​the smoke region, determine the size of the grid unit, divide the smoke region, obtain the detection grid unit, perform fire probability analysis on the detection grid unit, and generate the detection sequence; The corresponding smoke warning model is imported into the fire simulation engine to perform fire simulation, and the smoke concentration data of the areas where each smoke alarm is located during the fire simulation process is recorded to obtain fire simulation data.

[0008] Preferably, the steps of obtaining the real-time concentration data of each smoke alarm, calculating the matching relationship between the fire simulation data and the real-time concentration data, determining the location of the fire, and issuing a fire warning through a cloud server specifically include: Obtain the real-time concentration data of each smoke alarm and construct a smoke concentration prediction function for each smoke alarm based on the fire simulation data; Import the real-time concentration data into the smoke concentration prediction function, and calculate the matching rate between the fire simulation data and the real-time concentration data based on the prediction results; The fire area is screened based on the matching rate, the location of the fire is determined, recorded in the fire warning, and a fire warning is issued through the cloud server.

[0009] Preferably, in the step of performing fire probability analysis on the detection grid units and generating the detection sequence, the smoke area is graded for room risk, and different scoring values ​​are set according to the level of room risk. Q i , calculate the distance between each detection grid unit and the center of each room L i , calculate the risk coefficient between each detection grid unit and each room P=1 / L , calculate the comprehensive risk value of the detection grid unit According to the comprehensive risk value The sizes are sorted to determine the detection order of the detection grid cells.

[0010] Another object of the present invention is to provide a cloud-based smoke alarm system, the system comprising: An early warning model building module is used to build a smoke early warning model based on the installation location of the smoke alarm, wherein the smoke early warning model at least includes a building model and an alarm model, and the smoke alarm establishes a data connection with the cloud server; The model loading module is used to enter the smoke warning mode when the smoke concentration reaches the preset warning value, and load the smoke warning model of the corresponding area according to the location of the smoke alarm; The fire simulation module is used to obtain smoke concentration data of the smoke area, divide the smoke area into multiple detection grid units, perform fire simulation based on each detection grid unit, and obtain fire simulation data; The fire warning module is used to obtain the real-time concentration data of each smoke alarm, calculate the matching relationship between the fire simulation data and the real-time concentration data, determine the location of the fire, and issue a fire warning through the cloud server.

[0011] Preferably, the model loading module includes: A data acquisition unit, used to poll each smoke alarm at a preset time interval and acquire smoke concentration data of a corresponding area from the smoke alarm; The concentration determination unit is used to read the smoke concentration data in each area and determine whether the smoke concentration in the current area exceeds the preset warning value; The model segmentation unit is used to identify the number of the smoke alarm when the smoke concentration exceeds the preset warning value, and load the corresponding area in the smoke warning model according to the number.

[0012] Preferably, the fire simulation module comprises: A data extraction unit, used to obtain smoke concentration data detected by all smoke alarms in the current area according to the selected smoke area; The probability analysis unit is used to count the area of ​​the smoke area, determine the size of the grid unit, divide the smoke area, obtain the detection grid unit, perform fire probability analysis on the detection grid unit, and generate the detection sequence; The concentration simulation unit is used to import the corresponding smoke warning model into the fire simulation engine to perform fire simulation, record the smoke concentration data of the area where each smoke alarm is located during the fire simulation process, and obtain fire simulation data.

[0013] Preferably, the fire warning module includes: A prediction function building unit, used to obtain real-time concentration data of each smoke alarm, and build a smoke concentration prediction function for each smoke alarm based on fire simulation data; A data matching unit, used to import the real-time concentration data into the smoke concentration prediction function, and calculate the matching rate between the fire simulation data and the real-time concentration data based on the prediction result; The active warning unit is used to screen the fire area based on the matching rate, determine the location of the fire, record it in the fire warning, and issue a fire warning through the cloud server.

[0014] Preferably, in the step of performing fire probability analysis on the detection grid units and generating the detection sequence, the smoke area is graded for room risk, and different scoring values ​​are set according to the level of room risk. Q i , calculate the distance between each detection grid unit and the center of each room L i , calculate the risk coefficient between each detection grid unit and each room P =1 / L , calculate the comprehensive risk value of the detection grid unit According to the comprehensive risk value The sizes are sorted to determine the detection order of the detection grid cells.

[0015] The present invention provides a cloud-based management-based smoke alarm method, which constructs a smoke warning model. When the smoke concentration initially increases, the smoke warning model is used to simulate the fire situation, and the fire occurrence area is determined according to the simulation results and the actual detected smoke concentration changes, thereby achieving the purpose of fire warning, facilitating early disposal of the source of the fire and improving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a smoke alarm method based on cloud management provided by an embodiment of the present invention; Figure 2 A flowchart of the steps of entering a smoke warning mode and loading a smoke warning model of a corresponding area according to the position of a smoke alarm when the smoke concentration reaches a preset warning value provided by an embodiment of the present invention; Figure 3 A flowchart of the steps of obtaining smoke concentration data of a smoke area, dividing the smoke area into a plurality of detection grid units, performing fire simulation based on each detection grid unit, and obtaining fire simulation data provided by an embodiment of the present invention; Figure 4A flowchart of the steps of obtaining real-time concentration data of each smoke alarm, calculating the matching relationship between fire simulation data and real-time concentration data, determining the location of the fire, and issuing a fire warning through a cloud server provided by an embodiment of the present invention; Figure 5 An architecture diagram of a smoke alarm system based on cloud management provided by an embodiment of the present invention; Figure 6 An architectural diagram of a model loading module provided by an embodiment of the present invention; Figure 7 An architecture diagram of a fire simulation module provided by an embodiment of the present invention; Figure 8 An architectural diagram of a fire warning module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] like Figure 1 FIG. 1 is a flow chart of a smoke alarm method based on cloud management provided by an embodiment of the present invention, the method comprising: S100, constructing a smoke warning model based on the installation location of the smoke alarm, wherein the smoke warning model at least includes a building model and an alarm model, and the smoke alarm establishes a data connection with a cloud server.

[0019] In this step, a smoke warning model is constructed based on the installation position of the smoke alarm. A smoke alarm is installed in the building, and a proportional three-dimensional model is constructed in the three-dimensional software according to the size of the building, which is the smoke warning model. The smoke warning model only retains the frame information of the building, that is, the wall information, and the types of each room, such as bedrooms, living rooms, clothing stores, restaurants, and mobile phone stores. Different types of rooms have different fire risk levels. For rooms where fire is used and rooms with large traffic flow, the fire risk level is higher, and fire judgment needs to be prioritized. In the smoke warning model, the location of each smoke alarm in the building is marked to obtain a complete smoke warning model.

[0020] S200, when the smoke concentration reaches the preset warning value, the smoke warning mode is entered, and the smoke warning model of the corresponding area is loaded according to the position of the smoke alarm.

[0021] In this step, when the smoke concentration reaches the preset warning value, the smoke warning mode is entered. During the operation of each smoke alarm, the smoke alarm monitors the smoke concentration in the monitored area in real time to form smoke concentration data. When the smoke concentration monitored by a certain smoke alarm is greater than the preset value or the smoke concentration monitored by multiple adjacent smoke alarms is greater than the warning value (the warning value is lower than the preset value), the smoke warning mode is entered to locate the source of the smoke. At this time, the smoke warning model of the corresponding area is loaded according to the position of the smoke alarm that detects the smoke, and the area of ​​the loaded area is at least twice the area of ​​the area where the smoke is actually detected.

[0022] S300, obtaining smoke concentration data of a smoke area, dividing the smoke area into a plurality of detection grid units, performing fire simulation based on each detection grid unit, and obtaining fire simulation data.

[0023] In this step, the smoke concentration data of the smoke area is obtained. If there are 5 smoke alarms in the smoke area, the smoke concentration data detected by these 5 smoke alarms are obtained. The above data are collected in real time. In order to locate the source of the smoke, the area of ​​the currently loaded smoke warning model is calculated, and the grid size is determined according to the area. The loaded area is gridded according to the grid and divided into multiple detection grid units, that is, each detection grid unit is regarded as a fire starting point, and each detection grid unit is used as the fire starting point in turn. The combustion parameters of the fire starting point are set, so as to simulate the fire through the fire simulation engine. The fire simulation engine can use FDS (Fire Dynamics Simulator). After setting the combustion parameters, the smoke concentration at each smoke alarm is obtained at a preset time interval to obtain fire simulation data. Each fire starting point corresponds to at least one set of fire simulation data. By changing the combustion parameters of the fire starting point, the same fire starting point will generate multiple sets of fire simulation data.

[0024] S400, obtain the real-time concentration data of each smoke alarm, calculate the matching relationship between the fire simulation data and the real-time concentration data, determine the location of the fire, and issue a fire warning through the cloud server.

[0025] In this step, the real-time concentration data of each smoke alarm is obtained. During the fire simulation process, the smoke alarm will also monitor the smoke in real time to obtain real-time concentration data. The real-time concentration data records the smoke concentration corresponding to each moment. For each detection grid unit, the corresponding fire simulation data is retrieved, from which the smoke concentration change data corresponding to each smoke alarm is retrieved. The smoke concentration simulation coordinates are constructed according to the smoke concentration change data generated at each moment. The horizontal axis of the smoke concentration simulation coordinates is the time value, and the vertical axis is the smoke concentration. Function simulation is performed based on the smoke concentration simulation coordinates to obtain a smoke concentration prediction function. The fire simulation data is compared with the predicted value output by the smoke concentration prediction function to determine the matching relationship between the two. The detection grid unit with the highest matching rate is selected and used as the location of the fire, and a fire warning is issued through the cloud server.

[0026] like Figure 2 As shown, as a preferred embodiment of the present invention, when the smoke concentration reaches the preset warning value, the smoke warning mode is entered, and the step of loading the smoke warning model of the corresponding area according to the position of the smoke alarm includes: S201, polling each smoke alarm at a preset time interval to obtain smoke concentration data of a corresponding area from the smoke alarm.

[0027] In this step, each smoke alarm is polled at a preset time interval, such as obtaining smoke concentration data from each smoke alarm once every 1 second. When entering the smoke warning mode, the time detection is shortened, such as reducing the time interval from 1 second to 20 milliseconds, so as to obtain the latest smoke concentration data. If it is not in the smoke warning mode, using a longer time interval can reduce the amount of data collection and reduce energy consumption.

[0028] S202, reading the smoke density data in each area to determine whether the smoke density in the current area exceeds a preset warning value.

[0029] In this step, the smoke concentration data in each area is read. In the same area, there may be multiple smoke alarms, and each smoke alarm will generate a set of detection data. Each set of detection data is judged to determine whether there is an area where the smoke concentration exceeds the preset warning value. If so, the smoke warning mode is entered, otherwise the status quo is maintained.

[0030] S203, when the smoke concentration exceeds the preset warning value, identify the number of the smoke alarm, and load the corresponding area in the smoke warning model according to the number.

[0031] In this step, when the smoke concentration exceeds the preset warning value, the number of the smoke alarm is identified. Since the position of the alarm model corresponding to each numbered smoke alarm is recorded in the smoke warning model, the corresponding area can be directly located, which is the smoke area.

[0032] like Figure 3 As shown, as a preferred embodiment of the present invention, the steps of obtaining smoke concentration data of a smoke area, dividing the smoke area into a plurality of detection grid units, performing fire simulation based on each detection grid unit, and obtaining fire simulation data specifically include: S301, acquiring smoke concentration data detected by all smoke alarms in the current area according to the selected smoke area.

[0033] In this step, the area is expanded according to the selected smoke area, a larger detection area is selected, and the data generated by all smoke alarms in the area are retrieved to obtain smoke concentration data, which records the changes in smoke concentration at all locations in the area.

[0034] S302, counting the area of ​​the smoke region, determining the size of the grid unit, dividing the smoke region to obtain detection grid units, performing fire probability analysis on the detection grid units, and generating a detection sequence.

[0035] In this step, the area of ​​the smoke area is counted. The larger the area of ​​the smoke area, the larger the size of the grid unit is set, and the number of grid units is controlled accordingly. Conversely, the smaller the area of ​​the smoke area, the smaller the size of the grid unit is set. After determining the size of the grid unit, grid division is performed to obtain detection grid units, and fire probability analysis is performed on the detection grid units, specifically including: classifying the room risk of the smoke area and setting different scoring values ​​according to the level of room risk Q i , calculate the distance between each detection grid unit and the center of each room L i , calculate the risk coefficient between each detection grid unit and each room P =1 / L , calculate the comprehensive risk value of the detection grid unit According to the comprehensive risk value The sizes are sorted to determine the detection order of the detection grid cells.

[0036] S303, importing the corresponding smoke warning model into the fire simulation engine, performing fire simulation, recording the smoke concentration data of the areas where each smoke alarm is located during the fire simulation process, and obtaining fire simulation data.

[0037] In this step, the corresponding smoke warning model is imported into the fire simulation engine. The smoke warning model records the three-dimensional model of the entire building. When conducting fire analysis, the spread of fire will not involve all buildings in a short time. Therefore, the smoke warning model is segmented according to the smoke area, and the corresponding partial three-dimensional model is extracted from it and imported into the fire simulation engine. The fire simulation engine loads the partial three-dimensional model, takes each detection grid unit as the ignition point in turn, simulates the fire, simulates the diffusion of smoke, and then records the simulated smoke concentration of each alarm model at each simulation time value, and stores it as fire simulation data.

[0038] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of obtaining the real-time concentration data of each smoke alarm, calculating the matching relationship between the fire simulation data and the real-time concentration data, determining the location of the fire, and issuing a fire warning through the cloud server specifically include: S401, obtaining real-time concentration data of each smoke alarm, and constructing a smoke concentration prediction function for each smoke alarm based on fire simulation data.

[0039] In this step, the real-time concentration data of each smoke alarm is obtained. The real-time concentration data is the smoke concentration actually detected by each smoke alarm, while the fire simulation data records the predicted data obtained by the fire simulation engine. Specifically, the fire simulation data is analyzed. For example, during the simulation of the A alarm model, N groups of simulated smoke concentrations are generated. i The simulated smoke concentration at time is D i , construct smoke density simulation coordinates (A i , D i ), the smoke concentration was simulated by function fitting software (A i , D i ) is fitted to obtain the smoke concentration prediction function. The above process is to construct a continuous prediction function through several discrete data. The smoke concentration prediction function can be used to predict the corresponding smoke concentration at any time in a short period of time.

[0040] S402, importing the real-time concentration data into the smoke concentration prediction function, and calculating the matching rate between the fire simulation data and the real-time concentration data based on the prediction result.

[0041] In this step, the real-time concentration data is imported into the smoke concentration prediction function. Specifically, the collection time corresponding to the real-time concentration data is imported into the smoke concentration prediction function to obtain the smoke concentration prediction value at the corresponding moment. The smoke concentration prediction value is compared with the smoke concentration recorded in the real-time concentration data. If the difference is within the preset range, it is determined that the group of smoke concentration prediction values ​​matches the corresponding real-time concentration data. The matching data corresponding to each detection grid unit is counted. For example, the real-time concentration data corresponding to a smoke alarm contains G=20 groups of smoke concentrations, and the number of matches with the simulated smoke concentrations recorded in the fire simulation data is S=18, then the matching rate R=S / G, that is, 18 / 20=0.9.

[0042] S403, screen the fire area based on the matching rate, determine the location of the fire, record it in the fire warning, and issue a fire warning through the cloud server.

[0043] In this step, the fire area is screened based on the matching rate. Each detection grid unit corresponds to at least one set of matching rates. When multiple combustion parameters are set for each fire point, a set of matching rates will be generated under each combustion parameter of each detection grid unit. The detection grid unit under the combustion parameter with the highest matching rate is selected. The location of the detection grid unit is the predicted location of the fire, which is recorded in the fire warning and issued through the cloud server.

[0044] like Figure 5 As shown, a smoke alarm system based on cloud management is provided in an embodiment of the present invention, and the system includes: The warning model building module 100 is used to build a smoke warning model based on the installation location of the smoke alarm, wherein the smoke warning model at least includes a building model and an alarm model, and the smoke alarm establishes a data connection with the cloud server.

[0045] In this system, the warning model construction module 100 constructs a smoke warning model based on the installation position of the smoke alarm. A smoke alarm is installed in a building, and a proportional three-dimensional model is constructed in three-dimensional software according to the size of the building, which is the smoke warning model. The smoke warning model only retains the frame information of the building, that is, the wall information, and the types of each room, such as bedrooms, living rooms, clothing stores, restaurants, and mobile phone stores. Different types of rooms have different fire risk levels. For rooms where fire is used and rooms with a large flow of people, the fire risk level is higher, and fire judgment needs to be prioritized. In the smoke warning model, the location of each smoke alarm in the building is marked to obtain a complete smoke warning model.

[0046] The model loading module 200 is used to enter the smoke warning mode when the smoke concentration reaches the preset warning value, and load the smoke warning model of the corresponding area according to the location of the smoke alarm.

[0047] In this system, when the smoke concentration reaches the preset warning value, the model loading module 200 enters the smoke warning mode. During the operation of each smoke alarm, the smoke alarm monitors the smoke concentration in the monitored area in real time to form smoke concentration data. When the smoke concentration monitored by a certain smoke alarm is greater than the preset value or the smoke concentration monitored by multiple adjacent smoke alarms is greater than the warning value (the warning value is lower than the preset value), the smoke warning mode is entered to locate the source of the smoke. At this time, the smoke warning model of the corresponding area is loaded according to the position of the smoke alarm that detects the smoke, and the area of ​​the loaded area is at least twice the area of ​​the area where the smoke is actually detected.

[0048] The fire simulation module 300 is used to obtain smoke concentration data of the smoke area, divide the smoke area into a plurality of detection grid units, perform fire simulation based on each detection grid unit, and obtain fire simulation data.

[0049] In this system, the fire simulation module 300 obtains the smoke concentration data of the smoke area. If there are 5 smoke alarms in the smoke area, the smoke concentration data detected by the 5 smoke alarms are obtained. The above data are collected in real time. In order to locate the source of the smoke, the area of ​​the currently loaded smoke warning model is calculated, and the grid size is determined according to the area. The loaded area is gridded according to the grid and divided into multiple detection grid units, that is, each detection grid unit is regarded as a fire starting point, and each detection grid unit is used as the fire starting point in turn. The combustion parameters of the fire starting point are set, so as to perform fire simulation through the fire simulation engine. The fire simulation engine can use FDS (FireDynamics Simulator). After setting the combustion parameters, the smoke concentration at each smoke alarm is obtained at a preset time interval to obtain fire simulation data. Each fire starting point corresponds to at least one set of fire simulation data. By changing the combustion parameters of the fire starting point, the same fire starting point will generate multiple sets of fire simulation data.

[0050] The fire warning module 400 is used to obtain the real-time concentration data of each smoke alarm, calculate the matching relationship between the fire simulation data and the real-time concentration data, determine the location of the fire, and issue a fire warning through the cloud server.

[0051] In this system, the fire warning module 400 obtains the real-time concentration data of each smoke alarm. During the fire simulation process, the smoke alarm will also perform real-time smoke monitoring to obtain real-time concentration data. The real-time concentration data records the smoke concentration corresponding to each moment. For each detection grid unit, the corresponding fire simulation data is retrieved, from which the smoke concentration change data corresponding to each smoke alarm is retrieved. The smoke concentration simulation coordinates are constructed according to the smoke concentration change data generated at each moment. The horizontal axis of the smoke concentration simulation coordinates is the time value, and the vertical axis is the smoke concentration. Function simulation is performed based on the smoke concentration simulation coordinates to obtain a smoke concentration prediction function. The fire simulation data is compared with the predicted value output by the smoke concentration prediction function to determine the matching relationship between the two. The detection grid unit with the highest matching rate is selected and used as the location of the fire, and a fire warning is issued through the cloud server.

[0052] like Figure 6 As shown, as a preferred embodiment of the present invention, the model loading module 200 includes: The data acquisition unit 201 is used to poll each smoke alarm at a preset time interval to acquire smoke concentration data of a corresponding area from the smoke alarm.

[0053] In this module, the data acquisition unit 201 polls each smoke alarm at a preset time interval, such as obtaining smoke concentration data from each smoke alarm once every 1 second. When entering the smoke warning mode, the time detection is shortened, such as reducing the time interval from 1 second to 20 milliseconds, so as to obtain the latest smoke concentration data. If it is not in the smoke warning mode, using a longer time interval can reduce the amount of data collection and reduce energy consumption.

[0054] The concentration determination unit 202 is used to read the smoke concentration data in each area and determine whether the smoke concentration in the current area exceeds a preset warning value.

[0055] In this module, the concentration determination unit 202 reads the smoke concentration data in each area. In the same area, there may be multiple smoke alarms, and each smoke alarm will generate a set of detection data. Each set of detection data is determined to determine whether there is an area where the smoke concentration exceeds the preset warning value. If so, the smoke warning mode is entered, otherwise the status quo is maintained.

[0056] The model segmentation unit 203 is used to identify the serial number of the smoke alarm when the smoke concentration exceeds the preset warning value, and load the corresponding area in the smoke warning model according to the serial number.

[0057] In this module, the model segmentation unit 203 identifies the number of the smoke alarm when the smoke concentration exceeds the preset warning value. Since the position of the alarm model corresponding to each numbered smoke alarm is recorded in the smoke warning model, the corresponding area can be directly located, which is the smoke area.

[0058] like Figure 7 As shown, as a preferred embodiment of the present invention, the fire simulation module 300 includes: The data extraction unit 301 is used to obtain the smoke concentration data detected by all smoke alarms in the current area according to the selected smoke area.

[0059] In this module, the data extraction unit 301 expands the area according to the selected smoke area, selects a larger detection area, and retrieves the data generated by all smoke alarms in the area to obtain smoke concentration data, which records the changes in smoke concentration at all locations in the area.

[0060] The probability analysis unit 302 is used to count the area of ​​the smoke area, determine the size of the grid unit, divide the smoke area, obtain detection grid units, perform fire probability analysis on the detection grid units, and generate a detection sequence.

[0061] In this module, the probability analysis unit 302 counts the area of ​​the smoke area. The larger the area of ​​the smoke area, the larger the size of the grid unit is set, and the number of grid units is controlled accordingly. Conversely, the smaller the area of ​​the smoke area, the smaller the size of the grid unit is set. After determining the size of the grid unit, grid division is performed to obtain the detection grid unit, and the fire probability analysis is performed on the detection grid unit, which specifically includes: classifying the room risk of the smoke area and setting different scoring values ​​according to the level of room risk. Q i , calculate the distance between each detection grid unit and the center of each room L i , calculate the risk coefficient between each detection grid unit and each room P =1 / L , calculate the comprehensive risk value of the detection grid unit According to the comprehensive risk value The sizes are sorted to determine the detection order of the detection grid cells.

[0062] The concentration simulation unit 303 is used to import the corresponding smoke warning model into the fire simulation engine, perform fire simulation, record the smoke concentration data of the area where each smoke alarm is located during the fire simulation process, and obtain fire simulation data.

[0063] In this module, the concentration simulation unit 303 imports the corresponding smoke warning model into the fire simulation engine. The smoke warning model records the three-dimensional model of the entire building. When conducting fire analysis, the spread of fire will not involve all buildings in a short time. Therefore, the smoke warning model is segmented according to the smoke area, and the corresponding partial three-dimensional model is extracted from it and imported into the fire simulation engine. The fire simulation engine loads the partial three-dimensional model, takes each detection grid unit as the ignition point in turn, simulates the fire, simulates the diffusion of smoke, and then records the simulated smoke concentration of each alarm model at each simulation time value, and stores it as fire simulation data.

[0064] like Figure 8 As shown, as a preferred embodiment of the present invention, the fire warning module 400 includes: The prediction function construction unit 401 is used to obtain the real-time concentration data of each smoke alarm and construct a smoke concentration prediction function for each smoke alarm based on the fire simulation data.

[0065] In this module, the prediction function construction unit 401 obtains the real-time concentration data of each smoke alarm. The real-time concentration data is the smoke concentration actually detected by each smoke alarm, and the fire simulation data records the prediction data obtained by the fire simulation engine. Specifically, the fire simulation data is analyzed. For example, during the simulation of the A alarm model, N groups of simulated smoke concentrations are generated. i The simulated smoke concentration at time is D i , construct smoke density simulation coordinates (A i , D i ), the smoke concentration was simulated by function fitting software (A i , D i ) is fitted to obtain the smoke concentration prediction function. The above process is to construct a continuous prediction function through several discrete data. The smoke concentration prediction function can be used to predict the corresponding smoke concentration at any time in a short period of time.

[0066] The data matching unit 402 is used to import the real-time concentration data into the smoke concentration prediction function, and calculate the matching rate between the fire simulation data and the real-time concentration data based on the prediction result.

[0067] In this module, the data matching unit 402 imports the real-time concentration data into the smoke concentration prediction function. Specifically, the collection time corresponding to the real-time concentration data is imported into the smoke concentration prediction function to obtain the smoke concentration prediction value at the corresponding moment. The smoke concentration prediction value is compared with the smoke concentration recorded in the real-time concentration data. If the difference is within a preset range, it is determined that the group of smoke concentration prediction values ​​matches the corresponding real-time concentration data. The matching data corresponding to each detection grid unit is counted. For example, the real-time concentration data corresponding to a smoke alarm contains G=20 groups of smoke concentrations, and the number of matches with the simulated smoke concentrations recorded in the fire simulation data is S=18, then the matching rate R=S / G, that is, 18 / 20=0.9.

[0068] The active warning unit 403 is used to screen the fire area based on the matching rate, determine the location of the fire, record it in the fire warning, and issue a fire warning through the cloud server.

[0069] In this module, the active warning unit 403 screens the fire area based on the matching rate. Each detection grid unit corresponds to at least one set of matching rates. When multiple combustion parameters are set for each fire point, a set of matching rates will be generated under each combustion parameter of each detection grid unit. The detection grid unit under the combustion parameter with the highest matching rate is selected. The location of the detection grid unit is the predicted location of the fire, which is recorded in the fire warning and issued through the cloud server.

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smoke alarm method based on cloud management, characterized in that: The method comprises: Based on the installation location of the smoke alarm, a smoke warning model is constructed, wherein the smoke warning model at least includes a building model and an alarm model, and the smoke alarm establishes a data connection with a cloud server; When the smoke concentration reaches the preset warning value, the system enters the smoke warning mode and loads the smoke warning model of the corresponding area according to the location of the smoke alarm; Obtain smoke concentration data of the smoke area, divide the smoke area into multiple detection grid units, perform fire simulation based on each detection grid unit, and obtain fire simulation data; Obtain the real-time concentration data of each smoke alarm, calculate the matching relationship between the fire simulation data and the real-time concentration data, determine the location of the fire, and issue a fire warning through the cloud server.

2. The cloud-based smoke alarm method according to claim 1, characterized in that: When the smoke concentration reaches the preset warning value, the smoke warning mode is entered, and the smoke warning model of the corresponding area is loaded according to the position of the smoke alarm, including: Polling each smoke alarm at a preset time interval to obtain smoke concentration data of the corresponding area from the smoke alarm; Read the smoke concentration data in each area to determine whether the smoke concentration in the current area exceeds the preset warning value; When the smoke concentration exceeds the preset warning value, the number of the smoke alarm is identified and the corresponding area in the smoke warning model is loaded according to the number.

3. The cloud-based smoke alarm method according to claim 1, characterized in that: The step of obtaining smoke concentration data of the smoke area, dividing the smoke area into a plurality of detection grid units, performing fire simulation based on each detection grid unit, and obtaining fire simulation data specifically includes: According to the selected smoke area, obtain the smoke concentration data detected by all smoke alarms in the current area; Count the area of ​​the smoke region, determine the size of the grid unit, divide the smoke region, obtain the detection grid unit, perform fire probability analysis on the detection grid unit, and generate the detection sequence; The corresponding smoke warning model is imported into the fire simulation engine to perform fire simulation, and the smoke concentration data of the areas where each smoke alarm is located during the fire simulation process is recorded to obtain fire simulation data.

4. The cloud-based smoke alarm method according to claim 1, characterized in that: The steps of obtaining the real-time concentration data of each smoke alarm, calculating the matching relationship between the fire simulation data and the real-time concentration data, determining the location of the fire, and issuing a fire warning through the cloud server specifically include: Obtain the real-time concentration data of each smoke alarm and construct a smoke concentration prediction function for each smoke alarm based on the fire simulation data; Import the real-time concentration data into the smoke concentration prediction function, and calculate the matching rate between the fire simulation data and the real-time concentration data based on the prediction results; The fire area is screened based on the matching rate, the location of the fire is determined, recorded in the fire warning, and a fire warning is issued through the cloud server.

5. The cloud-based smoke alarm method according to claim 3, characterized in that: In the step of performing fire probability analysis on the detection grid units and generating the detection sequence, the smoke area is graded for room risk, and different scoring values ​​are set according to the level of room risk. Q i , calculate the distance between each detection grid unit and the center of each room L i , calculate the risk coefficient between each detection grid unit and each room P =1 / L , calculate the comprehensive risk value of the detection grid unit According to the comprehensive risk value The sizes are sorted to determine the detection order of the detection grid cells.

6. A cloud-based smoke alarm system, characterized in that: The system comprises: An early warning model building module is used to build a smoke early warning model based on the installation location of the smoke alarm, wherein the smoke early warning model at least includes a building model and an alarm model, and the smoke alarm establishes a data connection with the cloud server; The model loading module is used to enter the smoke warning mode when the smoke concentration reaches the preset warning value, and load the smoke warning model of the corresponding area according to the location of the smoke alarm; The fire simulation module is used to obtain smoke concentration data of the smoke area, divide the smoke area into multiple detection grid units, perform fire simulation based on each detection grid unit, and obtain fire simulation data; The fire warning module is used to obtain the real-time concentration data of each smoke alarm, calculate the matching relationship between the fire simulation data and the real-time concentration data, determine the location of the fire, and issue a fire warning through the cloud server.

7. The cloud-based smoke alarm system according to claim 6, characterized in that: The model loading module includes: A data acquisition unit, used to poll each smoke alarm at a preset time interval and acquire smoke concentration data of a corresponding area from the smoke alarm; The concentration determination unit is used to read the smoke concentration data in each area and determine whether the smoke concentration in the current area exceeds the preset warning value; The model segmentation unit is used to identify the number of the smoke alarm when the smoke concentration exceeds the preset warning value, and load the corresponding area in the smoke warning model according to the number.

8. The cloud-based smoke alarm system according to claim 6, characterized in that: The fire simulation module comprises: A data extraction unit, used to obtain smoke concentration data detected by all smoke alarms in the current area according to the selected smoke area; The probability analysis unit is used to count the area of ​​the smoke area, determine the size of the grid unit, divide the smoke area, obtain the detection grid unit, perform fire probability analysis on the detection grid unit, and generate the detection sequence; The concentration simulation unit is used to import the corresponding smoke warning model into the fire simulation engine to perform fire simulation, record the smoke concentration data of the area where each smoke alarm is located during the fire simulation process, and obtain fire simulation data.

9. The cloud-based smoke alarm system according to claim 6, characterized in that: The fire warning module comprises: A prediction function building unit, used to obtain real-time concentration data of each smoke alarm, and build a smoke concentration prediction function for each smoke alarm based on fire simulation data; A data matching unit, used to import the real-time concentration data into the smoke concentration prediction function, and calculate the matching rate between the fire simulation data and the real-time concentration data based on the prediction result; The active warning unit is used to screen the fire area based on the matching rate, determine the location of the fire, record it in the fire warning, and issue a fire warning through the cloud server.

10. The cloud-based smoke alarm system according to claim 8, characterized in that: In the step of performing fire probability analysis on the detection grid units and generating the detection sequence, the smoke area is graded for room risk, and different scoring values ​​are set according to the level of room risk. Q i , calculate the distance between each detection grid unit and the center of each room L i , calculate the risk coefficient between each detection grid unit and each room P =1 / L , calculate the comprehensive risk value of the detection grid unit According to the comprehensive risk value The sizes are sorted to determine the detection order of the detection grid cells.

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